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Simple Summary: As COVID-19 shifts from pandemic to endemic, emerging variants may be more or less virulent. Predicting whether an emerging COVID-19 variant has of high risk of causing severe disease is needed to plan for potential burdens on hospital capacity and protecting vulnerable populations. However, it takes time to do laboratory and animal experiments to determine whether a new genetic variant might be more severe, and the results may not be representative of when the virus infects humans. By the time there is epidemiological data on the severity of disease associated with a new variant, it can be too late for designing an optimal public health response. There is a critical need for computer models that can predict severe disease risk from genetic sequence data, which can be obtained from just the first few infections in a potential incoming wave. Two key challenges make computer modeling difficult: (1) sequence changes are complex, and (2) using historical data to predict future disease requires accounting for the confounding effects of changing patient demographics, improving therapeutics, and increased vaccination. In this paper, we introduce a novel interpretable deep learning architecture to solve this problem, demonstrating that it can make robust predictions for emerging variants. Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a modified SEIR model to study COVID-19 in Wuhan. The modified model is calibrated by the public number of COVID-19 hospitalization cases in Wuhan. The paper further uses this model to estimate the earliest date of COVID-19 infection in Wuhan, which is in agreement with some existing results. Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic significantly affected the clinical outcomes and management of patients with hematological malignancies, who are especially vulnerable to infections. At the time of the pandemic outbreak, when a highly virulent wild-type strain of SARS-CoV-2 (B.1) was responsible for the majority of infections, mortality rates among hematology-oncology patients was high. As no specific prevention or treatment was available at the time, patients were strictly advised to wear masks and practice physical distancing and hand hygiene. When anti-cancer treatment was needed, protocols with oral drugs on an outpatient basis were preferred to avoid hospitalization whenever possible. Despite this, many hemato-oncological patients died from COVID-19, secondary bacterial or fungal infections as well as from delayed or suboptimal antineoplastic treatment. Less virulent viral variants contributed to reducing the problem with COVID-19 in the general population, but patients with hematologic malignancies are still at high risk of suffering from SARS-CoV-2 or COVID-19 infection with a severe or long clinical course. These patients are advised to get vaccinated, wear a face mask and avoid poorly ventilated or crowded places. CDC guidelines recommend starting antiviral treatment as soon as possible in case of a positive SARS-CoV-2 test, i.e., within 5 days of the first symptoms. The COVID-19 pandemic is undoubtedly the most difficult health challenge of the 21st century with more than 600 million laboratory-confirmed SARS-CoV-2 infections and over 6.5 million deaths worldwide. The coronavirus pandemic contributed to rapid development of mRNA vaccines, which, along with new antiviral drugs, have been the subject of extensive research for many decades. Nevertheless, elderly, multi-morbid and immunocompromised patients continue to face a more severe clinical course and a higher risk of death from COVID-19, even now that the risk of COVID-19 in the general population is significantly reduced due to the introduction of global vaccination strategies. In this paper, we present the mechanisms of increased susceptibility to infectious complications and the evolution of the clinical course of COVID-19 in patients with hematological malignancies, taking into account the mutation of the virus and the introduction of vaccines and new antiviral drugs. We also present current recommendations for prophylactic and therapeutic management in patients with hematological malignancies. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper looks at changes in weight, physical activity, and food/dietary intake during the first year of the COVID-19 pandemic in undergraduate students enrolled in a Canadian university. The investigation used an online survey format to collect data at the end of the two lockdown periods over one year. Participants were recruited from universities across Canada. The results show a modest weight gain accompanied by a decrease in physical activity and an unhealthy change in diet. Specifically, students reported that their engagement in specific physical activities and overall activity levels decreased during the lockdowns. The number of calories also decreased over time, while the foods consumed decreased in nutritional value. Thus, the modest weight gain observed may have resulted from the decrease in physical activity and diet quality. In conclusion, Canadian food and physical activity recommendations were not met during the lockdowns caused by the pandemic, which highlights the physical and nutritional implications of confinement. This study examined changes in body mass and body mass index (BMI), physical activity, and dietary intake in Canadian university students during the first year of the COVID-19 pandemic. Two self-reported recall surveys were conducted: after the first lockdown in September 2020 (T1) and following the second lockdown in March 2021 (T2). Eligible participants were full-time undergraduate students attending a Canadian university and residing in Canada during the first year of the pandemic. At T1, 510 students (99 male, 411 female) completed the survey, and of those, 135 (32 males, 103 females) completed the survey at T2 (73% attrition). At both T1 and T2, most participants were 18–24 years of age (93% and 90%, respectively), Caucasian (73% and 78%, respectively), and resided in the province of Ontario (79% and 80%, respectively). Body mass increased from T1 to T2 (+0.91 ± 3.89 kg t(132) = −2.7, p = 0.008). BMI also increased from T1 to T2 (+0.30 ± 1.33 kg/m2 [t(130) = −2.5, p = 0.012), with a greater number of participants within the overweight range (19.8% versus 24.4%, respectively). At T1, 38% of the participants reported a decrease in physical activity, while the number of students reporting a decrease in activity increased to 56% at T2. Dietary energy intake decreased from 1678 ± 958 kcal/day at T1 to 1565 ± 842 kcal/day at T2 [c2(1) = 7.2, p = 0.007]. Diet quality also decreased, with participants not meeting the recommended daily allowance for essential macro and micronutrients. A decrease was observed in daily servings of fruits (−27%, p < 0.001), vegetables (−72%, p < 0.001), and grains (−68%, p < 0.001). In conclusion, despite a small decrease in dietary energy intake, a modest weight gain occurred during the first year of the COVID-19 pandemic in this cohort of Canadian university students, which was potentially related to decreased physical activity and diet quality. [ABSTRACT FROM AUTHOR]

Simple Summary: Cyclooxygenase 2 (COX2) inhibitors have been demonstrated to protect against hypoxia pathogenesis in several investigations. In several studies, it has been shown that COX2 inhibitors guard against the pathogenesis of hypoxia. However, there are conflicting findings on COX inhibitors' potency in treating COVID-19, and they have drawbacks. As a result, therapeutic COX2 inhibition may not always be beneficial, and further research into potential downstream mediators for hypoxic environment adaptability is required. According to research, those who are used to the lower oxygen levels at altitude may be more resistant to the negative effects of COVID-19. It indicates that COVID-19 poses a greater risk to people who live at lower elevations. It has been demonstrated that the COX2 pathway's downstream molecules adapt in people who live at high altitudes, which may help to explain why these people have a decreased prevalence of COVID-19 infection. Intermittent hypoxia training can be used to imitate the gene-environment interactions found in people who live at higher altitudes (IHT). It appears that COX-2 adaptation brought on by hypoxia exposure at altitude or IHT serves a significant therapeutic purpose. In this article, we highlight some of the most significant common genes associated with the pathophysiology of COVID-19 and hypoxia. We propose a common pathway between COVID-19 pathogenesis and hypoxia that influences apoptosis, proliferation, the immune system, and metabolism. We also emphasize the importance of researching people who reside at higher elevations to mimic their gene-environment interactions and contrast the results with IHT. Finally, we suggest COX2 as an upstream target for evaluating IHT's efficacy in preventing or mitigating the consequences of COVID-19 and other oxygen-related pathological conditions in the future. Cyclooxygenase 2 (COX2) inhibitors have been demonstrated to protect against hypoxia pathogenesis in several investigations. It has also been utilized as an adjuvant therapy in the treatment of COVID-19. COX inhibitors, which have previously been shown to be effective in treating previous viral and malarial infections are strong candidates for improving the COVID-19 therapeutic doctrine. However, another COX inhibitor, ibuprofen, is linked to an increase in the angiotensin-converting enzyme 2 (ACE2), which could increase virus susceptibility. Hence, inhibiting COX2 via therapeutics might not always be protective and we need to investigate the downstream molecules that may be involved in hypoxia environment adaptation. Research has discovered that people who are accustomed to reduced oxygen levels at altitude may be protected against the harmful effects of COVID-19. It is important to highlight that the study's conclusions only applied to those who regularly lived at high altitudes; they did not apply to those who occasionally moved to higher altitudes but still lived at lower altitudes. COVID-19 appears to be more dangerous to individuals residing at lower altitudes. The downstream molecules in the (COX2) pathway have been shown to adapt in high-altitude dwellers, which may partially explain why these individuals have a lower prevalence of COVID-19 infection. More research is needed, however, to directly address COX2 expression in people living at higher altitudes. It is possible to mimic the gene–environment interaction of higher altitude people by intermittent hypoxia training. COX-2 adaptation resulting from hypoxic exposure at altitude or intermittent hypoxia exercise training (IHT) seems to have an important therapeutic function. Swimming, a type of IHT, was found to lower COX-2 protein production, a pro-inflammatory milieu transcription factor, while increasing the anti-inflammatory microenvironment. Furthermore, Intermittent Hypoxia Preconditioning (IHP) has been demonstrated in numerous clinical investigations to enhance patients' cardiopulmonary function, raise cardiorespiratory fitness, and increase tissues' and organs' tolerance to ischemia. Biochemical activities of IHP have also been reported as a feasible application strategy for IHP for the rehabilitation of COVID-19 patients. In this paper, we aim to highlight some of the most relevant shared genes implicated with COVID-19 pathogenesis and hypoxia. We hypothesize that COVID-19 pathogenesis and hypoxia share a similar mechanism that affects apoptosis, proliferation, the immune system, and metabolism. We also highlight the necessity of studying individuals who live at higher altitudes to emulate their gene–environment interactions and compare the findings with IHT. Finally, we propose COX2 as an upstream target for testing the effectiveness of IHT in preventing or minimizing the effects of COVID-19 and other oxygen-related pathological conditions in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: In the past two years, the COVID-19 incidence curves and reproduction number R t have been the main metrics used by policy makers and journalists to monitor the spread of this global pandemic. However, these metrics are not always reliable in the short term, because of a combination of delay in detection, administrative delays and random noise. In this article, we present a complete model of COVID-19 incidence, faithfully reconstructing the incidence curve and reproduction number from the renewal equation of the disease and precisely estimating the biases associated with periodic weekly bias, festive day bias and residual noise. The sanitary crisis of the past two years has focused the public's attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number R t , defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable R t . Using R t , a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve. [ABSTRACT FROM AUTHOR]

Simple Summary: Coronavirus disease-2019 (COVID-19) affects many organs in the body, including the heart. One complication of particular concern is inflammation of the heart muscle, called myocarditis. This paper presents updated research data on COVID-19-associated myocarditis. Specifically, we review the incidence, potential mechanisms, blood and imaging tests that can be used to detect the disease. We emphasize that, in contrast with early reports, recent data suggest that myocarditis in the setting of COVID-19 is relatively uncommon, yet infected individuals are at a substantially increased risk for poor outcomes. It is important to continue research in this area. The direct and indirect adverse effects of SARS-CoV-2 infection on the cardiovascular system, including myocarditis, are of paramount importance. These not only affect the disease course but also determine clinical outcomes and recovery. In this review, the authors aimed at providing an update on the incidence of Coronavirus disease-2019 (COVID-19)-associated myocarditis. Our knowledge and experience relevant to this area continues to evolve rapidly since the beginning of the pandemic. It is crucial for the scientific and medical community to stay abreast of current information. Contrasting early reports, recent data suggest that the overall incidence of SARS-CoV-2-associated myocarditis is relatively low, yet infected individuals are at a substantially increased risk. Therefore, understanding the pathophysiology and diagnostic evaluation, including the use of serum biomarkers and imaging modalities, remain important. This review aims to summarize the most recent data in these areas as they relate to COVID-19-associated myocarditis. Given its increasing relevance, a brief update is included on the proposed mechanisms of myocarditis in COVID-19 vaccine recipients. [ABSTRACT FROM AUTHOR]

Simple Summary: The family of coronaviruses comprises a diverse set of strains and variants which cause diseases from the common cold to COVID-19. Moreover, they infect a wide array of hosts from bats, camels, birds, to humans. Studying coronaviruses through the lens of host specificity provides a unique perspective to understanding the evolution, diversity and dynamics of this family. In particular, this can reveal groups of different hosts infected by similar strains, giving clues on strains which were more likely to have evolved to jump from one host to another. In this work, we frame host specificity as a classification task, in designing a very compact numerical representation of the spike sequences of different coronaviruses. Based on this numerical representation, classification methods are able to detect the target host with high accuracy. Such an approach can used to efficiently scale to large volumes of sequences, in order to unveil trends in the host specificity of different coronavirus strains. The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models. [ABSTRACT FROM AUTHOR]

Simple Summary: Since its first description 35 years ago, the transcription factor NF-κB (nuclear factor κ-light-chain-enhancer of activated B cells) has been shown to be a key mediator of immune cell responses to inflammatory mediators, oxidative stress and genotoxic injury. Dysregulated NF-κB signalling drives inflammation in inflammatory disorders such as multiple sclerosis, rheumatoid arthritis or inflammatory bowel disease. Thus, re-establishing the appropriate regulation of NF-κB activity seems like a promising approach to treat inflammatory diseases. Current anti-inflammatory drugs have many, often serious, side effects. Thus, there is an unmet clinical need for safe and effective anti-inflammatory medicines that both decrease inflammatory mediator production and enhance endogenous anti-inflammatory and prorepair pathways. So far, traditional de novo drug discovery has fallen short of satisfying this need. Drug repurposing is a cost- and time-effective alternative to de novo drug development for the identification of novel applications and has already resulted in the identification of effective anti-inflammatories in the ongoing COVID-19 pandemic. In this paper we critically review NF-κB as a potential target for the development of anti-inflammatory drugs with an emphasis on drug repurposing as a strategy to identify new approaches to treat inflammatory diseases. NF-κB is a central mediator of inflammation, response to DNA damage and oxidative stress. As a result of its central role in so many important cellular processes, NF-κB dysregulation has been implicated in the pathology of important human diseases. NF-κB activation causes inappropriate inflammatory responses in diseases including rheumatoid arthritis (RA) and multiple sclerosis (MS). Thus, modulation of NF-κB signaling is being widely investigated as an approach to treat chronic inflammatory diseases, autoimmunity and cancer. The emergence of COVID-19 in late 2019, the subsequent pandemic and the huge clinical burden of patients with life-threatening SARS-CoV-2 pneumonia led to a massive scramble to repurpose existing medicines to treat lung inflammation in a wide range of healthcare systems. These efforts continue and have proven to be controversial. Drug repurposing strategies are a promising alternative to de novo drug development, as they minimize drug development timelines and reduce the risk of failure due to unexpected side effects. Different experimental approaches have been applied to identify existing medicines which inhibit NF-κB that could be repurposed as anti-inflammatory drugs. [ABSTRACT FROM AUTHOR]

The Special Issue compiles data on the role of metals in COVID-19 and focuses on other illnesses and biological processes that affect metal metabolism. We believe that the information provided in this Special Issue will be valuable to readers who are interested in metals and metal complexes/metal coordination compounds in general and to those interested in the role of elements in biological processes and diseases, including COVID-19. In the present Special Issue on "Metals and Metal Complexes in Diseases with a Focus on COVID-19: Facts and Opinions", an attempt has been made to include reports updating our knowledge of elements considered to be potential candidates for therapeutic applications and certain metal-containing species, which are extensively being examined towards their potential biomedical use due to their specific physicochemical properties. [Extracted from the article]

Simple Summary: COVID-19, with persistent and new onset of symptoms, such as fatigue, post-exertional malaise, and cognitive dysfunction that impact everyday functioning, is referred to as long-COVID under the general category of post-acute sequelae of the SARS-CoV-2 infection (PASC). It includes a wide range of signs and symptoms that can last weeks, months, or even years after infection, most of which are attributable to dysfunctions of the neurovegetative system. The causative mechanisms are still unknown, but autoimmunity and the production of autoantibodies targeting self-antigens via the molecular mimicry phenomenon seem to have a role. Here we evaluated the presence of autoantibodies against two proteins of vagal nuclei sharing a peptide with SARS-CoV-2 spike glycoprotein in sera from ongoing symptomatic COVID-19 patients (long-COVID) with cardiorespiratory symptoms, subjects vaccinated without a history of SARS-CoV-2 infection, and subjects not vaccinated without a history of SARS-CoV-2. Putative autoantibodies are present in both long-COVID-19 and vaccinated groups, suggesting that both viral infection and vaccination may trigger autoreactivity. However, the presence of autoantibodies is not sufficient for triggering autoimmunity, and other predisposing conditions must co-occur. Therefore, it is necessary to run further investigations to clarify the complex mechanisms involved in the development of long-COVID, providing knowledge which may offer further information for the prevention and treatment of the disease. As reported by the World Health Organization (WHO), about 10–20% of people have experienced mid- to long-term effects following SARS-CoV-2 infection, collectively referred to as post-COVID-19 condition or long-COVID, including some neurovegetative symptoms. Numerous findings have suggested that the onset of these neurovegetative symptoms upon viral infection may be caused by the production of autoantibodies through molecular mimicry phenomena. Accordingly, we had previously demonstrated that 22 of the human proteins sharing putatively immunogenic peptides with SARS-CoV-2 proteins are expressed in the dorsal motor nucleus and nucleus ambiguous. Therefore, if molecular mimicry occurs following severe forms of COVID-19, there could be transitory or permanent damage in some vagal structures, resulting in a lower vagal tone and all the related clinical signs. We investigated the presence of autoantibodies against two proteins of vagal nuclei sharing a peptide with SARS-CoV-2 spike glycoprotein using an immunoassay test on blood obtained from patients with cardiorespiratory symptoms in patients affected by ongoing symptomatic COVID-19 (long-COVID), subjects vaccinated without a history of SARS-CoV-2 infection, and subjects not vaccinated without a history of SARS-CoV-2 infection. Interestingly, putative autoantibodies were present in both long-COVID-19 and vaccinated groups, opening interesting questions about pathogenic mechanisms of the disease. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19-related literature has known a surge since the beginning of the pandemic. This surge prompted the creation of multiple literature exploration systems to help automate the exploration of scientific articles. In this work, we survey multiple COVID-19 literature exploration systems by exploring their most discriminative characteristics, give general design principles for these systems, and describe some of their limitations. The urgency of the COVID-19 pandemic caused a surge in the related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used. [ABSTRACT FROM AUTHOR]

Simple Summary: The review aims to explore the historical development of cardiac innervations and neuromodulation, tracing its origins back to the ancient Egyptian concept of "psychostasis"—the ritual weighing of the heart to determine one's fate in the afterlife. This ancient belief in the heart as the center of human wisdom, emotions, and memory laid the foundation for the later advancements in the scientific understanding of cardiac innervation. Importantly, this ancient cardiocentric worldview also established the ancestral ethical foundations for the study and application of cardiac neuromodulation. The Egyptians believed that the "lightness of the heart"—achieved through a balance of good and bad deeds—was crucial for one's wellbeing and fate in the afterlife. This ethical framework parallels the modern understanding of the importance of balanced cardiac autonomic control, mediated by the vagus nerve, for overall health and social engagement. The review chronicles key milestones, from Aristotle and Galen's early physiological studies, to the pioneering anatomical work of Leonardo da Vinci, Vesalius, and William Harvey's discovery of the circulatory system. It highlights the contributions of 17th century researchers like Richard Lower, who demonstrated the heart's neural control, and Albrecht von Haller, who discovered the heart's automaticity. The review emphasizes how the study of cardiac innervation and neuromodulation has regained prominence, particularly in understanding the cardiorespiratory symptoms and dysregulation observed in post-acute sequelae of SARS-CoV-2 infection (long COVID) and other acute respiratory infections. It draws parallels between the ancient concept of "weighing the heart" in psychostasis and the modern understanding of the "balance of different hormonal, chemical, electrical, and contrasting nervous stimuli" in cardiac neuromodulation. By tracing this historical evolution, the review aims to bridge the gap between ancient concepts like psychostasis and the modern applications of cardiac neuromodulation for cardiovascular health and disease management. Understanding the complex interplay between cardiac function, neural modulation, and overall wellbeing is crucial for addressing long-term cardiorespiratory issues, akin to the ancient notion of achieving "lightness of the heart". The review also highlights the relevance of the polyvagal theory, which emphasizes the role of the vagus nerve in regulating the heart's function and its connection to emotional and social engagement. This provides an ethical and physiological framework for understanding the importance of cardiac neuromodulation in promoting overall health and well-being. This review explores the historical development of cardiology knowledge, from ancient Egyptian psychostasis to the modern comprehension of cardiac neuromodulation. In ancient Egyptian religion, psychostasis was the ceremony in which the deceased was judged before gaining access to the afterlife. This ritual was also known as the "weighing of the heart" or "weighing of the soul". The Egyptians believed that the heart, not the brain, was the seat of human wisdom, emotions, and memory. They were the first to recognize the cardiocentric nature of the body, identifying the heart as the center of the circulatory system. Aristotle (fourth century BC) considered the importance of the heart in human physiology in his philosophical analyses. For Galen (third century AD), the heart muscle was the site of the vital spirit, which regulated body temperature. Cardiology knowledge advanced significantly in the 15th century, coinciding with Leonardo da Vinci and Vesalius's pioneering anatomical and physiological studies. It was William Harvey, in the 17th century, who introduced the concept of cardiac circulation. Servet's research and Marcello Malpighi's discovery of arterioles and capillaries provided a more detailed understanding of circulation. Richard Lower emerged as the foremost pioneer of experimental cardiology in the late 17th century. He demonstrated the heart's neural control by tying off the vagus nerve. In 1753, Albrecht von Haller, a professor at Göttingen, was the first to discover the heart's automaticity and the excitation of muscle fibers. Towards the end of the 18th century, Antonio Scarpa challenged the theories of Albrecht von Haller and Johann Bernhard Jacob Behrends, who maintained that the myocardium possessed its own "irritability", on which the heartbeat depended, and was independent of neuronal sensitivity. Instead, Scarpa argued that the heart required innervation to maintain life, refuting Galenic notions. In contemporary times, the study of cardiac innervation has regained prominence, particularly in understanding the post-acute sequelae of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection (PASC), which frequently involves cardiorespiratory symptoms and dysregulation of the intrinsic cardiac innervation. Recently, it has been recognized that post-acute sequelae of acute respiratory infections (ARIs) due to other pathogens can also be a cause of long-term vegetative and somatic symptoms. Understanding cardiac innervation and modulation can help to recognize and treat long COVID and long non-COVID-19 (coronavirus disease 2019) ARIs. This analysis explores the historical foundations of cardiac neuromodulation and its contemporary relevance. By focusing on this concept, we aim to bridge the gap between historical understanding and modern applications. This will illuminate the complex interplay between cardiac function, neural modulation, cardiovascular health, and disease management in the context of long-term cardiorespiratory symptoms and dysregulation of intrinsic cardiac innervations. [ABSTRACT FROM AUTHOR]

Simple Summary: A fundamental scientific question—where did SARS-CoV-2 come from?—has eluded the scientific community since it was first identified in December 2019. SARS-CoV-2/COVID-19 is still infecting humans. Like many other viruses, SARS-CoV-2 has been regarded as an RNA virus. However, the pathological knowledge of the cause of COVID-19 and the intrinsic drivers of virus replications are unknown. Finding an answer can help in understanding the virus and preventing the next pandemic. Many COVID-19 research results at the genomic level have been published in the literature. These published results have explored the pathological causes of COVID-19 infection from various aspects. Due to the limitations of research methodology, some published results can hardly be cross-validated from cohort to cohort. As a result, there is still an urgent need to study the root causes further. We aim to find an answer to the fundamental scientific question. We use a new AI algorithm to identify critical genes at the DNA methylation level. Our results are computational with biological implications. They are interpretable, accurate, and cross-validated. They can be reproduced from Excel sheets using the derived formula. Our findings demand rigorous and much deeper study. Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87–100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic showcases the impact of mitigation and elimination strategies across the globe, including the development of effective vaccines, antiviral drugs and diagnostic tools. However, the virus changes rapidly over time. Consequently, control strategies have been limited by time-consuming experimental acquisition of three-dimensional atomic protein structures of the fast-developing mutant 'variants' of the virus, which remains an unviable strategy for fast and effective disease control. Here, we use AlphaFold2 to model the atomic structure of the ever-changing SARS-CoV-2 spike protein in silico. AlphaFold2 is an artificial intelligence (AI) deep learning computational tool capable of producing models at experimental resolution in only a few hours. Structural models for major Variants of Concern (Alpha, Delta, and Omicron) and latitude-delimited haplotypes, sets of genetically linked and highly prevalent mutations that impact the epidemic calendar of the virus, were compared to the structure of the reference Wuhan strain. We find that patterns of structural change triggered by seasonal haplotype diversification could help predict the changing face of the virus, understand seasonal behavior, and develop more resilient vaccines and drugs. The slow experimental acquisition of high-quality atomic structures of the rapidly changing proteins of the COVID-19 virus challenges vaccine and therapeutic drug development efforts. Fortunately, deep learning tools such as AlphaFold2 can quickly generate reliable models of atomic structure at experimental resolution. Current modeling studies have focused solely on definitions of mutant constellations of Variants of Concern (VOCs), leaving out the impact of haplotypes on protein structure. Here, we conduct a thorough comparative structural analysis of S-proteins belonging to major VOCs and corresponding latitude-delimited haplotypes that affect viral seasonal behavior. Our approach identified molecular regions of importance as well as patterns of structural recruitment. The S1 subunit hosted the majority of structural changes, especially those involving the N-terminal domain (NTD) and the receptor-binding domain (RBD). In particular, structural changes in the NTD were much greater than just translations in three-dimensional space, altering the sub-structures to greater extents. We also revealed a notable pattern of structural recruitment with the early VOCs Alpha and Delta behaving antagonistically by suppressing regions of structural change introduced by their corresponding haplotypes, and the current VOC Omicron behaving synergistically by amplifying or collecting structural change. Remarkably, haplotypes altering the galectin-like structure of the NTD were major contributors to seasonal behavior, supporting its putative environmental-sensing role. Our results provide an extensive view of the evolutionary landscape of the S-protein across the COVID-19 pandemic. This view will help predict important regions of structural change in future variants and haplotypes for more efficient vaccine and drug development. [ABSTRACT FROM AUTHOR]

Simple Summary: SARS-CoV-2 XBB variant was isolated in Singapore in 2022. Sequence changes and their relation with spike protein structure is studied with respect to XBB subvariants. Structural and functional distinctions of the variants is also reported. Affonity binding between the spike protein and ACE2 is reported. The relation among sequence and structure has been studied. The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom. We study the impact of sequence changes on spike protein structure on the subvariants of XBB, with particular attention to the velocity of variant diffusion and virus activity with respect to its diffusion. We examine the structural and functional distinctions of the variants in three different conformations: (i) spike glycoprotein in complex with ACE2 (1-up state), (ii) spike glycoprotein (closed-1 state), and (iii) S protein (open-1 state). We also estimate the affinity binding between the spike protein and ACE2. The market binding affinity observed in specific variants raises questions about the efficacy of current vaccines in preparing the immune system for virus variant recognition. This work may be useful in devising strategies to manage the ongoing COVID-19 pandemic. To stay ahead of the virus evolution, further research and surveillance should be carried out to adjust public health measures accordingly. [ABSTRACT FROM AUTHOR]

Simple Summary: Procoagulant extracellular vesicle (EV) concentrations have been found to increase in several diseases, but the relative contribution of small (sEVs) and large (lEVs) EVs to plasma prothrombotic potential is poorly defined. Our study shows for the first time that the concentration of tissue factor (TF)pos sEVs is significantly higher than that of lEVs. Despite this, the TF-dependent procoagulant potential is primarily sustained by lEVs, although sEVs may also contribute to factor Xa generation when TF pathway inhibitor (TFPI) activity is reduced. Also, in thromboinflammatory conditions, such as COVID-19, the enhanced procoagulant potential that characterizes the infection is predominantly supported by lEVs, although both TFpos-sEVs and -lEVs increase during the acute phase of the disease and return to normal levels with infection remission. Therefore, circulating large EVs, instead of small ones, may be identified as a promising target for a future strategy aiming at reducing the procoagulant potential of blood. The relative contribution of small (sEVs) and large extracellular vesicles (lEVs) to the total plasma procoagulant potential is not yet well defined. Thus, we compared total and TFpos-sEVs and -lEVs isolated from healthy subjects and COVID-19 patients during the acute phase of the infection and after symptom remission in terms of (1) vesicle enumeration using nanoparticle tracking assay, imaging flow cytometry, and TF immunofluorescence localization in a single-vesicle analysis using microarrays; (2) cellular origin; and (3) TF-dependent Xa generation capacity, as well as assessing the contribution of the TF inhibitor, TFPI. In healthy subjects, the plasma concentration of CD9/CD63/CD81pos sEVs was 30 times greater than that of calceinpos lEVs, and both were mainly released by platelets. Compared to lEVs, the levels of TFpos-sEVs were 2-fold higher. The TF-dependent Xa generation capacity of lEVs was three times greater than that of sEVs, with the latter being hindered by TFPI. Compared to HSs, the amounts of total and TFpos-sEVs and -lEVs were significantly greater in acute COVID-19 patients, which reverted to the physiological values at the 6-month follow-up. Interestingly, the FXa generation of lEVs only significantly increased during acute infection, with that of sEV being similar to that of HSs. Thus, in both healthy subjects and COVID-19 patients, the TF-dependent procoagulant potential is mostly sustained by large vesicles. [ABSTRACT FROM AUTHOR]

Simple Summary: SARS-CoV-2 caused the COVID-19 health emergency, affecting millions of people worldwide. Samples collected from hospitalized or dead patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage. In combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin. [ABSTRACT FROM AUTHOR]

Simple Summary: It is important to know exactly the difference in changes in Complement C5a Receptor 1 (C5aR1) levels at onset and in Immunoglobulin G (IgG) levels after recovery between severe and non-severe coronavirus disease 2019 (COVID-19) patients to reduce the severity of the disease and prevent reinfection with severe acute respiratory syndrome coronavirus 2. We found that some of these changes in C5aR1 and IgG levels over time were dependent on their initial levels and not suitable for analysis by conventional statistical tests. We developed new t-tests that correctly examine the above changes. Our new t-test suggested a greater increase in C5aR1-levels at onset and a smaller decrease in IgG-levels after recovery in COVID-19 patients than non-COVID-19 patients, which were not detected by conventional statistical tests. Thus, the clinical trials should be analyzed with not only conventional statistical tests but also our new t-test. (1) Background: It is our purpose to identify the differences in the changes in Complement C5a receptor 1 (C5aR1) levels showing the degree of inflammation at onset and Immunoglobulin G (IgG) levels showing the extent of survival of the virus fragments after recovery between coronavirus disease 2019 (COVID-19) and pneumonia coronavirus disease (non-COVID-19) for saving patients' lives. (2) Methods: First, the studies showing these markers' levels in individual patients before and after the passage of time were selected from the PubMed Central® databases with the keywords (((COVID-19) AND individual) NOT review) AND C5a/IgG. Then, no changes in these markers' levels with conventional analyses were selected from the studies. Finally, the no changes were reexamined with our new two-tailed t-test using the values on the regression line between initial levels and changed levels instead of the mean or median of changed levels as the expected values of changed levels. (3) Results: Not conventional analyses but our new t-test suggested a greater increase in C5aR1-levels at onset and a smaller decrease in IgG-levels after recovery in COVID-19 patients than non-COVID-19 patients. (4) Conclusion: Our new t-test also should be used in clinics for COVID-19 patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Accumulating evidence suggests that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may have the potential to induce pancreatic β-cell damage, leading to diabetes onset in patients with coronavirus disease 2019 (COVID-19). However, controversial results have been reported among study groups. Here, we provide a comprehensive review of published findings that describe the potential relationship between SARS-CoV-2 infection (COVID-19) and pancreatic β-cell failure, and how this may contribute to the development of diabetes. SARS-CoV-2 infection primarily causes pulmonary symptoms; however, accumulating reports indicate that some patients with COVID-19 have multiple organ dysfunction or failure. Although diabetes is considered a risk factor for severe COVID-19, SARS-CoV-2 infection may also be a causal factor for diabetes mellitus in patients with COVID-19. According to the research reviewed in this paper, the pancreas and pancreatic β cells appear to be targets of SARS-CoV-2 and are damaged by direct or indirect effects of the infection. However, controversial results have been reported between study groups, mainly due to the limited number of cases with diabetes precipitated by COVID-19. In this review, we comprehensively discuss the published findings on the potential association between SARS-CoV-2 infection or COVID-19 and pancreatic β-cell damage leading to diabetes onset. These findings will further contribute to our understanding of the pathogenesis of diabetes mellitus. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 is a rapidly spreading and mutating pandemic. In the case of some people the disease can be fatal It has been observed that weight and age are parameters of comorbidity If it is difficult to take into account the uncertainties related to the combination of the two parameters to build up a model of simulation. Therefore, we propose in this article a SIR/SIH model with fuzzy parameters which allows us to simulate the pandemic in the absence of barrier actions and vaccines. In this paper, we propose a multi-group SIR to simulate the spread of COVID-19 in an island context. The multi-group aspect enables us to modelize transmissions of the virus between non-vaccinated individuals within an age group as well as between different age groups. In addition, fuzzy subsets and aggregation operators are used to account for the increased risks associated with age and obesity within these different groups. From a conceptual point of view, the model emphasizes the notion of Hospitalization which is the major stake of this pandemic by replacing the compartment R (Removed) by compartment H (Hospitalization). The experimental results were carried out using medical and demographic data from the archipelago, Guadeloupe (French West Indies) in the Caribbean. These results show that without the respect of barrier gestures, a first wave would concern the elderly then a second the adults and the young people, which conforms to the real data. [ABSTRACT FROM AUTHOR]

Simple Summary: The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first encountered in December of 2019 in Wuhan, China. As of now, there have been over 200 million infections and 4 million deaths attributed to the virus. Due to this, it has been a priority to find an effective preventative measure, and numerous vaccines have been developed. Although the developed vaccines share the target of blocking viral entry by the spike protein, their pharmacology and efficacy differs. As such, the mechanism of action and the elicited immune response of the most common COVID-19 vaccines have been compared to help determine which vaccine is most efficacious and is best suited to prevent reinfection and address viral mutations. It has been over a year since SARS-CoV-2 was first reported in December of 2019 in Wuhan, China. To curb the spread of the virus, many therapies and cures have been tested and developed, most notably mRNA and DNA vaccines. Federal health agencies (CDC, FDA) have approved emergency usage of these S gene-based vaccines with the intention of minimizing any further loss of lives and infections. It is crucial to assess which vaccines are the most efficacious by examining their effects on the immune system, and by providing considerations for new technological vaccine strategies in the future. This paper provides an overview of the current SARS-CoV-2 vaccines with their mechanisms of action, current technologies utilized in manufacturing of the vaccines, and limitations in this new field with emerging data. Although the most popular COVID-19 vaccines have been proven effective, time will be the main factor in dictating which vaccine will be able to best address mutations and future infection. [ABSTRACT FROM AUTHOR]

Simple Summary: Coronavirus disease 2019 (COVID-19) is a respiratory disease caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has led to millions of deaths globally. The rollout of SARS-CoV-2 vaccines has effectively reduced the morbidity and mortality of COVID-19, with messenger RNA (mRNA)-based vaccines being widely administrated. While neutralizing antibodies are crucial, CD8+ T cells induced by the vaccine may also play a significant role in early and long-term protection. This review explores the antiviral function of CD8+ T cells and their response to mRNA vaccines, as well as their role in immune protection in the lymph nodes. SARS-CoV-2 vaccines have played a crucial role in effectively reducing COVID-19 disease severity, with a new generation of vaccines that use messenger RNA (mRNA) technology being administered globally. Neutralizing antibodies have featured as the heroes of vaccine-induced immunity. However, vaccine-elicited CD8+ T cells may have a significant impact on the early protective effects of the mRNA vaccine, which are evident 12 days after initial vaccination. Vaccine-induced CD8+ T cells have been shown to respond to multiple epitopes of SARS-CoV-2 and exhibit polyfunctionality in the periphery at the early stage, even when neutralizing antibodies are scarce. Furthermore, SARS-CoV-2 mRNA vaccines induce diverse subsets of memory CD8+ T cells that persist for more than six months following vaccination. However, the protective role of CD8+ T cells in response to the SARS-CoV-2 mRNA vaccines remains a topic of debate. In addition, our understanding of CD8+ T cells in response to vaccination in the lymph nodes, where they first encounter antigen, is still limited. This review delves into the current knowledge regarding the protective role of polyfunctional CD8+ T cells in controlling the virus, the response to SARS-CoV-2 mRNA vaccines, and the contribution to supporting B cell activity and promoting immune protection in the lymph nodes. [ABSTRACT FROM AUTHOR]

Simple Summary: It is known that COVID-19 causes dynamic changes in the immune system. At different stages of the course of COVID-19, the immune cells may exhibit different patterns, which have not been fully uncovered. In this study, a machine-learning-based method was designed to deeply analyze the scRNA-seq data of three types of immune cells from patients with COVID-19, including B cells, T cells, and myeloid cells. Four levels of COVID-19 severity/outcome were involved for each cell type. As a result, several essential genes were obtained, and some of them could be confirmed to be related to SARS-CoV-2 infection. As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system. [ABSTRACT FROM AUTHOR]

Simple Summary: Infectious diseases have been part of human history. Countless epidemics have produced high mortality rates in vulnerable populations. With the understanding of the spread of these types of diseases, population groups have been able to adapt and better cope with infections. Given the COVID-19 pandemic, one of the strategies used is the modeling of infectious diseases with the aim of establishing protection measures for people and stopping the spread of the epidemic. Our study evaluates protection strategies through infectious disease modeling with COVID-19 data in a commune in Chile. The results of the simulations indicate that the model generates important protection for the population by recognizing the super-propagating people (bridge nodes). This type of protection can be key in the fight against COVID-19. Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-W α ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-W α , considering different protection budgets. Furthermore, we consider three different values for α ; in this way, we compare how the protection performs according to the value of α. [ABSTRACT FROM AUTHOR]

Simple Summary: Identified in 2019, the novel coronavirus SARS-CoV-2 causes the disease entity named COVID-19 (coronavirus disease-19). Similarly, in 2003, SARS-CoV that causes severe acute respiratory syndrome (SARS) was identified. This has raised concerns about coronaviruses as disease-causing agents in both humans and animals. This literature review provides an extensive discussion of the relationship between metal exposure and menopause in women with a particular focus on the impact of menopause on the course of COVID-19 and the relationship between metals and SARS-CoV-2 infection in humans. A growing number of reports point to the possible role of environmental factors in determining the age of onset of menopause. Specific metals, such as mercury, cadmium, arsenic and lead can lead to fertility disorders, to endocrine dysregulation, and in addition, their high blood concentrations correlate with the onset of menopause. Changing concentrations of hormones in the blood during this period of a woman's life can also have an impact on SARS-CoV-2 infection, and excessively high or low levels of metals may also be an important predictor for the course of COVID-19. Postmenopausal women are exposed to greater risk of serum biochemical changes, and with the possibility of nutritional disturbances, particularly involving trace minerals, the risk of age-related diseases is very high during this period. These adverse changes in serum trace minerals should be taken into consideration for the early diagnosis and prevention of menopause-related diseases. Dietary supplementation may be necessary, especially where levels are significantly reduced. We performed a manual search of scientific articles cited in major electronic databases (PubMed, EMBASE, Web of Science and Google Scholar) in November 2022 to identify studies relevant to the relationship between metals, COVID-19 and menopause. The effects of metals on the course of menopause is a broad topic and should certainly still be a subject of research, due to, among other things, continuing environmental pollution and the use of metals in many areas of life. [ABSTRACT FROM AUTHOR]

Simple Summary: One of the challenges facing the countries to contain the COVID-19 is to trace people that were in contact with an infected person. Failing to identify the possible infected people leads to unreported cases of the COVID-19, which results in massive infection among the population and even superinfection events. In this work, we study the impact of the lockdown implemented by three North African countries on reducing the infections in the pandemic's first wave. Then, we investigate the effect of the unreported cases in the increase of the number of infected people when each country relaxed the population's mobility in the "Eid" period, resulting in the second wave of the COVID-19. In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple's mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the inception of COVID-19, the international and national agencies across the globe have issued several guidelines and recommendations for the public as well as to the healthcare professionals. The fast spreading nature of this disease has brought various changes in the screening and treatment protocols in medicine and dentistry. Oral radiology is an integral part of oral screening and various dental treatment strategies. During oral radiology procedures, oral radiologists/radiology technicians come in direct contact with the patient as well as their body fluids such as saliva. It increases the risk of disease transmission from patients to oral radiologist and vice versa. Hence, proper infection control protocol should be implemented with new guidelines. This paper explains the practices to be followed in the oral radiology clinics at various stages whether in commercial set up or academic institute. Additionally, it explains the various precautions and new recommendations to be taken during various radiographic techniques. To add more, emphasis the importance of safe distancing, barrier protection and decontamination to be followed in dental clinics to prevent the spread of infection. Furthermore, the feasibility and procedure of online teaching methods for oral radiology clinics has been discussed. The pandemic of Coronavirus disease (COVID-19) has emerged as a global catastrophe that is plaguing mankind. In the past eight months since the world discovered about COVID-19, we learned a lot about server acute respiratory syndrome coronavirus 2 (SARS CoV-2) and perhaps there is much more to discover and understand about the virus. With the current understanding of the disease, we assume it will remain in an active state of transmission and progression among the community for a long time. Thus, it is advisable to adopt the disease's prevention protocol in our daily and work routine. During this pandemic patient requiring dental treatment cannot be neglected and the role of dental imaging is crucial in delivering treatment. Hence, this article attempts to provide an evidence-based compilation about the mode of transmission and clinical features of COVID-19. It also throws light on the potential source of disease transmission in the dental radiology setting. In addition, it suggests preventive measures to curb the infection and infrastructural model of the clinical setting that will assist in achieving control over the disease transmission. This article intends to project a strategy about protocols, infrastructure, and daily activities in a dental radiology office that institutions can adopt with modifications according to their local scenario. [ABSTRACT FROM AUTHOR]

Simple Summary: We presented a comprehensive, systematic literature review of all published New Psychoactive Substances (NPS)-related intoxications and fatalities during the COVID-19 pandemic (from January 2020 to March 2022). Public implications, such as isolation and social distancing, may have reduced consumption of some drugs. These stressful conditions brought an increase in the use of other drugs, with the illicit market and related misuse of drugs moving to different drugs of abuse, such as NPS. More than 200 cases were reported in Europe, UK, USA and Japan during the pandemic period, with synthetic opioid, synthetic cannabinoids and synthetic cathinones the most representative NPS classes. Importantly, the combined consumption of several NPS classes comprised 30% of all cases. Considering that the pandemic may have reduced the capabilities of forensic toxicology laboratories to report comprehensive information, the data could have led to an underestimation. In January 2020, the World Health Organization (WHO) issued a Public Health Emergency of International Concern, declaring the COVID-19 outbreak a pandemic in March 2020. Stringent measures decreased consumption of some drugs, moving the illicit market to alternative substances, such as New Psychoactive Substances (NPS). A systematic literature search was performed, using scientific databases such as PubMed, Scopus, Web of Science and institutional and government websites, to identify reported intoxications and fatalities from NPS during the COVID-19 pandemic. The search terms were: COVID-19, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2, coronavirus disease 2019, intox*, fatal*, new psychoactive substance, novel psychoactive substance, smart drugs, new psychoactive substance, novel synthetic opioid, synthetic opioid, synthetic cathinone, bath salts, legal highs, nitazene, bath salt, legal high, synthetic cannabinoid, phenethylamine, phencyclidine, piperazine, novel benzodiazepine, benzodiazepine analogue, designer benzodiazepines, tryptamine and psychostimulant. From January 2020 to March 2022, 215 NPS exposures were reported in Europe, UK, Japan and USA. Single NPS class intoxications accounted for 25, while mixed NPS class intoxications represented only 3 cases. A total of 130 NPS single class fatalities and 56 fatalities involving mixed NPS classes were published during the pandemic. Synthetic opioids were the NPS class most abused, followed by synthetic cathinones and synthetic cannabinoids. Notably, designer benzodiazepines were frequently found in combination with fentalogues. Considering the stress to communities and healthcare systems generated by the pandemic, NPS-related information may be underestimated. However, we could not define the exact impacts of COVID-19 on processing of toxicological data, autopsy and death investigations. [ABSTRACT FROM AUTHOR]

Simple Summary: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic coronavirus disease-2019 (COVID-19), which can lead to severe pneumonia and acute respiratory distress syndrome (ARDS). The immune system plays a key role in the defense against COVID-19. Nonetheless, if dysregulated and hyperactivated, the immune response can trigger the deleterious "cytokine storm". Multiple studies are aimed at finding potential immunomodulators to rebalance the immune response against the virus, thus limiting its dangerous effects. Moreover, the development of vaccines against SARS-CoV-2 has represented a global challenge. The aim of this narrative review is to describe the state-of-the-art knowledge on the immune system response in COVID-19 and to underline the main therapeutic strategies and the key role of vaccines. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative pathogen of coronavirus disease 19 (COVID-19). COVID-19 can manifest with a heterogenous spectrum of disease severity, from mild upper airways infection to severe interstitial pneumonia and devastating acute respiratory distress syndrome (ARDS). SARS-CoV-2 infection may induce an over activation of the immune system and the release of high concentrations of pro-inflammatory cytokines, leading to a "cytokine storm", a recognized pathogenetic mechanism in the genesis of SARS-CoV-2-induced lung disease. This overproduction of inflammatory cytokines has been recognized as a poor prognostic factor, since it can lead to disease progression, organ failure, ARDS and death. Moreover, the immune system shows dysregulated activity, particularly through activated macrophages and T-helper cells and in the co-occurrent exhaustion of lymphocytes. We carried out a non-systematic literature review aimed at providing an overview of the current knowledge on the pathologic mechanisms played by the immune system and the inflammation in the genesis of SARS-CoV-2-induced lung disease. An overview on potential treatments for this harmful condition and for contrasting the "cytokine storm" has also been presented. Finally, a look at the experimented experimental vaccines against SARS-CoV-2 has been included. [ABSTRACT FROM AUTHOR]

Simple Summary: SARS-CoV-2 infection is now known to be associated with several adverse events. However, not much is known about the effect it may have if contracted during pregnancy, on the proper development of the placenta or, subsequently, of the fetus itself. Studies have shown that there may be an increased incidence of developing pathological conditions, such as preterm delivery, fetal growth retardation, the onset of gestational diabetes or hypertensive disorders. In this study, several cases of women who became infected at different times during pregnancy (during the first, second or third trimester) were examined, and in particular it was assessed whether the infection had a role in altering the proper vascular development of the placenta. The greatest alterations were observed when infection occurred during the third trimester; alterations in the expression levels of certain markers of vasculogenesis such as the presence of fibrin deposits, lymphocyte infiltration in the villi, edema and thrombi were observed. Further studies are needed, however, as the mechanisms involved are not yet clear and therefore it is not yet possible to think of therapeutic strategies. During pregnancy, SARS-CoV-2 infection is associated with several adverse outcomes, including an increased risk of pre-eclampsia, preterm delivery, hypertensive disorders, gestational diabetes, and fetal growth restriction related to the development of placenta vascular abnormalities. We analyzed human placenta from full-term, uncomplicated pregnancies with SARS-CoV-2 infection during the first, second, or third trimesters of gestation. We studied, by the immunohistochemistry technique, the expression of CD34 and podoplanin (PDPN) as markers of vasculogenesis to find any differences. As secondary outcomes, we correlated maternal symptoms with placental histological alterations, including fibrin deposits, lymphocyte infiltration in the villi, edema, and thrombi. Our results showed a PDPN expression around the villous stroma as a plexiform network around the villous nucleus of fetal vessels; significant down-regulation was observed in the villous stroma of women infected during the third trimester. CD34 showed no changes in expression levels. During SARS-CoV-2 infection, the most common maternal symptoms were fever, anosmia, ageusia and asthenia, and the majority were treated with paracetamol, corticosteroids and azithromycin. Patients that required multiple symptomatic treatments evidenced a large amount of fibrin deposition in the villi. Certainly, PDPN plays a key role in healthy placental vasculogenesis and thus in its proper physiology, and SARS-CoV-2 surely alters its normal expression. Further studies are necessary to understand what mechanisms are being altered to try to avoid possible complications for both the mother and fetus in terms of the contagions that will still occur. [ABSTRACT FROM AUTHOR]

Simple Summary: Almost three years into the pandemic, older individuals still account for the vast majority of COVID-19 related deaths. While most older adults in the United States have been fully vaccinated, many have not yet completed the recommended course of booster vaccinations. To address U.S. data gaps on Vaccine Effectiveness (VE) in this high-risk population and to best formulate effective booster and other prevention policies, we conducted an observational study of VE in a 17 million Medicare population cohort including 5.7 million fully vaccinated individuals during the Delta variant phase of the pandemic. We observed significant VE in this higher risk population against infections and more so against hospitalizations, apparent waning vaccine-induced immunity, and found added protection from prior COVID-19 infection. We also uniquely report that VE increased over a period of weeks as the Delta surge progressed, suggesting that in addition to the critical protective role of vaccination, individual behavioral factors, such as increased masking or social distancing, likely contributed to this VE increase. Our results emphasize the need for a multipronged prevention strategy to combat the ongoing COVID-19 pandemic including not only vaccination but also complementary preventive measures to reduce the risk of infection, especially for individuals at the highest risk. Throughout the pandemic, individuals 65 years and older have contributed most COVID-19 related deaths. To best formulate effective vaccination and other prevention policies to protect older adults, large scale observational studies of these higher risk individuals are needed. We conducted a Vaccine Effectiveness (VE) study during the B.1.617.2 Delta variant phase of the pandemic in July and August 2021 in a cohort of 17 million Medicare beneficiaries of which 5.7 million were fully vaccinated. We found that individuals fully vaccinated with the Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273 vaccines in January 2021 had 2.5 times higher breakthrough infections and hospitalizations than those fully vaccinated in March 2021, consistent with waning of vaccine-induced immunity. Measuring VE weekly, we found that VE against hospitalization, and even more so against infection, increased from July 2021 through August 2021, suggesting that in addition to the protective role of vaccination, increased masking or social distancing might have contributed to the unexpected increase in VE. Ongoing monitoring of Medicare beneficiaries should be a priority as new variants continue to emerge, and the VE of the new bivalent vaccines remains to be established. This could be accomplished with a large Medicare claims database and the analytics platform used for this study. [ABSTRACT FROM AUTHOR]

The rampant spread of COVID-19 and the worldwide prevalence of infected cases demand a rapid, simple, and cost-effective Point of Care Test (PoCT) for the accurate diagnosis of this pandemic. The most common molecular tests approved by regulatory bodies across the world for COVID-19 diagnosis are based on Polymerase Chain Reaction (PCR). While PCR-based tests are highly sensitive, specific, and remarkably reliable, they have many limitations ranging from the requirement of sophisticated laboratories, need of skilled personnel, use of complex protocol, long wait times for results, and an overall high cost per test. These limitations have inspired researchers to search for alternative diagnostic methods that are fast, economical, and executable in low-resource laboratory settings. The discovery of Loop-mediated isothermal Amplification (LAMP) has provided a reliable substitute platform for the accurate detection of low copy number nucleic acids in the diagnosis of several viral diseases, including epidemics like Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). At present, a cocktail of LAMP assay reagents along with reverse transcriptase enzyme (Reverse Transcription LAMP, RT-LAMP) can be a robust solution for the rapid and cost-effective diagnosis for COVID-19, particularly in developing, and low-income countries. In summary, the development of RT-LAMP based diagnostic tools in a paper/strip format or the integration of this method into a microfluidic platform such as a Lab-on-a-chip may revolutionize the concept of PoCT for COVID-19 diagnosis. This review discusses the principle, technology and past research underpinning the success for using this method for diagnosing MERS and SARS, in addition to ongoing research, and the prominent prospect of RT-LAMP in the context of COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Simple Summary: For the last two years, COVID-19 has been rigorously studied aiming to identify novel prognostic and therapeutic avenues. Recently, T cell receptor profiling has emerged as a method to associate adaptive immunity with COVID-19 progression and severity. Such data are typically analyzed to explore T cell receptor properties and characteristics in the context of SARS-CoV-2 infection. However, the equally informative alternative analytic strategy of identifying any preferential recognition of viral antigens by the T-cell-mediated immune response is mostly overlooked. In this study, we propose a novel Machine-Learning-oriented approach for analyzing T cell receptor repertoires that is based on the concept of utilising the level at which each SARS-CoV-2 antigen is recognised by the available T cell receptors in each sample from COVID-19-convalescent and healthy cohorts. This approach also allowed us to observe a group of T cell receptors capable of recognising SARS-CoV-2 antigens that were already established in samples from the healthy cohort, leading us to the cross-reactivity phenomenon hypothesis. To explore this, all T cell receptors were examined for being able to recognise antigens from other pathogens and diseases, unveiling evidence of putative cross-reactivity with M. tuberculosis and Influenza virus, among others. During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases. [ABSTRACT FROM AUTHOR]

Simple Summary: As one of the two cases of soda lakes in the world, Wadi El-Natrun salt lakes are the intended isolation source in this study. They encompass a characteristic microbial niche of symbionts producing unique bioactive agents, among them carotenoids. Herein, halophilic carotenoid-producing microbes were isolated and purified. Based on the carotenoids content, the potent isolate, namely Virgibacillus halodenitrificans, was molecularly identified, and characterized physiologically, biochemically and morphologically as well. Its carotenoids content was optimized using statistical methods. Under optimized conditions, the maximum productivity was obtained and the extracted carotenoids were identified by UV-Vis analysis, FTIR, Raman spectroscopy, TLC and LC–MS, which confirmed such halophilic carotenoids composed of β-carotene, lutein and β-Apo-8′-carotenal mixture. Meanwhile, the carotenoids were utilized as a powerful antibacterial and antifungal agent. Notably, they exerted higher antibiofilm potentiality at a concentration of 20 μg/mL. Finally, halophilic carotenoids are competing as new biocidal agents, in particular with their biocompatibility and efficiency. Carotenoids, as phytonutrient pigments, are signified by their unique beneficial features that serve human health and the surrounding ecosystem. Haloalkaliphiles from soda lakes produce different natural bioactive molecules; however, their ability to produce carotenoids has been limited. Therefore, this study focused on the screening and isolation of carotenoid-producing haloalkaliphilic microbes. Out of 10 isolates, a powerful carotigenic bacterium was isolated, characterized phenotypically and identified on the molecular level as Virgibacillus halodenitrificans. By employing statistical approaches like Plackett–Burman design and central composite design, the influence of significant nutritional variables on carotenoids production was screened and optimized. Predictive modeling manifested that carotenoid yield was 36.42 mg/mL, a 2.12-fold enhancement compared to the basal conditions through inoculating 1.8% of bacterial biomass on optimized medium containing yeast extract (2 g/mL), peptone (10 g/mL) and NaCl (233.6 g/mL). The carotenoids content was confirmed by UV-Vis spectrum which detected a characteristic unique peak with left and right shoulders at 461 nm, 490 and 522 nm. However, FTIR and Raman spectroscopy showed the presence of several functional groups. Meanwhile, LC–MS confirmed that the examined carotenoids were composed of β-carotene, lutein and β-Apo-8′-carotenal mixture. As a bioactive agent, the carotenoids of V. halodenitrificans DASH showed characteristic antagonistic potency against a wide spectrum of Gram-positive and Gram-negative pathogens. Interestingly, a potent antifungal capacity was observed against Candida albicans, reflecting promising mycocidal efficacy against COVID-19 white fungal post-infections. Furthermore, carotenoids (20 μg/mL) inhibited the biofilm formation of P. aeruginosa and S. aureus by 54.01 ± 3.97% and 80.082 ± 0.895%, respectively. Our results proposed that haloalkaliphiles of Wadi El-Natrun lakes are promising sources of carotenoids that exhibited efficiency as safe, biocompatible and natural bioactive agents for environmental, medical and industrial applications. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 related symptoms and severity of infection vary greatly among individuals and althoμgh various factors including age, gender, obesity, and underlying co-morbidities are sμggested to be the confounding variables that affect the severity of disease, a small percentage of apparently healthy and young people also succumb to disease, this was especially true before the COVID-19 vaccines were developed. Recently, few studies have sμggested that COVID-19 severity is lower in individuals who followed a plant based and pescatarian diet. Aim of the current study was to examine whether symptoms experienced by COVID-19 patients can be predictive of mild and long-term disease pre-vaccination and if the intake of specific nutrients as well as serum 25(OH)D concentrations are related to the severity of symptoms experienced by individuals. Additionally, we performed longitudinal and cross-sectional analysis on antibodies against SARS-CoV-2 to examine the duration and degree of protection conferred by natural immunity after SARS-CoV-2 infection. We found several nutrients such as vitamin E, poly-unsaturated fatty acids, and fibre amongst others to be possibly linked to a reduced COVID-19 severity. The heterogeneity of the severity of symptoms of COVID-19 experienced by the young and healthy individuals is poorly understood. The present study was undertaken to mainly examine whether the respective diets and the type of symptoms experienced by patients are predictive of having long COVID-19. Disease severity was assessed with a symptomatology questionnaire and used to group 55 participants in asymptomatic (AS), mild symptoms (S) and long COVID (LC). We found that experiencing a higher number of symptoms as well as fatigue were predictors of developing LC whereas those who experienced rhinorrhea were less likely to develop LC. Blood samples were also taken to measure vitamin D [25(OH)D] concentrations and duration of spike IgG antibodies. In this study, serum 25(OH)D was not significantly different between 3 symptom groups with median (IQR) ng/mL levels of 22.0 (12.3) in the AS, 22.3 (7.5) in S, and 24.9 (9.4) in the LC group (p ≥ 0.05). The duration of IgG antibody response was found to vary greatly, with some individuals showing raised IgG over a year after infection. To examine whether dietary factors can influence the severity of symptoms, diet was analysed using 4–7-day food diaries as well as a Food Frequency Questionnaire (FFQ). Some nutrients such as vitamin E, polyunsaturated fatty acids, fibre, and iron were associated with lower severity of COVID-19. Lower intake of vitamin E was associated with having LC with a median (IQR) intake of 6.2 mg (3.8) seen in LC vs. 8.6 mg (7.2) in the AS group (p = 0.047). This pilot study has highlighted a few differences in the number and type of symptoms experienced by the young non-hospitalised individuals with mild and long COVID-19 and identified a few dietary components for their potential protective role against long COVID-19, however, the findings need to be confirmed with further large scale studies. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the early stages of the COVID-19 pandemic, it was recognized that male patients risked a more severe form of the disease. This suggested that, among other factors, a genetic predisposition could justify this issue. Thus, we investigated the CAG repeat polymorphism of the Androgen Receptor gene in a group of 142 recovered patients. Reduced androgenic signaling predisposes men to a more severe form: low testosterone levels and reduced androgen receptor activity (CAG > 23) expose the host to an excessive inflammatory response, leading downstream to the multi-organ damage seen in severe COVID-19. During the COVID-19 pandemic, the most severe form of the disease was most often seen in male patients. The aim of this study was to identify any male predispositions that could be used to predict the outcome of the disease and enable early intervention. We investigated CAG polymorphism in the androgen receptor gene and serum levels of testosterone and LH, which were considered as probably responsible for this predisposition. The study involved 142 patients who had recovered from COVID-19 at least three months previously and were classified according to their disease severity using the World Health Organization (WHO) classification. We observed a significant increase in the number of CAG repeats with increasing disease severity: the percentage of patients with more than 23 repeats increased two-fold from Grade I to Grade IV. Furthermore, testosterone levels were significantly lower in patients with severe disease. Reduced androgenic signaling could predispose men to a more severe form: low testosterone levels and a reduced androgen receptor activity (CAG > 23) expose the host to an excessive inflammatory response, leading downstream to the multi-organ damage seen in severe COVID-19. [ABSTRACT FROM AUTHOR]

Simple Summary: People who use drugs represent a category of patients to be prioritized for antiviral treatment for the purpose of hepatitis C elimination, due to their younger age and the major risk of viral transmission, acting as a virus reservoir. The treatment challenges for hepatitis C in this population are related to an ineffective linkage to care, poor adherence to treatment, and follow-up and the risk of re-infection. The COVID-19 pandemic has further exacerbated these conditions, increasing the concerns among clinicians regarding the effectiveness of their treatment. In our study, we describe a novel "patient-tailored" model-of-care for people who use drugs. The antiviral therapy was adapted to the needs of the patient and monitored remotely by a hepatological specialist, in order to decentralize the point of care within the addiction center. The study was conducted before and during the COVID-19 pandemic, clearly demonstrating the model's high effectiveness in the linkage to care, adherence, and response to antiviral therapy. People who use drugs (PWUDs) are generally considered "hard-to-treat" patients, due to adherence to HCV antiviral therapy or re-infection concerns. Linkage-to-care still remains a significant gap for HCV elimination, worsened by the COVID-19 pandemic. To reduce time-to-treat and improve treatment adherence, we have developed a patient-tailored model-of-care, decentralized within the addiction center and supervised remotely by hepatologists. From January 2017 to December 2020, patients were enrolled in one addiction care center in Southern Italy, where a complete hepatologic assessment, including blood chemistry, ultrasound, and transient elastography examination, was provided. DAAs treatment has been adapted on clinical features, also performing a daily administration during an outpatient visit, and monitored remotely by specialists via telemedicine interactions. Adherence was evaluated on the accomplishment of therapy or on the percentage of attended visits. From a total of 690 PWUDs, 135 had an active HCV infection and were enrolled in the study. All patients started the treatment within 3 weeks after HCV diagnosis. Six drop-outs were recorded, obtaining a sustained virological response at week 12 (SVR12) in 98.5% of PWUDs. There were only two cases of treatment failure, one of which is re-infection. No differences were found between the SVR12 rates before and during the COVID-19 pandemic. We obtained a high SVR12 rate, providing a comprehensive assessment within the addiction care center, tailoring the drug administration with a hepatologic remote stewardship. Our therapeutic model should improve the time-to-treat and treatment adherence in PWUDs. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent studies associated COVID-19 with coagulation dysfunction and increased risk of thromboembolism. Consequently, pulmonary embolism (PE) development and higher odds of mortality were observed in this group of patients. CRP levels, which are considered a sensitive marker of systemic inflammation, were linked to the higher rates of PE and immunologic processes associated with atherosclerosis development and thrombus formation. Several microelements, including zinc and copper, were shown to present anti-inflammatory and anti-oxidative characteristics. Due to these properties, and they're in-fluence on platelets activation and thrombus formation, zinc and copper should be evaluated as a potential therapeutic option during COVID-19. To date, many studies have proved that COVID-19 increases the incidence of thrombus formation and coagulopathies but the exact mechanism behind such a disease outcome is not well known. In this review we collect the information and discuss the pathophysiology of thrombus formation in patients with pulmonary embolism in the course of COVID-19 disease and the role of zinc and copper in the process. Supplementation of zinc and copper may be beneficial for COVID-19 patients due to its anti-inflammatory and anti-oxidative properties. On the other hand, excess of those microelements in the organism may be harmful, that is why marking the level of those micronutrients should be done at first. We also propose further investigation of diagnostic and therapeutic options of zinc and copper in course of COVID-19 thrombus formation to their potential in patient care, with particular emphasis on the dosage and the duration of their misbalance. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 is an infectious disease caused by SARS-CoV-2. The virus has rapidly spread to humans, causing the ongoing coronavirus pandemic. Enormous progress in finding therapies has been made, but an effective therapy is still absent. In this study, we propose a computational strategy aimed at identifying novel multi-target scaffolds against the virus. The proposed study was performed using bioinformatics methods, such as homology modeling, virtual screening and classical molecular dynamics, where 3D structures of targets were reconstructed and compounds with potential inhibitory activity against different virus targets were identified. Furthermore, a potential mechanism of action was proposed. Our study could provide new insights and approaches for the rapid identification of novel multi-target inhibitors of SARS-CoV-2. Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), an infectious disease that spreads rapidly in humans. In March 2020, the World Health Organization (WHO) declared a COVID-19 pandemic. Identifying a multi-target-directed ligand approach would open up new opportunities for drug discovery to combat COVID-19. The aim of this work was to perform a virtual screening of an exclusive chemical library of about 1700 molecules containing both pharmacologically active compounds and synthetic intermediates to propose potential protein inhibitors for use against SARS-CoV-2. In silico analysis showed that our compounds triggered an interaction network with key residues of the SARS-CoV-2 spike protein (S-protein), blocking trimer formation and interaction with the human receptor hACE2, as well as with the main 3C-like protease (3CLpro), inhibiting their biological function. Our data may represent a step forward in the search for potential new chemotherapeutic agents for the treatment of COVID-19. [ABSTRACT FROM AUTHOR]

Simple Summary: With the current coronavirus disease 2019 (COVID-19) crisis, humans have developed new habits and adapted to a novel socioeconomic reality. Indeed, measures including confinement and lockdown have led to mental health problems, economic crisis, and social isolation, among other consequences. These consequences, along with hand washing, sanitization, and face masks, would reduce our immunity against infections, including COVID-19. Such reduced immunity could impact not only our vulnerability to diseases but also the efficacy of vaccines that carry the biggest hope to putting an end to this COVID-19 pandemic. Thus, there is a need to review these approaches and optimize measures taken to limit the spread of COVID-19 by taking into consideration the possible impact of these measures on our immunity to fight COVID-19. The ongoing coronavirus disease 2019 (COVID-19) crisis has led to a new socioeconomic reality with the acquisition of novel habits. Measures imposed by governments and health authorities such as confinement and lockdown have had important consequences, including mental health problems, economic crisis, and social isolation. Combined with newly acquired habits such as hand washing, sanitization, and face masks, these have all directly and indirectly led to reduced immunity. Such effects on the immune system not only impact the epidemiological profile with respect to COVID-19 and other infectious diseases but also limit the efficacy of the ongoing anti-COVID-19 vaccination campaign. Therefore, there is a need to review these approaches and optimize measures towards better population immunity, which is much needed during such an epidemic. [ABSTRACT FROM AUTHOR]

Simple Summary: As of October 2021, over 245 million people have been infected and nearly 5 million have died due to COVID-19. Atherosclerosis is a lipid-driven chronic inflammation of the arterial wall with coronary artery disease (CAD) that may lead to acute coronary syndrome (ACS), which remains the main cause of death in developed countries. We believe that due to CAD development factors affecting the vessel wall, the SARS-CoV-2 infection may precipitate further advancement of future thromboembolic events by a plaque erosion or rupture. Therefore, this metanalysis aims to unambiguously establish the role of the history of CAD in mortality and severity of COVID-19 disease. Coronary artery disease (CAD) is the leading cause of death worldwide. Patients with pre-existing CAD were shown to have a more severe course of COVID-19, but this association has not been clarified. We performed a meta-analysis to determine the association between CAD and COVID-19 outcomes. We searched Scopus, Medline (PubMed), Web of Science, Embase, and Cochrane databases up to 2 November 2021. There were 62 studies with a total population of 49,286 patients included in the meta-analysis. CAD occurrence in survivor vs. non-survivor groups varied and amounted to 9.2% vs. 22.9%, respectively (OR = 0.33; 95%CI: 0.29 to 0.39; I2 = 70%; p < 0.001). CAD was also associated with increased severity of COVID-19 disease and was (10.8% vs. 5.6%, respectively, for severe vs. non-severe groups (OR = 2.28; 95%CI: 1.59 to 3.27; I2 = 72%; p < 0.001). The role of history of CAD in mortality and severe condition in COVID-19 presents itself as prominent—although a risk of bias in retrospective trials needs to be assessed, in case of our meta-analysis the statistically significant results when it comes to higher mortality among patients with CAD compared to non-CAD patients, a more severe condition observed in patients with CAD, and a visibly more frequent admission to intensive care unit in patients with CAD, it seems that an incidence of cardiovascular events plays a role in COVID-19 prognosis. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 is a disease caused by the SARS-CoV-2 coronavirus spreading mainly through person-to-person contact. It has caused millions of deaths around the world and lasting health problems in individuals who have survived the disease. This review concisely summarizes certain issues related to COVID-19 with a focus on elements and gives an update on clinical trials where some minerals will be tested/have been tested alone or in combination with drugs, vitamins, or plant extracts/herbal formulations in COVID-19 patients and in those at higher COVID-19 risk. The current report provides a brief overview of the clinical features, hematological/biochemical abnormalities, biomarkers, and AI-related strategies in COVID-19; presents in a nutshell the pharmacological and non-pharmacological therapeutic options; and concisely summarizes the most important aspects related to sociodemographic and behavioral factors as well as comorbidities having an impact on this disease. It also gives a brief outline of the effect of selected elements on immune response and collects data on the levels of micro-/macro-elements and toxic metals in the blood/urine of SARS-CoV-2 infected patients and on supplementation with minerals in COVID-19 subjects. Moreover, this review provides an overview of clinical trials based on the use of minerals alone or in combination with other agents that can provide effective responses toward SARS-CoV-2 infection. The knowledge compiled in this report lays the groundwork for new therapeutic treatments and further research on biomarkers that should be as informative as possible about the patient's condition and can provide more reliable information on COVID-19 course and prognosis. The collected results point to the need for clarification of the importance of mineral supplementation in COVID-19 and the relationships of the levels of some minerals with clinical improvement. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 emerged as a new disease with quick transmission and a high mortality rate at the end of 2019, caused by SARS-CoV-2. Common features of the coronavirus family helped resolve structural and entry mechanism characteristics of SARS-CoV-2. Still, rapid mutagenesis leads to the fast evolution of the virus and the emergence of new strains that differ in infectivity, morbidity, and mortality. Besides differences in the viral genome, genetic variability in the host defense and immune systems may also play a role in the outcome of virus–host interactions. Furthermore, epigenetic mechanisms may also influence the outcomes, including miRNA gene silencing and DNA methylation, which may be heavily influenced by SARS-CoV-2. Molecular biomarkers are intensively investigated as potential predictive and prognostic biomarkers of the disease course and treatment response. We reviewed new data regarding the mechanisms behind fast virus mutagenesis, infectivity, and potential human genetic and epigenetic characteristics that may lead to a more severe or lethal outcome of the disease. The rapid spread of COVID-19 outbreak lead to a global pandemic declared in March 2020. The common features of corona virus family helped to resolve structural characteristics and entry mechanism of SARS-CoV-2. However, rapid mutagenesis leads to the emergence of new strains that may have different reproduction rates or infectivity and may impact the course and severity of the disease. Host related factors may also play a role in the susceptibility for infection as well as the severity and outcomes of the COVID-19. We have performed a literature and database search to summarize potential viral and host-related genomic and epigenomic biomarkers, such as genetic variability, miRNA, and DNA methylation in the molecular pathway of SARS-CoV-2 entry into the host cell, that may be related to COVID-19 susceptibility and severity. Bioinformatics tools may help to predict the effect of mutations in the spike protein on the binding to the ACE2 receptor and the infectivity of the strain. SARS-CoV-2 may also target several transcription factors and tumour suppressor genes, thus influencing the expression of different host genes and affecting cell signalling. In addition, the virus may interfere with RNA expression in host cells by exploiting endogenous miRNA and its viral RNA. Our analysis showed that numerous human miRNA may form duplexes with different coding and non-coding regions of viral RNA. Polymorphisms in human genes responsible for viral entry and replication, as well as in molecular damage response and inflammatory pathways may also contribute to disease prognosis and outcome. Gene ontology analysis shows that proteins encoded by such polymorphic genes are highly interconnected in regulation of defense response. Thus, virus and host related genetic and epigenetic biomarkers may help to predict the course of the disease and the response to treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we conducted a quantitative assessment and compared the COVID-19 pandemic spread in two countries based on selected methods from the graph theory domain. The results indicate that while the applied experimental procedures are useful, we could draw limited conclusions about the dynamic nature of infection diffusion. We discussed the possible reasons for the above and used them to formulate research hypotheses that could serve the scientific community in future research efforts. Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology. [ABSTRACT FROM AUTHOR]

Simple Summary: The current coronavirus disease 2019 pandemic turned the attention of researchers to developing novel strategies to counteract virus infections. Despite several antiviral drugs being commercially available, there is an urgent need to identify novel molecules efficacious against viral infections that act through different mechanisms of action. In this context, our attention is focused on novel compounds acting on nuclear receptors, whose activity could be beneficial in viral infections, including coronavirus, hepatitis C virus, and cytomegalovirus. The manipulation of host metabolisms by viral infections has been demonstrated by several studies, with a marked influence on the synthesis and utilization of glucose, nucleotides, fatty acids, and amino acids. The ability of virus to perturb the metabolic status of the infected organism is directly linked to the outcome of the viral infection. A great deal of research in recent years has been focusing on these metabolic aspects, pointing at modifications induced by virus, and suggesting novel strategies to counteract the perturbed host metabolism. In this review, our attention is turned on PPARs, nuclear receptors controlling multiple metabolic actions, and on the effects played by PPAR ligands during viral infections. The role of PPAR agonists and antagonists during SARS-CoV-2, HCV, and HCMV infections will be analyzed. [ABSTRACT FROM AUTHOR]

Simple Summary: Coronavirus disease 2019 is a worldwide pandemic posing significant health risks. Medical imaging tools can be considered as a supporting diagnostic testing method for coronavirus disease since it uses available medical technologies and clinical findings. The classification of coronavirus disease using computed tomography chest images necessitates massive data collection and innovative artificial intelligence-based models. In this study, we explored the significant application of computer vision and an ensemble of deep learning models for automated coronavirus disease detection. In order to show the better performance of the proposed model over the recently developed deep learning models, an extensive comparative analysis is made, and the obtained results exhibit the superior performance of the proposed model on benchmark test images. Therefore, the proposed model has the potential as an automated, accurate, and rapid tool for supporting the detection and classification process of coronavirus disease. Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: This study proposes a new COVID-19 detection system called CGENet, based on computer vision and chest computed tomography images. First, an optimal backbone selection algorithm was proposed to determine the best backbone network for the CGENet adaptively. Then, we introduced a novel graph embedding mechanism to fuse the spatial relationship into the feature vectors. Finally, we chose the extreme learning machine as the classifier of the proposed CGENet to boost the classification performance. The proposed CGENet was evaluated on a public dataset using 5-fold cross-validation and compared with other algorithms. The results revealed that the proposed model achieved state-of-the-art classification performance. In all, the CGENet can be an effective and efficient tool that can assist COVID-19 diagnosis. Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Simple Summary: The study proposes an automated deep learning-based classification model, based on a Convolutional Neural Network, that demonstrates a rapid detection rate for COVID-19. The training dataset consists of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images which were then augmented. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models. MobileNetV2 showed enough promise to make it a candidate for further modification. The resulting model produced the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods. COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods. [ABSTRACT FROM AUTHOR]

Simple Summary: The recent infectious disease, coronavirus disease 2019, has become the novel pandemic event in the last decade after swine flu, which happened in 2009. While dealing with the pandemic, the challenge of gaining accurate identification results from abundant samples in a timely manner has still persisted. Here, in this study, we show the implementation of an optimized cloud workflow for a robust, yet accurate, identification process from these two latest pandemics events. This is a great example of how we integrate two current available technologies, next-generation sequencing and cloud computing, in practice into an applicable workflow for pandemics to tackle the issue of obtaining satisfactory results in a shorter time, while the abundant samples are available. Hopefully, the methods used in this study will intrigue more healthcare professionals to implement the cloud workflow as a part of the current identification method during the current or future pandemic and other infectious diseases as well. Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently become a novel pandemic event following the swine flu that occurred in 2009, which was caused by the influenza A virus (H1N1 subtype). The accurate identification of the huge number of samples during a pandemic still remains a challenge. In this study, we integrate two technologies, next-generation sequencing and cloud computing, into an optimized workflow version that uses a specific identification algorithm on the designated cloud platform. We use 182 samples (92 for COVID-19 and 90 for swine flu) with short-read sequencing data from two open-access datasets to represent each pandemic and evaluate our workflow performance based on an index specifically created for SARS-CoV-2 or H1N1. Results show that our workflow could differentiate cases between the two pandemics with a higher accuracy depending on the index used, especially when the index that exclusively represented each dataset was used. Our workflow substantially outperforms the original complete identification workflow available on the same platform in terms of time and cost by preserving essential tools internally. Our workflow can serve as a powerful tool for the robust identification of cases and, thus, aid in controlling the current and future pandemics. [ABSTRACT FROM AUTHOR]