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This paper assesses the association of the insertion/deletion ACE (angiotensin-converting enzyme) variant (rs1799752 I/D) and the serum ACE activity with the severity of COVID-19 as well as its impact on post-COVID-19, and we compare these associations with those for patients with non-COVID-19 respiratory disorders. We studied 1252 patients with COVID-19, 104 subjects recovered from COVID-19, and 74 patients hospitalized with a respiratory disease different from COVID-19. The rs1799752 ACE variant was assessed using TaqMan® Assays. The serum ACE activity was determined using a colorimetric assay. The DD genotype was related to risk for invasive mechanical ventilation (IMV) requirement as an indicator of COVID-19 severity when compared to the frequencies of II + ID genotypes (p = 0.025, OR = 1.428, 95% CI = 1.046–1.949). In addition, this genotype was significantly higher in COVID-19 and post-COVID-19 groups than in the non-COVID-19 subjects. The serum ACE activity levels were lower in the COVID-19 group (22.30 U/L (13.84–32.23 U/L)), which was followed by the non-COVID-19 (27.94 U/L (20.32–53.36 U/L)) and post-COVID-19 subjects (50.00 U/L (42.16–62.25 U/L)). The DD genotype of the rs1799752 ACE variant was associated with the IMV requirement in patients with COVID-19, and low serum ACE activity levels could be related to patients with severe disease. [ABSTRACT FROM AUTHOR]

The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers. [ABSTRACT FROM AUTHOR]

One of the main causes of maternal and neonatal morbidity and mortality is pre-eclampsia. It is characterized by a high sFlt1/PlGF ratio, according to prior research. Pregestational diseases in mothers may increase the risk of developing pre-eclampsia. Only a few studies have looked at the connection between maternal comorbidities before conception and the sFlt1/PlGF ratio. The most recent information regarding the association between maternal pregestational diseases and the ratio of sFlt1/PlGF is described in this review. The paper also examines current research suggesting that changes in pregnancy hormones and metabolites are related to a high sFlt1/PlGF ratio. Certain maternal disorders have been found to dramatically raise sFlt-1 and sFlt1/PlGF levels, according to an analysis of the literature. There is still debate about the data on the association between the sFlt1/PlGF ratio and maternal disorders such as HIV, acute coronary syndromes, cardiovascular function in the mother between 19 and 23 weeks of pregnancy, thyroid hormones, diabetes, and cancer. Additional research is needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Over the past few decades, a new area of reliability known as classes of life distributions has developed as a result of the creation of metrics for evaluating the success or failure of reliability. This paper proposes a new reliability class-test statistic for life distributions. In some reliability processes, such as convolution, mixture, and homogeneous shock models, the closure characteristics of the proposed class-test statistic are investigated. To compare the proposed class-test against some competitive tests, the Weibull, linear failure rate (LFR), and Makeham distributions are evaluated. In addition, the relationship between sample size, level of confidence, and critical values is considered to assess the efficacy of the proposed class-test. Furthermore, a Monte Carlo null distribution critical points simulation and some applications of the censored and uncensored data are performed to demonstrate the validity of the proposed class-test in reliability analysis. [ABSTRACT FROM AUTHOR]

A fractional order COVID-19 model consisting of six compartments in Caputo sense is constructed. The indirect transmission of the virus through susceptible populations by the shedding effect is studied. Equilibrium solutions are calculated, and basic reproduction ratio (that depends both on direct and indirect mode of transmission), existence and uniqueness, as well as stability analysis of the solution of the model, are studied. The paper studies the effect of optimal control policy applied to shedding effect. The control is the observation of standard hygiene practices and chemical disinfectants in public spaces. Numerical simulations are carried out to support the analytic result and to show the significance of the fractional order from the biological viewpoint. [ABSTRACT FROM AUTHOR]

In many disciplines, including pattern recognition, data mining, machine learning, image analysis, and bioinformatics, data clustering is a common analytical tool for data statistics. The majority of conventional clustering techniques are slow to converge and frequently get stuck in local optima. In this regard, population-based meta-heuristic algorithms are used to overcome the problem of getting trapped in local optima and increase the convergence speed. An asymmetric approach to clustering the asymmetric self-organizing map is proposed in this paper. The Interactive Autodidactic School (IAS) is one of these population-based metaheuristic and asymmetry algorithms used to solve the clustering problem. The chaotic IAS algorithm also increases exploitation and generates a better population. In the proposed model, ten different chaotic maps and the intra-cluster summation fitness function have been used to improve the results of the IAS. According to the simulation findings, the IAS based on the Chebyshev chaotic function outperformed other chaotic IAS iterations and other metaheuristic algorithms. The efficacy of the proposed model is finally highlighted by comparing its performance with optimization algorithms in terms of fitness function and convergence rate. This algorithm can be used in different engineering problems as well. Moreover, the Binary IAS (BIAS) detects coronavirus disease 2019 (COVID-19). The results demonstrate that the accuracy of BIAS for the COVID-19 dataset is 96.25%. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid variable-order mathematical model for multi-vaccination COVID-19 is analyzed. The hybrid variable-order derivative is defined as a linear combination of the variable-order integral of Riemann–Liouville and the variable-order Caputo derivative. A symmetry parameter σ is presented in order to be consistent with the physical model problem. The existence, uniqueness, boundedness and positivity of the proposed model are given. Moreover, the stability of the proposed model is discussed. The theta finite difference method with the discretization of the hybrid variable-order operator is developed for solving numerically the model problem. This method can be explicit or fully implicit with a large stability region depending on values of the factor Θ. The convergence and stability analysis of the proposed method are proved. Moreover, the fourth order generalized Runge–Kutta method is also used to study the proposed model. Comparative studies and numerical examples are presented. We found that the proposed model is also more general than the model in the previous study; the results obtained by the proposed method are more stable than previous research in this area. [ABSTRACT FROM AUTHOR]

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer's disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigation. [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]

In the context of the global COVID-19 pandemic, the fast and equitable distribution of effective vaccines worldwide is one of the challenges faced by international institutions in charge, as global equity in vaccine supply has not yet been achieved. Our paper explains the current state of ethical research on equity in global COVID-19 vaccine allocation, focusing on the COVAX Facility established by the WHO, acting as the global vaccine distributor. The article presents a detailed analysis of the first year of COVAX allocation in 2021 identifying problematic aspects of its allocation framework regarding the implementation of COVAX's fundamental allocation principles. We argue that the COVAX Facility has developed a proper concept to deal with global vaccine allocation—but to address uncovered defaults, we introduce the 'Prioritized Distribution of Equal Shares' model—a both ethical and practically feasible alternative allocation framework to protect the value of human lives in both high- and low-income countries through fair and fast global vaccine distribution in health emergencies. Nonetheless, we argue that the COVAX Facility remains the main organization to provide equitable access to vaccines. Yet, the global community has to consider further aspects such as patent protection, vaccine production and the lack of power of global structures to address the inequities that have arisen. Since new wars and further crises have arisen, a shift in public global attention endangers the processing of COVID-19-related issues. That is why now more than ever extensive efforts to achieve vaccine equity are needed. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Mycobacterium tuberculosis (Mtb) coinfection has been observed in a number of nations and it is connected with severe illness and death. The paper studies a reaction–diffusion within-host Mtb/SARS-CoV-2 coinfection model with immunity. This model explores the connections between uninfected epithelial cells, latently Mtb-infected epithelial cells, productively Mtb-infected epithelial cells, SARS-CoV-2-infected epithelial cells, free Mtb particles, free SARS-CoV-2 virions, and CTLs. The basic properties of the model's solutions are verified. All equilibrium points with the essential conditions for their existence are calculated. The global stability of these equilibria is established by adopting compatible Lyapunov functionals. The theoretical outcomes are enhanced by implementing numerical simulations. It is found that the equilibrium points mirror the single infection and coinfection states of SARS-CoV-2 with Mtb. The threshold conditions that determine the movement from the monoinfection to the coinfection state need to be tested when developing new treatments for coinfected patients. The impact of the diffusion coefficients should be monitored at the beginning of coinfection as it affects the initial distribution of particles in space. [ABSTRACT FROM AUTHOR]

This paper investigates the interrelations between stock returns and crude oil prices for European oil-importing/exporting countries. A vector autoregression (VAR) model is applied to estimate the significance of stock market responses to changes in oil prices during the pandemic period 2019–2021. A Granger causality test is applied to find the direction and the intensity of the relation between crude oil and the indices of the European stock markets. The findings of this paper hold with or without the COVID-19 pandemic episode and reveal the interaction between the European stock markets and the crude oil prices. The results indicate that in steady periods, before the COVID-19 outbreak and after the announcement of vaccinations, there is no interdependence between crude oil and stock prices, whereas in high volatility periods, the causality from stock markets to oil prices increases and both oil-exporting and -importing countries are equally influenced. These findings have implications both for investors and fund managers. [ABSTRACT FROM AUTHOR]

Engineering is traditionally considered a male domain with lower female participation despite various affirmative actions taken in recent decades. There is evidence of greater gender equality as a result of the COVID-19 pandemic and precautionary lockdown measures. With this in mind, this paper investigates whether women engineers in India were more adversely affected than their male counterparts by the COVID-19 pandemic. Such an impact may be explained by 'intersectional stigma', expanded upon in the literature on discrimination. The impact of such stigma varies in different countries based on socio-cultural factors. Through the use of ethnographic and statistical research methods on secondary and primary data from a sample of 384 engineers, this paper shows that the impact of COVID-19 is not significantly different between genders in engineering education and employment. This may be due to the high demand for digital engineering skills, and strong family support in Indian society. Engineering branch may play a relatively more important role than gender in terms of impact. This finding has repercussions for continuing engineering education (CEE) programs and regulatory bodies in India in terms of enhancing course content and the results may be used in developing affirmative programs in other regions. [ABSTRACT FROM AUTHOR]

COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models. [ABSTRACT FROM AUTHOR]

Purpose: The study discusses COVID-19's short-term impact on Israel's renewable energy start-up sector from March to July 2020. Results and contributions: The interviewed companies were experiencing supply chain disruption, logistical issues, and restrictions in work access, all of which negatively impacted business operations. Moreover, companies reported revenue losses and difficulty in securing funding, interpreted here as financial distress. In some cases, companies cut back on staff. Insights from the literature on the global financial crisis (GFC) were used here to discuss patterns discerned from the interviews. Policy recommendations are presented at the end based on both the interpretation of data and a literature review. Methodology: This paper combined the qualitative research analysis of COVID-19's impacts on energy start-up companies assessed by a questionnaire during interviews with a literature analysis on the previous GFC. Gap: This study aimed to clarify the issues experienced by the start-up companies in the renewable energy sector in Israel during the first months of the pandemic and contributes to the COVID-19 narrative in the energy sector, focusing on a topic that has not been thoroughly discussed at present. Relevance: The paper contains unique primary data on the short-term financial impact of COVID-19 on renewable energy start-up companies, and recommends policies to assist these companies and effectively respond to their financial needs in times of crisis. The paper points out that the absence of such a financial policy for start-up companies might pose risks to the companies' growth and innovation, and have harmful consequences for the renewable energy sector, energy transition, and climate goals. The attention currently given to initial short-term measures should shift toward a more structural and long-term approach. Impact: The paper wants to capture the attention of policymakers and the research community in evaluating and adequately addressing the financial needs of the start-up sector, which is a crucial segment of the economy, and is indispensable for achieving long-term goals such as energy transition. [ABSTRACT FROM AUTHOR]

Kōfuku no Kagaku is a most visible new religious actor in contemporary Japan thanks to the vast publishing activities of its founder and its widespread professional use of new media. This paper examines how Kōfuku no Kagaku engages with the COVID-19 pandemic in practical and doctrinal terms. Notably, early in the pandemic, Kōfuku no Kagaku's international promotion of spiritual cures and vaccines even prompted The New York Times to feature the group in a widely circulated article in April 2020. This paper outlines these "spiritual technologies" and examines their doctrinal rationale as well as the wider doctrinal appropriation of the COVID-19 pandemic. It will be shown how the latter is instrumentalised to echo Kōfuku no Kagaku's millenarian agenda. [ABSTRACT FROM AUTHOR]

Leaders on the forefront of the rapidly escalating climate crisis continually seek effective strategies to help communities stay engaged without burning out or spiraling into despair. This paper examines the concept of adaptive change for its potential to reframe disruption and intentionally harness its potential for building resilience in both practical and psychological ways. In particular, social science suggests that secure communal bonds lay the foundation for the adaptive ability to build resilience through and from disruption. Swiss history offers an intriguing example of this phenomenon: held up as a model for its social, political, and ecological resilience, Swiss democracy evolved as part of the restructuring of society after a series of disruptive historical pandemics. This paper uses the Swiss example and the current COVID-19 (Coronavirus Disease) pandemic in order to explore the potential of transcendent and adaptive sociological and theological frameworks for the development of robust concepts of resilience in the face of climate destabilization. It further argues that a wide theological interpretation of Eucharistic abundance offers a lens through which to claim the liberative resurrection of disruptions, even, or perhaps especially, in the extreme case of human or planetary annihilation. [ABSTRACT FROM AUTHOR]

The sequencing of SARS-CoV-2 provides essential information on viral evolution, transmission, and epidemiology. In this paper, we performed the whole-genome sequencing of SARS-CoV-2 using nanopore and Illumina sequencing to describe the circulation of the virus lineages in Armenia. The analysis of 145 full genomes identified six clades (19A, 20A, 20B, 20I, 21J, and 21K) and considerable intra-clade PANGO lineage diversity. Phylodynamic and transmission analysis allowed to attribute specific clades as well as infer their importation routes. Thus, the first two waves of positive case increase were caused by the 20B clade, the third peak caused by the 20I (Alpha), while the last two peaks were caused by the 21J (Delta) and 21K (Omicron) variants. The functional analyses of mutations in sequences largely affected epitopes associated with protective HLA loci and did not cause the loss of the signal in PCR tests targeting ORF1ab and N genes as confirmed by RT-PCR. We also compared the performance of nanopore and Illumina short-read sequencing and showed the utility of nanopore sequencing as an efficient and affordable alternative for large-scale molecular epidemiology research. Thus, our paper describes new data on the genomic diversity of SARS-CoV-2 variants in Armenia in the global context of the virus molecular genomic surveillance. [ABSTRACT FROM AUTHOR]

This paper assesses the influence on people's perception of the utility of the immunity passport (IP) program by sociodemographic factors, infectivity status, and the objective of its use. The material of this paper is a cross-sectional survey of 400 residents in Spain. The relation between utility perception and input variables is fitted with ordinary least squares (OLS) regression and linear quantile regression (LQR). The principal explanatory variable of usefulness perception is being vaccinated, especially when the objective of the IP is regulating mobility. The OLS estimate of the coefficient regression is (cr) = 0.415 (p = 0.001). We also found a positive and significant influence of that factor in all LQRs (cr = 0.652, p = 0.0026 at level (τ) = 0.75; cr = 0.482, p = 0.0047 at τ = 0.5 and cr = 0.201, p = 0.0385 at τ = 0.25). When the objective of the IP is regulating leisure, being vaccinated is relevant only to explain the central measures of usefulness perception. If the IP is used to regulate traveling, variables related to interviewees' infectivity have greater relevance than sociodemographic factors. When its objective is ruling assembly, the more important variables than being vaccinated are gender and age. To create an effective implementation of the IP, it is advisable to have a general agreement among the population on its convenience. Therefore, the findings in this study have important implications for public health decision-makers. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) continues to spread throughout the world, and it is necessary for us to implement effective methods to prevent and control the spread of the epidemic. In this paper, we propose a new model called Spatial–Temporal Attention Graph Convolutional Networks (STAGCN) that can analyze the long-term trend of the COVID-19 epidemic with high accuracy. The STAGCN employs a spatial graph attention network layer and a temporal gated attention convolutional network layer to capture the spatial and temporal features of infectious disease data, respectively. While the new model inherits the symmetric "space-time space" structure of Spatial–Temporal Graph Convolutional Networks (STGCN), it enhances its ability to identify infectious diseases using spatial–temporal correlation features by replacing the graph convolutional network layer with a graph attention network layer that can pay more attention to important features based on adaptively adjusted feature weights at different time points. The experimental results show that our model has the lowest error rate compared to other models. The paper also analyzes the prediction results of the model using interpretable analysis methods to provide a more reliable guide for the decision-making process during epidemic prevention and control. [ABSTRACT FROM AUTHOR]

Aim of the study: The aim of our review is to indicate and discuss the impact of cardiovascular risk factors, such as obesity, diabetes, lipid profile, hypertension and smoking on the course and mortality of COVID-19 infection. Background: The coronavirus disease 2019 (COVID-19) pandemic is spreading around the world and becoming a major public health crisis. All coronaviruses are known to affect the cardiovascular system. There is a strong correlation between cardiovascular risk factors and severe clinical complications, including death in COVID-19 patients. All the above-mentioned risk factors are widespread and constitute a significant worldwide health problem. Some of them are modifiable and the awareness of their connection with the COVID-19 progress may have a crucial impact on the current and possible upcoming infection. Data collection: We searched for research papers describing the impact of selected cardiovascular risk factors on the course, severity, complications and mortality of COVID-19 infection form PubMed and Google Scholar databases. Using terms, for example: "COVID-19 cardiovascular disease mortality", "COVID-19 hypertension/diabetes mellitus/obesity/dyslipidemia", "cardiovascular risk factors COVID-19 mortality" and other related terms listed in each subtitle. The publications were selected according to the time of their publications between January 2020 and December 2021. From the PubMed database we obtain 1552 results. Further studies were sought by manually searching reference lists of the relevant articles. Relevant articles were selected based on their title, abstract or full text. Articles were excluded if they were clearly related to another subject matter or were not published in English. The types of articles are mainly randomized controlled trial and systematic review. An additional criterion used by researchers was co-morbidities and age of patients in study groups. From a review of the publications, 105 of them were selected for this work with all subheadings included. Findings and Results: The intention of this review was to summarize current knowledge about comorbidities and development of COVID-19 infection. We tried to focus on the course and mortality of the abovementioned virus disease in patients with concomitant CV risk factors. Unfortunately, we were unable to assess the quality of data in screened papers and studies we choose because of the heterogenicity of the groups. The conducted studies had different endpoints and included different groups of patients in terms of nationality, age, race and clinical status. We decide to divide the main subjects of the research into separately described subtitles such as obesity, lipid profile, hypertension, diabetes, smoking. We believe that the studies we included and gathered are very interesting and show modern and present-day clinical data and approaches to COVID-19 infection in specific divisions of patients. [ABSTRACT FROM AUTHOR]

2020 will be remembered worldwide as the year of COVID-19 outbreak. The onset of this pandemic abruptly changed everybody's life and, in a particular manner, doctors' lives. Our hand surgery department became rapidly one of the first COVID-19-specialized wards in Italy, impacting considerably the authors' routines and activities. In this paper, the authors focus on how the demographics of patients with hand trauma changed and how they had to modify their activity. The authors retrospectively took into consideration all patients reaching their emergency department (ED) with hand trauma between 9 March 2020 (the day of the beginning of the first lockdown in Italy) and 8 March 2021 and compared them to those who reached the ED in the three previous years. Authors have analyzed the number of patients, their gender and age, the severity of their trauma, where the trauma occurred, the type of lesion, the percentage of patients who underwent surgery, and the percentage of patients who had an emergency admission. In the last year, the number of patients reaching the ED for a hand trauma has been reduced by two thirds (975 patients during the past year), the mean age of those patients has slightly increased, the severity of cases has increased, places of trauma and type of lesions have changed, and, lastly, the percentage of patients needing surgery who were admitted immediately has increased. This paper shows how the type of patients reaching the ED changed and discusses how surgeons evolved and modified their habits in treating those patients during the first lockdown and the year that followed. [ABSTRACT FROM AUTHOR]

The paper endeavours to explore and analyse some critical issues in the oil and gas market that cropped up around the spread of COVID-19 and tries to identify the key drivers and triggers pertaining therewith. The spread of the first wave that began in March 2020 is crucial because of the global economic downturn that ensued due to lockdown and imposed restrictions coupled with a protracted oil price war that began between Saudi Arabia and Russia. The paper tries to address some key research questions to understand the triggers and drivers around the pandemic. These are: (1) whether the behaviour of OPEC or its key players around the pandemic could be considered uniquely different; (2) what could the triggers be for the increased volatilities that cropped up in both physical and financial markets during the pandemic; (3) what was really different about the oil market crisis around the pandemic that transformed it to an unprecedented storage crisis; (4) what really went wrong with the much-hyped U.S. shale boom during the pandemic that led to the bankruptcy of several oil and gas companies, followed by huge job losses. The paper relies on a structured review of relevant secondary literature to address these exploratory questions and builds upon a retrospective rumination on the world oil market from 1960 to 2020. This is complemented by an analysis of supporting data and evidence obtained from various sources. Considering the intertwining of oil and financial markets around the pandemic, the lessons and findings from the paper would not only be highly relevant for policymakers and stakeholders in the oil and gas sector but would be equally relevant for those in the financial markets. [ABSTRACT FROM AUTHOR]

This paper attempts to identify the changes in religiosity among Catholic practitioners in Poland that occurred during the COVID-19 pandemic, based on the changes in the realization of religious practices by students (N = 354). A questionnaire prepared by the authors was used for the study. We sought to answer the question of the extent to which the socio-demographic characteristics of the subjects, a history of COVID-19, and the death of a family member due to SARS-CoV-2 infection differentiated the subjects' opinions about religiosity and their undertaking of religious practices. The paper also addresses the issue of media-facilitated religious practices. The study showed that the frequency of religious practices was influenced by the perceived religiosity of the family, the religiosity of the respondent, and the declaration concerning the belief in God. The same factors most often significantly differentiated respondents' opinions on COVID-19. No relationship was confirmed between having COVID-19 and subjects' beliefs about the pandemic or frequency of religious practice. Among the respondents, the pandemic did not intensify the practice of religion. In the face of danger, respondents did not turn to God; there was no revitalization of religion. The situation of limiting the physical experience communion of the Church was treated not as an acute undersupply but as an opportunity or a pretext to abandon the practice of religion. [ABSTRACT FROM AUTHOR]

The use of face masks has increased dramatically since the COVID-19 pandemic started in order to to curb the spread of the disease. Additionally, breakthrough infections caused by the Delta and Omicron variants have further increased the importance of wearing a face mask, even for vaccinated individuals. However, the use of face masks also induces attenuation in speech signals, and this change may impact speech processing technologies, e.g., automated speaker verification (ASV) and speech to text conversion. In this paper we examine Automatic Speaker Verification (ASV) systems against the speech samples in the presence of three different types of face mask: surgical, cloth, and filtered N95, and analyze the impact on acoustics and other factors. In addition, we explore the effect of different microphones, and distance from the microphone, and the impact of face masks when speakers use ASV systems in real-world scenarios. Our analysis shows a significant deterioration in performance when an ASV system encounters different face masks, microphones, and variable distance between the subject and microphone. To address this problem, this paper proposes a novel framework to overcome performance degradation in these scenarios by realigning the ASV system. The novelty of the proposed ASV framework is as follows: first, we propose a fused feature descriptor by concatenating the novel Ternary Deviated overlapping Patterns (TDoP), Mel Frequency Cepstral Coefficients (MFCC), and Gammatone Cepstral Coefficients (GTCC), which are used by both the ensemble learning-based ASV and anomaly detection system in the proposed ASV architecture. Second, this paper proposes an anomaly detection model for identifying vocal samples produced in the presence of face masks. Next, it presents a Peak Norm (PN) filter to approximate the signal of the speaker without a face mask in order to boost the accuracy of ASV systems. Finally, the features of filtered samples utilizing the PN filter and samples without face masks are passed to the proposed ASV to test for improved accuracy. The proposed ASV system achieved an accuracy of 0.99 and 0.92, respectively, on samples recorded without a face mask and with different face masks. Although the use of face masks affects the ASV system, the PN filtering solution overcomes this deficiency up to 4%. Similarly, when exposed to different microphones and distances, the PN approach enhanced system accuracy by up to 7% and 9%, respectively. The results demonstrate the effectiveness of the presented framework against an in-house prepared, diverse Multi Speaker Face Masks (MSFM) dataset, (IRB No. FY2021-83), consisting of samples of subjects taken with a variety of face masks and microphones, and from different distances. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics. [ABSTRACT FROM AUTHOR]

This paper presents a case study of the Inclusive Research Network (IRN), an inclusive research group founded by the Centre for Disability Studies, contributing to a National Disability Research Agenda (NDRA) setting project. IRN members with ID carried out their own consultations with disability stakeholders. This paper sets out the experiences of the IRN members in facilitating focus groups as part of phase 2 of the NDRA project. As this project took place during the COVID-19 pandemic and state lockdowns, this paper also provides a reflection on new ways of carrying out research and provides an insight into the process for other groups seeking to engage in inclusive research of this nature in a challenging and changing environment. IRN members showed great interest in being involved in the project, and their motivations aligned with the project's objectives. Facilitating the focus groups on Zoom took some preparation and posed accessibility challenges but enabled a more diverse group of people to attend. IRN members with ID improved their online facilitation skills and confidence but concluded that they preferred working face to face. IRN members overcame the challenges presented by online focus groups, and their involvement added value to the NDRA project. Researchers are encouraged to continue practicing inclusive research in times of remote working and virtual interaction and to share their experiences for mutual learning. [ABSTRACT FROM AUTHOR]

Understanding these factors can inform healthcare providers and policymakers to design interventions to enhance treatment adherence and long-term health outcomes for HIV patients. These studies shed light on various aspects of antiviral therapies, including treatment efficacy and tolerability. By analyzing the reasons behind treatment switches, this study uncovers factors influencing treatment adherence and interruption patterns [[19]]. This comprehensive collection of papers contains a wide range of studies and observations centered on antiviral therapies, with a particular focus on HIV and other viral infections such as monkeypox and SARS-CoV-2. [Extracted from the article]

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83 % accuracy and 93.39 % kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67 % accuracy and 86.67 % kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest. [ABSTRACT FROM AUTHOR]

This paper evaluates India's first officially approved self-administered rapid antigen test kit against COVID-19, a device called CoviSelf. The context is rural India. Rapid antigen tests (RATs) are currently popular in situations where vaccination rates are low, where sections of the community remain unvaccinated, where the COVID-19 pandemic continues to grow and where easy or timely access to RTPCR (reverse transcription-polymerase chain reaction) testing is not an option. Given that rural residents make up 66% of the Indian population, our evaluation focuses on the question of whether this self-administered RAT could help protect villagers and contain the Indian pandemic. CoviSelf has two components: the test and IT (information technology) parts. Using discourse analysis, a qualitative methodology, we evaluate the practicality of the kit on the basis of data in its instructional leaflet, reports about India's 'digital divide' and our published research on the constraints of daily life in Indian villages. This paper does not provide a scientific assessment of the effectiveness of CoviSelf in detecting infection. As social scientists, our contribution sits within the field of qualitative studies of medical and health problems. Self-administered RATs are cheap, quick and reasonably reliable. Hence, point-of-care testing at the doorsteps of villagers has much potential, but realising the benefits of innovative, diagnostic medical technologies requires a realistic understanding of the conditions in Indian villages and designing devices that work in rural situations. This paper forms part of a larger project regarding the COVID-19 pandemic in rural India. A follow-up study based on fieldwork is planned for 2022–2023. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlighted the importance of validated and updated scientific information to help policy makers, healthcare professionals, and the public. The speed in disseminating reliable information and the subsequent guidelines and policy implementation are also essential to save as many lives as possible. Trustworthy guidelines should be based on a systematic evidence review which uses reproducible analytical methods to collect secondary data and analyse them. However, the guidelines' drafting process is time consuming and requires a great deal of resources. This paper aims to highlight the importance of accelerating and streamlining the extraction and synthesis of scientific evidence, specifically within the systematic review process. To do so, this paper describes the COKE (COVID-19 Knowledge Extraction framework for next generation discovery science) Project, which involves the use of machine reading and deep learning to design and implement a semi-automated system that supports and enhances the systematic literature review and guideline drafting processes. Specifically, we propose a framework for aiding in the literature selection and navigation process that employs natural language processing and clustering techniques for selecting and organizing the literature for human consultation, according to PICO (Population/Problem, Intervention, Comparison, and Outcome) elements. We show some preliminary results of the automatic classification of sentences on a dataset of abstracts related to COVID-19. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient's body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy ( ~ 98.3 % ) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease. [ABSTRACT FROM AUTHOR]

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses. [ABSTRACT FROM AUTHOR]

Bacterial co-infections increase the severity of respiratory viral infections and are frequent causes of mortality in COVID-19 infected subjects. During the COVID-19 period, especially at the beginning of the pandemic, an inappropriate use of broad-spectrum antibiotic treatments has been frequently described, mainly due to prolonged hospitalization, especially in intensive care unit departments, and the use of immune-suppressive treatments as steroids. This misuse has finally led to the occurrence of infections by multi-drug resistant (MDR) bacteria in hospitalized COVID-19 patients. Although different reports assessed the prevalence of Gram-negative infections in COVID-19 infected patients, scarce data are currently available on bloodstream infections caused by Pseudomonas aeruginosa in hospitalized COVID-19 patients. The aim of our systematic review is to describe data on this specific population and to discuss the possible implications that these co-infections could have in the management of COVID-19 pandemics in the future. We systematically analysed the current literature to find all the relevant articles that describe the occurrence of P. aeruginosa bloodstream infections in COVID-19 patients. We found 40 papers that described in detail P. aeruginosa HAIs-BSI in COVID-19 patients, including 756,067 patients overall. The occurrence of severe infections due to MDR bacteria had a significant impact in the management of hospitalized patients with COVID-19 infections, leading to a prolonged time of hospitalization and to a consequent increase in mortality. In the near future, the increased burden of MDR bacteria due to the COVID-19 pandemic might partially be reduced by maintaining the preventive measures of infection control implemented during the acute phase of the COVID-19 pandemic. Finally, we discuss how the COVID-19 pandemic changed the role of antimicrobial stewardship in healthcare settings, according to the isolation of MDR bacteria and how to restore on a large scale the optimization of antibiotic strategies in COVID-19 patients. [ABSTRACT FROM AUTHOR]

Due to its severity, the outbreak of COVID-19 led to unprecedented levels of social isolation that affected educational institutions, among others. Digital technologies such as cloud computing and video broadcasting helped the adoption of e-learning during the crisis. However, the speed and efficiency of e-learning adoption during the COVID-19 period varied across countries. This paper compares the adoption of e-learning in European countries before and during the COVID-19 pandemic and the relationship between the pandemic, e-learning, and economic development. First, the adoption of e-learning in European countries before and during the pandemic is compared. Second, using fuzzy C-means clustering, homogeneous groups of European countries are formed based on e-learning indicators for the periods before and during the pandemic. Third, GDP per capita is used as an indicator of economic development and severity indices are used as an indicator of the severity of the response to the pandemic to compare the different clusters. The research results show that economically and digitally advanced countries led the adoption of e-learning in both the period before and the period during the pandemic. However, they also responded less strictly to the pandemic. Less-advanced countries responded more strictly to the pandemic, likely due to a lack of healthcare resources, and also fell behind in the adoption of e-learning. [ABSTRACT FROM AUTHOR]

Since December 2019, many statistical spatial–temporal methods have been developed to track and predict the spread of the COVID-19 pandemic. In this paper, we analyzed the COVID-19 dataset which includes the number of biweekly infected cases registered in Ontario from March 2020 to the end of June 2021. We made use of Bayesian Spatial–temporal models and Area-to-point (ATP) and Area-to-area (ATA) Poisson Kriging models. With the Bayesian models, spatial–temporal effects and government intervention effects on infection risk are considered while the ATP Poisson Kriging models are used to display the spread of the pandemic over space. [ABSTRACT FROM AUTHOR]

Hospitals generate huge amounts of nonwoven residues daily. This paper focused on studying the evolution of nonwoven waste generated in the Francesc de Borja Hospital, Spain, over the last few years and its relation to the COVID-19 pandemic. The main objective was to identify the most impacting pieces of nonwoven equipment in the hospital and to analyze possible solutions. The carbon footprint of the nonwoven equipment was studied through a life-cycle assessment. The results showed an apparent increase in the carbon footprint in the hospital from 2020. Additionally, due to the higher annual volume, the simple nonwoven gown used primarily for patients had a higher carbon footprint over a year than the more sophisticated surgical gowns. It can be concluded that developing a local circular economy strategy for medical equipment could be the solution to avoid the enormous waste generation and the carbon footprint of nonwoven production. [ABSTRACT FROM AUTHOR]

Vaccination against any infectious disease is considered to be an effective way of controlling it. This paper studies a fractional order model with vaccine efficacy and waning immunity. We present the model's dynamics under vaccine efficacy, the impact of immunization, and the waning of the vaccine on coronavirus infection disease. We analyze the model under their equilibrium points. The model under the equilibrium points is discussed and proven that it is locally asymptotically stable if R v is lesser than unity. We present the backward bifurcation hypothesis of the model and show that there is a parameter β 2 that causes the backward bifurcation in the imperfect vaccine model. We show certain assumptions when ψ = 1 for the imperfect vaccine case; the model is both stable globally asymptotically at the disease-free ( R 0 ≤ 1 ) and endemic cases ( R 0 > 1 ). By using infected cases from the recent wave throughout Pakistan, we shall estimate the model parameters and calculate the numerical value of the basic reproductive number R 0 ≈ 1.2591 . We present the comprehensive graphical results for the realistic parameter values and show many useful suggestions regarding the elimination of the infection from society. The vaccination efficacy that provides an important role in disease elimination is discussed in detail. [ABSTRACT FROM AUTHOR]

This paper investigates the month-wise impact of COVID-19 conditions on residential load due to people's presence at home during office hours in Memphis city, Tennessee, USA. The energy consumption patterns of four consumers are analyzed based on the data available from pre-COVID to continuing COVID-19 situations. It is observed that the energy consumptions from April 2020 to June 2021 for all families have increased during office hours than that of pre-COVID months. Therefore, the impacts of the increased loads on distribution transformer are analyzed in terms of hottest spot, top-oil temperature, and loss of transformer life. Moreover, an experimental setup is made to produce the harmonics in currents of non-linear residential load which has detrimental effects on temperature rise of distribution transformer. In addition, this work proposes implementation of harmonic filter at the load side considering the impact of harmonics produced by loads to reduce the temperature rise due to the increased load consumption and presence of harmonics in currents produced by the load. The rise in temperatures and the loss of life of distribution transformer with and without the proposed solutions are simulated in MATLAB to show the efficacy of the proposed solution. Moreover, cost value analysis among different methods, which can be implemented to reduce the adverse impact on distribution transformer, are provided to rank the available methods. [ABSTRACT FROM AUTHOR]

Coriolus versicolor (CV) is a common species from the Polyporaceae family that has been used in traditional Chinese herbal medicine for over 2000 years. Among well-described and most active compounds identified in CV are polysaccharopeptides, such as polysaccharide peptide (PSP) and Polysaccharide-K (PSK, krestin), which, in some countries, are already used as an adjuvant agent in cancer therapy. In this paper, research advances in the field of anti-cancer and anti-viral action of CV are analyzed. The results of data obtained in in vitro and in vivo studies using animal models as well as in clinical research trials have been discussed. The present update provides a brief overview regarding the immunomodulatory effects of CV. A particular focus has been given to the mechanisms of direct effects of CV on cancer cells and angiogenesis. A potential use of CV compounds in anti-viral treatment, including therapy against COVID-19 disease, has also been analyzed based on the most recent literature. Additionally, the significance of fever in viral infection and cancer has been debated, providing evidence that CV affects this phenomenon. [ABSTRACT FROM AUTHOR]

Designing efficient vaccination programs that consider the needs of the population is very relevant to prevent reoccurrence of the COVID-19 pandemic. The government needs to provide vaccination points to give out vaccine doses to the population. In this paper, the authors analyze the location of vaccination points whilst addressing the inhabitants' preferences. Two objectives that prevent crowding of inhabitants are considered. The government aims for the minimum distance between located vaccination points is maximized, and for the number of inhabitants that attend the different vaccination points to be equitable. One of the key aspects of this problem is the assumption that inhabitants freely choose the located vaccination point to go. That decision affects the objectives of the government, since crowding at vaccination points may appear due to the inhabitants' decisions. This problem is modeled as a bi-objective, bi-level program, in which the upper level is associated to the government and the lower level to the inhabitants. To approximate the Pareto front of this problem, a cross-entropy metaheuristic is proposed. The algorithm incorporates criteria to handle two objective functions in a simultaneous manner, and optimally solve the lower-level problem for each government decision. The proposed algorithm is tested over an adapted set of benchmark instances and pertinent analysis of the results is included. An important managerial insight is that locating far vaccination points does not lead us to a more equitable allocation of inhabitants. [ABSTRACT FROM AUTHOR]

Objective: The coronavirus belongs to the family of Coronaviridae, which are not branched single-stranded RNA viruses. COVID-19 creates respiratory problems and infections ranging from mild to severe. The virus features mechanisms that serve to delay the cellular immune response. The host's response is responsible for the pathological process that leads to tissue destruction. Temporomandibular disorders are manifested by painful jaw musculature and jaw joint areas, clicks, or creaks when opening or closing the mouth. All these symptoms can be disabling and occur during chewing and when the patient yawns or even speaks. The pandemic situation has exacerbated anxieties and amplified the vulnerability of individuals. Therefore, from this mechanism, how the COVID-19 pandemic may have increased the incidence of temporomandibular disorders is perceived. The purpose of this review is to evaluate whether COVID-19-related anxiety has caused an increase in temporomandibular dysfunction symptoms in adults to children. Methods: PubMed, Web of Science, Lilacs, and Scopus were systematically searched, until 30 July 2022, to identify studies presenting: the connection between COVID-19 with temporomandibular disorders. Results: From 198 papers, 4 studies were included. Literature studies have shown that the state of uncertainty and anxiety has led to an increase in the incidence of this type of disorder, although not all studies agree. Seventy-three studies were identified after viewing all four search engines; at the end of the screening phase, only four were considered that met the PECO, the planned inclusion, and the exclusion criteria. All studies showed a statistically significant correlation between temporomandibular disorders and COVID-19 with a p < 0.05. Conclusions: All studies agreed that there is an association between COVID-19 and increased incidence of temporomandibular disorders. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) is a respiratory disease that appeared in 2019 caused by a virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is still spreading and causing deaths around the world. There is a real concern of SARS-CoV-2 coinfection with other infectious diseases. Tuberculosis (TB) is a bacterial disease caused by Mycobacterium tuberculosis (Mtb). SARS-CoV-2 coinfection with TB has been recorded in many countries. It has been suggested that the coinfection is associated with severe disease and death. Mathematical modeling is an effective tool that can help understand the dynamics of coinfection between new diseases and well-known diseases. In this paper, we develop an in-host TB and SARS-CoV-2 coinfection model with cytotoxic T lymphocytes (CTLs). The model investigates the interactions between healthy epithelial cells (ECs), latent Mtb-infected ECs, active Mtb-infected ECs, SARS-CoV-2-infected ECs, free Mtb, free SARS-CoV-2, and CTLs. The model's solutions are proved to be nonnegative and bounded. All equilibria with their existence conditions are calculated. Proper Lyapunov functions are selected to examine the global stability of equilibria. Numerical simulations are implemented to verify the theoretical results. It is found that the model has six equilibrium points. These points reflect two states: the mono-infection state where SARS-CoV-2 or TB occurs as a single infection, and the coinfection state where the two infections occur simultaneously. The parameters that control the movement between these states should be tested in order to develop better treatments for TB and COVID-19 coinfected patients. Lymphopenia increases the concentration of SARS-CoV-2 particles and thus can worsen the health status of the coinfected patient. [ABSTRACT FROM AUTHOR]

Ongoing impacts of the COVID-19 pandemic highlight structural inequities in our educational systems and force educators to ask prominent questions about the role of school as conditions continue to shift. This paper explores challenges and opportunities for moving forward in the fields of teaching and teacher education. Through presenting a framework of education for liberation, sociocultural learning theory, and fostering cultural assets, the value of live, in-person teaching is highlighted. Embracing this moment in history through honoring the lived experiences of students and striving for more collective approaches to teaching and learning are discussed as future possibilities in education. [ABSTRACT FROM AUTHOR]

In this paper, we ask how the Finnish Roma community handled the challenging times of the COVID-19 pandemic, and how the information reached Roma communities. The previous studies indicate existing health inequalities between Finnish Roma and the general population. The point of departure for the article is crisis communication between Finnish Roma and national authorities. The themes of racialization, mistrust and Roma relying on peers as a source of information and support became apparent. From this setting, this article sheds light on the way of speaking, usage of language, (Finnish) as a social construction of ethnic identity and expression of (non)belonging. At the same time, the usage of languge and codeswitching reveals social stratifications among the Finnish Roma population. Findings indicate that subtle forms of exclusion of Roma prevail in Finland and are materialized as a "left alone mentality". Consequently, Roma persons working as civil servants or NGO workers would potentially constitute a crucial link between health authorities and the Roma population. This link, however, should be constantly maintained. The study is a qualitative study and based on thematic interviews among Roma actors. [ABSTRACT FROM AUTHOR]

Introduction: SARS-CoV-2, responsible for the coronavirus disease (COVID-19) pandemic, may impact other systems apart from the respiratory system, including the nervous system. In this systematic review, we aimed to establish the prevalence and determinants of neuropathic pain amongst COVID-19-infected individuals. Methodology: A literature search in the PubMed database was performed and 11 papers were eligible for inclusion in this systematic review and meta-analysis. Results: The pooled prevalence of COVID-19-related neuropathic pain was 6.7% (95% CI: 4.7–9.5%) for hospitalised patients during the acute phase and 34.3% (95% CI: 14.3–62%) for long COVID patients. The identified risk factors for COVID-19-related neuropathic pain development included depression, COVID-19 severity and azithromycin use. Conclusions: Neuropathic pain is a very common symptom in long COVID, indicating the urgency for further research in this direction. [ABSTRACT FROM AUTHOR]

Cellular immunity against SARS-CoV-2 is an important component of the immune response to the virus. At present, two such tests based on interferon-gamma release (interferon-γ release assays, IGRAs) are available—Quan-T-Cell SARS-CoV-2 by EUROIMMUN and T-SPOT.COVID by Oxford Immunotec. In this paper, we compared the results of these two tests in 90 subjects employed at the Public Health Institute Ostrava who had previously undergone COVID-19 infection or were vaccinated against that disease. To the best of our knowledge, this is the first head-to-head comparison of these two tests evaluating T-cell-mediated immunity against SARS-CoV-2. In addition, we also evaluated humoral immunity in the same individuals using the in-house virus neutralization test and IgG ELISA assay. The evaluation yielded similar results for both IGRAs, with Quan-T-Cell appearing to be insignificantly (p = 0.08) more sensitive (all 90 individuals were at least borderline positive) than T-SPOT.COVID (negative results found in five patients). The overall qualitative (presence/absence of immune response) agreement of both tests with virus neutralization test and anti-S IgG was also excellent (close or equal to 100% in all subgroups, with the exception of unvaccinated Omicron convalescents, a large proportion of whom, i.e., four out of six subjects, were IgG negative while at least borderline positive for T-cell-mediated immunity measured by Quan-T). This implies that the evaluation of T-cell-mediated immunity is a more sensitive indicator of immune response than the evaluation of IgG seropositivity. This is true at least for unvaccinated patients with a history of being infected only by the Omicron variant, but also likely for other groups of patients. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19, possesses an unusually large positive-sense, single-stranded viral RNA (ssvRNA) genome of about ~29,903 nucleotides (nt). In many respects, this ssvRNA resembles a very large, polycistronic messenger RNA (mRNA) possessing a 5′-methyl cap (m7GpppN), a 3′- and 5′-untranslated region (3′-UTR, 5′-UTR), and a poly-adenylated (poly-A+) tail. As such, the SARS-CoV-2 ssvRNA is susceptible to targeting by small non-coding RNA (sncRNA) and/or microRNA (miRNA), as well as neutralization and/or inhibition of its infectivity via the human body's natural complement of about ~2650 miRNA species. Depending on host cell and tissue type, in silico analysis, RNA sequencing, and molecular-genetic investigations indicate that, remarkably, almost every single human miRNA has the potential to interact with the primary sequence of SARS-CoV-2 ssvRNA. Individual human variation in host miRNA abundance, speciation, and complexity among different human populations and additional variability in the cell and tissue distribution of the SARS-CoV-2 angiotensin converting enzyme-2 (ACE2) receptor (ACE2R) appear to further contribute to the molecular-genetic basis for the wide variation in individual host cell and tissue susceptibility to COVID-19 infection. In this paper, we review recently described aspects of the miRNA and ssvRNA ribonucleotide sequence structure in this highly evolved miRNA–ssvRNA recognition and signaling system and, for the first time, report the most abundant miRNAs in the control superior temporal lobe neocortex (STLN), an anatomical area involved in cognition and targeted by both SARS-CoV-2 invasion and Alzheimer's disease (AD). We further evaluate important factors involving the neurotropic nature of SARS-CoV-2 and miRNAs and ACE2R distribution in the STLN that modulate significant functional deficits in the brain and CNS associated with SARS-CoV-2 infection and COVID-19's long-term neurological effects. [ABSTRACT FROM AUTHOR]

The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As a case study, the total number of death cases in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning and control. In this paper, the proposed model for making a time-series forecast for daily and total infected cases, death cases, and recovered cases for the countrywide Algeria COVID-19 dataset is a two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used to assess the model's performance: mean absolute error (MAE), root mean squared error (RMSE), R 2 , and mean absolute percentage error (MAPE). The results generated with TDGRU are compared with actual numbers as well as predictions with conventional time-series techniques, such as autoregressive integrated moving average (ARIMA), machine learning model of linear regression (LR), and the time series-based deep learning method of long short-term memory (LSTM). The experiment results on different time horizons show that the TDGRU model outperforms the other forecasting methods that deliver correct predictions with lower prediction errors. Furthermore, since this TDGRU is based on a relatively simpler architecture than the LSTM, in comparison to LSTM-based models, it features a significantly reduced number of parameters, a shorter training period, a lower memory storage need, and a more straightforward hardware implementation. [ABSTRACT FROM AUTHOR]

Drawing a discriminative pattern in quantitative datasets is often represented to return a high utility pattern (HUP). The traditional methods output patterns with a utility above a pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting the results in a timely manner to strengthen the interaction between the mining system and users. Pattern sampling can return results with a probability guarantee in a short time, and it could be a candidate technology to mine such discriminative patterns. In this paper, a novel approach named HUPSampler is proposed to sample one potential HUP, which is extracted with probability significance according to its utility in the database. HUPSampler introduces an interval constraint on the length of HUP and randomly extracts an integer k according to the utility proportion firstly; then, the HUPs could be obtained efficiently from a random tree by using a pattern growth way, and finally, it returns a HUP of length k randomly. The experimental study shows that HUPSampler is efficient in regard to memory usage, runtime, and utility distribution. In addition, case studies show that HUPSampler can be significantly used in analyzing the COVID-19 epidemic by identifying critical locations. [ABSTRACT FROM AUTHOR]