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Objective. Aging is a growing public health concern for people, organizations, and governments. The current study was undertaken to provide insights into the global research output on geriatric nursing. Methods. A bibliometric study was implemented using the WoS database for the period from 1900 to 2020. Various tools and measures were used to analyze and visualized. Results. The search strategy found 4923 papers. The oldest paper was written by Beverly C. Andre in 1953. As team size increases, so does the number of citations. The USA was the active country and the highest number of coauthors. New York University was an active institution. Stig Karlsson was the most active author in Geriatric Nursing with 28 articles from Sweden, followed by Koen Milisen and Sandman, with 26 articles each from Sweden and Belgium. The most frequent words in this field were depression, malnutrition, education, Alzheimer's disease, and dementia. The latest research themes in this field were COVID-19, interprofessional locomotive syndrome, emergency nursing, and public health. The most influential papers were specified. Journal of the American Geriatrics Society was the most active journal. Conclusions. Geriatric nursing is a rooted field and has received special attention in the last decade. Policymakers, especially in developing countries, should pay attention to geriatric nursing as a specialty of nursing to solve aging issues they would face considering the increasing elderly population. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID-19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan-Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far-reaching problem in medical-imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID-19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine-learning healthcare models. [ABSTRACT FROM AUTHOR]

Membrane-bound angiotensin-converting enzyme 2 ( ACE2 ) receptor acts as the entry point for the novel coronavirus, SARS-CoV-2. Polymorphisms in the ACE2 gene may alter viral binding, regulate the expression of ACE2, and thus, affect disease severity. In this study, 68 COVID-19 patients with varying degrees of severity and 40 healthy controls were enrolled. The genetic landscape of the ACE2 gene was explored by whole exome sequencing of 29 individuals, and specific regions of ACE2 were analyzed for the rest of the participants via PCR, followed by barcode-tagged sequencing. The mean soluble ACE2 level in the plasma of healthy controls and patients did not vary significantly but was higher in the patient group (3.77 ± 1.55 ng/mL vs. 3.94 ± 1.42 ng/mL). Analysis of exon 1, exon 2, and exon 8 of the ACE2 gene revealed that these regions are highly conserved in our population. Investigation of exon 11 and its flanking intronic region revealed that deletions in a stretch of 18T nucleotides in the noncoding region significantly decrease ACE2 levels in plasma, as individuals harboring wild-type variants had higher plasma ACE2 levels compared to those harboring T1del, T2del, and T3del variants. However, the intronic variants were not found to be significantly associated with disease severity., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2023 Rubaiat Ahmed et al.)

Hackathons are collaborative events that bring together diverse groups to solve predefined challenges. The COVID-19 pandemic caused by SARS-CoV-2 has emphasized the need for portable and reproducible genomics analysis pipelines to study the genetic susceptibility of the human host and investigate human-SARS-CoV-2 protein interactions. To build and strengthen institutional capacities in OMICS data analysis applied to host-pathogen interaction (HPI), the PHINDaccess project organized two hackathons in 2020 and 2021. These hackathons are aimed at developing bioinformatics pipelines related to the SARS-CoV-2 viral genome, its phylodynamic transmission, and the identification of human genome host variants, with a focus on addressing global health challenges, particularly in low- and middle-income countries (LMIC). This paper outlines the preparation, proceedings, and lessons learned from these hackathons, including the challenges faced by participants and our recommendations based on our experience for organizing hackathons in LMIC and beyond., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Kais Ghedira et al.)

Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devastating losses of life. Deep learning (DL) could be used to detect and classify COVID-19 for potential image-based diagnosis. Materials and Methods. This paper proposes an improved deep convolutional neural network (IDConv-Net) to detect and classify COVID-19 using X-ray and computed tomography (CT) images. Before the training phase, preprocessing methods such as filtering, data normalization, classification variable encoding, and data augmentation were used in conjunction with the proposed IDConv-Net to increase the effectiveness of the detection and classification processes. To extract essential features, deep CNN is then employed. As a result, the suggested model can identify patterns and relationships crucial to the image classification task, resulting in more precise and useful diagnoses. Python and Keras (with TensorFlow as a backend) were used to carry out the experiment. Results. The proposed IDConv-Net was tested using chest X-rays and CT images collected from hospitals in Sao Paulo, Brazil, and online databases. After evaluating the model, the proposed IDConv-Net achieved an accuracy of 99.53% and 98.41% in training and testing for CT images and 97.49% and 96.99% in training and testing for X-ray images, respectively. Further, the area under the curve (AUC) value is 0.954 and 0.996 for X-ray and CT images, respectively, indicating the excellent performance of the proposed model. Conclusion. The findings of our proposed IDConv-Net model confirm that the model outperformed compared to existing COVID-19 detection and classification models. The IDConv-Net outperforms current state-of-the-art models by 2.25% for X-rays and 2.81% for CT images. Additionally, the IDConv-Net training approach is significantly quicker than the current transfer learning models. [ABSTRACT FROM AUTHOR]

The virus responsible for the coronavirus viral pandemic is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Emerging SARS-CoV-2 variants caused by distinctive mutations within the viral spike glycoprotein of SARS-CoV-2 are considered the cause for the rapid spread of the disease and make it challenging to treat SARS-CoV-2. The manufacturing of appropriate efficient vaccines and therapeutics is the only option to combat this pandemic. Nanomedicine has enabled the delivery of nucleic acids and protein-based vaccines to antigen-presenting cells to produce protective immunity against the coronavirus. Nucleic acid-based vaccines, particularly mRNA nanotechnology vaccines, are the best prevention option against the SARS-CoV-2 pandemic worldwide, and they are effective against the novel coronavirus and its multiple variants. This review will report on progress made thus far with SARS-CoV-2 vaccines and beyond employing nanotechnology-based nucleic acid vaccine approaches., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2023 Maluta Steven Mufamadi et al.)

Objective. Artificial intelligence-powered screening systems of coronavirus disease 2019 (COVID-19) are urgently demanding since the ongoing outbreak of SARS-CoV-2 worldwide. Chest CT or X-ray is not sufficient to support the large-scale screening of COVID-19 because mildly-infected patients do not have imaging features on these images. Therefore, it is imperative to exploit supplementary medical imaging strategies. Traditional Chinese medicine has played an essential role in the fight against COVID-19. Methods. In this paper, we conduct two kinds of verification experiments based on a newly-collected multi-modality dataset, which consists of three types of modalities: tongue images, chest CT scans, and X-ray images. First, we study a binary classification experiment on tongue images to verify the discriminative ability between COVID-19 and non-COVID-19. Second, we design extensive multimodality experiments to validate whether introducing tongue image can improve the screening accuracy of COVID-19 based on chest CT or X-ray images. Results. Tongue image screening of COVID-19 showed that the accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew correlation coefficient (MCC) of the improved AlexNet and Googlenet both reached 98.39%, 98.97%, 96.67%, and 99.11%. The fusion of chest CT and tongue images used a tandem multimodal classifier fusion strategy to achieve optimal classification, and the results and screening accuracy of COVID-19 reached 98.98%, resulting in a significant improvement of 4.75% the highest accuracy in 375 years compared with the single-modality model. The fusion of chest x-rays and tongue images also had good classification accuracy. Conclusions. Both experimental results demonstrate that tongue image not only has an excellent discriminative ability for screening COVID-19 but also can improve the screening accuracy based on chest CT or X-rays. To the best of our knowledge, it is the first work that verifies the effectiveness of tongue image on screening COVID-19. This paper provides a new perspective and a novel solution that contributes to large-scale screening toward fast stopping the pandemic of COVID-19. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) which emerged in Wuhan, China, in December 2019, is widely controlled now in China. However, the global epidemic is still severe. To study and comment on Hubei's approaches for responding to the disease, the paper considered some factors such as suspected cases (part of them are influenza patients or common pneumonia patients, etc.), quarantine, patient classification (three types), clinically diagnosed cases, and lockdown of Wuhan and Hubei. After that, the paper established an S E L I H R model based on the surveillance data of Hubei published by the Hubei Health Commission from 10 January 2020 to 30 April 2020 and used the fminsearch optimization method to estimate the optimal parameters of the model. We obtained the basic reproduction number R 0 = 3.1571 from 10 to 22 January. R 0 was calculated as 2.0471 from 23 to 27 January. From 28 January to 30 April, R 0 = 1.5014. Through analysis, it is not hard to find that the patients without classification during the period of confirmed cases will result in the cumulative number of cases in Hubei to increase. In addition, regarding the lockdown measures implemented by Hubei during the epidemic, our simulations also show that if the lockdown time of either Hubei or Wuhan is advanced, it will effectively curb the spread of the epidemic. If the lockdown measures are not taken, the total cumulative number of cases will increase substantially. From the results of the study, it can be concluded that the lockdown, patient classification, and the large-scale case screening are essential to slow the spread of COVID-19, which can provide references for other countries or regions. [ABSTRACT FROM AUTHOR]

This paper has reviewed the global research on the pandemic critical care research during the COVID-19 from 2020 to 2022. To this end, a bibliometric and cluster analysis by full counting has been carried out using VOSviewer software and bibliographic data extracted from the Scopus database. The research found and studied 2778 documents. The types of research documents were limited to an article (81.46%), a letter (9.43%), an editorial (3.92%), a note (3.92%), a conference paper (0.90), and a short survey (0.04%). The results show an incessant increase in the number of research documents published and citations received during the COVID-19 pandemic. The U.S., U.K., Italy, and France have been shown to be the most productive countries, and there is a predominance of European institutions supporting and fostering research on pandemic critical care. Cecconi, M. (Italy) and Shankar-Hari, M. (U.K.) produced the highest number of research documents. Mapping of citation, co-citation, co-authorship, and keyword cooccurrence highlighted the hotspot, knowledge structure, and important themes. Citation dynamics for the top-cited research documents revealed static discourse. By reviewing the evolutionary trends of pandemic critical care research investigated factors, such as the influential works, main research topics, and the research frontiers, this paper reveals the scientific literature production's main research objectives and directions that could be addressed and explored in future studies. This paper reveals the scientific literature production's main research objectives and directions that could be addressed and explored in future studies after reviewing the evolutionary trends of pandemic critical care research during the COVID-19 and the investigated factors, such as influential works, main research topics, and research frontiers., Competing Interests: Author declares no conflict of interest., (Copyright © 2022 Mohammed Ageel.)

The global malaria morbidity and mortality witnessed an increase from 2019 to 2020 partly due to disruptions in control programs' activities imposed by the COVID-19 pandemic. Therefore, there is still a significant burden of malaria in Cameroon which needs attention from all fronts to attain elimination goals. It is normally expected that a typical forest ecology that has undergone urbanization and subjected to high rates of ecological instabilities should also have a shift from characteristic perennial malaria transmission and a shift in the type of malaria endemicity plaguing such distorted forest ecology. In this observational comparative study, we randomly enrolled participants from rural and urban settings of a forest zone during a low malaria transmission period, which coincided with the onset of COVID-19 pandemic. An optimized structured questionnaire was employed, to collect socio-demographic data and associated risk factors. The CareStart™ Malaria HRP2 antigen test was performed on participants from both settings to determine the prevalence of community asymptomatic malaria. Of 307 participants, 188 (61.0%) were from the rural, while 119 (38.8%) from the urban community. The overall prevalence of asymptomatic malaria (27.0%) detected Plasmodium falciparum antigen in 83 participants. The urban community's prevalence was 4.2% (5 positives) while the rural community's was 41.5% (78 positives). In simple logistic regression models, rural forest community and farm around the house were statistically significant predictors of testing positive (coefficient 2.8, 95% CI 1.8-3.7, p value<0.001) and (coefficient 3.1, 95% CI 1.1-5.1, p value =0.003), respectively. In the multivariate model, the strongest predictor of testing positive was living in a rural community, with p < 0.001 and odds ratio of 10.9 (95% CI, 3.8-31.8). These results indicate that during a low transmission period, the prevalence of asymptomatic malaria differs between depleted urban and rural forested settings, suggesting a need for strategic target intervention for the control of asymptomatic malaria., Competing Interests: The authors declare no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Clarisse E. Mbah et al.)

Objective: This study is aimed at investigating the pattern of change occurring in respiratory pathogens before and after the outbreak of COVID-19, a type of viral pneumonia for which a pandemic was declared (March 2020). The results were analyzed by gender and age to identify the association between personal hygiene and prevention of infection by respiratory pathogens., Methods: A retrospective analysis was performed on 39,814 sputum, bronchial aspirate, and transtracheal aspirate samples obtained from 15,398 patients visiting a university hospital, located in Chungcheongnam-do, South Korea, between January 2018 and December 2021. From 4,454 patients whose samples were culture positive for bacteria, 6,389 strains were isolated and further cultured., Results: The mean age of the outpatients with respiratory pathogens was 66.2 years, and the comparison of the culture test results by gender showed that 64.9% (2,892/4,454) were male and 35.1% (1,562/4,454) were female. Compared to the pre-COVID-19 pandemic period, the number of outpatients with a request for respiratory microbial cultures after the onset of the pandemic was reduced by 20.7% and the number of outpatients with a positive culture result was reduced by 23.0%. The number of respiratory samples received was reduced by 6.7% after the pandemic, while the sample positive rate was reduced by 18.3%. Among the isolated microbial strains, there was a significant decrease of 43.1% for the Acinetobacter baumannii complex, 60.5% for Streptococcus pneumoniae , 67.2% for Haemophilus influenzae , and 78.1% for Moraxella catarrhalis when compared with pre-COVID-19 levels. The distribution of respiratory microbial strains by age group showed that the highest percentage of isolated strains was in patients in their 70s., Conclusions: The improvements in personal hygiene due to the COVID-19 pandemic exerted a substantial influence on the pattern of change in other common respiratory microorganisms, which highlights the importance of personal hygiene management in the prevention of respiratory infections., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Ki Yeon Kim et al.)

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea., Competing Interests: There is no potential conflict of interest in our paper, and all authors have seen the manuscript and approved to submit to your journal., (Copyright © 2022 Malek Badr et al.)

Objective: Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis., Methods: Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy., Results: From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis., Conclusion: The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come., Competing Interests: The authors have no conflict of interest., (Copyright © 2022 Madhumita Pal et al.)

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Ashwani Sharma et al.)

Objectives: Given the high prevalence of the coronavirus and the high risk of virus transfer to dentists, the use of mouthwashes, which can potentially eliminate this virus, is suggested before dental procedures. Since these mouthwashes may affect the bond strength of composite resin restorations to teeth, this study was conducted to investigate the effect of recommended mouthwashes on the shear bond strength of composite resin restorations to dentin and enamel in selective etch and rinse and two-step self-etch bonding systems., Methods: Five groups of posterior teeth ( n = 15) were selected for five groups of cetylpyridinium chloride 0.07%, povidone-iodine 1%, hydrogen peroxide 1%, and chlorhexidine 0.2% as mouthwash and distilled water as the control group. The buccal enamel and lingual dentin of each tooth were rinsed after immersion in a mouthwash. After 20 seconds of enamel acid-etching and 15 seconds of dentin priming, they were impregnated with an adhesive, and composite cylinders were placed on the dentin and enamel surfaces of the tooth. The shear bond strength test was performed after 24 hours, and results were analyzed by ANOVA and paired t -test ( α = 0.05)., Results: The mean shear bond strength of enamel to composite was significantly ( p < 0.05) higher than that of dentin to composite in each study group, but no significant difference was found between the mean shear bond strength of composite to enamel ( p = 0.199) and to dentin ( p = 0.335) after the use of mouthwashes and that of the control group., Conclusion: The use of mouthwashes used in this study did not have negative effects on the shear bond strength of composite to enamel and dentin., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Farzaneh Shirani et al.)

COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4 + cell, CD8 + cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Lei Chen et al.)

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F -score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Qunsheng Zou et al.)

Objectives: To develop and implement a "semi-presential" technology platform to support urgent and priority dental care for the elderly in the context of the COVID-19 pandemic among the Chilean population., Methods: A dental mobile clinic was implemented along with the development of a technological platform designed to support emergency and priority dental procedures, including teleconsultation with specialists. Under strict biosafety protocols, dental care was provided in five Chilean regions between February and May 2021. Sociodemographic, medical, and dental data were recorded., Results: A total of 135 patients over sixty years old, with a mean age of 72 years, were treated, 48 males and 87 females were attended between February and May 2021 in five different regions of Chile. 53.3% required immediate or urgent treatment, and 24.4% were derived to specialists from whom 60.6% needed immediate or urgent treatment. 74.3% of teleconsultations were derived to an oral pathology specialist., Conclusion: It was shown that a "semi-presential" technology platform implemented in a mobile dental clinic can help elderly people who are impeded to look for traditional dental assistance during a pandemic., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Víctor Beltrán et al.)

We determined player-to-player distance, body-to-ball contact, and exercise intensity during three training modalities in various football populations. 213 participants were recruited, ranging from 9-year-old boys to young men and 11-year-old girls to middle-aged women. All groups were analysed with video-filming and GPS-based Polar Pro monitors during three types of football training for 20 min, i.e., COVID-19-modified training (CMT) with >2-metre player-to-player distance, small-sided games (SSG), and simulated match-play with normal rules (SMP), in randomised order. Time spent in a danger zone (1.5 m) per-percent-infected-player (DZ PPIP) ranged from 0.015 to 0.279% of playing time. DZ PPIP for SSG was higher ( P < 0.05) than CMT and SMP. The average number of contacts (within 1.5 m) with a potentially infected player ranged from 12 to 73 contacts/hour. SSG had more ( P < 0.05) contacts than CMT and SMP, with SMP having a higher ( P < 0.05) number of contacts than CMT. Time/contact ranged from 0.87 to 3.00 seconds for the groups. No player-to-player and body-to-ball touches were registered for CMT. Total player-to-player contacts were 264% higher ( P < 0.05) in SSG than SMP, ranging from 80 to 170 and 25 to 56 touches, respectively. In all groups, a greater total distance was covered during SMP compared to CMT (38-114%; P < 0.05). All groups performed more high-intensity running (33-54%; P < 0.05) and had higher heart rates during SMP compared to CMT. Different types of football training all appear to exert a minor COVID-19 infection risk; however, COVID-19-modified training may be safer than small-sided game training, but also match-play. In contrast, exercise intensity is lower during COVID-19-modified training than match-play., Competing Interests: The authors declare that there is no conflict of interests regarding the publication of this paper., (Copyright © 2022 Magni Mohr et al.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) detection can be an effective complementary tool to the reverse transcription-polymerase chain reaction (RT-PCR) test in estimating the true burden of coronavirus diseases 2019 (COVID-19) and can serve as baseline data, especially after the roll-out of vaccines against SARS-CoV-2. In this study, we aim to determine the seropositivity of SARS-CoV-2 IgG among people in Dhaka, Bangladesh. Volunteers, mostly asymptomatic people from Dhaka, were enrolled between October 2020 and February 2021. After obtaining participants' signed consents, blood samples were tested for SARS-CoV-2 IgG antibody, following the standard protocol of testing within 72 hours of collection. SARS-CoV-2 IgG was positive in 42% (101/239) of the cases. No difference was observed in terms of IgG positivity and IgG levels when stratified by age, gender, and blood group. However, RT-PCR-positive cases presented higher IgG levels compared to RT-PCR-negative/RT-PCR-not performed cases. SARS-CoV-2 IgG was found in 31% (32/102) and 28% (19/67) of RT-PCR-negative and RT-PCR-not performed cases, respectively. For RT-PCR-positive but SARS-CoV-2 IgG-negative cases ( n = 13), the average time gap between the RT-PCR and SARS-CoV-2 IgG tests of six months indicates a gradual reduction of IgG. Eight cases for which samples were tested at two time points, three months apart, showed presented a decline in IgG levels with time (median IgG index of 2.55 in the first sample versus 1.22 in the second sample). Our findings reveal that several mild/asymptomatic cases that were RT-PCR-negative/not tested exist in the community, and IgG levels reduce in the human body over time., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Zabed Bin Ahmed et al.)

Early in the COVID-19 pandemic, asymptomatic transmission represented an important challenge for controlling the spread of SARS-CoV-2 through the traditional public health strategies. Further understanding of the contribution of asymptomatic infections to SARS-CoV-2 transmission has been of crucial importance for pandemic control. We conducted a retrospective epidemiological study to characterize asymptomatic COVID-19 cases occurred in the Apulia region, Italy, during the first epidemic wave of COVID-19 outbreak (February 29-July 7, 2020). We analyzed data collected in a regional platform developed to manage surveillance activities, namely, investigation and follow-up of cases and contacts, contact tracing, and laboratory and clinical data collection. We included all asymptomatic cases that were laboratory-confirmed during the appropriate follow-up, defined as persons infected with SARS-CoV-2 who did not develop symptoms/clinical signs of the disease. Between February 29 and July 7, 2020, a total of 4,536 cases were diagnosed with COVID-19 among 193,757 tests performed. The group of persons with asymptomatic SARS-CoV-2 infection consisted of 903 cases; the asymptomatic proportion was 19.9% (95% CI: 18.8-21.1%); this decreased with increasing age (OR: 0.89, 95% CI: 0.83-0.96; p = 0.001), in individuals with underlying comorbidities (OR: 0.55, 95% CI: 0.41-0.73; p < 0.001), and in males (OR: 0.69, 95% CI: 0.54-0.87; p = 0.002). The median asymptomatic SARS-CoV-2 RNA positive period was 19 days (IQR: 14-31) and the cumulative proportion of persons with resolution of infection 14 days after the first positive PCR test was 74%. As the public health community is debating the question of whether asymptomatic and late spreaders could sustain virus transmission in the communities, such cases present unique opportunities to gain insight into SARS-CoV-2 adaptation to human host. This has important implications for future COVID-19 surveillance and prevention., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Domenico Martinelli et al.)

Introduction: Coronavirus disease 2019 (COVID-19) is a global public health crisis. However, whether it can cause respiratory dysfunction or physical and psychological disorders in patients remains unknown. Thus, this study was conducted to investigate the respiratory function, activities of daily living, quality of life, and mental status of patients with COVID-19. Participants and outcomes . Data was collected from the follow-up of eligible patients who attended the fever clinic of three hospitals in Jiangxi Province, from March to May 2020. The outcomes included respiratory muscle function, degree of dyspnea, aerobic capacity, activities of daily living, quality of life, and mental status., Results: A total of 139 patients (72 men and 67 women) were included in this study. The proportions of mild, moderate, severe, and critical cases of COVID-19 were 7.1% (10 cases), 68.3% (95 cases), 20.1% (28 cases), and 4.2% (6 cases), respectively. The rates of abnormal maximal inspiratory pressure were 10.0%, 25.2%, 25.0%, and 16.7%, respectively. There were 50%, 65.3%, 50%, and 66.7% of the patients with abnormal dyspnea in the four clinical classifications, respectively. Patients generally show a decline in quality of life, anxiety, and depression symptoms., Conclusions: Respiratory dysfunction, decreased quality of life, and psychological disorders were present in each clinical classification of COVID-19. Therefore, it is necessary to carry out respiratory rehabilitation and psychological intervention for COVID-19 patients., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Moyi Li et al.)

Background and Purpose. Stroke represents one of the most important causes of morbidity (Just over hundred million patients with disabling of ongoing effects of stroke at a given time, globally) and mortality (the second leading cause of death) worldwide. Innovative system biology-based approach is likely to increase the understanding of the underpinning of acute stroke promise to enhance stroke prevention, acute treatment, and neurorehabilitation. Recent growing body of evidence with shared pathobiology with COVID-19 and the critically important role of inflammation in the context of stroke points to far-reaching consequences of acute stroke, just as in the case of COVID-19 (postacute event issues as well as long term issues). So far, stroke is typically defined by late-appearing disease manifestation by the range of stroke subtypes as defined by the WHO or American Stroke Association. This definition neglects the underlying pathobiological mechanisms such as low-grade chronic inflammation and already compromised vascular system. Diseases such as stroke are hardly a simple result of a single problem but rather a complex cascade of pathobiological processes and interactions in a complex biochemical environment. The evidence of changes in innate immunity and adaptive immunity during the index event of acute stroke and recovery over next 3-12 months can be easily elicited with simple bedside blood tests such as neutrophil-lymphocyte ratio (NLR) with well over 300 published papers including several systematic reviews and meta-analyses confirming this. Global standard operating procedures (SOP) of stroke care are dictated by the national and international stroke guidelines at present. It is imperative to explore the evidence of system biology approach in current stroke guidelines. This is likely to be a key turning point in managing stroke across the continuum (prevention, management of acute event, and rehabilitation). Methods. We systematically searched for guideline recommendation on the day-to-day use of peripheral inflammatory markers such as NLR published in the English language between January 1, 2005, and October 2020. Any other evidence of system biology-based approach or recommendation was explored within the selected guidelines for this scoping review. Only the latest guideline per writing group was selected. Each guideline was analyzed independently by 2 to 4 authors to determine clinical scenarios explained/given, scientific evidence used, and recommendations presented in the context of system biology. Results. The scoping review found 2,911 titles at the beginning of the search. Final review included with 15 guidelines. Stroke-related organizations wrote sixty-five percent of the guidelines while national ministries wrote a fewer number of guidelines. We were primarily interested in recommendations for acute management in AIS published in the English language. Fifteen eligible guidelines were identified from 15 different countries/regions. None of the guidelines recommended the routine use of peripheral markers of inflammation, such as NLR, among their acute assessment and management recommendations. None of the existing guidelines explored the system biology approach to one of the most complex diseases affecting the human brain, stroke. Conclusions. This systematic review has identified a significant evidence-practice gap in all existing national stroke guidelines published in English medium as of October 2020. These guidelines included the only current "living stroke guidelines," stroke guidelines from Australia with a real opportunity to modernize the living stroke guidelines with systems biology approach, and provide 2020 vision towards better stroke care globally. Investigation of complex disease such as stroke is best served through a systems biology approach. One of the easiest places to start is simple blood tests such as total white cell count and NLR. Systems biology approach point us towards simple tools such immune-inflammatory index (SII) and serial systemic immune inflammatory indices (SSIIi) which should pave the way for the stroke physician community address the challenges in systems biology approach in stroke care. These challenges include translating bench research to the bedside, managing big data (continuous pulse, blood pressure, sleep, oxygen saturation, progressive changes in NLR, SII, SSIIi, etc.). Working with an interdisciplinary team also provides a distinct advantage. Recent adoption of historic WHO-IGAP calls for immediate action. The 2022 World Brain Day campaign on Brain Health for All is the perfect opportunity to raise awareness and start the process. [ABSTRACT FROM AUTHOR]

Background: Policy changes are often necessary to contain the detrimental impact of epidemics such as those brought about by coronavirus disease (COVID-19). In the earlier phases of the emergence of COVID-19, China was the first to impose strict restrictions on movement (lockdown) on January 23rd, 2020. A strategy whose effectiveness in curtailing COVID-19 was yet to be determined. We, therefore, sought to study the impact of the lockdown in reducing the incidence of COVID-19., Methods: Daily cases of COVID-19 that occurred in China which were registered between January 12th and March 30th, 2020, were extracted from the Johns Hopkins CSSE team COVID-19 ArcGIS® dashboards. Daily cases reported were used as data points in the series. Two interrupted series models were run: one with an interruption point of 23 January 2020 (model 1) and the other with a 14-day deferred interruption point of 6th February (model 2). For both models, the magnitude of change (before and after) and linear trend analyses were measured, and β -coefficients reported with 95% confidence interval (CI) for the precision., Results: Seventy-eight data points were used in the analysis. There was an 11% versus a 163% increase in daily cases in models 1 and 2, respectively, in the preintervention periods ( p ≤ 0.001). Comparing the period immediately following the intervention points to the counterfactual, there was a daily increase of 2,746% ( p < 0.001) versus a decline of 207% ( p = 0.802) in model 2. However, in both scenarios, there was a statistically significant drop in the daily cases predicted for this data and beyond when comparing the preintervention periods and postintervention periods ( p < 0.001)., Conclusion: There was a significant decrease the COVID-19 daily cases reported in China following the institution of a lockdown, and therefore, lockdown may be used to curtail the burden of COVID-19., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Mooketsi Molefi et al.)

The human transmembrane protease serine 2 ( TMPRSS2 ) protein plays an important role in prostate cancer progression. It also facilitates viral entry into target cells by proteolytically cleaving and activating the S protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the current study, we used different available tools like SIFT, PolyPhen2.0, PROVEAN, SNAP2, PMut, MutPred2, I-Mutant Suite, MUpro, iStable, ConSurf, ModPred, SwissModel, PROCHECK, Verify3D, and TM-align to identify the most deleterious variants and to explore possible effects on the TMPRSS2 stability, structure, and function. The six missense variants tested were evaluated to have deleterious effects on the protein by SIFT, PolyPhen2.0, PROVEAN, SNAP2, and PMut. Additionally, V160M, G181R, R240C, P335L, G432A, and D435Y variants showed a decrease in stability by at least 2 servers; G181R, G432A, and D435Y are highly conserved and identified posttranslational modifications sites (PTMs) for proteolytic cleavage and ADP-ribosylation using ConSurf and ModPred servers. The 3D structure of TMPRSS2 native and mutants was generated using 7 meq as a template from the SwissModeller group, refined by ModRefiner, and validated using the Ramachandran plot. Hence, this paper can be advantageous to understand the association between these missense variants rs12329760, rs781089181, rs762108701, rs1185182900, rs570454392, and rs867186402 and susceptibility to SARS-CoV-2., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Asmae Saih et al.)

Introduction. Many COVID-19 patients display adverse symptoms, such as reduced physical ability, poor quality of life, and impaired pulmonary function. Therefore, this systematic review is aimed at evaluating the effectiveness of physical exercise on various psychophysiological indicators among COVID-19 patients who may be at any stage of their illness (i.e., critically ill, hospitalized, postdischarge, and recovering). Methods. A systematic search was conducted in PubMed, Scopus, ScienceDirect, Web of Science, and Google Scholar from 2019 to 2021. Twenty-seven studies, which assessed a total of 1525 patients, were included and analysed. Results. Overall, data revealed significant improvements in the following parameters: physical function, dyspnoea, pulmonary function, quality of life (QOL), lower limb endurance and strength, anxiety, depression, physical activity level, muscle strength, oxygen saturation, fatigue, C-reactive protein (CRP), interleukin 6 (IL-6), tumour necrosis factor-alpha (TNF-α), lymphocyte, leukocytes, and a fibrin degradation product (D-dimer). Conclusions. Physical training turns out to be an effective therapy that minimises the severity of COVID-19 in the intervention group compared to the standard treatment. Therefore, physical training could be incorporated into conventional treatment of COVID-19 patients. More randomized controlled studies with follow-up evaluations are required to evaluate the long-term advantages of physical training. Future research is essential to establish the optimal exercise intensity level and assess the musculoskeletal fitness of recovered COVID-19 patients. This trial is registered with CRD42021283087. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is shed in the stool of infected individuals and can be detected in sewage and wastewater contaminated with infected stool. This study is aimed at detecting the virus and its potential survival in sewage and wastewater in Ghana. The cross-sectional study included samples from 16 validated environmental surveillance sites in 7 regions of Ghana. A total of 354 samples composed of wastewater (280) and sewage (74) were collected from November 2020 to November 2022. Overall, 17% of the samples were positive for SARS-CoV-2 by real-time PCR, with 6% in sewage and 11% in wastewater. The highest number of positive samples was collected from the Greater Accra Region (7.3%) with the least recorded in the Bono East Region (0.6%). Further characterization of the positive samples using the next-generation sequencing (NGS) approach yielded two variants: Alpha (B.1.1.7) and Delta (AY.36). Attempts to isolate SARS-CoV-2 in the Vero cell line were not successful probably due to the low viral load concentrations (Ct values > 35) or prolonged exposure to high temperatures rendering the virus noninfectious. Our findings suggest that SARS-CoV-2 RNA in sewage and wastewater may not be infectious, but the prevalence shows that the virus persists in the communities within Ghana. [ABSTRACT FROM AUTHOR]

The world is presently in crisis facing an outbreak of a health-threatening microorganism known as COVID-19, responsible for causing uncommon viral pneumonia in humans. The virus was first reported in Wuhan, China, in early December 2019, and it quickly became a global concern due to the pandemic. Challenges in this regard have been compounded by the emergence of several variants such as B.1.1.7, B.1.351, P1, and B.1.617, which show an increase in transmission power and resistance to therapies and vaccines. Ongoing researches are focused on developing and manufacturing standard treatment strategies and effective vaccines to control the pandemic. Despite developing several vaccines such as Pfizer/BioNTech and Moderna approved by the U.S. Food and Drug Administration (FDA) and other vaccines in phase 4 clinical trials, preventive measures are mandatory to control the COVID-19 pandemic. In this review, based on the latest findings, we will discuss different types of drugs as therapeutic options and confirmed or developing vaccine candidates against SARS-CoV-2. We also discuss in detail the challenges posed by the variants and their effect on therapeutic and preventive interventions. [ABSTRACT FROM AUTHOR]

The efficacy and safety of traditional Chinese medicine (TCM) paired with western medicine in the treatment of patients with COVID-19 remains controversial. This meta-analysis was performed to identify the effects of TCM. Seven electronic databases were reviewed from the inception of these databases to 30 June 2022. A quality assessment of the included studies was performed with the Cochrane Collaboration's tool to provide a score of high, unclear, or low risk of bias. The standard software program (Stata, version 12.0, statistical software) was used for endpoint analyses. A total of 13 RCTs involving 1398 patients conducted in China were included. The cross-sectional data from various studies were plotted, and the results illustrated that the statistically higher rates of total effectiveness (RR, 1.357; 95% CI, 1.259 to 1.464; P < 0.001), improvement of chest CT (RR, 1.249; 95% CI, 1.143 to 1.356; P < 0.001), and cough improvement (RR, 1.228; 95% CI, 1.057 to 1.570; P = 0.012) and a lower incidence of conversion to severe cases (RR, 0.408; 95% CI, 0.275 to 0.605; P < 0.001) were demonstrated in the TCM group than that of the control group. Of note, the subgroup on specific TCM of Lianhua Qingwen (LQ) revealed that the experiment group was associated with a higher rate of total effectiveness (RR, 1.248; 95% CI, 1.136 to 1.371; P < 0.001) and improvement of chest CT (RR, 1.226; 95% CI, 1.110 to 1.356; P < 0.001) and a lower rate of conversion to severe cases (RR, 0.469; 95% CI, 0.311 to 0.707; P < 0.001). However, there was no significant difference in fever improvement (RD, 0.110; 95% CI, -0.063 to 0.283; P = 0.213). The findings of this meta-analysis suggest that TCM combined with western medicine is more effective in treating COVID-19 via relieving symptoms, promoting patients' recovery, and cutting the rate of patients developing into severe conditions. However, given the relevant possible biases in our study, adequately powered and better-designed studies with long-term follow-up are required to reach a firmer conclusion. [ABSTRACT FROM AUTHOR]

A rare type of pneumonia later on referred to as COVID-19 was reported in China in December 2019. Investigations revealed that this disease is caused by a coronavirus previously identified as SARS-CoV-2, and since then, it has become a global pandemic with new strains emerging rapidly as a result of genetic mutations. Various therapeutic options are being explored in order to eradicate this pandemic even though approved vaccine candidates are being currently rolled out globally. Most medicinal plant extracts have astonishing properties, and they can therefore be used in the biosynthesis of effective antiviral nanoparticles. In this systematic review, we aimed to highlight the specific attributes that make Azadirachta indica (neem plant) a suitable candidate for the biosynthesis of anti-SARS-CoV-2 nanoparticles. A systematic investigation was therefore carried out in PubMed, Scopus, Web of Science, and AJOL databases with the keywords "Nanoparticles," "Biosynthesis," "Antivirals," "SARS-CoV-2," and "Azadirachta indica." 1216 articles were retrieved by the 21st of February 2022, but we screened studies that reported data on biomedical and antimicrobial assessment of Azadirachta indica extracts. We also screened studies that were reporting nanoparticles possessing antiviral properties against SARS-C0V-2, narrowing our results to 98 reports. Herein, the SARS-CoV-2 viral structure is briefly discussed with nanoparticles of biomedical importance in the design of SARS-CoV-2 antivirals. Most importantly, we focused on the biomedical and antiviral properties of Azadirachta indica extracts that could be of importance in the design of potential anti-SARS-CoV-2 nanoformulations. [ABSTRACT FROM AUTHOR]

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task. [ABSTRACT FROM AUTHOR]

Background. COVID-19 is a social health problem. Several risk factors threaten students, and schools can provide a suitable environment for managing and performing health promotion programs. Given the extensive spread of the disease and the existence of multilevel components affecting the adoption of preventive behaviors, understanding the views and opinions of the audience about the barriers and facilitators affecting the behavior using qualitative studies can be one of the ways to have a successful intervention. Materials and Methods. This was a qualitative study performed by a directed content analysis method while using the PRECEDE model (predisposing, reinforcing, enabling constructs in educational diagnosis and evaluation). In total, 38 individuals (teachers, parents, and students) were entered into the study using the purposive sampling method. Data were collected by semistructured interviews, and the implemented data were systematically classified into five stages (condensed meaning units, code, subcategory, category, and theme) and were arranged and analyzed. Results. The findings are classified into three themes of predisposing, enabling, and reinforcing factors. In addition, perceived sensitivity, perceived intensity, mental beliefs, behavior outcome evaluation, and perceived power were considered as subcategories of predisposing factors, whereas normative beliefs and motivation for adherence to protocols were subcategories of reinforcing factors, and control beliefs were subcategories of enabling factors. Conclusion. Our findings could be used as a guide to design educational interventions aiming at promoting COVID-19 prevention behaviors in schools. [ABSTRACT FROM AUTHOR]

Background. The novel coronavirus first emerged in Wuhan, China, and quickly spread across the globe, spanning various countries and resulting in a worldwide pandemic by the end of December 2019. Given the current advances in treatments available for COVID-19, mesenchymal stem cell (MSC) therapy seems to be a prospective option for management of ARDS observed in COVID-19 patients. This present study is aimed at exploring the therapeutic potential and safety of using MSC obtained by isolation from health cord tissues in the treatment of patients with COVID-19. Methods. A systematic search was done based on the guidelines of the PRISMA 2020 statement. A literature search was executed using controlled vocabulary and indexing of trials to evaluate all the relevant studies involving the use of medical subject headings (MeSH) in electronic databases like PubMed, Embase, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and clinicaltrials.gov up to 31 December 2021. The protocol was registered in the PROSPERO register with ID CRD42022301666. Findings. After screening finally, 22 remaining articles were included in this systematic review. The studies revealed that MSC exosomes are found to be superior to MSC alone in terms of safety owing to being smaller with a lesser immunological response which leads to free movement in blood capillaries without clumping and also cannot further divide, thus reducing the oncogenic potential of MSC-derived exosomes as compared to MSC only. The studies demonstrated that the lungs healed with the use of exosomes compared to how they presented initially at the hospital. MSCs are found to increase the angiogenesis process and alveolar reepithelization, reducing markers like TNF alpha, TGF beta, and COL I and III, reducing the growth of myofibroblasts and increasing survivability of endothelium leading to attenuated pulmonary fibrosis and even reversing them. Interpretation. We can conclude that the use of mesenchymal stem cells or their derived exosomes is safe and well-tolerated in patients with COVID-19. It improves different parameters of oxygenation and helps in the healing of the lungs. The viral load along with different inflammatory cells and biomarkers of inflammation tend to decrease. Chest X-ray, CT scan, and different radiological tools are used to show improvement and reduced ongoing destructive processes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection. [ABSTRACT FROM AUTHOR]

While the world continues to grapple with the devastating effects of the SARS-nCoV-2 virus, different scientific groups, including researchers from different parts of the world, are trying to collaborate to discover solutions to prevent the spread of the COVID-19 virus permanently. Henceforth, the current study envisions the analysis of predictive models that employ machine learning techniques and mathematical modeling to mitigate the spread of COVID-19. A systematic literature review (SLR) has been conducted, wherein a search into different databases, viz., PubMed and IEEE Explore, fetched 1178 records initially. From an initial of 1178 records, only 50 articles were analyzed completely. Around (64%) of the studies employed data-driven mathematical models, whereas only (26%) used machine learning models. Hybrid and ARIMA models constituted about (5%) and (3%) of the selected articles. Various Quality Evaluation Metrics (QEM), including accuracy, precision, specificity, sensitivity, Brier-score, F1-score, RMSE, AUC, and prediction and validation cohort, were used to gauge the effectiveness of the studied models. The study also considered the impact of Pfizer-BioNTech (BNT162b2), AstraZeneca (ChAd0x1), and Moderna (mRNA-1273) on Beta (B.1.1.7) and Delta (B.1.617.2) viral variants and the impact of administering booster doses given the evolution of viral variants of the virus. [ABSTRACT FROM AUTHOR]

COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease has been identified as a world pandemic by the WHO. Deep learning-based approaches are being widely used to diagnose the COVID-19 cases, but the limitation of immensity in the publicly available dataset causes the problem of model over-fitting. Modern artificial intelligence-based techniques can be used to increase the dataset to avoid from the over-fitting problem. This research work presents the use of various deep learning models along with the state-of-the-art augmentation methods, namely, classical and generative adversarial network- (GAN-) based data augmentation. Furthermore, four existing deep convolutional networks, namely, DenseNet-121, InceptionV3, Xception, and ResNet101 have been used for the detection of the virus in X-ray images after training on augmented dataset. Additionally, we have also proposed a novel convolutional neural network (QuNet) to improve the COVID-19 detection. The comparative analysis of achieved results reflects that both QuNet and Xception achieved high accuracy with classical augmented dataset, whereas QuNet has also outperformed and delivered 90% detection accuracy with GAN-based augmented dataset. [ABSTRACT FROM AUTHOR]

COVID-19 is a global pandemic viral infection that has affected millions worldwide. Limited data is available on the effect of COVID-19 on hematological parameters in Saudi Arabia. This study is aimed at examining the role of hematological parameters among COVID-19 patients admitted to King Khalid Hospital in Najran, Saudi Arabia. This is a retrospective, hospital-based study of 514 cases who were recruited during August to October 2020. 257 COVID-19 patients formed the study group, and a further 257 negative subjects formed the control group. Anemia was significantly elevated in positive subjects over controls (respectively, 64.2% and 35.8%), with patients 2.5 times more likely to be anemic (p < 0.01). Thrombocytopenia was higher in patients over controls (respectively, 62% and 38%), with patients ~1.7 times more likely to be thrombocytopenic (p < 0.01). Moreover, leukopenia was significantly higher in patients over controls (respectively, 71% and 29%), with positive subjects ~2.6 times more likely to be leukopenic. Our study results indicate that mild anemia associated with leukopenia may have diagnostic value for COVID-19. Careful assessment of hematological parameters, at baseline and throughout the disease path, will assist physicians in formulating personalized approaches to treatment and promptly offer intensive care to those in greater need. [ABSTRACT FROM AUTHOR]

Increasing outbreaks of new pathogenic viruses have promoted the exploration of novel alternatives to time-consuming vaccines. Thus, it is necessary to develop a universal approach to halt the spread of new and unknown viruses as they are discovered. One such promising approach is to target lipid membranes, which are common to all viruses and bacteria. The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has reaffirmed the importance of interactions between the virus envelope and the host cell plasma membrane as a critical mechanism of infection. Metadichol®, a nanolipid emulsion of long-chain alcohols, has been demonstrated as a strong candidate that inhibits the proliferation of SARS-CoV-2. Naturally derived substances, such as long-chain saturated lipid alcohols, reduce viral infectivity, including that of coronaviruses (such as SARS-CoV-2) by modifying their lipid-dependent attachment mechanism to human host cells. The receptor ACE2 mediates the entry of SARS-CoV-2 into the host cells, whereas the serine protease TMPRSS2 primes the viral S protein. In this study, Metadichol® was found to be 270 times more potent an inhibitor of TMPRSS2 (E C 50 = 96 ng / mL) than camostat mesylate (E C 50 = 26000 ng / mL). Additionally, it inhibits ACE with an EC50 of 71 ng/mL, but it is a very weak inhibitor of ACE2 at an EC50 of 31 μg/mL. Furthermore, the live viral assay performed in Caco-2 cells revealed that Metadichol® inhibits SARS-CoV-2 replication at an EC90 of 0.16 μg/mL. Moreover, Metadichol® had an EC90 of 0.00037 μM, making it 2081 and 3371 times more potent than remdesivir (E C 50 = 0.77 μ M) and chloroquine (E C 50 = 1.14 μ M), respectively. [ABSTRACT FROM AUTHOR]

Since its outbreak, the coronavirus (COVID-19) pandemic has caused havoc on people's lives. All activities were paused due to the virus's spread across the continents. Researchers have been working hard to find new medication treatments for the COVID-19 pandemic. The World Health Organization (WHO) recommends that safety and self-measures play a major role in preventing the virus from spreading from one person to another. Wireless technology is playing a critical role in avoiding viral propagation. This technology mainly comprises of portable devices that assist self-isolated patients in adhering to safe precautionary measures. Government officials are currently using wireless technologies to identify infected people at large gatherings. In this research, we gave an overview of wireless technologies that assisted the general public and healthcare professionals in maintaining effective healthcare services during COVID-19. We also discussed the possible challenges faced by them for effective implementation in day-to-day life. In conclusion, wireless technologies are one of the best techniques in today's age to effectively combat the pandemic. [ABSTRACT FROM AUTHOR]

Background. The risk of acquiring COVID-19 during a pandemic is a major concern among health care workers. Dental professionals being in close proximity to the patients had been exposed more than other health care workers. Hence, all the standard operating procedures (SOPs) are strictly advised to be followed. Methods. A detailed relevant literature search was conducted in international databases such as PubMed, Web of Science, and Science Direct, from January 2020 to November 2020. All the studies that provided recommendations regarding endodontic procedures during the COVID-19 pandemic were included, and those that were not in the English language, case reports, book chapters, and short communications were excluded in this review. In the end, only 6 articles were selected for the systematic review considering that complete information regarding the provision of dental care in the time of COVID-19 with diagnostic accuracy (STARD) was provided. Results. Endodontic treatments were restricted to only emergency dental procedures, and all other patients have advised medications and catered through teledentistry. Endodontic emergencies were advised to be carried out with minimal aerosol production procedures. Conclusion. Provision of endodontic care during COVID-19 restricted to only emergency dental procedures by strictly following standard operating procedures. A protocol for COVID-19 prevention was followed by all the dentists and the dental staff in the dental clinics. [ABSTRACT FROM AUTHOR]

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic poses a special risk for both immunosuppressed patients, especially transplant recipients. Although the knowledge about this infection is growing, many uncertainties remain, particularly regarding the kidney. Kidney transplant recipients (KDRs) should be considered immunocompromised hosts since a potential risk for infection, comorbidity, and immunosuppression exposure exists. Additionally, the management of immunosuppressive agents in KDRs remains challenging. Potential drug interactions with immunosuppressive treatment escalated the risk of unwanted side effects. In this review, we aimed to attain an augmented awareness and improved management immunosuppressant for COVID-19 KDRs. [ABSTRACT FROM AUTHOR]

In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method's performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F 1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability. [ABSTRACT FROM AUTHOR]