Role of apoptotic protease activating factor-1 in CD4+ depletion during HIV progression.

Objective: This study investigates the role of Apoptotic Protease Activating Factor-1 (APAF-1) in CD4+ cell depletion among human immunodeficiency virus (HIV) patients. Materials and Methods: This is a cross-sectional study in which 105 participants were enrolled, including 60 confirmed HIV-positive patients and 45 HIV-negative controls. HIV-positive patients were further divided based on CD4+ cell counts: Group 1 (<200), Group 2 (200-499), and Group 3 (≥500). An enzyme-linked immunoassay was used to measure APAF-1 levels, and CD4+ T-cell counts were enumerated using a Cyflow counter. Independent student's t-test, Kruskal-Wallis, and Spearman's correlation were utilized as needed. Results: Results showed significant reductions in lymphocytes, platelets, red blood cells, hemoglobin, albumin, and CD4+ cell values among HIV-infected individuals compared to controls. Conversely, APAF-1 and total protein levels were elevated in HIV-positive patients. Among HIV-positive groups, those with CD4+ cell counts <200 exhibited the highest median serum APAF-1 concentration. However, these differences were not statistically significant when compared with the other seropositive groups with CD4+ cell counts between 200 and 499 (P = 0.6726) and CD4+ cell counts of 500 or greater (P = 0.4325). The control group had the lowest median SAPAF-1 concentration, significantly different from HIV-positive groups. Positive correlations were observed between CD4+ counts and lymphocytes, hemoglobin, and hypoalbuminemia, while negative correlations were found between these parameters and APAF-1 levels. Conclusion: APAF-1 is a host factor that potentially contributes to CD4+ cell depletion. Similarly, APAF-1, serum total protein, and albumin levels were found to be predictive of disease progression and could serve as valuable diagnostic biomarkers in the monitoring of HIV/AIDS.

Implications of SARS-CoV-2 genetic diversity and mutations on pathogenicity of the COVID-19 and biomedical interventions.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused an unprecedented global health emergency. The COVID-19 pandemic has claimed over 350,000 human lives within five months of its emergence, especially in the USA and the European continent. This study analysed the implications of the genetic diversity and mutations in SARS-CoV-2 on its virulence diversity and investigated how these factors could affect the successful development and application of antiviral chemotherapy and serodiagnostic test kits, and vaccination. METHODS: All the suitable and eligible full text articles published between 31st December 2019 and 31st May 2020 were filtered and extracted from "PubMed", "Scopus", "Web of Science", and "Hinari" and were critically reviewed. We used the Medical Subject Headings (MeSH) terms "COVID-19, "Mutation", "Genetic diversity", "SARS-CoV-2", "Virulence", "Pathogenicity", "Evolution" and "SARS-CoV-2 transmission" for this search. RESULTS: Our search showed that SARS-CoV-2 has persistently undergone significant mutations in various parts of its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). In particular, the S protein was found to be the key determinant of evolution, transmission, and virulence of SARS-CoV-2, and could be a potential target for vaccine development. Additionally, RdRp could be a major target in the development of antivirals for the treatment of COVID-19. CONCLUSION: Given the critical importance of mutations in the pathogenicity of SARS-CoV-2 and in the development of sero-diagnostics, antivirals, and vaccines, this study recommends continuous molecular surveillance of SARS-CoV-2. This approach would potentially prompt identification of new mutants and their impact on ongoing biomedical interventions and COVID-19 control measures.

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. METHODS: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. RESULTS: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. CONCLUSIONS: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.