One of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB. The genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)). A total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores. Through the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.

Background: Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research. Objective: This review aims to assess the effectiveness of user-generated social media texts in predicting depression and evaluate the influence of demographic, language, social media activity, and temporal features on predicting depression on social media texts through machine learning. Methods: We searched studies from 11 databases (CINHAL [through EBSCOhost], PubMed, Scopus, Ovid MEDLINE, Embase, PubPsych, Cochrane Library, Web of Science, ProQuest, IEEE Explore, and ACM digital library) from January 2008 to August 2023. We included studies that used social media texts, machine learning, and reported area under the curve, Pearson r, and specificity and sensitivity (or data used for their calculation) to predict depression. Protocol papers and studies not written in English were excluded. We extracted study characteristics, population characteristics, outcome measures, and prediction factors from each study. A random effects model was used to extract the effect sizes with 95% CIs. Study heterogeneity was evaluated using forest plots and P values in the Cochran Q test. Moderator analysis was performed to identify the sources of heterogeneity. Results: A total of 36 studies were included. We observed a significant overall correlation between social media texts and depression, with a large effect size (r=0.630, 95% CI 0.565-0.686). We noted the same correlation and large effect size for demographic (largest effect size; r=0.642, 95% CI 0.489-0.757), social media activity (r=0.552, 95% CI 0.418-0.663), language (r=0.545, 95% CI 0.441-0.649), and temporal features (r=0.531, 95% CI 0.320-0.693). The social media platform type (public or private; P<.001), machine learning approach (shallow or deep; P=.048), and use of outcome measures (yes or no; P<.001) were significant moderators. Sensitivity analysis revealed no change in the results, indicating result stability. The Begg-Mazumdar rank correlation (Kendall τb=0.22063; P=.058) and the Egger test (2-tailed t34=1.28696; P=.207) confirmed the absence of publication bias. Conclusions: Social media textual content can be a useful tool for predicting depression. Demographics, language, social media activity, and temporal features should be considered to maximize the accuracy of depression prediction models. Additionally, the effects of social media platform type, machine learning approach, and use of outcome measures in depression prediction models need attention. Analyzing social media texts for depression prediction is challenging, and findings may not apply to a broader population. Nevertheless, our findings offer valuable insights for future research. Trial Registration: PROSPERO CRD42023427707; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023427707

Childhood asthma represents a heterogeneous disease resulting from the interaction between genetic factors and environmental exposures. Currently, finding reliable biomarkers is necessary for the clinical management of childhood asthma. However, only a few biomarkers are being used in clinical practice in the pediatric population. In the long run, new biomarkers for asthma in children are required and would help direct therapy approaches. This study aims to identify potential childhood asthma biomarkers using a genetic-driven biomarkers approach. Herein, childhood asthma-associated Single Nucleotide Polymorphisms (SNPs) were utilized from the GWAS database to drive and facilitate the biomarker of childhood asthma. We uncovered 466 childhood asthma-associated loci by extending to proximal SNPs based on r² > 0.8 in Asian populations and utilizing HaploReg version 4.1 to determine 393 childhood asthma risk genes. Next, the functional roles of these genes were subsequently investigated using Gene Ontology (GO) term enrichment analysis, a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and a protein–protein interaction (PPI) network. MCODE and CytoHubba are two Cytoscape plugins utilized to find biomarker genes from functional networks created using childhood asthma risk genes. Intriguingly, 10 hub genes (IL6, IL4, IL2, IL13, PTPRC, IL5, IL33, TBX21, IL2RA, and STAT6) were successfully identified and may have been identified to play a potential role in the pathogenesis of childhood asthma. Among 10 hub genes, we strongly suggest IL6 and IL4 as prospective childhood asthma biomarkers since both of these biomarkers achieved a high systemic score in Cytohubba’s MCC algorithm. In summary, this study offers a valuable genetic-driven biomarker approach to facilitate the potential biomarkers for asthma in children.

Antiretroviral treatment (ART) appropriately and regularly used decreases the human immunodeficiency virus (HIV) viral load in the bloodstream, preventing HIV-infected people from spreading the infection to others. Disparities in ART adoption persists in East and Southern Africa, with low HIV-positive status knowledge being the primary factor. We investigated individual and household characteristics of HIV-positive status awareness among adults with long-term HIV infection in four East and Southern African countries: Eswatini, Malawi, Tanzania, and Zimbabwe. The study analyzed data from surveys conducted in Eswatini, Malawi, Tanzania, and Zimbabwe in 2015–2016. Only individuals who tested positive for HIV through rapid tests were included in the analysis. Those who already knew they were HIV-positive were categorized as aware, while those who reported being negative, never tested, or didn’t know their status were categorized as unaware. Statistical models were used to examine various factors related to HIV awareness. Pooled and country-specific odds ratios were computed. The percentage of people who knew they had HIV ranged from 58% (Tanzania and Malawi) to 87% (Eswatini). After adjusting for other variables, young persons in all countries were less likely to be aware of their HIV-positive status. Gender, marital status, education, working status, household wealth, and urbanization level of households were also associated with HIV-positive status awareness but inconsistent across countries. HIV-positive status awareness in these four East and Southern African nations remained unsatisfactory as compared to the United Nations’ 95% guideline, indicating that testing and knowledge of HIV testing in this region still has a lot of potential for improvement. The observed variations among nations may be attributable to differences in HIV pandemic culture and policies. The findings of this study will assist governments determining which subpopulations to target to boost adoption of HIV testing services, as well as in designing and development of policies.

Background: Depressive symptoms are associated with both long-lasting and short-term repetitive mood disorders and affect a person’s ability to function and lead a rewarding life. In addition to predisposing genetic causes, other factors such as socioeconomic and demographic factors, and chronic diseases have also been reported to associate with depression. In this study, we analyzed the association between history of chronic diseases and presentation of depressive symptoms amongst Turkish individuals.Methods: We employed the 2019 Turkey health survey to analyze data of 11,993 individuals aged 15+ years. Depressive symptoms were assessed using the eight-item Patient Health Questionnaire (PHQ-8) coded with a binary measure, a score of 10 as moderate-severely depressed. A number of sociodemographic characteristics were adjusted for in the analyses. Logistic regression models were used to test the association between chronic diseases and depressive symptoms in the study sample.Results: Our analysis revealed that 6.24% of the 11,993 participants had reported an episode of depressive symptoms. The prevalence of depressive symptoms in men was 1.85% and in women, it was 2.34 times higher. Participants who had previously reported experiencing coronary heart diseases (AOR = 7.79, 95% CI [4.96–12.23]), urinary incontinences (AOR = 7.90, 95% CI [4.93–12.66]), and liver cirrhosis (AOR = 7.50, 95% CI [4.90–10.42]) were approximately eight times likely to have depressive symptoms. Similarly, participants with Alzheimer’s disease (AOR = 6.83, 95% CI [5.11–8.42]), kidney problems (AOR = 6.63, 95% CI [4.05–10.85]), and history of allergies (AOR = 6.35, 95% CI [4.28–9.23]) had approximately seven-fold odds of reporting episodes of depressive symptoms. The odds of presenting with depressive symptoms amongst participants aged ≥ 50 were higher than in individuals aged ≤ 49 years.Conclusion: At individual level, gender and general health status were associated with increased odds of depression. Furthermore, a history of any of the chronic diseases, irrespective of age, was a positive predictor of depression in our study population. Our findings could help to serve as a reference for monitoring depression amongst individuals with chronic conditions, planning health resources and developing preventive and screening strategies targeting those exposed to predisposing factors.