Human Immunodeficiency Virus Infection and Acquired Immunodeficiency Syndrome (<scp>HIV</scp>/<scp>AIDS</scp>): An Overview

Prabhu, S.R.; Wagoner, Nicholas Van

doi:10.1002/9781119826781.ch5

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…Combined with other antibiotic drugs, it can treat bacterial infections [30]. The disease of Acquired Immunodeficiency Syndrome (AIDS) is caused by the human immunodeficiency virus (HIV), which is a viral infection [39]. HIV causes the immune system to lose its function and makes the patient susceptible to diseases [41].…”

Section: Case Analysismentioning

confidence: 99%

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

Xing,

Zhang,

Wang

2023

Preprint

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Clinical studies have shown that microbes are closely related to the occurrence of diseases in the human body. It is beneficial for treating diseases by means of microbes to modulate the activity and toxicity of drugs. Therefore, it is significant in predicting associations between drugs and microbes. Recently, there are several computational models for addressing the issue. However, most of them only focus on drug-related microbes and neglect related diseases, which can lead to insufficient training. Here we introduce a new model (called MDMD) is proposed to predict drug-related microbes based on the Metapaths from a heterogeneous network constructed by using the data of Diseases, Microbes, Drugs, the associations of microbe-disease and disease-drug. The MDMD uses an aggregation of the metapath features that can effectively abundance the embedding of the features for different types of nodes and edges in the heterogeneous networks. Then, the MDMD uses the attention mechanism to mark the importance of the metapath vector for each node type which can improve the quality of feature embedding. Experimental results demonstrate that the MDMD improves accuracy by 1.9% compared with other models. The MDMD is also used to predict the microbes of two drugs Lamivudine and Tenofovir which are the antiretroviral drugs used to treat the Acquired Immune Deficiency Syndrome(AIDS). The results show that 90-95% of microbes are reported in the PubMed. Mycobacterium tuberculosis(Mtb) is a specific microbe only predicted by the MDMD. An online platform of the MDMD is available in https://mdmd2023.bit1024.top/, in which the source code of the MDMD and the data in the work can be downloaded.

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Section: Case Analysismentioning

confidence: 99%

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

Xing,

Zhang,

Wang

2023

Preprint

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“…Human immunodeficiency virus (HIV), which later causes acquired immunodeficiency syndrome (AIDS), is one of the global health threats ( 1 ). According to the Joint United Nations Programme on HIV/AIDS, 2022 Global HIV and AIDS Statistics fact sheet, 84.2 million people have become infected with HIV since the HIV epidemic began, and 40.1 million have died from AIDS-related illnesses.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of co-trimoxazole resistance among HIV-infected individuals in Ethiopia: a systematic review and meta-analysis

Assefa,

Girmay

2024

Front. Med.

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BackgroundCo-trimoxazole is used as a prophylaxis for human immunodeficiency virus (HIV) patients to prevent opportunistic infections. Its widespread use results in the emergence of co-trimoxazole resistance, which is a significant problem. This systematic review and meta-analysis determined the pooled prevalence of co-trimoxazole resistance among HIV-infected individuals in Ethiopia.MethodsThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was applied to report this study. The protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the assigned number CRD42024532240. Article search was performed using electronic databases such as PubMed, Medline, EMBASE, Google Scholar, Hinari, Web of Science, Science Direct, and African Journals Online. Data were extracted using a Microsoft Excel spreadsheet and analyzed using STATA version 11.0 software. A random-effects model was used to estimate the pooled effect size of co-trimoxazole resistance across studies with a 95% confidence interval. The heterogeneity was checked using I2 statistic. The presence of publication bias was determined using a funnel plot and Egger’s test with a p-value <0.05 evidence of statistically significant bias. Subgroup and sensitivity analyses were performed.ResultsTwenty-two studies with 5,788 HIV-infected individuals were included. The pooled prevalence of co-trimoxazole resistance was 61.73% (95% CI: 53.10–70.37%), with heterogeneity (I2 = 87.7%) and statistical significance (p < 0.001). A higher co-trimoxazole resistance was observed in HIV-infected individuals with urinary tract infection; 82.10% (95% CI: 75.03–89.17%). Among the bacterial spp., higher resistance to co-trimoxazole was observed in Escherichia coli; 70.86% (95% CI: 53.44–88.27%) followed by Salmonella spp.; 67.66% (95% CI: 41.51–93.81%) and Proteus spp.; 66.23% (95% CI: 34.65–97.82%).ConclusionThere is a higher prevalence of co-trimoxazole resistance in HIV-infected individuals in Ethiopia. This alarms WHO’s recommendation of co-trimoxazole prophylaxis guidelines to review and update it. Additionally, a nationwide assessment of co-trimoxazole resistance in Ethiopia as a whole is required.Systematic review registration: identifier: CRD42024532240.

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Human Immunodeficiency Virus Infection and Acquired Immunodeficiency Syndrome (HIV/AIDS): An Overview

Cited by 2 publications

References 37 publications

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

MDMD: a computational model for predicting drug-related microbes based on the aggregated metapaths from a heterogeneous network

Prevalence of co-trimoxazole resistance among HIV-infected individuals in Ethiopia: a systematic review and meta-analysis

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