The genital tract and rectal microbiomes: their role in HIV susceptibility and prevention in women
Bibliographic record
Abstract
INTRODUCTION: Young women in sub-Saharan Africa are disproportionately affected by HIV, accounting for 25% of all new infections in 2017. Several behavioural and biological factors are known to impact a young woman's vulnerability for acquiring HIV. One key, but lesser understood, biological factor impacting vulnerability is the vaginal microbiome. This review describes the vaginal microbiome and examines its alterations, its influence on HIV acquisition as well as the efficacy of HIV prevention technologies, the role of the rectal microbiome in HIV acquisition, advances in technologies to study the microbiome and some future research directions. DISCUSSION: Although the composition of each woman's vaginal microbiome is unique, a microbiome dominated by Lactobacillus species is generally associated with a "healthy" vagina. Disturbances in the vaginal microbiota, characterized by a shift from a low-diversity, Lactobacillus-dominant state to a high-diversity non-Lactobacillus-dominant state, have been shown to be associated with a range of adverse reproductive health outcomes, including increasing the risk of genital inflammation and HIV acquisition. Gardnerella vaginalis and Prevotella bivia have been shown to contribute to both HIV risk and genital inflammation. In addition to impacting HIV risk, the composition of the vaginal microbiome affects the vaginal concentrations of some antiretroviral drugs, particularly those administered intravaginally, and thereby their efficacy as pre-exposure prophylaxis (PrEP) for HIV prevention. Although the role of rectal microbiota in HIV acquisition in women is less well understood, the composition of this compartment's microbiome, particularly the presence of species of bacteria from the Prevotellaceae family likely contribute to HIV acquisition. Advances in technologies have facilitated the study of the genital microbiome's structure and function. While next-generation sequencing advanced knowledge of the diversity and complexity of the vaginal microbiome, the emerging field of metaproteomics, which provides important information on vaginal bacterial community structure, diversity and function, is further shedding light on functionality of the vaginal microbiome and its relationship with bacterial vaginosis (BV), as well as antiretroviral PrEP efficacy. CONCLUSIONS: A better understanding of the composition, structure and function of the microbiome is needed to identify opportunities to alter the vaginal microbiome and prevent BV and reduce the risk of HIV acquisition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".