Simian Immunodeficiency Virus Susceptibility, Immunology, and Microbiome in the Female Genital Tract of Adolescent Versus Adult Pigtail Macaques
Bibliographic record
Abstract
In Sub-Saharan Africa, young women 15–24 years of age account for nearly 30% of all new HIV infections, however, biological and epidemiological factors underlying this disproportionate infection rate are unclear. In this study, we assessed biological contributors of SIV/HIV susceptibility in the female genital tract (FGT) using adolescent (n = 9) and adult (n = 10) pigtail macaques (PTMs) with weekly low-dose intravaginal challenges of SIV. Immunological variables were captured in vaginal tissue of PTMs by flow cytometry and cytokine assays. Vaginal biopsies were profiled by proteomic analysis. The vaginal microbiome was assessed by 16S rRNA sequencing. We were powered to detect a 2.2-fold increase in infection rates between age groups, however, we identified no significant differences in susceptibility. This model cannot capture epidemiological factors or may not best represent biological differences of HIV susceptibility. No immune cell subsets measured were significantly different between groups. Inflammatory marker MCP-1 was significantly higher (adj p = .02), and sCD40L trended higher (adj p = .06) in vaginal cytobrushes of adults. Proteomic analysis of vaginal biopsies showed no significant (adj p < .05) protein or pathway differences between groups. Vaginal microbiomes were not significantly different between groups. No differences were observed between age groups in this PTM model, however, these animals may not reflect biological factors contributing to HIV risk such as those found in their human counterparts. This model is therefore not appropriate to explore human adolescent differences in HIV risk. Young women remain a key population at risk for HIV infection, and there is still a need for comprehensive assessment and intervention strategies for epidemic control of this uniquely vulnerable population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".