HIV Infection in Uncircumcised Men Is Associated With Altered CD8 T-cell Function But Normal CD4 T-cell Numbers in the Foreskin
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV)-infected (HIV+) men are more susceptible to sexually transmitted infections, and may be superinfected by HIV. We hypothesized that HIV induces immune alterations in the foreskin that may impact the subsequent acquisition/clearance of genital coinfections. METHODS: Foreskin tissue and blood were obtained from 70 HIV-uninfected and 20 HIV+ men undergoing circumcision. T cells were characterized by flow cytometry, immunohistochemistry, and polymerase chain reaction. RESULTS: There was substantial influx of CD8 T-cells into the foreskins of HIV+ men (108.8 vs 23.1 cells/mm(2); P < .001); but foreskin CD4 T-cell density was unchanged (43.0 vs 33.7/mm(2); P = .67), despite substantial blood depletion (409.0 vs 877.8 cells/µL; P < .001). While frequencies of foreskin C-C chemokine receptor type 5(+) (CCR5(+)) T cells, T regulatory cells, and T-helper 17 cells were unaltered in HIV+ men, CD8 T-cell production of tumor necrosis factor α (TNFα) was decreased. HIV-specific CD8 T cells were present in the foreskins of HIV+ men, although their frequency and function was reduced compared to the blood. CONCLUSIONS: Foreskin CD4 T-cell density and CCR5 expression were not reduced during HIV infection, perhaps explaining susceptibility to HIV superinfection. Foreskin CD8 T-cell density was increased, but decreased production of TNFα may enhance susceptibility to genital coinfections in HIV+ men.
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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) | 0.002 | 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".