Cervical HIV-Specific IgA in a Population of Commercial Sex Workers Correlates with Repeated Exposure But Not Resistance to HIV
Bibliographic record
Abstract
We conducted a comprehensive cross-sectional analysis of total and HIV-specific cervical antibody levels in HIV-1-resistant, uninfected, and infected women in order to examine the role of HIV-specific antibody responses in the female genital tract and examine the effect on antibody levels of various epidemiologic factors in this population. Cervical lavages were collected from 272 subjects of the Pumwani commercial sex worker cohort. Total and HIV-specific genital tract IgA and IgG levels were measured using an ELISA and correlated with behavioral and demographic factors. No significant difference was seen between cervical HIV-specific IgA levels in infected, uninfected, and resistant individuals, nor were any correlations between cervical HIV-specific IgA and neutralization capacity or viral shedding seen. We did, however, note increased HIV-specific IgA in HIV-negative women with four or more clients per day, and decreased HIV-specific IgA in both long-term nonprogressors and long-term survivors. These results show that there is not a strong cohort-wide correlation between HIV-specific cervical IgA levels and resistance to infection by HIV-1 as previously believed, but there is a correlation between exposure to HIV and HIV-specific cervical IgA. Our findings do not preclude the possibility that functional differences in the cervical IgA of HEPS women may play a role in resistance, but argue that HIV-specific responses may not be a universal protective factor. They also indicate that resistance to HIV is a complex condition related to more factors than exposure. Further studies of correlates of immune protection in these individuals would be beneficial to the field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".