A High-throughput Bead-based Affinity Assay Enables Analysis of Genital Protein Signatures in Women At Risk of HIV Infection
Bibliographic record
Abstract
Women at high risk of HIV infection, including sex workers and those with active genital inflammation, have molecular signatures of immune activation and epithelial barrier remodeling in samples of their genital mucosa. These alterations in the local immunological milieu are likely to impact HIV susceptibility. We here analyze host genital protein signatures in HIV uninfected women, with high frequency of condom use, living in HIV-serodiscordant relationships. Cervicovaginal secretions from women living in HIV-serodiscordant relationships ( n = 62) were collected at three time points over 12 months. Women living in HIV-negative seroconcordant relationships (controls, n = 25) were sampled at one time point. All study subjects were examined for demographic parameters associated with susceptibility to HIV infection. The cervicovaginal samples were analyzed using a high-throughput bead-based affinity assay. Proteins involved in epithelial barrier function and inflammation were increased in HIV-serodiscordant women. By combining several methods of analysis, a total of five proteins (CAPG, KLK10, SPRR3, elafin/PI3, CSTB) were consistently associated with this study group. Proteins analyzed using the affinity set-up were further validated by label-free tandem mass spectrometry in a partially overlapping cohort with concordant results. Women living in HIV-serodiscordant relationships thus had elevated levels of proteins involved in epithelial barrier function and inflammation despite low prevalence of sexually transmitted infections and a high frequency of safe sex practices. The identified proteins are important markers to follow during assessment of mucosal HIV susceptibility factors and a high-throughput bead-based affinity set-up could be a suitable method for such evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".