Pregnancy associates with alterations to the host and microbial proteome in vaginal mucosa
Bibliographic record
Abstract
PROBLEM: Pregnant women are at increased risk of HIV acquisition, but the biological mechanisms contributing to this observation are not well understood. METHOD OF STUDY: Here, we assessed host immune and microbiome differences in the vaginal mucosa of healthy pregnant and non-pregnant women using a metaproteomics approach. Cervicovaginal lavage (CVL) samples were collected from 23 pregnant and 25 non-pregnant women. RESULTS: Mass spectrometry analysis of CVL identified 550 human proteins and 376 bacterial proteins from 11 genera. Host proteome analysis indicated 56 human proteins (10%) were differentially abundant (P < .05) between pregnant and non-pregnant women, including proteins involved in angiogenesis (P = 3.36E-3), cell movement of phagocytes (P = 1.34E-6), and permeability of blood vessels (P = 1.27E-4). The major bacterial genera identified were Lactobacillus, Gardnerella, Prevotella, Megasphaera, and Atopobium. Pregnant women had higher levels of Lactobacillus species (P = .017) compared with non-pregnant women. Functional pathway analysis indicated that pregnancy associated with changes to bacterial metabolic pathway involved in energy metabolism, which were increased in pregnant women (P = .035). CONCLUSION: Overall, pregnant women showed differences in the cervicovaginal proteome and microbiome that may be important for HIV infection risk.
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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 | 0.001 | 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".