Proteomic Analysis of Human Cervico-Vaginal Fluid
Bibliographic record
Abstract
Human cervico-vaginal fluid (CVF) is a mixture of fluids originating from the vagina, cervix, endometrium, and oviduct. CVF has been shown to play an important role in protecting the vagina from infection. We used "bottom-up" proteomic approaches to characterize the protein repertoire of human CVF. We applied two different sample prefractionation methods, one-dimensional-SDS-PAGE (1D-SDS-PAGE) and strong cation-exchange chromatography, followed by LC-MS/MS and bioinformatic analysis. We identified a total of 685 proteins. Strong cation-exchange chromatography prefractionation resulted in a larger number of proteins identified when compared with 1D-SDS-PAGE. Extracellular or membrane proteins made up 30% of the proteins identified, according to Genome Ontology (GO) classifications. We confirmed the presence of defense-related proteins, such as haptoglobin, defensins, and lactoferrin; and identified new ones such as azurocidin and dermcidin. We also identified many serine and cysteine proteases, including 6 members of the kallikrein family (KLKs 6, 7, 10, 11, 12, and 13). The same KLKs were also confirmed quantitatively by ELISA assays. Knowledge of the CVF proteome will aid in the discovery of potential biomarkers for gynecological malignancies and infections and provide additional clues for its physiological functions.
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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 | 0.001 | 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".