A potential role for tissue kallikrein-related peptidases in human cervico-vaginal physiology
Bibliographic record
Abstract
Human tissue kallikrein-related peptidases (KLK) are a family of 15 genes located on chromosome 19q13.4 that encode secreted serine proteases with trypsin- and/or chymotrypsin-like activity. Relatively large levels of many KLKs are present in human cervico-vaginal fluid (CVF) and in the supernatant of cultured human vaginal epithelial cells. Many KLKs are also hormonally regulated in vaginal epithelial cells, particularly by glucocorticoids and estrogens. The physiological role of KLK in the vagina is currently unknown; however, analysis of the CVF proteome has revealed clues for potential KLK functions in this environment. Here, we detail potential roles for KLKs in cervico-vaginal physiology. First, we suggest that KLKs play a role in the vagina similar to their role in skin physiology: (1) in the desquamation of vaginal epithelial cells, similar to their activity in the desquamation of skin corneocytes; and (2) in their ability to activate antimicrobial proteins in CVF as they do in sweat. Consequently, we hypothesize that dysregulated KLK expression in the vagina could lead to the development of pathological conditions such as desquamative inflammatory vaginitis. Second, we propose that KLKs may play a role in premature rupture of membranes and pre-term birth through their cleavage of fetal membrane extracellular matrix proteins.
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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".