Glycosidase and Proteinase Activity of Anaerobic Gram-Negative Bacteria Isolated From Women With Bacterial Vaginosis
Bibliographic record
Abstract
BACKGROUND: It is known that glycosidases and proteases are produced by the anaerobic gram-negative bacteria associated with bacterial vaginosis (BV). We hypothesized that these enzymes enzymatically degrade mucins, thereby destroying the mucus gel that otherwise helps protect against sexually transmitted pathogens, including HIV. GOAL: The goal was to determine glycosidase and protease production by vaginal bacteria associated with BV and to compare these with symptoms and signs of abnormal discharge and to test vaginal fluid viscosity. STUDY DESIGN: The anaerobic gram-negative rods recovered from the vaginas of 153 women with normal flora, intermediate flora, or BV were tested for production of sialidase, fucosidase, galactosidase, glucosaminidase, and glycine and arginine aminopeptidases. RESULTS: Women with BV had higher frequencies and concentrations of bacteria producing mucin-degrading enzymes than did women with intermediate and normal flora (P < 0.001). Women with higher concentrations of bacteria producing mucin-degrading enzymes were more likely to have a thin discharge associated with BV (P < 0.001). The viscosity of diluted vaginal fluid samples from women with BV was significantly lower than those from women with normal flora (P = 0.001). CONCLUSION: These data support the hypothesis that BV organisms degrade the protective mucus gel.
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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 | 0.000 | 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".