Selection, Phenotyping and Identification of Acid and Hydrogen Peroxide Producing Bacteria from Vaginal Samples of Canadian and East African Women
Bibliographic record
Abstract
The common but poorly understood condition known as bacterial vaginosis (BV) increases vulnerability to HIV infection and is associated with the absence of H(2)O(2)-producing Lactobacillus. Vaginal lactic acid bacteria (LAB) produce anti-HIV factors such as organic acids and hydrogen peroxide (H(2)O(2)), and may bind and inactivate HIV particles during scavenging of mannose. These factors define potential criteria for initial selection of candidate probiotics to block heterosexual transmission of HIV. Therefore, the primary goal of this study was to characterize acid production on mannose and H(2)O(2) production in vaginal isolates from Canadian adolescents (192 isolates, 16 individuals) and commercial sex workers in Nairobi, Kenya (576 isolates, 96 individuals). Selection of isolates from H(2)O(2)-detecting media suggested an idiosyncratic individual-level profile and extensive phenotypic diversity, including the identification of a subset of "double-strong" acid- and H(2)O(2)-producers with phenotypes similar to well-characterized probiotic strains. Molecular fingerprinting of all isolates by capillary electrophoresis of 16S-23S rRNA interspacer amplicons was coupled with chaperonin-60 universal target (cpn60 UT) sequencing in a subset, tentatively identifying 96% of isolates although only 19% were sequenced. Most isolates belonged to Lactobacillus, Streptococcus, Bifidobacterium or Gardnerella, with a total of 37 species in 15 genera, as well as 5 potentially novel organisms, identified in this study. This sensitivity was likely enhanced by phenotype-based selection on two chromogenic media formulations. Identification of double-strong isolates may provide a rational basis for selection and further characterization of vaginal probiotics, with potential application as part of HIV prevention initiatives in western Canada and East Africa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".