Lactobacillus pentosus KCA1 Decreases Vaginal and Gut Microbiota Associated with Bacterial Vaginosis
Bibliographic record
Abstract
Introduction: Bacterial vaginosis (BV) which affects 14%–50% of reproductive-aged women in Nigeria is misdiagnosed and underreported. Treatment option is antibiotics, which leads to recurrent infections. The objectives of this study are three folds: first, to determine effects of oral feeding of Lactobacillus pentosus KCA1 on the vaginal and gut microbiota of women diagnosed with BV; second, to measure the level of two pro-inflammatory cytokines interleukin-1 (IL-1)-beta and IL-6 before and after KCA1 consumption; and third, to determine the relative abundance of bacterial metabolic genes. Materials and Methods: Seven women diagnosed with BV by Nugent score (7–10) were recruited to provide vaginal and gut sample before and after 14-day oral intake of 3 g of L. pentosus KCA1. The DNA from the swabs was processed for 16S rRNA metagenomics using Illumina MiSeq platform. The paired-end sequence FASTQ reads were imported into Illumina Basespace pipeline for quality check. In addition, EzBioCloud pipeline was used for alpha- and beta-diversity estimation using PKSSU4.0 version and open reference UCLUST_MC2 for operational taxonomic units picking at 97% cutoff. Blood samples were analyzed using ELISA technique. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was used to predict the metabolic functions from the 16S rRNA gene dataset. Results: On average, there was no significant difference at P = 0.05 in the alpha-indices typified by Shannon index. The beta-diversity showed different clustering positions with principal coordinate analysis. However, at individual taxonomic categories, there was a significant decrease in the relative abundance of some genera associated with BV after KCA1 feeding with a corresponding increase of Lactobacillus genus. Bacterial genes related to defense systems were upregulated in the vagina. There was a two-fold downregulation of IL-1-beta after consumption of KCA1. Conclusion: Our findings suggest that L. pentosus KCA1 taken orally lowers pro-inflammatory cytokine and IL-1-beta and decreases the relative abundance of BV-associated bacteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".