Influence of Luseogliflozin on Vaginal Bacterial and Fungal Populations in Japanese Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: Selective sodium-glucose cotransporter 2 inhibitors, known to lower the blood glucose levels by promoting the urinary glucose excretion, can predispose to genitourinary infections. This prospective study investigated the influence of selective sodium-glucose cotransporter 2 inhibitors luseogliflozin on the vaginal flora of the pre- and postmenopausal women with type 2 diabetes mellitus. Methods: Twelve premenopausal and 24 postmenopausal female Japanese patients with type 2 diabetes mellitus took luseogliflozin 2.5 mg once daily for 6 months. The intravaginal fungal and bacterial populations, together with the body weight and serum parameters of diabetes mellitus and lipid metabolism were measured before and after the treatment. Results: After luseogliflozin treatment, the body weight, body mass index and hemoglobin A1c decreased, and the serum levels of high-density lipoprotein cholesterol increased significantly. Luseogliflozin treatment revealed to increase vaginal colony concentrations of Enterococcus faecalis (P = 0.0077) and E. coli (P = 0.0201) in premenopausal patients, and Enterococcus faecalis (P = 0.0051) and Candida albicans (P = 0.0355) in postmenopausal patients. In both pre- and postmenopausal patients, colony concentrations of Staphylococcus spp . had decreased (P = 0.0261 and P = 0.0161). Conclusions: Treatment with selective sodium-glucose cotransporter 2 inhibitors luseogliflozin was associated with changes of the vaginal flora. These findings provide basic data on the increased susceptibility to genital infections during luseogliflozin treatment. J Clin Med Res. 2021;13(5):309-316 doi: https://doi.org/10.14740/jocmr4504
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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) | 0.000 | 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) | 0.001 | 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".