Pre- and post-LEEP: analysis of the female urogenital tract microenvironment and its association with sexual dysfunction
Bibliographic record
Abstract
Abstract Background The loop electrosurgical excision procedure (LEEP) to treat cervical dysplasia (CD) is known to alter the cervical microbiota, the community of bacteria that play a central role in female genital health. Perturbations to the microbiota of the female urogenital tract (FUT), including the urethra, vagina, and cervix, have been linked with symptoms of sexual dysfunction (SD), though correlations among LEEP, the microenvironment, and SD have not yet been described. Aims To characterize the FUT microbiota before and after LEEP and investigate possible associations with SD. Methods Females undergoing LEEP for CD were recruited to participate in the study. Urinary samples and vaginal and cervical swabs were collected immediately before and 3 months after treatment. Bacterial communities were characterized by 16S rRNA next-generation sequencing. Self-report surveys assessing demographics, medical history, and sexual function were completed at the same intervals. Outcomes Microbiota taxonomy and Female Sexual Function Index (FSFI) scores. Results Alpha diversity revealed a significant decrease in species richness in the FUT microbiota post-LEEP. Beta diversity demonstrated significant differences among the cervical, urinary, and vaginal microenvironments pre- and post-LEEP. Lactobacillus spp were the dominant microbial genus in the cervical microenvironment pre- and post-LEEP. Although the vaginal and urinary microenvironments were characterized by Prevotella pre-LEEP, they were colonized by Lactobacillus post-LEEP. Following LEEP, some participants experienced a significant increase in proinflammatory bacteria, including the genera Gardnerella, Megasphaera, Sneathia, Parvimonas, and Peptostreptococcus. Others experienced significant decreases in inflammatory and protective bacteria post-LEEP, including Butyricicoccus, Terriporobacter, Intestinimonas, and Negativibacillus. Overall there were no significant changes in pre- and post-LEEP FSFI scores. However, post-LEEP FSFI scores were seemingly associated with changes in inflammatory bacteria in some participants. Clinical Implications There is an overall reduction in FUT microbiota dysbiosis post-LEEP. However, we show variability as some participants experienced persistent dysbiosis of FUT microbiota and elevated FSFI scores, suggesting that therapies to treat dysbiosis of FUT microbiota may reduce FSFI scores, thereby improving SD symptoms. Strengths and Limitations We demonstrate novel associations among urogenital sites, microbiota changes, LEEP, and SD. The small sample size and inability of species classification are limitations. Conclusion Diverse inflammatory microbiota characterizes CD in the FUT, and LEEP mostly returns microenvironments to a healthy state. However, some participants have persistent inflammatory bacteria post-LEEP, suggesting a non-uniform healing response. This study provides an impetus for future longitudinal studies to monitor and restore FUT microenvironments post-LEEP, aimed at mitigating postoperative SD symptoms.
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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) | 0.002 | 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".