Analysis of Female Urogenital Tract Microenvironment Pre- and Post LEEP and Impact on Sexual Dysfunction
Bibliographic record
Abstract
ABSTRACT Introduction The microbiome of the female urogenital tract (FUT), including the urinary tract, the vagina, and the cervix, contains different organisms which are essential for maintaining a stable microenvironment. Little is known about the interrelatedness of these three regions despite their close anatomical relationship. It is possible that dysbiosis in one region because of disease, such as cervical dysplasia (CD), can impact other FUT microbiomes. Electrocautery of the cervix during the Loop Electrosurgical Excision Procedure (LEEP) effectively treats CD yet is known to alter the local microbiome. The impact of CD and its treatment (LEEP) on the FUT microbiomes has yet to be investigated. Given the potential for tissue damage from electrocautery, it is likely that the cervical microbiome pre- and post-LEEP could have a bacterial profile that reflects a persistent pro-inflammatory environment. It is possible that persistent dysbiosis may be a mechanism of the FSD that has been reported in a subpopulation of post-LEEP patients, though this correlation has never been investigated. Objective This study examined the bacterial profile of the FUT in patients with CD before and after treatment with LEEP. It also evaluated the sexual function of patients pre-and post-LEEP using validated surveys and compared the survey responses to the patient bacterial profiles. Methods Twenty-five participants with CD undergoing LEEP were consented and recruited. Vaginal and cervical swabs as well as urine samples were collected to examine the FUT microbiomes. All participants completed an online self-report survey including full FSFI before LEEP and three months post-LEEP. 16S rRNA analysis was performed to determine the presence and relative abundance of bacteria in the samples. Qualitative and statistical analysis were performed on survey responses using NVivo12 and SPSS, respectively. Results The cervical, vaginal, and urethral microbiomes displayed significant similarity (beta diversity, p = <0.0001) likely demonstrating a functional relationship between these three regions for the first time. Notably, this study found the relative abundance of Prevotella in participants with CD pre-LEEP significantly increased (p = <0.001) in only the cervical microbiome. This showed that the cervix had unique bacterial proportions compared to the vagina, though existing studies often examine them together. There was a further significant increase (p = 0.0186) in Prevotella in the cervical microbiome of participants post-LEEP versus pre-LEEP. The findings suggest that on average, patients with CD have a cervical microbiome in dysbiosis. This study also identified a subset of participants with decreased sexual function post-LEEP and correlative microbiome dysbiosis. Further bacterial analysis regarding these profiles is ongoing. Conclusions This study was the first to determine the interrelatedness between regions of the FUT microbiomes, and importantly showed that patients with CD have a cervical microbiome in dysbiosis. It also showed that CD patients may have persistent inflammation after treatment with LEEP, which could result in FSD detected by self-report surveys. Information gained from characteristic FUT bacterial profiles may be translated into therapies to regulate the microbiome pre- and post-LEEP and may indicate that the environment of the FUT can be optimized to facilitate healing following electrocautery procedures. Disclosure No
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".