(109) Analysis of Patient-Specific Microbiome Profiles and Possible Association with Sexual Dysfunction After Cervical Dysplasia Treatment
Notice bibliographique
Résumé
Abstract Introduction Electrocautery of the cervix during the loop electrosurgical excision procedure (LEEP) effectively treats cervical dysplasia (CD). Most patients do not report adverse symptoms post-operatively. However, a subset of patients has reported sexual issues and psychosexual sequalae, including some impact to their quality of life. The etiology of these symptoms has not been investigated, especially in a functional capacity. Moreover, there is some evidence that LEEP alters the cervical microbiome, from a small number of studies. Given the close anatomical relatedness of the pelvis, regions in close proximity to the cervix should also be considered, such as the vagina and urethra. Together, these three regions comprise the female urogenital tract (FUT), which should be examined for persistent inflammatory bacterial profiles post-LEEP. Correlations between LEEP patient-reported symptoms of psychological and sexual issues, and bacterial microbiome profiles, have not yet been investigated. Objective To analyze overall and individualized bacterial profiles (cervix, vagina, urethra) of patients with CD before and after treatment with LEEP and investigate associations with sexual functioning. Methods Women with typical female internal organs and external genitalia, undergoing LEEP treatment for CD, were recruited to participate in the study. Urethral samples, in addition to vaginal and cervical swabs, were collected immediately before treatment and 3 months post-treatment. Bacterial community analysis was characterized by 16S ribosomal RNA gene analysis. Self-report online surveys assessing demographics, medical history, and sexual function (FSFI) were completed by participants at the same time intervals. Chi square analyses and t-tests were conducted. Results Alpha diversity (observed species richness) revealed a significant decrease in species richness in the FUT microbiome post-LEEP (P < 0.05). Beta diversity demonstrated significant differences between the cervical, urinary, and vaginal microbiomes pre- and post-LEEP (P < 0.05). Lactobacillus was observed to be significantly higher in the cervical microbiome (P < 0.05), and Prevotella, Dialister, and Ruminococcus were significantly lower in the cervical microbiome (P < 0.05), when compared to the urinary and vaginal microbiomes pre- and post-LEEP. Analysis of individual microbiome patient profiles revealed individuals who showed discordant trends when compared to data summarizing averages. Namely, a subset of participants experienced a significant increase (P < 0.05) in pro-inflammatory bacteria post-LEEP with correlative changes in SD. These results suggest there may be a non-uniform healing response post-LEEP, and that individualized microbiome analysis could reveal regional dysbiosis that could be subsequently treated and managed. Conclusions A diverse inflammatory bacterial community characterizes CD in the FUT, and treatment with LEEP mostly returns the microbiomes to a healthy state. However, some participants showed increased inflammatory bacterial profiles post-LEEP, potentially demonstrating a non-uniform healing response. This study provides a basis for future studies to screen and restore FUT microbiomes post-LEEP, with the aim of detecting and treating any bothersome symptoms reported by patients. Disclosure No
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».