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Record W3023840458 · doi:10.1186/s40168-020-00804-1

The neovaginal microbiome of transgender women post-gender reassignment surgery

2020· article· en· W3023840458 on OpenAlexafffund
Kenzie Birse, Kateryna Kratzer, Christina Farr Zuend, Sarah Mutch, Laura Noël‐Romas, Alana Lamont, Max Abou, Emília M. Jalil, Valdiléa G. Veloso, Beatriz Grinsztejn, Ruth Khalili Friedman, Kristina Broliden, Frideborg Bradley, Vanessa Poliquin, Fan Li, Carolyn Yanavich, Adam Burgener, Grace M. Aldrovandi

Bibliographic record

VenueMicrobiome · 2020
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthNational Institute of Allergy and Infectious DiseasesNational Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institutes of HealthInternational Maternal Pediatric Adolescent AIDS Clinical Trials Network
KeywordsRoseburiaBiologyPrevotellaMicrobiomePeptostreptococcusBacterial vaginosisGynecologyZoologyLactobacillusPhysiologyInternal medicineGastroenterologyMicrobiologyBacteroidesBioinformaticsGeneticsMedicineBacteria

Abstract

fetched live from OpenAlex

Abstract Background Gender reassignment surgery is a procedure some transgender women (TW) undergo for gender-affirming purposes. This often includes the construction of a neovagina using existing penile and scrotal tissue and/or a sigmoid colon graft. There are limited data regarding the composition and function of the neovaginal microbiome representing a major gap in knowledge in neovaginal health. Results Metaproteomics was performed on secretions collected from the neovaginas (n = 5) and rectums (n = 7) of TW surgically reassigned via penile inversion/scrotal graft with (n = 1) or without (n = 4) a sigmoid colon graft extension and compared with secretions from cis vaginas (n = 32). We identified 541 unique bacterial proteins from 38 taxa. The most abundant taxa in the neovaginas were Porphyromonas (30.2%), Peptostreptococcus (9.2%), Prevotella (9.0%), Mobiluncus (8.0%), and Jonquetella (7.2%), while cis vaginas were primarily Lactobacillus and Gardnerella. Rectal samples were mainly composed of Prevotella and Roseburia. Neovaginas (median Shannon’s H index = 1.33) had higher alpha diversity compared to cis vaginas (Shannon’s H = 0.35) (p = 7.2E−3, Mann-Whitney U test) and were more similar to the non-Lactobacillus dominant/polymicrobial cis vaginas based on beta diversity (perMANOVA, p = 0.001, r2 = 0.342). In comparison to cis vaginas, toll-like receptor response, amino acid, and short-chain fatty acid metabolic pathways were increased (p < 0.01), while keratinization and cornification proteins were decreased (p < 0.001) in the neovaginal proteome. Conclusions Penile skin-lined neovaginas have diverse, polymicrobial communities that show similarities in composition to uncircumcised penises and host responses to cis vaginas with bacterial vaginosis (BV) including increased immune activation pathways and decreased epithelial barrier function. Developing a better understanding of microbiome-associated inflammation in the neovaginal environment will be important for improving our knowledge of neovaginal health.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.060
GPT teacher head0.254
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2020
Admission routes2
Has abstractyes

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