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Record W3204065639 · doi:10.3389/fcimb.2021.709372

Microbiome Compositions From Infertile Couples Seeking In Vitro Fertilization, Using 16S rRNA Gene Sequencing Methods: Any Correlation to Clinical Outcomes?

2021· article· en· W3204065639 on OpenAlexaff
Somadina I. Okwelogu, Joseph Ifeanyichukwu Ikechebelu, Nneka R. Agbakoba, Kingsley C. Anukam

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsOntario Genomics
Fundersnot available
KeywordsBiologyGardnerella vaginalisLactobacillus gasseriLactobacillusPrevotellaRuminococcusMicrobiologyLactobacillus crispatusBacteroidetesFirmicutesHaemophilusMicrobiomePeptostreptococcusBacteroides thetaiotaomicronUreaplasmaBacteroidesBacterial vaginosis16S ribosomal RNAGeneticsMycoplasmaBacteriaFeces

Abstract

fetched live from OpenAlex

Background Bacterial infections are usually suspected in infertile couples seeking IVF with no clear understanding of the microbial compositions present in the seminal fluids and vaginal niche of the patients. We used next-generation sequencing technology to correlate microbiota compositions with IVF clinical outcomes. Methods Thirty-six couples were recruited to provide seminal fluids and vaginal swabs. Bacterial DNA was extracted, and V4 region of the 16S rRNA was amplified and sequenced in a pair-end configuration on the Illumina MiSeq platform rendering 2 × 150 bp sequences. Microbial taxonomy to species level was generated using the Greengenes database. Linear discriminant analysis (LDA) effect size (LEfSe) was used to identify biologically and statistically significant differences in relative abundance. Results Seminal fluid microbiota compositions had lower bacterial concentrations compared with the vagina, but species diversity was significantly higher in seminal fluid samples. Azoospermic subjects had more relative abundance of Mycoplasma and Ureaplasma. In Normospermic semen, Lactobacillus (43.86%) was the most abundant, followed by Gardnerella (25.45%), while the corresponding vaginal samples, Lactobacillus (61.74%) was the most abundant, followed by Prevotella (6.07%) and Gardnerella (5.86%). Conclusions Semen samples with positive IVF were significantly colonized by Lactobacillus jensenii ( P =0.002), Faecalibacterium ( P =0.042) and significantly less colonized by Proteobacteria , Prevotella , Bacteroides , and lower Firmicutes / Bacteroidetes ratio compared with semen samples with negative IVF. Vaginal samples with positive IVF clinical outcome were significantly colonized by Lactobacillus gasseri , less colonized by Bacteroides and Lactobacillus iners. This study has opened a window of possibility for Lactobacillus replenishments in men and women before IVF treatment.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.355
Teacher spread0.309 · 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

Citations54
Published2021
Admission routes1
Has abstractyes

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