Assessing the testicular sperm microbiome: a low-biomass site with abundant contamination
Bibliographic record
Abstract
RESEARCH QUESTION: The semen harbours a diverse range of microorganisms. The origin of the seminal microbes, however, has not yet been established. Do testicular spermatozoa harbour microbes and could they potentially contribute to the seminal microbiome composition? DESIGN: The study included 24 samples, comprising a total of 307 testicular maturing spermatozoa. A high-throughput sequencing method targeting V3 and V4 regions of 16S rRNA gene was applied. A series of negative controls together with stringent in-silico decontamination methods were analysed. RESULTS: Between 50 and 70% of all the detected bacterial reads accounted for contamination in the testicular sperm samples. After stringent decontamination, Blautia (P = 0.04), Cellulosibacter (P = 0.02), Clostridium XIVa (P = 0.01), Clostridium XIVb (P = 0.04), Clostridium XVIII (P = 0.02), Collinsella (P = 0.005), Prevotella (P = 0.04), Prolixibacter (P = 0.02), Robinsoniella (P = 0.04), and Wandonia (P = 0.04) genera demonstrated statistically significant abundance among immature spermatozoa. CONCLUSIONS: Our results indicate that the human testicle harbours potential bacterial signature, though in a low-biomass, and could contribute to the seminal microbiome composition. Further, applying stringent decontamination methods is crucial for analysing microbiome in low-biomass site.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".