Case series – COVID-19 is unlikely to affect male fertility: Results of histopathological and reverse transcriptase polymerase chain reaction analysis
Bibliographic record
Abstract
INTRODUCTION: We aimed to analyze the testicular histopathology of men who died with active COVID-19 infection. METHODS: We performed autopsy of eight consecutive men who died of COVID-19 pneumonia. Lung and testis tissue of all men were stained for SARS-CoV-2 nucleocapsid, angiotensin-converting enzyme 2 (ACE-2) receptor immunohistochemistry (IHC). H&E was performed to assess for spermatogenesis and evidence of testicle tissue damage. Reverse transcriptase polymerase chain reaction (RT-PCR) analysis for SARS-CoV-2 was performed on matched lung and bilateral testicular tissue samples from all men. RESULTS: Patient age ranged from 50-79 years. SARS-CoV-2 viral RNA was detected by RTPCR in testis tissue in one man. All eight testicle specimens that underwent IHC for ACE2 receptor showed uniformly strong immunoreactivity against all testicle cell populations. By H&E, all testis specimens showed no inflammation, vascular thrombosis, vasculitis, or morphological evidence of viral changes. One case showed diminished but not absent spermatogenesis, consistent with patient age. CONCLUSIONS: Our results suggest that SARS-CoV-2 is unlikely to affect male fertility. Contrary to all prior histological studies, our results showed no evidence of damage to reproductive tissues that might impair fertility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".