Vimentin as a contributing factor in SARS-CoV-2-induced orchitis on postmortem testicular autopsy of COVID-19 cases: A case-control study
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) was identified in China in late December 2019 and led to a pandemic that resulted in millions of confirmed cases and deaths. The causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), uses distinct receptors and co-receptors to enter host cells. Vimentin has emerged as a potential co-receptor for SARS-CoV-2 due to the high level of vimentin expression in testis tissue. Objective: The present study investigated the link between vimentin expression level and SARS-CoV-2-induced orchitis. Materials and Methods: In this case-control study, testis autopsy samples were collected immediately after the death of both COVID-19 cases and a control group that included individuals who died due to accidental causes. Gene expression and immunohistochemical assays were conducted to evaluate the level of vimentin expression, cell proliferation, and leukocyte infiltration. Results: A significant expression of vimentin and infiltration of immune cells (CD68+, CD38+, and CD138+) in the testicular tissue of COVID-19 cases, along with extensive immunoglobulin G precipitation and reduced inhibin expression (p = 0.001) were observed. Additionally, gene expression analysis revealed increased expression of vimentin and decreased expression of the proliferation markers Ki67 and proliferating cell nuclear antigen, suggesting that SARS-CoV-2 may disrupt spermatogenesis through immune responses and the arrest of cell proliferation. Conclusion: There may be a strong link between vimentin expression and COVID-19-induced orchitis. Further studies are needed to confirm these findings. Considering some limitations, vimentin can be used as a biomarker option for testicular damage following COVID-19-induced orchitis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".