Characterization of sperm chromatin quality in testicular cancer and Hodgkin's lymphoma patients prior to chemotherapy
Bibliographic record
Abstract
BACKGROUND: Although the incidences of testicular cancer and Hodgkin's lymphoma have increased in young men over the past decade, combination chemotherapy has improved survival. As fertility is of importance to these patients, characterization of sperm chromatin structure is needed. We assessed sperm chromatin in testicular cancer and Hodgkin's lymphoma patients prior to chemotherapy, in comparison with control community and idiopathic infertile volunteers. METHODS: DNA damage was assessed with the sperm chromatin structure assay (SCSA), terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) and comet assays; reactive thiols (SH) and DNA compaction were determined with the monobromobimane (mBBr) and chromomycin A3 (CMA3) assays, respectively. RESULTS: Both testicular cancer (37%) and Hodgkin's lymphoma (81%) patients had normospermic samples with increased DNA damage, compared with controls. Cancer patients also had higher reactive thiols and CMA3 staining, indicating low DNA compaction. CONCLUSIONS: Sperm DNA integrity and compaction were affected in testicular cancer and Hodgkin's lymphoma patients prior to chemotherapy. Although SCSA, TUNEL and comet assays all detected DNA damage, the latter was optimal for use in cancer patients. A combination of the comet assay with tests that evaluate sperm DNA compaction, such as flow cytometry-based CMA3 and mBBr assays, is a reliable strategy to characterize sperm chromatin quality in cancer patients at the time of sperm banking.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".