Investigation of Male Infertility Using Quantitative Comparative Proteomics
Bibliographic record
Abstract
Male factors account for 40% of infertility cases. The identification of differentially expressed proteins on spermatozoa from fertile and infertile men can help in the elucidation of the molecular basis of male infertility. The aim of this study was to compare sperm proteomes from 3 different groups: fertile men, normozoospermic men consulting for infertility, and normozoospermic men with an impaired capacity for fertilization (IVF-failure). We used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling, and LC-MS analysis to identify proteins that are differentially expressed. A total of 348 unique proteins were identified and quantified. The analysis identified 33 proteins that were differentially expressed in the IVF-failure group vs the fertile group. Comparison of the infertile and fertile groups revealed that 18 proteins appeared to be differentially expressed. Four proteins were similarly altered in the IVF-failure and infertile groups: semenogelin 1 (SEMG1), prolactin-induced protein (PIP), glyceraldehyde-3-phosphate dehydrogenase (GAPDHS), and phosphoglycerate kinase 2 (PGK2). These protein markers were selected for validation using multiple reactions monitoring mass spectrometry (MRM-MS) and further confirmed by Western blot analysis. Overall, these results suggest that a panel of proteins may be used as biomarkers for future studies of infertility.
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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) | 0.000 | 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) | 0.001 | 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".