Analysis of the Sperm Head Protein Profiles in Fertile Men: Consistency across Time in the Levels of Expression of Heat Shock Proteins and Peroxiredoxins
Bibliographic record
Abstract
We investigated the identity and quantitative variations of proteins extracted from human sperm heads using a label-free Gel-MS approach. Sperm samples were obtained from three men with high sperm counts at three different time points. This design allowed us to analyse intra-individual and inter-individual variations of the human sperm head proteome. Each time point was analyzed in triplicate to minimize any background artifactual effects of the methodology on the variation analyses. Intra-individual analysis using the spectral counting method revealed that the expression levels of 90% of the common proteins identified in three samples collected at various time-points, separated by several months, had a coefficient of variation of less than 0.5 for each man. Across individuals, the expression level of more than 80% of the proteins had a CV under 0.7. Interestingly, 83 common proteins were found within the core proteome as defined by the intra- and inter-variation analyses set criteria (CV<0.7). Some of these uniformly expressed proteins were chaperones, peroxiredoxins, isomerases, and cytoskeletal proteins. Although there is a significant level of inter-individual variation in the protein profiles of human sperm heads even in a well-defined group of men with high sperm counts, the consistent expression levels of a wide range of proteins points to their essential role during spermatogenesis.
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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".