Macrophage migration inhibitory factor in the human epididymis and semen
Bibliographic record
Abstract
During epididymal transit, mammalian spermatozoa acquire new proteins involved in the acquisition of motility and of male gamete fertilising ability. We have previously shown that membranous vesicles called epididymosomes are involved in the transfer of epididymal-originating proteins to spermatozoa. The cytokine macrophage migration inhibitory factor (MIF) is one of these proteins but the role played by MIF in relation to epididymal sperm maturation still remains unclear. As this protein has already been shown to bear different functions depending on its location, we investigated its distribution along the epididymis and in different compartments of human semen. Northern and Western blot analysis as well as immunohistochemical studies show that MIF is expressed all along the epididymis with a higher level of transcript in the proximal segment. MIF is associated with two types of membranous vesicles, i.e. epididymosomes and prostasomes, the latter being prostate-originating membranous vesicles present in the semen. In semen, MIF is associated with spermatozoa, prostasomes as well as the soluble fraction. The amount of MIF in the seminal fluid varies from one individual to another but does not correlate with the amount of MIF associated with ejaculated spermatozoa. There is a negative correlation between the amount of sperm-associated MIF and the percentage of motility in different semen samples. Sperm separation using discontinuous Percoll gradient centrifugation shows a higher amount of MIF associated with poorly motile spermatozoa compared to highly motile spermatozoa present in the lower Percoll fraction. These results are discussed with regards to the possible involvement of MIF in sperm motility acquisition during the epididymal transit.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".