Artificially selected human sperm morphology after swim-up processing
Bibliographic record
Abstract
The swim-up technique is a clinical practice used to select highly motile sperm cells from patient ejaculates to use in assisted fertilization. The aim of this study was to investigate whether the length of different sperm-cell components is related to gamete function. Thus, we explored whether swim-up technique selects for longer sperm cells than mean sperm cells from unprocessed ejaculates. Sperm midpiece, tail endpiece, and total length were measured before and after the swim-up selection by means of contrast-phase and electron microscopy. Correlations between sperm dimensions, sperm motility, and sperm concentration were also investigated. Swim-up selected cells with longer midpiece compared with the unprocessed fractions (5.8 μm (CI 5.52–6.16 μm) vs. 5.3 μm (CI 4.97–5.61 μm), p < 0.05) and shorter tail endpiece (7.8 μm (CI 7.11–8.44 μm) vs. 8.5 μm (CI 7.81–9.14 μm), p < 0.05 after meta-analysis), whereas no effect of swim-up selection was detected on the total sperm cell length. Individuals producing high sperm concentrations had longer sperm midpiece than had men producing lower sperm concentrations. It is concluded that short sperm flagellar tips with long midpieces may be used as biomarkers in infertility therapy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".