Flagellar Propulsion of Sperm Cells Against a Time‐Periodic Interaction Force
Bibliographic record
Abstract
Abstract Sperm cells undergo complex interactions with external environments, such as a solid‐boundary, fluid flow, as well as other cells before arriving at the fertilization site. The interaction with the oviductal epithelium, as a site of sperm storage, is one type of cell‐to‐cell interaction that serves as a selection mechanism. Abnormal sperm cells with poor swimming performance, the major cause of male infertility, are filtered out by this selection mechanism. In this study, collinear bundles, consisting of two sperm cells, generate propulsive thrusts along opposite directions and allow to observe the influence of cell‐to‐cell interaction on flagellar wave‐patterns. The developed elasto‐hydrodynamic model demonstrates that steric and adhesive forces lead to highly symmetrical wave‐pattern and reduce the bending amplitude of the propagating wave. It is measured that the free cells exhibit a mean flagellar curvature of 6.4 ± 3.5 rad mm‐1 and a bending amplitude of 13.8 ± 2.8 rad mm‐1. After forming the collinear bundle, the mean flagellar curvature and bending amplitude are decreased to 1.8 ± 1.1 and 9.6 ± 1.4 rad mm‐1, respectively. This study presents consistent theoretical and experimental results important for understanding the adaptive behavior of sperm cells to the external time‐periodic force encountered during sperm–egg interaction.
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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 | 0.000 | 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) | 0.001 | 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".