Connecting Droplet Adhesion with Sperm Kinematics: A New Paradigm in Sperm Quality Monitoring
Bibliographic record
Abstract
Abstract Conventional microscopy‐driven sperm health monitoring systems suffer from high infrastructural costs and complex protocols. Here, a simple, economical sperm motility assessment system is proposed. Based on a cantilever‐deflection‐based direct force measurement system capable of detecting wetting forces in the range of ≈µN, it is found that the adhesion of live and motile sperm cell‐laden droplets is dependent on sperm motility in the suspension. Further, it is observed that the sperm motility inside the droplet decreases with time, and the adhesion of the concerned droplet with a master substrate demonstrates an increasing trend. Contrary to an immotile cell, the motile sperm, due to its inherent nature of swimming parallel to the contact line, induces a lower restrictive force on the receding triple contact line of the droplet. The study establishes a potential avenue by which sperm cell motility can be predicted via measuring the adhesion of the sperm‐cell‐laden droplets with a standard surface using the simple and automatable cantilever‐deflection method. These findings can pave a pathway toward developing a user‐friendly, expertise‐independent diagnostic platform for in‐house sperm health monitoring.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".