Red LED Light Irradiation Increases the Resistance Against Environmental Stress of Frozen Bovine Sperm Thawed in Suboptimal Conditions
Bibliographic record
Abstract
This work sought to test the positive effect of red LED light on frozen bovine sperm resistance to thawing in suboptimal conditions. Moreover, a preliminary study explores whether this improvement could have any repercussions on in vivo fertilizing ability. Thus, frozen bull semen straws (n = 16) were thawed (a) with irradiation for 5 min at 20 °C (2 min light, 1 min darkness, 2 min light; PHOTO); (b) without irradiation for 5 min at 20 °C (ET); or (c) through immersion into a water bath at 38 °C for 40 s as standard control (CONTROL). Sperm quality and preliminary, purely descriptive AI trials were performed. The PHOTO samples demonstrated significantly (p < 0.05) improved percentages of intact acrosomes, progressive motility, DNA condensation and fragmentation, and viable sperm with high ROS/superoxides. The viability of PHOTO samples decreased significantly (p < 0.05) when compared with the ET ones. Overall results of both the PHOTO and ET samples were poorer than those of the CONTROL. Otherwise, the PHOTO straws yielded greater pregnancy rates (64.0% vs. 49.4% in CONTROL) when evaluated at two different farms. The results suggest that irradiating cryopreserved bovine sperm during thawing in suboptimal conditions could improve AI pregnancy rates, although more in vivo studies are needed to support this conclusion.
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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) | 0.002 | 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".