The effect of discrete wavelengths of visible light on the developing murine embryo
Bibliographic record
Abstract
PURPOSE: A current focus of the IVF field is non-invasive imaging of the embryo to quantify developmental potential. Such approaches use varying wavelengths to gain maximum biological information. The impact of irradiating the developing embryo with discrete wavelengths of light is not fully understood. Here, we assess the impact of a range of wavelengths on the developing embryo. METHODS: Murine preimplantation embryos were exposed daily to wavelengths within the blue, green, yellow, and red spectral bands and compared to an unexposed control group. Development to blastocyst, DNA damage, and cell number/allocation to blastocyst cell lineages were assessed. For the longer wavelengths (yellow and red), pregnancy/fetal outcomes and the abundance of intracellular lipid were investigated. RESULTS: Significantly fewer embryos developed to the blastocyst stage when exposed to the yellow wavelength. Elevated DNA damage was observed within embryos exposed to blue, green, or red wavelengths. There was no effect on blastocyst cell number/lineage allocation for all wavelengths except red, where there was a significant decrease in total cell number. Pregnancy rate was significantly reduced when embryos were irradiated with the red wavelength. Weight at weaning was significantly higher when embryos were exposed to yellow or red wavelengths. Lipid abundance was significantly elevated following exposure to the yellow wavelength. CONCLUSION: Our results demonstrate that the impact of light is wavelength-specific, with longer wavelengths also impacting the embryo. We also show that effects are energy-dependent. This data shows that damage is multifaceted and developmental rate alone may not fully reflect the impact of light exposure.
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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 | 0.001 |
| 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".