MyoD–<i>lacZ</i> transgenes are early markers in the neural retina, but MyoD function appears to be inhibited in the developing retinal cells
Bibliographic record
Abstract
Recent findings suggest that eye and skeletal muscle development in vertebrates share the same regulatory network. In that network, Pax3 gene is apparently activated through Dach/Eya/Six feedback loop to mediate MyoD-driven myogenesis. The purpose of this study was to investigate previously reported MyoD-lacZ expression in the developing mouse neural retina and to gain insight into the potential role of MyoD in the embryonic retinal cells. The analysis of MD6.0-lacZ and 258/-2.5lacZ transgenic embryos revealed that the retinal temporal expression pattern of the two transgenes resembled their expression pattern in the MyoD-dependent precursor muscle cells. However, MyoD transcripts and protein could not be found in the sites of MyoD-lacZ retinal expression. Furthermore, our immunohistochemical analysis suggests the existence of diverse factors (e.g., Pax6 and Chx10) within the retinal cells that differentially and inappropriately activate the two transgenes. Finally, the retinal phenotype observed in Pax7-/- knock-out mice suggests a role for Pax7 in photoreceptor cell differentiation, retinal lamination and in the etiopathology of retinoblastoma. Taken together, our data suggest that the MyoD gene evolved a different mechanism to achieve its down-regulation within the retina than that of the Myf5 gene.
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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 | 0.001 |
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".