Fetal ocular movements and retinal cell differentiation: analysis employing DNA microarrays.
Bibliographic record
Abstract
As developmental biologists we study the role of fetal movements in providing continuity between prenatal and postnatal life. There are two major categories of fetal motility. The first category consists of movements that have an obvious effect on the survival or development of the fetus (e.g., changes of position, sucking and swallowing). The second category consists of fetal movements that anticipate postnatal functions. For example, fetal ocular movements (FOMs) predict postnatal eye function (e.g., motion vision) of the newborn and therefore represent an important indicator of fetal health. However, while the clinical significance of fetal motility is obvious, its biological significance is elusive. We propose to use retina of genetically modified mouse embryos to study the biological role of FOMs in the genesis of cell diversity and organ functional maturation. Our results have already demonstrated the importance of fetal eye motility in the differentiation of cholinergic amacrine cells (CACs) in the retina (Kablar, 2003). Apparently, these cells are sensitive to motion and also responsible for motion vision. In the current report, we suggest employing the unique opportunity provided by the mouse Myf5-/-:MyoD-/- knock-outs that lack skeletal musculature and FOMs, microarray analysis and the follow-up experiments to identify a group of candidate genes that are essential for the molecular regulation of CAC differentiation and in turn for the functional maturation of the visual system towards its ability to perform motion vision. Finally, the molecules identified via this approach may be important in the mechanochemical signal transduction pathways employed during the process of conversion of a mechanical stimulus into an instruction understandable by the developing retinal neurons and glia cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".