AP‐2α is required after lens vesicle formation to maintain lens integrity
Bibliographic record
Abstract
BACKGROUND: Transcription factors are critical in regulating lens development. The AP-2 family of transcription factors functions in differentiation, cell growth and apoptosis, and in lens and eye development. AP-2α, in particular, is important in early lens development, and when conditionally deleted at the placode stage defective separation of the lens vesicle from the surface ectoderm results. AP-2α's role during later stages of lens development is unknown. To address this, the MLR10-Cre transgene was used to delete AP-2α from the lens epithelium beginning at embryonic day (E) 10.5. RESULTS: The loss of AP-2α after lens vesicle separation resulted in morphological defects beginning at E18.5. By P4, a small highly vacuolated lens with a multilayered epithelium was evident in the MLR10-AP-2α mutants. Epithelial cells appeared elongated and expressed fiber cell specific βB1 and γ-crystallins. Epithelial cell polarity and lens cell adhesion was disrupted and accompanied by the misexpression of ZO-1, N-Cadherin, and β-catenin. Cell death was observed in the mutant lens epithelium between postnatal day (P) 14 and P30, and correlated with altered arrangements of cells within the epithelium. CONCLUSIONS: Our findings demonstrate that AP-2α continues to be required after lens vesicle separation to maintain a normal lens epithelial cell phenotype and overall lens integrity and to ensure correct fiber cell differentiation.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".