Holoprosencephaly: signaling interactions between the brain and the face, the environment and the genes, and the phenotypic variability in animal models and humans
Bibliographic record
Abstract
Holoprosencephaly (HPE) is the most common developmental defect of the forebrain characterized by inadequate or absent midline division of the forebrain into cerebral hemispheres, with concomitant midline facial defects in the majority of cases. Understanding the pathogenesis of HPE requires knowledge of the relationship between the developing brain and the facial structures during embryogenesis. A number of signaling pathways control and coordinate the development of the brain and face, including Sonic hedgehog, Bone morphogenetic protein, Fibroblast growth factor, and Nodal signaling. Mutations in these pathways have been identified in animal models of HPE and human patients. Because of incomplete penetrance and variable expressivity of HPE, patients carrying defined mutations may not manifest the disease at all, or have a spectrum of defects. It is currently unknown what drives manifestation of HPE in genetically at-risk individuals, but it has been speculated that other gene mutations and environmental factors may combine as cumulative insults. HPE can be diagnosed in utero by a high-resolution prenatal ultrasound or a fetal magnetic resonance imaging, sometimes in combination with molecular testing from chorionic villi or amniotic fluid sampling. Currently, there are no effective preventive methods for HPE. Better understanding of the mechanisms of gene-environment interactions in HPE would provide avenues for such interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".