EPAS 1, congenital heart disease, and high altitude: disclosures by genetics, bioinformatics, and experimental embryology
Bibliographic record
Abstract
Abstract The high-altitude environment is a challenge for human settlement. Low oxygen concentrations, extreme cold, and a harsh arid climate are doubtlessly challenges for the colonization of the Tibetan plateau. I am delighted to comment on the article of Pan et al. (2018) on mutations in endothelial PAS domain-containing protein 1 (EPAS1) in congenital heart disease in Tibetans. In humans, the EPAS1 gene is responsible for coding EPAS1 protein, an alias of which is HIF2α, an acronym for hypoxia-inducible factor 2 alpha. EPAS1 is a type of hypoxia-inducible factors, which are collected as a group of transcription factors involved in body response to oxygen level. EPAS1 gene is active under hypoxic conditions and plays an essential role in the development of the heart and in the management of the catecholamine balance, mutations of which have been identified in neuroendocrine tumors. In this article, Pan et al. investigated Tibetan patients with and without non-syndromic congenital heart disease. They identified two novel EPAS1 gene mutations, of which N203H mutation significantly affected the transcription activity of the vascular endothelial growth factor (VEGF) promoter, particularly in situations of hypoxia. VEGF is a downstream target of HIF-2 (other than HIF-1), and the expression levels of either HIF-1α or HIF-2α correlate positively to VEGF expression. Pan et al.’s data may be of incitement to further evaluate protein–protein interaction and using experimental animal models. Moreover, it may also be a stimulus for setting up genetic epidemiologic studies for other populations living at high altitudes.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".