Bibliographic record
Abstract
The discovery that mutations in the genes encoding two endothelium-restricted transforming growth factor (TGF)-β receptors – endoglin (1) and activin-like kinase 1 (ALK1) (2) – account for most, if not all cases of the human vascular disorder hereditary hemorrhagic telangiectasia (HHT) (3) firmly established the significance of direct TGF-β signaling in endothelial cells (ECs) and in vascular development. Indeed, embryonically lethal vascular phenotypes result from homozygous deletions in no less than eight components of the TGF-β signaling system, namely TGF-β1 (4), ALK1 (5), endoglin (6), ALK5(7), TGF-β receptor II (TβRII) (8), Smad5 (9), TGF-β–activated kinase (TAK)-1 (10), and furin (11) (Table 35-2). TGF-βs are key regulators of three-dimensional blood vessel structure, site-specific EC differentiation, and interactions between ECs and their immediate microenvironments. TGF-β and other superfamily members, influence ECs through direct activation and through stimuli that result in the release of endothelium-directed mediators from neighboring cells. Moreover, TGF-β–elicited EC-derived signals stimulate recruitment and differentiation of pericytes and vascular smooth muscle cells (VSMCs). TGF-β pathway activation is necessary for endothelial–mesenchymal transdifferentiation during formation of the cardiac cushion and, consequently, the cardiac valves. TGF-β derived from ECs regulates the phenotype of surrounding VSMCs. More than 40 members of the TGF-β superfamily (12), which includes TGF-β1, TGF-β2, TGF-β–activated kinase (TAK)-1 and TGF-β3, activins, bone morphogenetic proteins (BMPs), growth differentiation factors (GDFs), inhibin, and muellerian inhibitory substance (MIS), have been identified so far. Pathways for TGF-βs, BMPs, and activins, all known to stimulate ECs, are shown in Table 35–1.
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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 | 0.000 | 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".