Abstract 4572: Reduced Cardiac Progenitors in the Right Ventricle of Cyanotic Heart Lesions: Insights into RV Adaptation
Bibliographic record
Abstract
The RV in hypoplastic left heart syndrome (HLHS) and Tetralogy of Fallot (TOF) is exposed to hypoxia and abnormal load which may lead to RV dysfunction despite surgical repair. The ability of hypoxia to upregulate angiogenic signaling in the RV is not known. We measured vascular endothelial growth factor (VEGF) mediated regulation of cardiac progenitors in the RV of patients with HLHS and TOF. Methods: RV myocardial samples were obtained from 6 pts with HLHS (age 0.96±1.5 yrs), 6 pts with TOF (age 3.5±6.3 yrs) and 9 age-matched controls with structurally normal hearts (age 0.16±0.3 yrs) at surgery/transplant/autopsy. The following were measured: VEGF, thymosin β-4 (recruits progenitors), Nkx2.5 (myocyte precursor), Flk-1 (smooth muscle progenitor), CD34 (endothelial progenitor), and von willebrand factor (vWf) (endothelial marker). Results: (i) VEGF and thymosin β-4 expression was lower in HLHS vs controls. This was associated with reduced cardiac progenitors and lower myocardial capillary density vs controls (0.14±0.01 vs 0.55±0.16, p<0.05). (ii) VEGF expression was preserved but thymosin β-4 was reduced in RV in TOF. This was associated with preserved myocyte progenitors but reduction in endothelial and smooth muscle lineages and reduced capillary density vs controls (0.13±0.03 vs 0.55±0.16, p<0.05) (Figure 1 ). Conclusion: Hypoxia fails to induce an angiogenic response in the RV in cyanotic heart lesions. This may be due to reduced VEGF (HLHS) or impaired coupling of VEGF to thymosin β-4 (TOF). This maladaptation may contribute to post-operative RV dysfunction and lower RV regenerative capacity in later life. Figure 1: Reduced capillary density (red) in HLHS and TOF
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".