Fetal Reprogramming and Senescence in Hypoplastic Left Heart Syndrome and in Human Pluripotent Stem Cells during Cardiac Differentiation
Bibliographic record
Abstract
Hypoplastic left heart syndrome (HLHS) is a severe cardiac malformation characterized by left ventricle (LV) hypoplasia and abnormal LV perfusion and oxygenation. We studied hypoxia-associated injury in fetal HLHS and human pluripotent stem cells during cardiac differentiation to assess the effect of microenvironmental perturbations on fetal cardiac reprogramming. We studied LV myocardial samples from 32 HLHS and 17 structurally normal midgestation fetuses. Compared with controls, the LV in fetal HLHS samples had higher nuclear expression of hypoxia-inducible factor-1α but lower angiogenic growth factor expression, higher expression of oncogenes and transforming growth factor (TGF)-β1, more DNA damage and senescence with cell cycle arrest, fewer cardiac progenitors, myocytes and endothelial lineages, and increased myofibroblast population (P < 0.05 versus controls). Smooth muscle cells (SMCs) had less DNA damage compared with endothelial cells and myocytes. We recapitulated the fetal phenotype by subjecting human pluripotent stem cells to hypoxia during cardiac differentiation. DNA damage was prevented by treatment with a TGF-β1 inhibitor (P < 0.05 versus nonhypoxic cells). The hypoplastic LV in fetal HLHS samples demonstrates hypoxia-inducible factor-1α up-regulation, oncogene-associated cellular senescence, TGF-β1-associated fibrosis and impaired vasculogenesis. The phenotype is recapitulated by subjecting human pluripotent stem cells to hypoxia during cardiac differentiation and rescued by inhibition of TGF-β1. This finding suggests that hypoxia may reprogram the immature heart and affect differentiation and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".