Cellular origins and translational approaches to congenital diaphragmatic hernia
Bibliographic record
Abstract
Congenital Diaphragmatic Hernia (CDH) is a complex developmental abnormality characterized by abnormal lung development, a diaphragmatic defect and cardiac dysfunction. Despite significant advances in management of CDH, mortality and morbidity continue to be driven by pulmonary hypoplasia, pulmonary hypertension, and cardiac dysfunction. The etiology of CDH remains unknown, but CDH is presumed to be caused by a combination of genetic susceptibility and external/environmental factors. Current research employs multi-omics technologies to investigate the molecular profile and pathways inherent to CDH. The aim is to discover the underlying pathogenesis, new biomarkers and ultimately novel therapeutic targets. Stem cells and their cargo, non-coding RNAs and agents targeting inflammation and vascular remodeling have produced promising results in preclinical studies using animal models of CDH. Shortcomings in current therapies combined with an improved understanding of the pathogenesis in CDH have given rise to novel promising experimental treatments that are currently being evaluated in clinical trials. This review provides insight into current developments in translational research, ranging from the cellular origins of abnormal cardiopulmonary development in CDH and the identification of novel treatment targets in preclinical CDH models at the bench and their translation to clinical trials at the bedside.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".