PGC-1α activity and mitochondrial dysfunction in preterm infants
Bibliographic record
Abstract
Extremely low gestational age neonates (ELGANs) are born in a relatively hyperoxic environment with weak antioxidant defenses, placing them at high risk for mitochondrial dysfunction affecting multiple organ systems including the nervous, respiratory, ocular, and gastrointestinal systems. The brain and lungs are highly affected by mitochondrial dysfunction and dysregulation in the neonate, causing white matter injury (WMI) and bronchopulmonary dysplasia (BPD), respectively. Adequate mitochondrial function is important in providing sufficient energy for organ development as it relates to alveolarization and axonal myelination and decreasing oxidative stress via reactive oxygen species (ROS) and reactive nitrogen species (RNS) detoxification. Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) is a master regulator of mitochondrial biogenesis and function. Since mitochondrial dysfunction is at the root of WMI and BPD pathobiology, exploring therapies that can regulate PGC-1α activity may be beneficial. This review article describes several promising therapeutic agents that can mitigate mitochondrial dysfunction through direct and indirect activation and upregulation of the PGC-1α pathway. Metformin, resveratrol, omega 3 fatty acids, montelukast, L-citrulline, and adiponectin are promising candidates that require further pre-clinical and clinical studies to understand their efficacy in decreasing the burden of disease from WMI and BPD in preterm infants.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".