Perinatal origins of bronchopulmonary dysplasia—deciphering normal and impaired lung development cell by cell
Bibliographic record
Abstract
Bronchopulmonary dysplasia (BPD) is a multifactorial disease occurring as a consequence of premature birth, as well as antenatal and postnatal injury to the developing lung. BPD morbidity and severity depend on a complex interplay between prenatal and postnatal inflammation, mechanical ventilation, and oxygen therapy as well as associated prematurity-related complications. These initial hits result in ill-explored aberrant immune and reparative response, activation of pro-fibrotic and anti-angiogenic factors, which further perpetuate the injury. Histologically, the disease presents primarily by impaired lung development and an arrest in lung microvascular maturation. Consequently, BPD leads to respiratory complications beyond the neonatal period and may result in premature aging of the lung. While the numerous prenatal and postnatal stimuli contributing to BPD pathogenesis are relatively well known, the specific cell populations driving the injury, as well as underlying mechanisms are still not well understood. Recently, an effort to gain a more detailed insight into the cellular composition of the developing lung and its progenitor populations has unfold. Here, we provide an overview of the current knowledge regarding perinatal origin of BPD and discuss underlying mechanisms, as well as novel approaches to study the perturbed lung development.
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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 | 0.001 |
| 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".