Ventilation-Induced Brain Injury in Preterm Neonates: A Review of Potential Therapies
Bibliographic record
Abstract
Mechanical ventilation is a risk factor for cerebral inflammation and brain injury in preterm neonates. The risk increases proportionally with the intensity of treatment. Recent studies have shown that cerebral inflammation and injury can be initiated in the delivery room. At present, initiation of intermittent positive pressure ventilation (IPPV) in the delivery room is one of the least controlled interventions a preterm infant will likely face. Varying pressures and volumes administered shortly after birth are sufficient to trigger pathways of ventilation-induced lung and brain injury. The pathways involved in ventilation-induced brain injury include a complex inflammatory cascade and haemodynamic instability, both of which have an impact on the brain. However, regardless of the strategy employed to deliver IPPV, any ventilation has the potential to have an impact on the immature brain. This is particularly important given that preterm infants are already at a high risk for brain injury simply due to immaturity. This highlights the importance of improving the initial respiratory support in the delivery room. We review the mechanisms of ventilation-induced brain injury and discuss the need for, and the most likely, current therapeutic agents to protect the preterm brain. These include therapies already employed clinically, such as maternal glucocorticoid therapy and allopurinol, as well as other agents, such as erythropoietin, human amnion epithelial cells and melatonin, already showing promise in preclinical studies. Their mechanisms of action are discussed, highlighting their potential for use immediately after birth.
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 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 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".