Bibliographic record
Abstract
PURPOSE OF REVIEW: Ventilator-induced lung injury (VILI) is a ubiquitous iatrogenic clinical problem in critical care. Aside from avoiding large tidal volumes, little progress has been made in identifying effective clinical strategies to minimize this injury. With recent rapid development in bioinformatics and high-throughput molecular technology, the genetic basis of lung injury has been intensively investigated. This review will describe recent insights and potential therapies developed in the field. RECENT FINDINGS: Much progress has been made in delineating the possible genes and gene products involved in VILI through various mechanisms such as early induced genes, capillary leak, apoptosis, fibrin deposition, inflammatory cytokines, oxidative stress, disrupted angiogenesis, and neutrophil infiltration. Some studies have translated bench findings to the bedside in an attempt to identify clinically important genetic susceptibility, which could aid in the identification of at-risk individuals who might benefit from careful titration of mechanical ventilation. Genetic insights also provide candidate pharmaceutical approaches that may ameliorate VILI in the future. SUMMARY: Much relevant information exists for investigators and clinicians interested in VILI. Future research will interlink evolving data to provide a more integrated picture of the molecular mechanisms involved in VILI enabling translation of the most promising candidate therapies.
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) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".