Advances in molecular acute lung injury/acute respiratory distress syndrome and ventilator-induced lung injury: the role of genomics, proteomics, bioinformatics and translational biology
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss emerging technologies and their application to translation biology research in the field of acute lung injury/acute respiratory distress syndrome and ventilator-induced lung injury. RECENT FINDINGS: Significant advances in the field of innovative therapeutics rely on our ability to identify and validate targets derived from biomedical breakthrough discoveries. The review considers recent studies that have creatively applied and integrated genomics, bioinformatics and/or proteomics to identifying novel candidate molecular targets. The focus is to present how innovative strategies have been exploited and combined with elegant translational biology experiments to advance the field of molecular acute respiratory distress syndrome/acute lung injury and ventilator-induced lung injury. SUMMARY: Renewed efforts to define the clinical phenotype have coincided with the availability of novel technology that has the potential to address critical molecular aspects of the syndrome. Convergence of these two approaches is expected to bring about a better understanding of acute lung injury and consequently further advances in treatment.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".