Ventilator-induced Lung Injury: Role of Protein–Protein Interaction in Mechanosensation
Bibliographic record
Abstract
For critically ill patients, mechanical ventilation is a commonly used life-supporting modality, but ventilation per se could also induce lung injury. Mechanical forces-induced cell damage and inflammatory responses have been considered as one of major mechanisms of ventilator-induced lung injury (VILI). Mechanotransduction related to VILI has been the subject of several recent reviews, which focused on the mechanical force-induced signal cascades. In this article, we will discuss the initial processes, mechanosensation, by which physical forces can be sensed by the cells and converted into biochemical reactions for intracellular signaling. In addition to suggested mechanosensors, such as stretch-activated ion channels, extracellular matrix-integrin-cytoskeleton complex, and growth factor receptors, we would like to introduce a new concept of intracellular mechanosensation through specific protein-protein interactions. Proteins associated with the cytoskeleton could transmit physical forces, and bind with signaling-related enzymes through specific functional domains and motifs. These interactions could lead to activation or inactivation of the enzymes, and subsequently alter the signal transduction processes in the cells. Understanding these mechanisms will help us to develop new strategies for the management of VILI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".