Phenotyping the heterogeneity of Acute Respiratory Distress Syndrome (ARDS) to test new pro-reparative treatments in human preclinical models
Bibliographic record
Abstract
It is becoming increasingly clear that ARDS heterogeneity is largely responsible for the lack of efficacy of most treatments in clinical trials. Innovative human preclinical models should be developed to study ARDS heterogeneity and to identify effective subphenotype-targeted interventions to restore alveolar integrity and resolve ARDS. Importantly, our previous work has revealed a pro-repair role for K+ channels. Our aims were 1) to define subphenotypes of ARDS patients with specific inflammatory/damage biomarkers in their bronchoalveolar lavage (BAL) in combination with their clinical data and 2) to develop preclinical models of human alveolar epithelial cells (hAECs) and precision cut lung slices (PCLS) from healthy donors and patients with ARDS and/or pre-existing respiratory diseases to assess the benefit of new pro-reparative treatments, in the presence of BAL with different subphenotypes. BAL were first phenotyped at the cellular and molecular level. hAEC cultures and PCLS were then exposed to BAL with different subphenotypes and treated with specific K+ channel activators, before assessing the early and late steps of epithelial repair/regeneration after injury in hAEC and alveolar integrity in PCLS. Our data revealed two BAL subphenotypes (hypo vs hyper-inflammatory/damaging) with a deleterious effect on early and late repair processes and markers of functional integrity in hAECs/PCLS from healthy donors and patients with respiratory diseases. This deleterious effect was reversed by K+ channel activators. Our findings will pave the way for new and effective precision/personalized medicine approaches in ARDS.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".