Cell-based therapies for the acute respiratory distress syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute respiratory distress syndrome (ARDS) is a multifaceted lung disease with no current effective therapy. Many clinical trials using conventional pharmacologic therapies have failed, suggesting the need to examine alternative approaches. Thus, attention has focused on the therapeutic potential of cell-based therapies for ARDS, with promising results demonstrated in relevant preclinical disease models. We review data concerning the therapeutic promise of cell-based therapies for ARDS. RECENT FINDINGS: Recent experimental studies provide further evidence for the potential of cell-based therapies in ARDS. A number of cell types, particularly mesenchymal stem/stromal cells (MSCs), bone marrow-derived mononuclear cells, endothelial progenitor cells, and embryonic stem cells have been demonstrated to reduce mortality and modulate the inflammatory and remodeling processes in relevant preclinical ARDS models. Multiple insights have emerged in regard to the mechanisms by which cell therapies - particularly MSCs - exert their effects, with evidence supporting direct cell-mediated and paracrine-mediated mechanisms of action. Diverse paracrine mechanisms exist, including the release of cytokines, growth factors (such as keratinocyte growth factor), and antimicrobial peptides, and transfer of cellular contents such as peptides, nucleic acids, and mitochondria via either microvesicular or direct cell-cell contact-mediated transfer. SUMMARY: Cell-based therapies offer considerable promise for the treatment of ARDS. While MSC-based therapies are being rapidly advanced toward clinical testing, clear therapeutic potential exists for other cell types for ARDS. A greater understanding of current knowledge gaps should further enhance the therapeutic potential of cell-based therapies for ARDS.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".