Advances in Stem Cell and Cell-Based Gene Therapy Approaches for Experimental Acute Lung Injury: A Review of Preclinical Studies
Bibliographic record
Abstract
Given the failure of pharmacological interventions in acute respiratory distress syndrome (ARDS), researchers have been actively pursuing novel strategies to treat this devastating, life-threatening condition commonly seen in the intensive care unit. There has been considerable research on harnessing the reparative properties of stem and progenitor cells to develop more effective therapeutic approaches for respiratory diseases with limited treatment options, such as ARDS. This review discusses the preclinical literature on the use of stem and progenitor cell therapy and cell-based gene therapy for the treatment of preclinical animal models of acute lung injury (ALI). A variety of cell types that have been used in preclinical models of ALI, such as mesenchymal stem cells, endothelial progenitor cells, and induced pluripotent stem cells, were evaluated. At present, two phase I trials have been completed and one phase I/II clinical trial is well underway in order to translate the therapeutic benefit gleaned from preclinical studies in complex animal models of ALI to patients with ARDS, paving the way for what could potentially develop into transformative therapy for critically ill patients. As we await the results of these early cell therapy trials, future success of stem cell therapy for ARDS will depend on selection of the most appropriate cell type, route and timing of cell delivery, enhancing effectiveness of cells (i.e., potency), and potentially combining beneficial cells and genes (cell-based gene therapy) to maximize therapeutic efficacy. The experimental models and scientific methods exploited to date have provided researchers with invaluable knowledge that will be leveraged to engineer cells with enhanced therapeutic capabilities for use in the next generation of clinical trials.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".