Cell-Based Therapies for Lung Vascular Diseases: Lessons for the Future
Bibliographic record
Abstract
Pulmonary arterial hypertension or acute respiratory distress syndrome (ARDS) represent diseases caused in large part by lung endothelial injury and inflammation. Pulmonary arterial hypertension is a progressive and debilitating lung disorder, which is characterized by hallmark pathological features in the small peripheral pulmonary arteries, including intimal hyperplasia and fibrosis, medial hypertrophy, increased deposition of extracellular matrix, infiltration of inflammatory mediators, and in many instances of advanced disease, plexiform lesions. ARDS is a clinically important complication of severe acute lung injury and a significant cause of morbidity and mortality in critically ill patients. Acute lung injury/ARDS is characterized by breakdown of the air-blood barrier, with the accumulation of proteinaceous fluid and influx of inflammatory cells into the alveolar airspace, all of which are likely mediated by endothelial dysfunction and disruption of the pulmonary endothelial barrier. Preclinical studies suggest that cellular therapies may offer unique and effective alternatives for respiratory diseases that currently have limited (or no) treatment options. Encouraging results from studies using progenitor or mesenchymal stem cells suggest that these cells may exert their therapeutic benefits by promoting vascular repair and regeneration and/or by modulating the pathological immune responses. Furthermore, by combining genetic engineering with cell therapy, it may be possible to enhance the regenerative function of these cells, particularly in the context of adult autologous cell therapy, and therefore provide additional benefits that may overcome many of the limitations of cell or gene therapy alone.
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 | 0.001 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".