Extracellular vesicles in lung health, disease, and therapy
Bibliographic record
Abstract
Both physiological homeostasis and pathological disease processes in the lung typically result from complex, yet coordinated multicellular responses that are synchronized via paracrine and endocrine intercellular communication pathways. Of late, extracellular vesicles have emerged as important information shuttles that can coordinate and disseminate homeostatic and disease signals. In parallel, extracellular vesicles in biological fluids such as sputum, mucus, epithelial lining fluid, edema fluid, the pulmonary circulation, pleural fluid, and lymphatics have emerged as promising candidate biomarkers for diagnosis and prognosis in lung disease. Extracellular vesicles are small, subcellular, membrane-bound vesicles containing cargos from parent cells such as lipids, proteins, genetic information, or entire organelles. These cargos endow extracellular vesicles with biologically active information or functions by which they can reprogram their respective target cells. Recent studies show that extracellular vesicles found in lung-associated biological fluids play key roles as biomarkers and effectors of disease. Conversely, administration of naïve or engineered extracellular vesicles with homeostatic or reparative effects may provide a promising novel protective and regenerative strategy to treat lung disease. To highlight this rapidly developing field, the American Journal of Physiology-Lung Cellular and Molecular Physiology is now launching a special Call for Papers on extracellular vesicles in lung health, disease, and therapy. This review aims to set the stage for this call by introducing extracellular vesicles and their emerging roles in lung physiology and pathobiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".