Mitochondrial Extracellular Vesicles: A Promising Avenue for Diagnosing and Treating Lung Diseases
Bibliographic record
Abstract
Mitochondria, pivotal organelles governing cellular biosynthesis, energy metabolism, and signal transduction, maintain dynamic equilibrium through processes such as biogenesis, fusion, fission, and mitophagy. Growing evidence implicates mitochondrial dysfunction in a spectrum of respiratory diseases including acute lung injury/acute respiratory distress syndrome, bronchial asthma, pulmonary fibrosis, chronic obstructive pulmonary disease, and lung cancer. Consequently, identifying methods capable of ameliorating damaged mitochondrial function is crucial for the treatment of pulmonary diseases. Extracellular vesicles (EVs), nanosized membrane vesicles released by cells into the extracellular space, facilitate intercellular communication by transferring bioactive substances or signals between cells or organs. Recent studies have identified abundant mitochondrial components within specific subsets of EVs, termed mitochondrial extracellular vesicles (mitoEVs), whose contents and compositions vary with disease progression. Moreover, mitoEVs have demonstrated reparative mitochondrial functions in injured recipient cells. However, a comprehensive understanding of mitoEVs is currently lacking, limiting their clinical translation prospects. This Review explores the biogenesis, classification, functional mitochondrial cargo, and biological effects of mitoEVs, with a focus on their role in pulmonary diseases. Emphasis is placed on their potential as biological markers and innovative therapeutic strategies in pulmonary diseases, offering fresh insights for mechanistic studies and drug development in various pulmonary disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".