What do we know about extracellular vesicles in patients with idiopathic pulmonary fibrosis? a scoping review
Bibliographic record
Abstract
Extracellular Vesicles (EVs), released by all cell types and detectable in biological samples, carry a variety of biological molecules. These molecules mediate communication and signaling with both local and distant cells, potentially playing a role in the pathogenesis of diseases, including Interstitial Lung Diseases and, more specifically, Idiopathic Pulmonary Fibrosis. To better understand the role of EVs in IPF, a systematic search was performed in PubMed, Scopus, and Ovid databases. These searches were conducted from January 1st, 2019, the period during which the MISEV 2018 guidelines were published, to August 31st, 2024. The SANRA scale was used for quality assessment. A total of 691 papers were screened, and 16, in the end, were definitively enrolled for our evaluation. The studies were reviewed in the following steps: 1) the nomenclature used to define EVs; 2) conformity with the MISEV 2018 guidelines; 3) the biological samples used to isolate EVs; 4) the main conclusion of each manuscript. There was significant heterogeneity among the publications in all the aforementioned steps, such as the type and source of EVs and the analysis of EVs content, primarily a wide array of different miRNAs in the various publications. Despite these differences, the emerging role of EVs and their potential usefulness both in therapies and clinical practice is of growing interest.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".