The Therapeutic Effect of Extracellular Vesicles on Asthma in Pre-Clinical Models: A Systematic Review Protocol
Bibliographic record
Abstract
Asthma is the most common pediatric disease, characterized by chronic airway inflammation and airway hyperresponsiveness. There are several management options for asthma, but no specific treatment. Extracellular vesicles (EVs) are powerful cellular mediators of endocrine, autocrine and paracrine signalling, and can modulate biophysiological function in vitro and in vivo. A thorough investigation of therapeutic effects of EVs in asthma has not been conducted. Therefore, this systematic review is designed to synthesize recent literature on the therapeutic effects of EVs on physiological and biological outcomes of asthma in pre-clinical studies. An electronic search of Web of Science, EMBASE, MEDLINE, and Scopus will be conducted on manuscripts published in the last five years that adhere to standardized guidelines for EV research. Grey literature will also be included. Two reviewers will independently screen the selected studies for title and abstract, and full text based on the eligibility criteria. Data will be extracted, narratively synthesized and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This systematic review will summarize the current knowledge from preclinical studies investigating the therapeutic effects of EVs on asthma. The results will delineate whether EVs can mitigate biological hallmarks of asthma, and if so, describe the underlying mechanisms involved in the process. This insight is crucial for identifying key pathways that can be targeted to alleviate the burden of asthma. The data will also reveal the origin, dosage and biophysical characteristics of beneficial EVs. Overall, our results will provide a scaffold for future intervention and translational studies on asthma treatment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".