Mesenchymal Stromal Cell-Derived Extracellular Vesicles for Neonatal Lung Disease: Tiny Particles, Major Promise, Rigorous Requirements for Clinical Translation
Bibliographic record
Abstract
Extreme preterm birth disrupts late lung development and puts newborns at risk of developing chronic lung disease, known as bronchopulmonary dysplasia (BPD). BPD can be associated with life-long complications, and currently no effective treatment is available. Cell therapies are entering the clinics to curb complications of extreme preterm birth with several clinical trials testing the feasibility, safety and efficacy of mesenchymal stromal cells (MSCs). The therapeutic effect of MSCs is contained in their secretome, and nanosized membranous structures released by the MSCs, known as extracellular vesicles (EVs), have been shown to be the therapeutic vectors. Driven by this discovery, the efficacy of EV-based therapy is currently being explored in models of BPD. EVs derived from MSCs, contain a rich cargo of anti-inflammatory and pro-angiogenic molecules, making them suitable candidates to treat multifactorial diseases such as BPD. Here, we review the state-of-the-art of preclinical studies involving MSC-derived EVs in models of BPD and highlight technical and regulatory challenges that need to be addressed before clinical translation. In addition, we aim at increasing awareness regarding the importance of rigorous reporting of experimental details of EV experiments and to increase the outreach of the current established guidelines amongst researchers in the BPD field.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".