Extracellular Vesicles as a Potential Therapy for Neonatal Conditions: State of the Art and Challenges in Clinical Translation
Bibliographic record
Abstract
Despite advances in intensive care, several neonatal conditions typically due to prematurity affect vital organs and are associated with high mortality and long-term morbidities. Current treatment strategies for these babies are only partially successful or are effective only in selected patients. Regenerative medicine has been shown to be a promising option for these conditions at an experimental level, but still warrants further exploration for the development of optimal treatment. Although stem cell-based therapy has emerged as a treatment option, studies have shown that it is associated with potential risks and hazards, especially in the fragile population of babies. Recently, extracellular vesicles (EVs) have emerged as an attractive therapeutic alternative that holds great regenerative potential and is cell-free. EVs are nanosized particles endogenously produced by cells that mediate intercellular communication through the transfer of their cargo. Currently, EVs are garnering considerable attention as they are the key effectors of stem cell paracrine signaling and can epigenetically regulate target cell genes through the release of RNA species, such as microRNA. Herein, we review the emerging literature on the therapeutic potential of EVs derived from different sources for the treatment of neonatal conditions that affect the brain, retinas, spine, lungs, and intestines and discuss the challenges for the translation of EVs into clinical practice.
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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 | 0.002 | 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) | 0.004 | 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".