Unique Patterns of Circulating Extracellular Vesicles in Preterm Infants During Adaptation to Extra‐Uterine Life
Bibliographic record
Abstract
ABSTRACT There is growing interest in the role of extracellular vesicles (EVs) in neonatal pathology. This study aimed to characterise circulating EVs following preterm birth. This single‐centre prospective observational study included cord and postnatal plasma from preterm ( n = 101) and full‐term infants ( n = 66). EVs were analysed using nanoparticle tracking analysis, flow cytometry, proteomics and procoagulant activity assay. We found changes in the concentration, size, cellular origin and proteomic content of circulating EVs in preterm infants during perinatal adaptation. To understand if these changes were related to prematurity or normal adaptation to extrauterine life, they were also investigated in term infants. There was a dramatic increase in the concentration of small and large EVs on Day 3 in the preterm group; specific subsets of platelet (CD42b + and CD62P + ), endothelial (VEGFR2) and tissue factor EVs were elevated. Differentially expressed proteins relating to haemostasis, pulmonary physiology and immunity were identified between Day 1 and 3 in preterm infants. These changes have never previously been described in a large cohort of preterm infants and differ from healthy term infants. These findings have major implications for future neonatal EV studies, particularly the timing of sample collection. Further work is required to understand the clinical implications of this unique EV profile following preterm birth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".