Time-course full profiling of circulating miRNAs in neurologically deceased organ donors: a proof of concept study to understand the onset of the cytokine storm
Bibliographic record
Abstract
Neurologically deceased organ donors (NDDs) generally display an immune response involving an intense production of pro-inflammatory cytokines referred to as the cytokine storm. The sudden surge of inflammatory mediators in circulation promotes tissue and organ damages and ultimately leads to poor transplant outcome. As microRNAs (miRNAs) are frequently proposed as key regulators of inflammation and are relatively stable in circulation, changes in their profiles could play a role in the onset of the cytokine storm in NDDs. In this proof-of-concept study, we sought to investigate differentially abundant circulating miRNAs in a temporal manner between neurological death and organ recovery and to assess the association between specific miRNAs and levels of inflammatory cytokines in blood. Plasma samples from five NDDs were obtained at multiple time points between organ donation consent and organ recovery. Using a time-course analysis and miRNA sequencing, we identified 32 plasma miRNAs fluctuating between consent and organ recovery (false discovery rate; q-value < 0.1). Eleven miRNAs relatively abundant (>100 reads) and detected in all samples were selected for further biological pathway analysis (miR-486-3p, miR-103a-3p, miR-106b-3p, miR-182-5p, miR-101-3p, miR-10a-5p, miR-125a-5p, miR-146b-5p, miR-26a-5p, miR-423-5p, miR-92b-3p). These miRNAs targeted genes such as c-JUN (TNF signalling pathway) and eEF2 (AMPK pathway), suggesting a potential role in regulation of inflammation. Our results contribute to a better understanding of the miRNAs dynamic after neurological death in organ donors and could potentially be used to predict the related early cytokine storm.Trial registration: ClinicalTrials.gov ID NCT03786991. Registered December 2018
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".