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Record W4414070791 · doi:10.1080/07366205.2025.2555657

Impact of ambient temperature exposure on miRNA stability in human plasma

2025· article· en· W4414070791 on OpenAlexaff
Véronique Desgagné, Imad Soukar, Marie‐France Hivert, Luigi Bouchard, Perrie F. O’Tierney-Ginn

Bibliographic record

VenueBioTechniques · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsmicroRNAHuman plasmaDigital polymerase chain reactionMessenger RNAPlasmaPlasma concentrationBlood plasma

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are considered more stable than mRNA, but the impact of progressive thawing of biological samples after freezing as may happen during shipping delays has not been quantified. To address this, we utilized digital PCR to estimate the absolute concentrations of select miRNAs following progressive thawing of human plasma and maintenance at ambient temperature. Specifically, we quantified let-7b-3p, miR-144-5p, miR-150-5p, miR-517a-3p, miR-524-5p, and miR-1283, which have varying abundance in plasma. We observed a trend indicating a decline in miRNA concentration as plasma samples were progressively thawed. Notably, miR-150-5p and miR-517a-3p were the least stable and were degraded by 32% and 52% respectively after 24 hours of ambient temperature storage. We found that the variation in sensitivity to temperature was not due to the GC content of the miRNAs nor their initial abundance, suggesting that other factors, such as protein interactors and vesicles carrying these miRNAs, may impact sensitivity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.009
GPT teacher head0.296
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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