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Record W4401192968 · doi:10.1016/j.bbrc.2024.150482

Circulating non-coding RNA in type 1 diabetes mellitus as a source of potential biomarkers – An emerging role of sex difference

2024· review· en· W4401192968 on OpenAlexfundno aff
Lucyna Stachowiak, Weronika Kraczkowska, Aleksandra Świercz, Paweł P. Jagodzińśki

Bibliographic record

VenueBiochemical and Biophysical Research Communications · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsnot available
FundersCentre for Jewish Studies, University of TorontoMinistry of Education and Science
KeywordsCircular RNARNAmicroRNANon-coding RNABiologyDiabetes mellitusLong non-coding RNAComputational biologyBioinformaticsType 2 diabetesType 2 Diabetes MellitusGeneticsGeneEndocrinology

Abstract

fetched live from OpenAlex

Non-coding RNAs (ncRNAs), such as microRNA, long non-coding RNA, and circular RNA, are considered essential regulatory molecules mediating many cellular processes. Moreover, an increasing number of studies have investigated the role of ncRNAs in cancers and various metabolic disorders, including diabetes mellitus. Interestingly, some circulating ncRNA detected in body fluids may serve as novel biomarkers. There is still a lack of conventional biomarkers that detect the early stage of type 1 diabetes mellitus. Many circulating microRNA, long non-coding RNA, and circular RNA show aberrant expression in type 1 diabetes patients compared to healthy individuals. However, most studies have focused on circulating microRNA rather than long non-coding RNA or circular RNA. In addition, a few studies have evaluated sex differences in ncRNA biomarkers. Therefore, this article summarises current knowledge about circulating ncRNAs as potential biomarkers for type 1 diabetes and explores the effects of sex on such biomarkers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Review · Consensus signal: Review
Teacher disagreement score0.302
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.399
Teacher spread0.337 · 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 teacher head, not a consensus.

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

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

Citations4
Published2024
Admission routes1
Has abstractyes

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