Characterization of anti-steatogenic long noncoding RNAs and their epigenetic influence on the development of metabolic fatty liver disease – a systematic review
Bibliographic record
Abstract
Introduction and aim. Numerous transcriptomic studies have demonstrated that the development of metabolically associated fatty liver disease is accompanied by changes in the expression level of long noncoding RNAs (lncRs). The aim: to present a brief description of the role of anti-steatogenic lncRs in the epigenetic influence on the development of metabolically associated fatty liver disease, analyzing the data of modern scientific literature. Material and methods. An analysis of 64 reports over the past 10 years was conducted from the databases PubMed; MEDLINE; EMBASE; Cochrane Systematic Reviews Database; BIOSIS which were selected using the indicated keywords. Quality aspects were assessed using the adapted Newcastle–Ottawa Scale, PROSPERO CRD420250652980. Analysis of the literature. Hypoexpression of AC012668 – increased representation of miR-380-5p, activation of LRP2; B4GALT1- AS1/lncSHGL, MEG3 – activation of lipogenesis, gluconeogenesis in hepatocytes; FLRL2 – inhibition of BMAL1 and SIRT1; Gm16551 – increased expression of ACC1, SCD1; HR1 – activation of SREBP1c. LncLSTR – activation of cytochrome Cyp8b1 transcription; MRAK052686 reduction of FABP7 expression. Conclusion. The formation of hepatosteatosis is supported by a decrease in the expression level of anti-steatogenic lncRs, such as AC012668, B4GALT1-AS1/lncSHGL, MEG3, FLRL2, Gm16551, lncHR1, lncLSTR, MRAK052686. LncRs overexpression is compensatory in escalating inflammation, hyperglycemia.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".