miR-145 improves metabolic inflammatory disease through multiple pathways
Bibliographic record
Abstract
Chronic inflammation plays a pivotal role in insulin resistance and type 2 diabetes, yet the mechanisms are not completely understood. Here, we demonstrated that serum LPS levels were significantly higher in newly diagnosed diabetic patients than in normal control. miR-145 level in peripheral blood mononuclear cells decreased in type 2 diabetics. LPS repressed the transcription of miR-143/145 cluster and decreased miR-145 levels. Attenuation of miR-145 activity by anti-miR-145 triggered liver inflammation and increased serum chemokines in C57BL/6 J mice. Conversely, lentivirus-mediated miR-145 overexpression inhibited macrophage infiltration, reduced body weight, and improved glucose metabolism in db/db mice. And miR-145 overexpression markedly reduced plaque size in the aorta in ApoE-/- mice. Both OPG and KLF5 were targets of miR-145. miR-145 repressed cell proliferation and induced apoptosis partially by targeting OPG and KLF5. miR-145 also suppressed NF-κB activation by targeting OPG and KLF5. Our findings provide an association of the environment with the progress of metabolic disorders. Increasing miR-145 may be a new potential therapeutic strategy in preventing and treating metabolic diseases such as type 2 diabetes and atherosclerosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".