Role of Heme Oxygenase in Inflammation, Insulin-Signalling, Diabetes and Obesity
Bibliographic record
Abstract
Diabetes and obesity are chronic conditions associated with elevated oxidative/inflammatory activities with a continuum of tissue insults leading to more severe cardiometabolic and renal complications including myocardial infarction and end-stage-renal damage. A common denominator of these chronic conditions is the enhanced the levels of cytokines like tumour necrosis factor-alpha (TNF-alpha), interleukin (IL-6), IL-1beta and resistin, which in turn activates the c-Jun-N-terminal kinase (JNK) and NF-kappaB pathways, creating a vicious cycle that exacerbates insulin resistance, type-2 diabetes and related complications. Emerging evidence indicates that heme oxygenase (HO) inducers are endowed with potent anti-diabetic and insulin sensitizing effects besides their ability to suppress immune/inflammatory response. Importantly, the HO system abates inflammation through several mechanisms including the suppression of macrophage-infiltration and abrogation of oxidative/inflammatory transcription factors like NF-kappaB, JNK and activating protein-1. This review highlights the mechanisms by which the HO system potentiates insulin signalling, with particular emphasis on HO-mediated suppression of oxidative and inflammatory insults. The HO system could be explored in the search for novel remedies against cardiometabolic diseases and their complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".