Editorial: Molecular mechanisms underlying obesity and their links with other comorbidities
Bibliographic record
Abstract
Worldwide, obesity is becoming more prevalent and represents a major health concern for everyone (Khine et al., 2022). Obesity etiology involves many factors, including dietary, behavior, education, and genetic (Lin and Li, 2021). Some molecular mechanisms during adipocyte differentiation like the expression of transcription factors and the crosstalk between the protein kinase B (AKT)/glycogen synthase kinase-3β(GSK-3β) signaling pathway and the 5′-adenosine monophosphate-activated protein kinase (AMPK)-acetyl-CoA carboxylase (ACC) pathway, have been unveiled for their roles in governing obesity (Khine et al., 2022). Obesity is a well-recognized complex multifactorial disease that can pose several illness consequences, e.g., hyper-glycemia, hyperlipidemia, hypertension, cardiovascular disease, and other less common comorbidities (e.g., infertility, aging, infectious diseases, and cancers) (Gutierrez-Cuevas et al Crohn's disease. Taking these facts into account, prebiotics, probiotics, or fecal microbial 81 transplantation may serve as an effective means of managing type 2 diabetes in the gut and adipose 82 tissue (Patra et al., 2023). 83An anti-diabetic compound derived from fungal metabolites and semi-synthetic derivatives, 84 pyridylnidulin (PN), was found to induce glucose uptake. PN was tested in diet-induced obesity mice 85 to its effects on liver lipid metabolism and anti-diabetic properties. Through dietary 86 intervention with a high-fat diet for six weeks, male C57BL/6 mice were exposed to obesity as well as 87 pre-diabetic conditions. In these obese mice, PN (40 or To this end, all contributions to this research topic offer impactful insights into molecular mechanisms 106 that mediate obesity and its comorbidities. However, additional frontier research is yet needed to fulfil 107 the knowledge gaps about the link between obesity and its associated diseases.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".