Insights into the role of macrophage migration inhibitory factor in obesity and insulin resistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-fat diet (HFD)-induced obesity has emerged as a state of chronic low-grade inflammation characterised by a progressive infiltration of immune cells, particularly macrophages, into obese adipose tissue. Adipose tissue macrophages (ATM) present immense plasticity. In early obesity, M2 anti-inflammatory macrophages acquire an M1 pro-inflammatory phenotype. Pro-inflammatory cytokines including TNF-α, IL-6 and IL-1β produced by M1 ATM exacerbate local inflammation promoting insulin resistance (IR), which consequently, can lead to type-2 diabetes mellitus (T2DM). However, the triggers responsible for ATM recruitment and activation are not fully understood. Adipose tissue-derived chemokines are significant players in driving ATM recruitment during obesity. Macrophage migration inhibitory factor (MIF), a chemokine-like inflammatory regulator, is enhanced during obesity and is directly associated with the degree of peripheral IR. This review focuses on the functional role of macrophages in obesity-induced IR and highlights the importance of the unique inflammatory cytokine MIF in propagating obesity-induced inflammation and IR. Given MIF chemotactic properties, MIF may be a primary candidate promoting ATM recruitment during obesity. Manipulating MIF inflammatory activities in obesity, using pharmacological agents or functional foods, may be therapeutically beneficial for the treatment and prevention of obesity-related metabolic diseases.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it