From neutrophils to macrophages: differences in regional adipose tissue depots
Bibliographic record
Abstract
Currently, we do not fully understand the underlying mechanisms of how regional adiposity promotes metabolic dysregulation. As adipose tissue expands, there is an increase in chronic systemic low-grade inflammation due to greater infiltration of immune cells and production of cytokines. This chronic inflammation is thought to play a major role in the development of metabolic complications and disease such as insulin resistance and diabetes. We know that different adipose tissue depots contribute differently to the risk of metabolic disease. People who have an upper body fat distribution around the abdomen are at greater risk of disease than those who tend to store fat in their lower body around the hips and thighs. Thus, it is conceivable that adipose tissue depots contribute differently to the inflammatory milieu as a result of varied infiltration of immune cell types. In this review, we describe the role and function of major resident immune cells in the development of adipose tissue inflammation and discuss their regional differences in the context of metabolic disease risk. We find that although initial studies have found regional differences, a more comprehensive understanding of how immune cells interrupt adipose tissue homeostasis is needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".