Macrophage JAK2 deficiency protects against high-fat diet-induced inflammation
Bibliographic record
Abstract
Abstract During obesity, macrophages can infiltrate metabolic tissues, and contribute to chronic low-grade inflammation, and mediate insulin resistance and diabetes. Recent studies have elucidated the metabolic role of JAK2, a key mediator downstream of various cytokines and growth factors. Our study addresses the essential role of macrophage JAK2 in the pathogenesis to obesity-associated inflammation and insulin resistance. During high-fat diet (HFD) feeding, macrophage-specific JAK2 knockout (M-JAK2−/−) mice gained less body weight compared to wildtype littermate control (M-JAK2+/+) mice and were protected from HFD-induced systemic insulin resistance. Histological analysis revealed smaller adipocytes and qPCR analysis showed upregulated expression of some adipogenesis markers in visceral adipose tissue (VAT) of HFD-fed M-JAK2−/−mice. There were decreased crown-like structures in VAT along with reduced mRNA expression of some macrophage markers and chemokines in liver and VAT of HFD-fed M-JAK2−/−mice. Peritoneal macrophages from M-JAK2−/−mice andJak2knockdown in macrophage cell line RAW 264.7 also showed lower levels of chemokine expression and reduced phosphorylated STAT3. However, leptin-dependent effects on augmenting chemokine expression in RAW 264.7 cells did not require JAK2. Collectively, our findings show that macrophage JAK2 deficiency improves systemic insulin sensitivity and reduces inflammation in VAT and liver in response to metabolic stress.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".