Palmitate differentially regulates the polarization of differentiating and differentiated macrophages
Bibliographic record
Abstract
The tissue accumulation of M1 macrophages in patients with metabolic diseases such as obesity and type 2 diabetes mellitus has been well-documented. Interestingly, it is an accumulation of M2 macrophages that is observed in the adipose, liver and lung tissues, as well as in the circulation, of patients who have had major traumas such as a burn injury or sepsis; however, the trigger for the M2 polarization observed in these patients has not yet been identified. In the current study, we explored the effects of chronic palmitate and high glucose treatment on macrophage differentiation and function in murine bone-marrow-derived macrophages. We found that chronic treatment with palmitate decreased phagocytosis and HLA-DR expression in addition to inhibiting the production of pro-inflammatory cytokines. Chronic palmitate treatment of bone marrows also led to M2 polarization, which correlated with the activation of the peroxisome proliferator-activated receptor-γ signalling pathway. Furthermore, we found that chronic palmitate treatment increased the expression of multiple endoplasmic reticulum (ER) stress markers, including binding immunoglobulin protein. Preconditioning with the universal ER stress inhibitor 4-phenylbutyrate attenuated ER stress signalling and neutralized the effect of palmitate, inducing a pro-inflammatory phenotype. We confirmed these results in differentiating human macrophages, showing an anti-inflammatory response to chronic palmitate exposure. Though alone it did not promote M2 polarization, hyperglycaemia exacerbated the effects of palmitate. These findings suggest that the dominant accumulation of M2 in adipose tissue and liver in patients with critical illness may be a result of hyperlipidaemia and hyperglycaemia, both components of the hypermetabolism observed in critically ill patients.
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.
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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".