Deconstructing metabolic inflammation using cellular systems
Bibliographic record
Abstract
Over the past years, we have embarked in a systematic analysis of the effect of obesity or fatty acids on circulating monocytes, microvascular endothelial cells, macrophages, and skeletal muscle cells. With the use of cell culture strategies, we have deconstructed complex physiological systems and then reconstructed "partial equations" to better understand cell-to-cell communication. Through these approaches, we identified that in high saturated fat environments, cell-autonomous proinflammatory pathways are activated in monocytes and endothelial cells, promoting monocyte adhesion and transmigration. We think of this as a paradigm of the conditions promoting immune cell infiltration into tissues during obesity. In concert, it is possible that muscle and adipose tissue secrete immune cell chemoattractants, and indeed, our tissue culture reconstructions reveal that myotubes treated with the saturated fatty acid palmitate, but not the unsaturated fatty acid palmitoleate, release nucleotides that attract monocytes and other compounds that promote proinflammatory classically activated "(M1)-like" polarization in macrophages. In addition, palmitate directly triggers an M1-like macrophage phenotype, and secretions from these activated macrophages confer insulin resistance to target muscle cells. Together, these studies suggest that in pathophysiological conditions of excess fat, the muscle, endothelial and immune cells engage in a synergistic crosstalk that exacerbates tissue inflammation, leukocyte infiltration, polarization, and consequent insulin resistance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".