Bibliographic record
Abstract
Obesity usually is accompanied by inflammation of fat tissue, with a prominent role of visceral fat. Chronic inflammation in obese fat tissue is of a lower grade than acute immune activation for clearing the tissue from an infectious agent. It is the loss of adipocyte metabolic homeostasis that causes activation of resident immune cells for supporting tissue functions and regaining homeostasis. Initially, the excess influx of lipids and glucose in the context of overnutrition is met by adipocyte growth and proliferation. Eventual lipid overload of hypertrophic adipocytes leads to endoplasmic reticulum stress and the secretion of a variety of signals causing increased sympathetic tone, lipolysis by adipocytes, lipid uptake by macrophages, matrix remodeling, angiogenesis, and immune cell activation. Pro-inflammatory signaling of adipocytes causes the resident immune system to release increased amounts of pro-inflammatory and other mediators resulting in enhanced tissue-protective responses. With chronic overnutrition, these protective actions are insufficient, and death of adipocytes as well as senescence of several tissue cell types is seen. This structural damage causes the expression or release of immunostimulatory cell components resulting in influx and activation of monocytes and many other immune cell types, with a contribution of stromal cells. Matrix remodeling and angiogenesis is further intensified as well as possibly detrimental fibrosis. The accumulation of senescent cells also may be detrimental via eventual spread of senescence state from affected to neighboring cells by the release of microRNA-containing vesicles. Obese visceral fat inflammation can be viewed as an initially protective response in order to cope with excess ambient nutrients and restore tissue homeostasis but may contribute to tissue damage at a later stage.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".