Bibliographic record
Abstract
Obesity, characterized by excess adipose mass, is a growing epidemic that increases the risk for developing cardiovascular disease and type 2 diabetes. Different mechanisms linking obesity with these comorbidities have been postulated, but remain poorly understood. Adipose tissue secretes a number of hormone‐like compounds, termed adipokines, that are important for the maintenance of normal glucose metabolism. Alterations in the secretion of adipokines are believed to contribute to the undesirable changes in glucose metabolism that commonly occur with obesity. Ultimately, this can result in the development of type 2 diabetes. We have identified a small secreted protein, chemerin, as a novel adipokine. Our study has shown that in mouse models of obesity, serum chemerin levels are significantly elevated and the expression of chemerin and its receptors, chemokine‐like receptor 1 and chemokine (C‐C motif) receptor‐like 2 are altered in white adipose, liver, pancreas, and skeletal muscle tissue. In addition, administration of exogenous chemerin exacerbates glucose intolerance and decreases serum insulin levels in obese mouse models. Therefore, chemerin has an important role in glucose homeostasis and may influence the metabolic derangements that result in obesity and type 2 diabetes. Funding: CIHR.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".