Non-shivering thermogenesis and its current advances in clinical trials targeting obesity
Bibliographic record
Abstract
Obesity is a major risk factor for adverse cardiometabolic events such as diabetes and cardiovascular diseases. Cardiometabolic diseases are the number one cause of death globally. Despite being the leading cause of death, many therapeutics targeted at its risk factors such as obesity have limited effectiveness. This limited effectiveness warrants research into novel strategies to combat obesity. Past literature established an inverse relationship between obesity and thermogenic activity. Research in thermogenesis has made unprecedented progress in the past decade. Based on this progress, thermogenesis has been proposed as a novel target for treating obesity. Thermogenesis is targeted due to its ability to expend excess energy such as fat in the form of heat. This conversion from fat to heat is mostly done by brown and brite adipocytes in brown adipose tissue (BAT). This review presents current advances in clinical trials related to the therapeutic application of non-shivering thermogenesis. Each clinical trial topic is highlighted and summarized. This paper summarized sympathetic nervous system activation (cold-induced, pharmacologically activated, and thyroid hormones), and transient receptor potential (TRP) channels on non-shivering thermogenesis. Advanced knowledge in non-shivering thermogenesis allows researchers to harness its vast therapeutic potential to combat obesity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".