Functional characterization of human brown adipose tissue metabolism
Bibliographic record
Abstract
Brown adipose tissue (BAT) has long been described according to its histological features as a multilocular, lipid-containing tissue, light brown in color, that is also responsive to the cold and found especially in hibernating mammals and human infants. Its presence in both hibernators and human infants, combined with its function as a heat-generating organ, raised many questions about its role in humans. Early characterizations of the tissue in humans focused on its progressive atrophy with age and its apparent importance for cold-exposed workers. However, the use of positron emission tomography (PET) with the glucose tracer [18F]fluorodeoxyglucose ([18F]FDG) made it possible to begin characterizing the possible function of BAT in adult humans, and whether it could play a role in the prevention or treatment of obesity and type 2 diabetes (T2D). This review focuses on the in vivo functional characterization of human BAT, the methodological approaches applied to examine these features and addresses critical gaps that remain in moving the field forward. Specifically, we describe the anatomical and biomolecular features of human BAT, the modalities and applications of non-invasive tools such as PET and magnetic resonance imaging coupled with spectroscopy (MRI/MRS) to study BAT morphology and function in vivo, and finally describe the functional characteristics of human BAT that have only been possible through the development and application of such tools.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".