Adipose depots differ in cellularity, adipokines produced, gene expression, and cell systems
Bibliographic record
Abstract
The race to manage the health concerns related to excess fat deposition has spawned a proliferation of clinical and basic research efforts to understand variables including dietary uptake, metabolism, and lipid deposition by adipocytes. A full appreciation of these variables must also include a depot-specific understanding of content and location in order to elucidate mechanisms governing cellular development and regulation of fat deposition. Because adipose tissue depots contain various cell types, differences in the cellularity among and within adipose depots are presently being documented to ascertain functional differences. This has led to the possibility of there being, within any one adipose depot, cellular distinctions that essentially result in adipose depots within depots. The papers comprising this issue will underscore numerous differences in cellularity (development, histogenesis, growth, metabolic function, regulation) of different adipose depots. Such information is useful in deciphering adipose depot involvement both in normal physiology and in pathology. Obesity, diabetes, metabolic syndrome, carcass composition of meat animals, performance of elite athletes, physiology/pathophysiology of aging, and numerous other diseases might be altered with a greater understanding of adipose depots and the cells that comprise them-including stem cells-during initial development and subsequent periods of normal/abnormal growth into senescence. Once thought to be dormant and innocuous, the adipocyte is emerging as a dynamic and influential cell and research will continue to identify complex physiologic regulation of processes involved in adipose depot physiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".