Adipose depots differ in cellularity, adipokines produced, gene expression, and cell systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it