Morphologie de l'adipocyte humain : méthodologie, dysfonction adipeuse et altérations cardiométaboliques
Bibliographic record
Abstract
Excess accumulation of visceral fat is a major marker of the cardiometabolic alterations associated with obesity such as dyslipidemia, insulin resistance and chronic low-grade inflammation.Conversely, for the same level of adiposity, preferential accumulation of subcutaneous adipose tissue has a neutral or protective effect.Several factors may contribute to the development of visceral adiposity and its relationship with cardiometabolic risk factors.The general objective of this master thesis is to examine the contribution of diet and the morphology of adipocytes in the pathophysiology of visceral obesity in humans.To achieve this goal, we first conducted a literature analysis on the modulation of fat distribution by nutritional components.This analysis revealed that the effect of nutrients on body fat distribution was mediated in large part by its effect on total fat accumulation.Second, we conducted a critical review of the literature on adipocyte hypertrophy as a marker of adipose tissue dysfunction.This study confirmed that adipocyte size of both subcutaneous and visceral depots is a strong predictor of alterations in the lipid profile and in glucose-insulin homeostasis, independently of adiposity.We also found that each of the measurement techniques for fat cell sizing generated variable results on the entire range of body mass index values.Third, in an original study aimed at comparing three measurement methods for cell sizing, we reported that the choice of technique had little impact on the associations between adipocyte size and cardiometabolic risk factors as well as the various adiposity indices, regional or total.In conclusion, human visceral adipocyte hypertrophy is an important measure to evaluate the risk associated with 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".