Circulating plasma fibronectin affects tissue insulin sensitivity, adipocyte differentiation, and transcriptional landscape of adipose tissue in mice
Bibliographic record
Abstract
Plasma fibronectin (pFN) is a hepatocyte-derived circulating extracellular matrix protein that affects cell morphology, adipogenesis, and insulin signaling of adipocytes in vitro. In this study, we show pFN accrual to adipose tissue and its contribution to tissue homeostasis in mice. Hepatocyte-specific conditional Fn1 knockout mice (Fn1-/-ALB) show a decrease in adipose tissue FN levels and enhanced insulin sensitivity of subcutaneous (inguinal), visceral (epididymal) adipose tissue on a normal diet. Diet-induced obesity model of the Fn1-/-ALB mouse showed normal weight gain and whole-body fat mass, and normal adipose tissue depot volumes and unaltered circulating leptin and adiponectin levels. However, Fn1-/-ALB adipose depots showed significant alterations in adipocyte size and gene expression profiles. The inguinal adipose tissue on a normal diet, which had alterations in fatty acid metabolism and thermogenesis suggesting browning. The presence of increased beige adipocyte markers Ucp1 and Prdm16 supported this. In the inguinal fat, the obesogenic diet resulted in downregulation of the browning markers and changes in gene expression reflecting development, morphogenesis, and mesenchymal stem cell maintenance. Epididymal adipose tissue showed alterations in developmental and stem cell gene expression on both diets. The data suggests a role for pFN in adipose tissue insulin sensitivity and cell profiles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".