Circulating plasma fibronectin affects normal adipose tissue insulin sensitivity and adipocyte differentiation
Bibliographic record
Abstract
Abstract Plasma fibronectin (pFN), a liver-derived, circulating protein, has been shown to affect adipocyte morphology, adipogenesis, and insulin signalling in preadipocytes in vitro . In this study, we show via injections of fluorescence-labelled pFN to mice in vivo its abundant accrual visceral and subcutaneous adipose tissues (VAT and SAT). Diet-induced obesity model of liver-specific conditional Fn1 knockout (pFN KO), showed no altered weight gain or differences, whole-body fat mass or SAT or VAT volumes after 20- week HFD-feeding, however, mice showed significantly improved glucose clearance and whole-body insulin sensitivity on normal diet. Furthermore, in vivo insulin sensitivity assay revealed significant increase in AKT phosphorylation in pFN KO SAT on normal diet as well as in normal and obese VAT of the pFN KO. Histological assessment of the pFN KO depots showed significant increase in small adipocytes on normal diet, which was particularly prominent in SAT. RNA sequencing of the normal diet-fed pFN versus control SAT revealed alterations in fatty acid metabolism and thermogenesis suggesting presence of beige adipocytes. VAT RNA sequencing after HFD showed alternations in genes reflecting stem cell populations. Our data suggests that the absence of pFN alters cell pools in AT favoring cells with increased insulin sensitivity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".