Fibronectin extra domain a limits liver dysfunction and protects mice during acute inflammation
Bibliographic record
Abstract
The primary transcript of fibronectin (FN) undergoes alternative splicing to generate different isoforms, including FN containing the Extra Domain A (FN_EDA+), whose expression is regulated spatially and temporarily during developmental and disease conditions including acute inflammation. The role of FN_EDA+ during sepsis, however, remains elusive. Mice constitutively express the EDA domain of fibronectin (EDA+/+); lacking the FN EDA domain (EDA−/−) or with a conditional ablation of EDA + inclusion only in liver produced FN (alb-CRE+EDA floxed mice) thus expressing normal plasma FN were used. Systemic inflammation and sepsis were induced by either LPS injection (70 mg/kg) or by cecal ligation and puncture (CLP) Neutrophils isolated from septic patients were tested for neutrophil binding ability. We observed that EDA+/+ were protected toward sepsis as compared to EDA−/− mice. Also alb-CRE+EDA floxed mice presented reduced survival, thus indicating a key role for EDA in protecting toward sepsis. This phenotype was associated with improved liver and spleen inflammatory profile. Ex vivo experiments showed that neutrophils bind to a larger extent to an FN_EDA + coated surface as compared to FN, thus potentially limiting their over-reactivity. Our study demonstrates that the inclusion of the EDA domain in fibronectin dampens the nflammatoryi consequences of sepsis.
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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) | 0.001 | 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 | 0.001 | 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".