Matrix Gla Protein Expression in Pericytes and Myofibroblasts Contributes to Renal Fibrosis
Bibliographic record
Abstract
Renal fibrosis is the main pathological change observed with the progression of chronic kidney disease (CKD) which predicts kidney outcomes. The ability to detect fibrosis early in the disease course may be crucial to identify those at the highest risk of CKD progression. Clinical studies have observed increased expression of serum matrix Gla protein (MGP), a potent inhibitor of soft tissue calcification, in CKD patients. In a cross-sectional study of CKD patients, we found that serum MGP levels were associated with albuminuria and waist circumference after controlling for kidney function which modified the association between MGP and albuminuria. To examine the impact of MGP on the onset and progression of CKD, various mouse models were used in the current study. Using Cre-reporter, Rosa Tomato ;Mgp-Cre , mice and a new ‘knock-in' model expressing hemagglutinin epitope-tagged MGP, it was identified that pericytes in healthy kidneys and myofibroblasts in the folic acid (FA)-injured kidneys are the primary sources of MGP production. FA injection in Mgp -/- mice induced significantly less renal fibrosis in comparison to the control mice due to a reduced number of pericytes and attenuated Notch signaling. In a complementary experiment, restoration of Mgp expression in myofibroblasts in Mgp -/- mice leads to renal fibrosis as severe as control mice. This work suggests that MGP expression in myofibroblasts exacerbates renal fibrosis in FA-injured kidneys.
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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 | 0.001 | 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) | 0.001 | 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".