Reduced expression of VAChT increases renal fibrosis
Bibliographic record
Abstract
Chronic kidney disease (CKD) is associated with several other long-lasting conditions such as diabetes and cardiovascular diseases and it is a significant contributor to mortality worldwide. Obstructive kidney disease is one of the leading causes of CKD in children and may result from a wide variety of pathologic processes. Recent studies have shown that α7 nicotinic acetylcholine receptor (α7 nAChR) activation in the cholinergic anti-inflammatory pathway reduces production of inflammatory mediators and consequently prevents tissue injury and death. Here, we examined the role of endogenous release of acetylcholine on the development of fibrosis in renal tissue using a model of unilateral ureter obstruction (UUO)-induced CKD, in which obstruction promotes inflammation-mediated kidney damages. To interfere with acetylcholine secretion, we used mice in which the vesicular acetylcholine transporter is genetically reduced (VAChT KD(hom) mice). We observed a higher renal damage in VAChT mutant mice when compared to wild type controls, exemplified by higher proteinuria and increased amount of type 1 collagen in the kidney tissue, indicating accentuated fibrogenesis. These results were accompanied by enhanced localized kidney inflammation, with increased TH1/TH17 profile response. Administration of PNU-282987, a selective agonist of α7 nAChR, significantly attenuated kidney injury after UUO in VAChT KD(hom) mice, indicating that the lack of acetylcholine release decrease the action of the cholinergic anti-inflammatory pathway, promoting an up-regulation of pro-inflammatory and pro-fibrotic pathways. These results suggest that physiological activation of the cholinergic anti-inflammatory pathway regulates inflammatory responses in the kidney suggesting a new therapeutic approach for kidney disease.
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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 | 0.001 | 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".