Overexpression of mm9_circ_013935 alleviates renal inflammation and fibrosis in diabetic nephropathy via the miR-153-3p/NFIC axis
Bibliographic record
Abstract
Circ-RBM4 (mm9_circ_013935) has been revealed to have low expression in the renal tissues of diabetic nephropathy (DN) mice, and its underlying regulatory mechanism remains unexplored. The high glucose (HG) - treated mouse podocytes were used to establish DN cell models. A cell counting kit-8 assay was used to examine the viability of mouse podocytes. The expression of proteins related to fibrosis (collagen I, collagen III, fibronectin) was detected using Western blot. The concentration of inflammation cytokines (tumor necrosis factor α, interleukin 1β (IL-1β), IL-8) in mouse podocytes was assessed by ELISA. The interaction between genes was explored by luciferase reporter assays. HG treatment decreased the viability and elevated the expression of fibrosis and inflammation factors in mouse podocytes. Circ-RBM4 expression was downregulated in HG-treated mouse podocytes. Circ-RBM4 overexpression reversed HG-induced increase in levels of proteins related to fibrosis and the concentration of inflammation factors. The miR-153-3p was revealed to bind with circ-RBM4 and directly targeted nuclear factor I/C (NFIC) in mouse podocytes. Rescue assays indicated that circ-RBM4 attenuated HG-induced fibrosis and inflammation response in mouse podocytes by inhibiting miR-153-3p expression or upregulating NFIC expression. Circ-RBM4 alleviated the renal inflammation and renal fibrosis in DN by targeting the miR-153-3p/NFIC axis.
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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 | 0.001 |
| 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) | 0.003 | 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".