Preventing Renal IschemiaReperfusion Injury Using Small Interfering RNA by Targeting Complement 3 Gene
Bibliographic record
Abstract
The complement system is one of the important mediators of renal ischemia-reperfusion injury (IRI). We hypothesized that efficient silencing of C3, which is the central component on which all complement activation pathways converge, could be achieved using small interfering RNA (siRNA), and that this would result in overall inhibition of complement activation, thereby preventing IRI in kidneys. A series of experiments was conducted, using a mouse model of IRI and vector-delivered C3-specific siRNA. We demonstrated the following: (1) renal expression of C3 increases as a result of IRI; (2) by incorporation into a pRNAT U6.1 vector, siRNA can be delivered to renal cells in vivo; (3) systemically delivered siRNA is effective in reducing the expression of C3 in an experimentally induced mouse kidney model of IRI; (4) similarly, siRNA reduces complement-mediated IRI-related effects, both in terms of renal injury (as evidenced by renal function and histopathology examination) and mouse mortality and (5) silencing the production of C3 diminishes in vivo production of TNF-alpha. This study implies that siRNA represents a novel approach to preventing IRI in kidneys and might be used in a variety of clinical settings, including transplantation and acute tubular necrosis.
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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) | 0.000 | 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) | 0.001 | 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".