The Role of Repeat Administration of Adventitial Delivery of Lentivirus-shRNA-Vegf-A in Arteriovenous Fistula to Prevent Venous Stenosis Formation
Bibliographic record
Abstract
PURPOSE: To determine if a second dose of a lentivirus mediated small hairpin RNA that inhibits Vegf-A gene expression (LV-shRNA-Vegf-A) can improve lumen vessel area (LVA) of the outflow vein of an arteriovenous fistula (AVF) and decrease venous neointimal hyperplasia. MATERIALS AND METHODS: Chronic kidney disease was created in C57BL/6 mice; 28 days later, an AVF was created by connecting the right carotid artery to the ipsilateral jugular vein. Immediately after AVF creation, 5 × 10(6) plaque-forming units of LV-shRNA-Vegf-A or control shRNA was administered to the adventitia of the outflow vein, and a second dose of the same treatment was administered 14 days later. Animals were sacrificed at 21 days, 28 days, and 42 days after AVF creation for reverse transcription polymerase chain reaction and histomorphometric analyses. RESULTS: By day 21, there was a 125% increase in the average LVA (day 21, P = .11), with a decrease in cell proliferation (day 21, P = .0079; day 28, P = .28; day 42, P = .5), decrease in α-smooth muscle cell actin staining (day 21, P < .0001; day 28, P < .05; day 42, P = .59), and decrease in hypoxic stress (day 21, P < .001; day 28, P = .28; day 42, P = .46) in LV versus control shRNA vessels. CONCLUSIONS: A second dose of LV-shRNA-Vegf-A administration results in a moderate improvement in LVA at day 21.
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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 | 0.000 | 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".