Adenoviral gene transfer of bioactive TGFbeta1 to the rodent eye as a novel model for anterior subcapsular cataract.
Bibliographic record
Abstract
PURPOSE: To produce a gene-transfer model of rodent anterior subcapsular cataracts (ASC) using a replication-deficient, adenoviral vector containing active TGFbeta1. Establishment of this model will be important for further investigations of TGFbeta-induced signaling cascades in ASC. METHODS: Adenovirus containing the transgene for active TGFbeta1 (AdTGFbeta1), beta-galactosidase (AdLacZ), green fluorescent protein (AdGFP) or no transgene (AdDL) was injected into the anterior chamber of C57Bl/6, Smad3 WT and Smad3 KO mice. Four and 21 days after injection, animals were enucleated and eyes were processed and examined by routine histology. Immunolocalization of markers indicative of epithelial to mesenchymal transition (EMT), fibrosis, proliferation and apoptosis was also carried out. RESULTS: By day 4, treatment with AdLacZ demonstrated transgene expression in multiple structures of the anterior chamber including the lens epithelium. In contrast to AdDL, treatment with AdTGFbeta1 produced alphaSMA-positive subcapsular plaques in all three groups of mice, which shared features reminiscent of human ASC. At day 21, plaques remained alphaSMA-positive and extensive extracellular matrix deposition was observed. The AdTGFbeta1 model was further employed in Smad3 deficient mice and this resulted in the development of small ASC. CONCLUSIONS: Gene transfer of active TGFbeta1 using an adenoviral vector produced cataractous plaques four days postinjection, which were found to develop independent of functional Smad3.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".