Tofacitinib Ameliorates Retinal Vascular Leakage in a Murine Model of Diabetic Retinopathy with Type 2 Diabetes
Bibliographic record
Abstract
We have previously reported that inhibition of the Janus kinase 1 (JAK1) signaling ameliorates IL-17A-mediated blood-retinal barrier (BRB) dysfunction. Higher levels of IL-17A have been observed in the blood and intraocular fluids in patients with diabetic retinopathy (DR), in particular those with diabetic macular oedema. This study aimed to understand whether JAK1 inhibition could prevent BRB dysfunction in db/db mice, a model of type 2 diabetes (T2D). An in vitro study showed that high glucose treatment disrupted the junctional distribution of claudin-5 in bEnd3 cells and ZO-1 in ARPE19 cells and that tofacitinib citrate treatment prevented high glucose-mediated tight junction disruption. Albumin leakage, accompanied by increased levels of the phosphorylated form of JAK1 (pJAK1), was observed in three-month-old db/db mice. Treatment of two-and-a-half-month-old db/db mice with tofacitinib citrate for two weeks significantly reduced retinal albumin leakage and reduced pJAK1 expression. pJAK1 expression was also detected in human DR retina. Our results suggest that JAK1 inhibition can ameliorate BRB dysfunction in T2D, and JAK1 inhibitors such as tofacitinib citrate may be re-purposed for the management of diabetic macular oedema.
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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 | 0.000 |
| 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 | 0.002 |
| 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".