IL-33 regulates Müller cell-mediated retinal inflammation and neurodegeneration in diabetic retinopathy
Bibliographic record
Abstract
Diabetic retinopathy (DR) is characterised by dysfunction of the retinal neurovascular unit, leading to visual impairment and blindness. Müller cells are key components of the retinal neurovascular unit and diabetes has a detrimental impact on these glial cells, triggering progressive neurovascular pathology of DR. Amongst many factors expressed by Müller cells, interleukin-33 (IL-33) has an established immunomodulatory role, and we investigated the role of endogenous IL-33 in DR. The expression of IL-33 in Müller cells increased during diabetes. Wild-type and Il33-/- mice developed equivalent levels of hyperglycaemia and weight loss following streptozotocin-induced diabetes. Electroretinogram a- and b-wave amplitudes, neuroretina thickness, and the numbers of cone photoreceptors and ganglion cells were significantly reduced in Il33-/- diabetic mice compared with those in wild-type counterparts. The Il33-/- diabetic retina also exhibited microglial activation, sustained gliosis, and upregulation of pro-inflammatory cytokines and neurotrophins. Primary Müller cells from Il33-/- mice expressed significantly lower levels of neurotransmitter-related genes (Glul and Slc1a3) and neurotrophin genes (Cntf, Lif, Igf1 and Ngf) under high-glucose conditions. Our results suggest that deletion of IL-33 promotes inflammation and neurodegeneration in DR, and that this cytokine is critical for regulation of glutamate metabolism, neurotransmitter recycling and neurotrophin secretion by Müller cells.
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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".