Semaphorin 3A and VEGF promote inflammation in retinopathy
Bibliographic record
Abstract
Immunological activity in the CNS is largely dependent on an innate immune response and is present in health and heightened in diseases such as diabetic retinopathy, multiple sclerosis, amyotrophic lateral sclerosis and Alzheimer's disease. The molecular dynamics governing immune cell recruitment to sites of injury and disease in the CNS remain ill-defined. We identify a subset of mononuclear phagocytes (MPs) that responds to local chemotactic cues that are conserved between central neurons, vessels and immune cells. We provide evidence that patients suffering from late stage proliferative diabetic retinopathy (PDR) produce elevated levels of Semaphorin 3A (SEMA3A) which counterintuitively acts as a potent attractant for Neuropilin-1 (NRP-1)-positive MPs. These pro-angiogenic MPs are selectively recruited to sites of pathological neovascularization in response to locally produced SEMA3A as well as VEGF- NRP-1-positive MPs play a critical role in disease progression as NRP-1-deficient MPs (LysM-Cre/Nrp1fl/fl) fail to enter the retina in a model of oxygen-induced retinopathy (OIR) that serves as a proxy for PDR. This leads to decreased vascular degeneration and diminished pathological pre-retinal neovascularization. Intravitreal administration of a NRP-1-derived trap effectively mimics the therapeutic benefits observed in LysM-Cre/Nrp1fl/fl mice. Our findings identify NRP-1 as an obligate receptor for immune chemotaxis and accretion in neovascular retinal disease. Understanding the signals that influence neuroimmune interplay may provide valuable therapeutic insight for treatments to counter destructive neuroinflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".