Eyeing central neurons in vascular growth and reparative angiogenesis
Bibliographic record
Abstract
The generation of blood vessels is a highly synchronized process requiring the coordinated efforts of several vascular and nonvascular cell populations as well as a stringent orchestration by the tissue being vascularized. Stereotyped angiogenesis is vital for both developmental growth and to restore tissue metabolic supply after ischemic events. Central neurons such as those found in the brain, spinal cord, and retina are vast consumers of oxygen and nutrients and therefore require high rates of perfusion by functional vascular networks to ensure proper sensory transmission. During a metabolic mismatch, such as that occurring during a cerebrovascular infarct or in ischemic retinopathies, there is increasing evidence that central neurons have an inherent ability to influence the vascular response to injury. With a focus on the retina and retinal ischemic disorders, this review explores the ever-growing evidence suggesting that central neurons have the propensity to impact tissue vascularization and reparative angiogenesis. Moreover, it addresses the paradoxical ability of severely ischemic neurons to hinder vascular regrowth and thus segregate the most severely injured zones of nervous tissue. The topics covered here are pertinent for future therapeutic strategies because promoting and steering vascular growth may be beneficial for ischemic disorders.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".