Editorial: Novel Therapeutic Target and Drug Development in Neurovascular Retinal Diseases
Bibliographic record
Abstract
In this research topic, Fouda et al. provided a systematic overview of the arginase pathway in acute retina and brain injury, and discussed the possibility of modulating this pathway to treat ischemia-induced neurodegeneration.Shetty and Corson summarized the vulnerability of endothelial cells to mitochondrial heme loss, and proposed that targeting intracellular heme via inhibiting heme synthesis or blocking heme transport may be a novel strategy to decrease retinal neovascularization.Further exploration of neural-vascular metabolism and interaction is needed.Endothelial cells utilize glucose, fatty acid and glutamine as substrates for energy and biomass for cell homeostasis and growth (Falkenberg et al., 2019).On the other hand, photoreceptors require glucose and fatty acids for energy production and function (Joyal et al., 2016).Therefore, when considering interventions for metabolic modulation, it is necessary to take into account the overall impact on various retinal cell types.In addition, the interaction of metabolic pathways in retinopathies also requires further investigation.Recently, low serine with increase in deoxysphingolipids is reported to correlate with macular disease (Gantner et al., 2019).Wang et al. revealed significant metabolic disturbances (such as amino acids and ketone bodies) in aqueous humor of patients with Posner-Schlossman syndrome that were identified with metabolomics.Further exploration of retinal metabolic interactions between amino acid, lipid pathways, and others would definitely attract great interests.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".