Synthesis and Biomedical Potential of Azapeptide Modulators of the Cluster of Differentiation 36 Receptor (CD36)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The innovative development of azapeptide analogues of growth hormone releasing peptide-6 (GHRP-6) has produced selective modulators of the cluster of differentiation 36 receptor (CD36). The azapeptide CD36 modulators curb macrophage-driven inflammation and mitigate atherosclerotic and angiogenic pathology. In macrophages activated with Toll-like receptor-2 heterodimer agonist, they reduced nitric oxide production and proinflammatory cytokine release. In a mouse choroidal explant microvascular sprouting model, they inhibited neovascularization. In murine models of cardiovascular injury, CD36-selective azapeptide modulators exhibited cardioprotective and anti-atherosclerotic effects. In subretinal inflammation models, they altered activated mononuclear phagocyte metabolism and decreased immune responses to alleviate subsequent inflammation-dependent neuronal injury associated with retinitis pigmentosa, diabetic retinopathy and age-related macular degeneration. The translation of GHRP-6 to potent and selective linear and cyclic azapeptide modulators of CD36 is outlined in this review which highlights the relevance of turn geometry for activity and the biomedical potential of prototypes for the beneficial treatment of a wide range of cardiovascular, metabolic and immunological disorders.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it