Effect of Hexadecyl Azelaoyl Phosphatidylcholine on Cardiomyocyte Apoptosis in Myocardial Ischemia-Reperfusion Injury: A Hypothesis
Bibliographic record
Abstract
Reperfusion after myocardial ischemia can induce cardiomyocyte death, known as myocardial reperfusion injury. The pathophysiology of the process of reperfusion suggests the confluence multiple pathways. Recent studies have focused on the inflammatory response, which is considered to be the main mechanism during the process of myocardial ischemia-reperfusion injury and can cause cardiomyocyte apoptosis. Peroxisome proliferator-activated receptors gamma activated by endogenous ligands and exogenous ligand can decrease the inflammatory response in cardiomyocytes. Thiazolidinediones are synthetic, high-affinity, selective ligands for peroxisome proliferator-activated receptors gamma, and can inhibit the inflammatory response, decrease myocardial infarct size, and protect cardiac function. However, thiazolidinediones, including rosiglitazone and pioglitazone, can also contribute to adverse cardiovascular events such as congestive heart failure. Therefore, there are some limitations to the use of thiazolidinediones. Most endogenous ligands were of low affinity until hexadecyl azelaoyl phosphatidylcholine was identified as a high-affinity ligand and agonist for peroxisome proliferator-activated receptors gamma. Hexadecyl azelaoyl phosphatidylcholine binds recombinant peroxisome proliferator-activated receptors with an affinity (Kd(app) ≈40 nM) which is equivalent to rosiglitazone. Therefore, hexadecyl azelaoyl phosphatidylcholine is a specific peroxisome proliferator-activated receptors gamma agonist. Given these findings, we hypothesized that the use of hexadecyl azelaoyl phosphatidylcholine can activate the peroxisome proliferator-activated receptors gamma signal pathways and prevent the inflammatory response process of myocardial ischemia-reperfusion injury, with reduced cardiomyocyte apoptosis and death.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".