Effect of L-Arginine on the Serum Level of Advanced Glycation End Products in Patients with Post Infarction Chronic Heart Failure
Bibliographic record
Abstract
Post-infarction heart failure with preserved ejection fraction (HFpEF) determines a great morbidity and mortality, and given the physiopathology implications of advanced glycation end products (AGEs) in the genesis of myocardial dysfunction. As known endothelial dysfunction is an independent predictor for cardiovascular disease. L-Arginine is the amino acid with potential to improve endothelial function which leading to prevention and treatment of cardiovascular diseases, and we think that L-Arginine may decrease the serum AGEs. We aimed to estimate the value of AGEs in post-infarction HFpEF patients, and detect the effect of L-Arginine on the serum level of AGEs in post-infarction HFpEF pts. all individuals (25) included aged 40 to 80 years, 20(80%) males and 5(20%) females were diagnosed with (HFpEF) according to ESC guidelines (2012), and their functional class according to NYHA classification for HF. 20(80%) patients of them have myocardial infarction in anamnesis. 1st group:13 patients with HFpEF and history of myocardial infarction with L-Arginine added to their standard treatment. 2nd group:7 patients with HFpEF and history of myocardial infarction with standard treatment (without L-Arginine). Comparsion group: 5 patients with HFpEF with standard treatment. We prescribed L. Arginine aspartate (Tivortin 4.2gm) intravenously once daily for 10 days for all 1st group patients. The levels of total cholesterol, triglycerides, glucose, white blood cells, erythrocyte sedimentation rate and AGEs serum level were deterimined. AGEs serum level increased markedly increased in middle-age pts with post infarction HFpEF. Inclusion of L-arginine aspartate in complex of treatment for post infarction HFpEF contributed to the significant decrease AGEs level in >60 years old patients.
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 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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".