Potential Effects of Opium on Pro-inflammatory Cytokines in Coronary Artery Disease by Interfering with Anti-inflammatory Drugs
Bibliographic record
Abstract
BACKGROUND: Opium is one of the factors that may interfere with coronary artery disease (CAD). This study aimed to investigate the role of opium in certain pro-inflammatory and anti-inflammatory cytokines in CAD patients with and without opium dependence on regular prescription medicines. METHODS: Seventy-seven patients with suspected CAD were selected as candidates for coronary angiography in this case-control study. They were categorized into three groups:1) CAD opiumaddicted (CAD+OA, n=30); 2) CAD non-opium-addicted (CAD, n=30); and 3) non-opium-addicted with no CAD individuals as a control group (Ctrl, n=17). Routine medications, including aspirin, atorvastatin, bisoprolol, valsartan, losartan, clopidogrel, metoprolol, isosorbide, trinitrate glyceryl, captopril, and carvedilol, were administered to these patients. ELISA was performed to quantify plasma levels of interleukin-23 (IL-23), IL-17, IL-1β, transforming growth factor beta (TGF-β), and IL-10. RESULTS: A significantly higher level of IL-23 was found in the CAD+OA group than in the CAD and control groups. In addition, in the CAD+OA group, the mean difference in TGF-β levels was significantly lower than that in CAD patients, whereas no significant difference was found between the Ctrl group and the CAD+OA and CAD groups. No significant differences were observed in the mean levels of IL-17, IL-1β, or IL-10 among the groups. DISCUSSION: Considering the significant increase in plasma levels of IL-23, a pro-inflammatory cytokine, in the CAD+OA group compared with the other two groups, opium may play an important role in increasing inflammation. Also, given the important role of IL-23 in the differentiation of Th17 cells (producers of IL-17), the lack of increase in IL-17 and IL-1β in the three groups, the decrease in TGF-β levels in the CAD+OA group compared with the CAD group, and the fact that CAD and CAD+OA patients were treated with anti-inflammatory drugs, it can be assumed that these drugs modulate cytokine expression. CONCLUSION: Opium was found to contribute to the induction of inflammation by interfering with cardiovascular medications, resulting in deterioration of CAD complications. Additionally, certain medications, including aspirin, glyceryl trinitrate, atorvastatin, and clopidogrel, played a significant role in regulating the expression of cytokines.
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".