In-vitro antiplatelet effect of melatonin in healthy individuals and patients with type 2 diabetes mellitus.

PURPOSE: The incidence of acute myocardial infarctions (AMI) shows circadian variation typically peaking during morning hours with a decline at night. However, this variation does not occur in patients with diabetes mellitus (DM). The night's decline of AMI may be partially explained by melatonin-related platelet inhibition. Whether this effect is absent in diabetic patients is unknown. The aim was to study the effect of melatonin on in-vitro platelet aggregation in healthy individuals and patients with type 2 DM. METHODS: Platelet aggregation was measured in blood samples from healthy individuals (n = 15) and type 2 DM patients (n = 15) using multiple electrode aggregometry. Adenosine diphosphate (ADP), arachidonic acid (ASPI) and thrombin (TRAP) were used as agonists. Aggregability for each subject was tested after adding melatonin in two concentrations. RESULTS: In healthy individuals, melatonin inhibited platelet aggregation in both higher (10-5 M) and lower concentrations (10-9 M) induced by ADP, ASPI, and TRAP (p < 0.001, p = 0.002, p = 0.029, respectively). In DM patients, melatonin did not affect platelet aggregation in both concentrations induced by ADP, ASPI, and TRAP. Melatonin decreased platelet aggregation induced by ADP, ASPI, and TRAP significantly more in healthy individuals compared to patients with DM. (p = 0.005, p = 0.045 and p = 0.048, respectively). CONCLUSION: Platelet aggregation was inhibited by melatonin in healthy individuals. In-vitro antiplatelet effect of melatonin in type 2 DM patients is significantly attenuated.

Technical and practical aspects of artificial intelligence in cardiology.

Artificial intelligence (AI) is here to stay. It is not a future anymore, and there are many particular problems in cardiology that are already being solved via machine learning (ML), and many more are to come. AI cannot solve complex tasks yet, and probably this will not change in the upcoming years. Therefore, cardiologists do not have to be afraid that computers will replace them. However, cardiologists who will not be able to use ML algorithms in their clinical practice will be replaced by those who will. (Fig. 2, Ref. 50). Keywords: artificial intelligence, cardiology, potential machine learning, survival models, classification algorithms, computer vision, automated analysis of various imaging examinations, ECG interpretation, phenotype clustering, pathophysiological mechanisms.