Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.
(1) Background: Health services that were already under pressure before the COVID-19 pandemic to maximize its impact on population health, have not only the imperative to remain resilient and sustainable and be prepared for future waves of the virus, but to take advantage of the learnings from the pandemic to re-configure and support the greatest possible improvements. (2) Methods: A review of articles published by the Special Issue on Population Health and Health Services to identify main drivers for improving the contribution of health services on population health is conducted. (3) Health services have to focus not just on providing the best care to health problems but to improve its focus on health promotion and disease prevention. (4) Conclusions: Implementing innovative but complex solutions to address the problems can hardly be achieved without a multilevel and multisectoral deliberative debate. The CHRODIS PLUS policy dialog method can help standardize policy-making procedures and improve network governance, offering a proven method to strengthen the impact of health services on population health, which in the post-COVID era is more necessary than ever.
Introduction: Acute myocardial infarction (AMI) remains one of the leading causes of death worldwide during cardiovascular diseases. An important step in the secondary prevention of recurrent myocardial infarction is cardiac rehabilitation (CR). However, with the onset of the global COVID-19 pandemic, the CR programs in many clinics were limited due to the quarantine measures. Knowledge about the effects of CR on quality of life and exercise tolerance in AMI patients with COVID is scarce. Aim: To evaluate the use of a modular CR program on quality of life and exercise tolerance among post-AMI patients with COVID-19 recovery, and in those with no history of COVID-19 infection. Material and methods: This study included 118 patients with or recovering from acute myocardial infarction. They were divided into 2 groups: the first group included 86 patients, who had slight "ground-glass opacity" changes on the computed tomography (CT) scans, and the second group comprised 32 patients, who had no history of coronavirus infection or no change on CT scan of the lungs during the pandemic. The CR program was modified due to the pandemic era. Results: Physical tolerance increased in both groups after CR 3.6 months as compared to before the CR program (duration of training in seconds (p < 0.05), a 6-minute walk test (p < 0.05), the maximal oxygen consumption (VO2max) (p < 0.05), and the metabolic equivalent of task (MET) (p < 0.05)). Similarly, quality of life measures improved in both groups. Treatment satisfaction was higher in the first group at the beginning and the end of CR. Conclusions: The modular CR program improves exercise capacity and quality of life with AMI and COVID-19 similar to that of patients without AMI. Patients after COVID-19 should undergo rehabilitation
Aim: The current study aimed to investigate the potential antiproliferative activity of metformin, the effective concentration range, and the mechanism of action. Materials & methods: Human breast cancer cells, MCF-7 were treated with a serial dilution of metformin (10–150 μM) for 24 and 48 h. Potential antiproliferative activity of metformin and its ability in inducing cellular apoptosis and autophagy were also investigated. Results: Metformin inhibited MCF-7 proliferation in a concentration and time dependent manner, with 80 μM as the most effective concentration. Compared with nontreated cells, metformin induced significant levels of autophagy and apoptosis, which were confirmed by the reduction of mTOR and BCL-2 protein expression. Conclusion: The study confirms the antiproliferative activity of metformin, which may likely occur through AMPK signaling pathway.
Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.
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