Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females

We analysed features relevant for differentiation between males and females based on the data available from the Human Connectome Project (HCP) S1200 dataset. We used 354 features containing cognitive and emotional measures as well as measures derived from task functional magnetic resonance imaging (MRI) and structural brain MRI. The paper presents a thorough analysis of this extensive set of features using a machine learning approach with a goal of identifying features that have the ability to differentiate between males and females. We used two state of the art classification algorithms with different properties: support vector machine (SVM) and random forest classifier (RFC). For each classifier the hyperparameters were obtained and classifiers were optimized using nested cross validation and grid search. This resulted in the classification performance of 91% and 89% accuracy using SVM and RFC, respectively. Using SHAP (SHapley Additive exPlanations) method we obtained relevance of features as indicators of sex differences and identified features with high discriminative power for sex classification. The majority of top features were brain morphological measures, and only a small proportion were features related to cognitive performance. Our results demonstrate the importance and advantages of using a machine learning approach when analysing sex differences

Long-term Effectiveness of Liraglutide in Association with Patients' Baseline Characteristics in Real-life Setting in Croatia: an Observational, Retrospective, Multicenter Study

INTRODUCTION: Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are recommended therapy for type 2 diabetes (T2DM) and liraglutide is the most used worldwide. We assessed the glycemic efficacy and extra-glycemic effects of liraglutide during 36 months' follow-up of individuals with poorly regulated T2DM under routine clinical practice and sought to identify the phenotype of treatment responders. METHODS: A total of 207 individuals were included. The primary endpoint was the proportion of participants with HbA1c < 7.0% and/or weight reduction. Secondary endpoints included changes in lipids, blood pressure, fasting c-peptide, and antidiabetic treatment during follow-up of 3 years. RESULTS: Liraglutide was prescribed to 89.8% of participants already on at least two antidiabetic medications and 18% on insulin. Subject's mean age was 53.28 ± 9.42 years with duration of diabetes 8.29 ± 4.89 years. Baseline HbA1c was 8.5 ± 1.3% and body mass index (BMI) was 39 ± 4.5 kg/m2. Reduction of HbA1c was observed in 84.4% of participants, and 89.2% experienced average weight reduction of 5 kg. A composite outcome (reduction of HbA1c with any weight loss) was achieved in 76.2% of patients. After 6 months on liraglutide treatment, 38.1% of participants achieved target HbA1c level < 7%. This effect was maintained for 36 months in 50.8% of subjects. Increase in c-peptide was evident after 24 months (p = 0.030). Participants experienced a significant reduction in systolic blood pressure (BP) (p = 0.003), while there was no effect on diastolic BP, lipid profile, or liver enzymes. The number of participants treated with sulfonylurea decreased from 60.8% to 17.5%, while the number treated with insulin and sodium-glucose co-transporter-2 (SGLT-2) inhibitor increased (17.6% to 24.6% and 2.5% to 36.8%, respectively). Independent predictors of durability of HbA1c reduction were initial BMI (p = 0.004), HbA1c (p < 0.001), systolic BP (p = 0.007), and cholesterol (p = 0.020). Moreover, female gender and shorter duration of diabetes were independent predictors for HbA1c reduction. CONCLUSION: Liraglutide shows sustained glycemic and extra-glycemic effects when used for treatment of obese poorly regulated individuals with T2DM

Long-Term Effectiveness of Liraglutide in Association with Patients’ Baseline Characteristics in Real-Life Setting in Croatia: An Observational, Retrospective, Multicenter Study

<p><b>Article full text</b></p> <p><br></p> <p>The full text of this article can be found <a href="https://link.springer.com/article/10.1007/s13300-017-0324-x"><b>here</b>.</a></p> <p><br></p> <p><b>Provide enhanced content for this article</b></p> <p><br></p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p> </p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p><br></p> <p>Other enhanced features include, but are not limited to:</p> <p><br></p> <p>• Slide decks</p> <p>• Videos and animations</p> <p>• Audio abstracts</p> <p>• Audio slides</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p

MOESM1 of The influence of dopamine-beta-hydroxylase and catechol O-methyltransferase gene polymorphism on the efficacy of insulin detemir therapy in patients with type 2 diabetes mellitus

Additional file 1. Additional tables