The life expectancy increase in patients with cancer requires more attention to the quality of life and mental health status of this population. The aim of the current research is to change the perspective on the evolution of oncological disorders, regardless of their localization, stage, or complications, in terms of survival chances, by exploring the essential role of psychiatric medication on the core physical and mental health-related outcomes. This is a prospective, naturalistic study, that enrolled 284 oncological patients, distributed in three groups, diagnosed with lung cancer, metastatic lung cancer or other types of cancer, who received psychotropic medication for their mood symptoms, and changes in the primary outcomes (i.e., patients’ mood and pain, and quality of the mental status of the caregivers) were monitored for six months. The results reflected the efficacy of psychopharmacological intervention initiated early after the diagnosis of cancer was formulated, according to the scores on the Integrated Palliative Care Outcome Scale (IPOS). The severity of pain, the intensity of depression, and anxiety in family members decreased significantly during six months of the standard-of-care psychotropic medication administered for depressive disorders. In the long term, the objective is to change the oncological protocols to accommodate an early introduction of psychiatric medication in the evolution of oncological disease, even in the context of moderate physical symptoms with uncertain etiology, which causes a deterioration in overall functionality and quality of life. A new notion, created by the first author, is supported by this research, i.e., “long-term oncological survivorship care,” which will replace the former concept of palliative care, which refers to “end-of-life care,” or “comfort care,” in relation to the oncological context.

Background: Type 2 Diabetes Mellitus (DM) is another pandemic of 21 century, and its control is of immense importance. Researchers developed many predictor models using soft computing techniques. The present study developed a prediction model for Type 2 DM using machine learning classifiers. The analysis excludes plasma glucose concentration and insulin concentration as predictors to explore relationships with other predictors.
Methods: This cross-sectional study enrolled 108 participants aged 25 to 67 years from SMS Medical College, Jaipur (Rajasthan, India), after approval from the ethics committee. The study developed a prediction model using machine learning techniques. The classifiers used in the application include decision trees, support vector machines, K-nearest neighbors, and ensemble learning classifiers. A total of 25 predictors were collected and underwent feature reduction. The response levels include diabetes mellitus, prediabetes, and no diabetes mellitus. The models were run using three predictors and a response variable. The prediction model with the best accuracy and area under the receiver operator characteristic curve was selected.
Results: The features that vary among the three groups include age, WHR, biceps skinfold thickness, total lipids, phospholipids, triglycerides, total cholesterol, LDL, VLDL, and serum creatinine, and family history of DM. After feature reduction, the age, biceps skinfold thickness, and serum creatinine were run on the Classification learner application to predict the diabetic category. The best model was subspace discriminant with accuracy, sensitivity, specificity, and AUC under the ROC curve was 62.4%, 74%, 94%, and 0.70, respectively. Conclusion: The present study concludes that age, biceps skinfold thickness, and serum creatinine combination have higher specificity in predicting type 2 DM. The study emphasized the selection of appropriate predictors along with newer machine learning algorithms.

The presence of depression, substance use disorders other than alcohol and alcohol use disorders are not signifi cant in the differentiation of ADHD patients from the population of personality disorder nonADHD patients. The overall severity and the impact on functionality as assessed with the presence of hospitalizations and the WFIRS scale show a signifi cant importance in differentiating the intensity of ADHD symptomatology.

ADHD and personality disorders are known to have clinical presentations that sometimes share different symptoms. Their characteristics are pervasive, relatively stable over the years. This study tries to determine the prevalence of attention deficit and hyperactivity/im-pulsivity symptoms in a population of individuals with a personality disorder. [...]