Gallstone disease (GD) is a common gastrointestinal disease. Although traditional diagnostic techniques, such as ultrasonography, CT, and MRI, detect gallstones, they have some limitations, including high cost and potential inaccuracies in certain populations. This study proposes a machine learning-based prediction model for gallstone disease using bioimpedance and laboratory data. A dataset of 319 samples, comprising161 gallstone patients and 158 healthy controls, was curated. The dataset comprised 38 attributes of the participants, including age, weight, height, blood test results, and bioimpedance data, and it contributed to the literature on gallstones as a new dataset. State-of-the-art machine learning techniques were performed on the dataset to detect gallstones. The experimental results showed that vitamin D, C-reactive protein (CRP) level, total body water, and lean mass are crucial features, and the gradient boosting technique achieved the highest accuracy (85.42%) in predicting gallstones. The proposed technique offers a viable alternative to conventional imaging techniques for early prediction of gallstone disease.

In this study, it was aimed to evaluate the severity of depression and fatigue in patients with type 2 diabetes mellitus (T2DM), aged ≥ 60 years, with poor diabetes control. Between December 2018 and June 2019, 310 patients aged ≥ 60 years, with hemoglobin A1C ≥ 10%, followed-up with the diagnosis of T2DM for at least 3 years in the internal medicine outpatient clinics of Bursa Yüksek İhtisas Training and research hospital were included in the study. The geriatric depression scale (GDS) and fatigue severity scale (FSS) questionnaires were administered. Patients were analyzed according to their sociodemographic and clinical characteristics, according to their GDS and FSS scores. The GDS and FSS scores were higher in the female patients than in the male patients, those with diabetes aged ≥ 21 years than those aged < 21 years, those using premixed insulin than those using basal bolus insulin and oral antidiabetic drug for + basal insulin, and those living alone than in those living with their families. The FSS score was higher in patients with vitamin D levels < 20 ng/mL. The factors affecting the GDS score were the FGS and FSS scores in the multivariate analysis. The factors affecting the FSS score were the GDS, diabetes age, hemoglobin level, and vitamin D level in the multivariate analysis. Poorly controlled diabetes affects elderly patients more in terms of their mental and physical health. Therefore, these patients should be considered in terms of psychosocial aspects to increase treatment compliance and effects.

Herbal medicines are believed to be effective in the treatment of many diseases. The genius Teucrium is a member of Lamiaceae family, which contains more than 300 species. Antimutagenic, anticancer, hypoglycemic, antibacterial, anti-inflammatory, antipyretic, antiulcer, hypolipidemic and antidiarrheal effects of Teucrium polium have been reported in the studies. In this article, we describe an acute kidney injury (AKI), which is thought to have developed as a result of T. polium use for the treatment of diabetes mellitus. It should not be forgotten that herbal medicine is an important reason for AKI. In addition, community should be made aware of the danger of herbal medicines which especially have potential renal and hepatic side effects.