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Objective: Abdominal involvement of COVID-19 is a current issue. We aimed to evaluate hepatic and pancreatic density alterations on computed tomography (CT) and to analyze whether these alterations had a relationship with chest CT score and laboratory findings.Methods: Patients with reverse transcription-polymerase chain reactionconfirmed COVID-19 from March 11, 2020, to February 6, 2021, were retrospectively analyzed. Patients were divided into nonprogressive and progressive groups according to their chest CT scores. Liver and pancreas density, and liver-to-spleen (L/S) ratio were calculated. Laboratory findings, medication, intensive care unit stay, and survival were noted.Results: There were 51 patients in the nonprogressive group and 123 patients in the progressive group. The median (minimum to maximum) L/S value of the nonprogressive group was 1 (0.28-1.53) at admission and 1.06 (0.33-1.83) at follow-up ( P < 0.001). In the progressive group, the median L/S value was 1.08 (0.35-1.51) at admission and 0.92 (0.33-1.75) at follow-up ( P < 0.001). A significant difference was found between the 2 groups at admission and follow-up ( P = 0.010 and P < 0.001, respectively). Pancreatic density measured at follow-up was significantly lower in the progressive group ( P = 0.045). In the progressive group, aspartate aminotransferase, total bilirubin, creatinine, urea, C-reactive protein, D-dimer, and white blood cell values were higher; albumin and lymphocyte values were lower ( P < 0.05). Conclusions:Patients with COVID-19 with progressive CT scores may have a decrease in L/S values, and their pancreatic density is lower than nonprogressives. Aspartate aminotransferase, total bilirubin, creatinine, urea, C-reactive protein, D-dimer, and white blood cell values tend to be higher in patients with a high chest CT score.

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Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.

Objective Bipolar disorder (BD) is an inflammatory and metabolic disease. The disease and the drugs used to treat it may affect cardiovascular disease (CVD) risk. The aim of this study is to investigate arterial stiffness in patients with BD and compare them with healthy controls. Methods Thirty-nine patients with BD type I in remission and 39 healthy control subjects were included in the study. Carotid and femoral artery intima-media thickness (IMT) and arterial thickness parameters were measured by Doppler ultrasonography. Results The elastic modulus value of the carotid artery was significantly higher in the patients than in the control group ( p = 0.015). Although the IMT of both carotid and femoral artery was thicker in patients than in healthy control subjects, this difference was not statistically significant ( p = 0.105; p = 0.391). There was a significant positive correlation between chlorpromazine equivalent dose and femoral elastic modulus value ( p = 0.021, r = 0.539). There was a positive correlation between lithium equivalent dose and carotid compliance; a significant negative correlation between lithium equivalent dose and carotid elastic modulus was also determined (both p = 0.007, r = 0.466; p = 0.027, r = −0.391, respect-ively). No predictor was observed between drug dose and arterial stiffness parameters. Conclusion Arterial stiffness might be investigated for its potential to reduce CVD risk in patients with BD. Given the established CVD complications in this patient population, further studies are needed to determine whether the results are specific to antipsychotic treatment or BD and to clarify the potential arterial protective effects of mood stabilizers.