The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse drug reactions (ADRs), making it the 4th most common cause of mortality in the USA. Drug-drug interactions (DDls) and their impact on patients represent critical challenges for the healthcare system. To reduce the incidence of ADRs, this study focuses on identifying DDls using a machine-learning approach. Drug-related information was obtained from various free databases, including DrugBank, BioGRID and Comparative Toxicogenomics Database. Eight similarity matrices between drugs were created as covariates in the model in order to assess their infiuence on DDls. Three distinct machine learning algorithms were considered, namely, logistic regression (LR), extreme Gradient Boosting (XGBoost) and neural network (NN). Our study examined 22 notable drugs and their interactions with 841 other drugs from DrugBank. The accuracy of the machine learning approaches ranged from 68% to 78%, while the F1 scores ranged from 78% to 83%. Our study indicates that enzyme and target similarity are the most significant parameters in identifying DDls. Finally, our data-driven approach reveals that machine learning methods can accurately predict DDls and provide additional insights in a timely and cost-effective manner.

Patients in intensive care units (ICUs) are often physically unable to communicate with their physicians. Thus, the sharing of information about the on-going treatment of the patients in ICUs is directly related to the communication attitudes governing a patient’s relatives and the physician. This study aims to analyze the attitudes displayed by the relatives of patients and the physician with the purpose of determining the communication between the two parties. For data collection, two similar survey forms were created in context of the study; one for the relatives of the patients and one for the ICU physicians. The questionnaire included three sub-dimensions: informing, empathy and trust. The study included 181 patient relatives and 103 ICU physicians from three different cities and six hospitals. Based on the results of the questionnaire, identification of the mutual expectations and substance of the messages involved in the communication process between the ICU patients’ relatives and physicians was made. The gender and various disciplines of the physicians and the time of the conversation with the patients’ relatives were found to affect the communication attitude towards the patient. Moreover, the age of the patient’s relatives, the level of education, the physician’s perception, and the contact frequency with the patient when he/she was healthy were also proven to have an impact on the communication attitude of the physician. This study demonstrates the mutual expectations and substance of messages in the informing, empathy and trust sub-dimensions of the communication process between patient relatives and physicians in the ICU. The communication between patient relatives and physicians can be strengthened through a variety of training programs to improve communication skills.

We aimed to evaluate the effects of morphological characteristics of the solitary pulmonary nodules (SPN) on metabolic activity assessment. To the best of our knowledge, this is the first study to compare the volumetric metabolic activity parameters according to the morphologic parameters of the nodules. In this retrospective study, 18F-FDG positron emission tomography and computed tomography scans performed between 2011 and 2018 were evaluated by a nuclear and diagnostic radiologist. One hundred thirteen patients with SPNs with biopsy-proven diagnosis were included. SPNs were classified as solid, partially solid (PS), and ground glass opacity (GGO). SPN diameter, SUVmax, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and density were significantly higher in the malignant group. SUVmax, MTV, TLG increased in direct proportion to the diameter. There was no a significant difference between GGO, PS, and solid nodules in terms of SUVmax values. MTV and TLG values increased in parallel with the density of the nodules, but this increase was only significant in the malignant group. There was a significant difference between SPNs <2 cm and SPNs ≥2 cm in terms of MTV, while there was no difference in terms of SUVmax. The cut-off value determined by the ROC curve was found to be 4.39 for SUVmax, 7.33 mL for MTV and 31.88 g for TLG. The cut-off values for SUVmax of solid and subsolid nodules were close to each other, but cut-off values for MTV and TLG were higher in solid nodules. SUVmax, MTV, and TLG are affected by diameter and attenuation. We suggest using different MTV and TLG cut-off values for solid and subsolid nodules, but we suggest using same SUVmax values. MTV can be a more reliable parameter than SUVmax in prediction of malignancy in smaller nodules.

Dates have great importance in cancer diseases. However, the date variables themselves are not analyzed. This study aims to evaluate the descriptive statistics of diagnosis, operation, and last examination dates in gastric carcinoma patients by circular analysis methods. Totally 502 gastric carcinoma patients were enrolled in the study. The mean month of diagnosis date was found in nearly November (∼10.86) for females and May (∼5.17) for male patients. The mean month of operation date was found March (∼3.24) for females, and July & August (∼7.79) for males. The mean month of the last examination date was found as February & March (∼2.61) for females, and May (∼4.85) for males. Moreover, the mean day of the week for diagnosis date was found Thursday (∼5.50) for both female and male patients. The fitting of distributions of all variables was checked, also, according to von Mises, Rayleigh, and Kuiper’s tests. When the days and months were analyzed by classical descriptive statistics, the results were obtained completely different from the circular analyses results. Therefore, the dates and times should be analyzed in certain diseases to give an idea for physicians.