The effect of anatomic differences on the relationship between renal artery and diaphragmatic crus

Background: The aim of this study is to investigate the effect of anatomic differences on the relationship between renal artery and diaphragmatic crus via the touch of two structures. Materials and methods: The study included dynamic computed tomography (CT) scans of 308 patients performed mainly for characterisation of liver and renal masses. Anatomic differences including the thickness of the diaphragmatic crus, the localisation of renal artery ostium at the wall of aorta, the level of renal artery origin with respect to superior mesenteric artery were evaluated. Statistical relationships between renal artery-diaphragmatic crus contact and the anatomic differences were assessed. Results: Thickness of the diaphragmatic crus at the level of renal artery origin exhibited a statistically significant relationship to renal artery-diaphragmatic crus contact at the left (p < 0.001) and right side (p < 0.001). There was a statistically significant relationship between high renal artery origin and renal artery- -diaphragmatic crus contact at the left (p < 0.001) and right side (p = 0.01). The localisation of renal artery ostium at the wall of aorta (right side, p = 0.436, left side, p = 0.681) did not demonstrate a relationship to renal artery-diaphragmatic crus contact. Conclusions: Thickness of the diaphragmatic crus and high renal artery origin with respect to superior mesenteric artery are crucial anatomic differences determining the relationship of renal artery and diaphragmatic crus. (Folia Morphol 2018; 77, 1: 22–28)

Modeling and Validation of 2-DOF Rail Vehicle Model Based on Electro–Mechanical Analogy Theory Using Theoretical and Experimental Methods

This paper presents theoretical and experimental results on modeling and simulation of two degrees of freedom rail vehicle by using electro-mechanical similarity theory. In this study, the equations of motion were derived using Newton’s second law of motion and then mechanical and equivalent electrical circuits were obtained with the help of a free body diagram. A schema in Simulink allowing analyzing of the behavior of the primary and secondary suspension was created. In order to verify the electrical model, transfer function and schema were developed in Simulink. The simulation results were compared with the experimental data and the comparison showed that the results of the mechanical experiments were close to the simulation results, but the electrical results showed better periodic behavior

Secondary bacterial infection rates among patients with COVID-19

Objective The aim of this study was to determine the factors and rates of secondary bacterial infections developed in patients after the diagnosis of COVID-19 and antimicrobial susceptibility to guide the empirical treatment and contribute to epidemiological data. Materials and Methods In our study, 1,055 patients diagnosed with COVID-19, hospitalized at Recep Tayyip Erdogan University Training and Research Hospital, Rize, between the dates March 24, 2020 and December 31, 2020, were recruited. The diagnoses of all patients were confirmed by positive SARS-CoV-2 polymerase chain reaction (PCR) tests. In addition, the blood and respiratory tract cultures of the patients recruited in the study were analyzed retrospectively. Results Ninety-two (8.7%) patients were found to have microbiologically proven respiratory or circulatory tract infections via microbial culture results. Respiratory tract infections were detected as monomicrobial in 44 patients and as polymicrobial in 17 patients, among a total of 61 patients. In addition, 59 (64.1%) patients were male patients, and 33 (35.9%) were female patients. Among the microorganisms grown in blood cultures, coagulase-negative staphylococci with a percentage of 31% and Acinetobacter baumannii with a percentage of 27.5% were prominent. In respiratory tract cultures, A. baumannii constitutes the majority with a percentage of 33.3%, followed by Staphylococcus aureus and Klebsiella pneumoniae with a percentage of 9.5% each. The most resistant bacteria were A. baumannii, resistant to all antibiotics other than colistin. Conclusion Secondary bacterial infection rates in patients with COVID-19 are lower than influenza pandemic. However, the frequency of empirical antibiotics use seems relatively high