Safety in Surgery:Evaluation of Safety and Efficiency in Use of Aminoglycosides in Acute Appendicitis

Background: Aminoglycosides are used as empirical antibiotic treatment of intraabdominal infections which are caused by Gram negative bacteria and for which the treatment of choice is surgery. Aminoglycosides maintain good efficacy against these bacteria and reduce the need for prescribing fluoroquinolone, cephalosporin and carbapenem antibiotics which contribute to the development of resistant bacterial strains. In recent years, several clinical trials and international guidelines have advised against the use of aminoglycosides owing largely to doubts about their effectiveness and to the concern for their known nephrotoxicity and ototoxicity. Aim: In our study, we aimed to prove whether aminoglycosides are appropriate agents in the treatment of acute appendicitis. Methods: Retrospectively, patients with acute appendicitis we included in the trial. Demographic characteristics, comorbidities, clinical signs and symptoms, the type of antibiotic and surgical treatment were analyzed. The effect of independent variables on the occurrence of complications was calculated using Student’s T-test and Fisher’s precise test. The effect of aminoglycosides on the loss of kidney function was determined by means of a linear regression method. Results: 300 patients proved acute appendicitis were included in the study. Univariate statistical analysis showed that the risk factors for postoperative complications in treating acute appendicitis were: age over 76 years (p Conclusion: Aminoglycoside antibiotics are a safe and effective treatment of acute appendicitis;our not published data are positive of AGs use in acute cholecystitis and left colon diverticulitis which requires surgery. If used for a limited time period, they do not increase the risk for kidney injury and remain a stable low level of all over complications.

Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.