Over the past decade, absorption, distribution, metabolism, and excretion (ADME) property evaluation has become one of the most important issues in the process of drug discovery and development. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques had been widely used to estimate ADME properties of chemical compounds. Traditional prediction methods usually try to build a functional relationship between a set of molecular descriptors and a given ADME property. Although traditional methods have been successfully used in many cases, the accuracy and efficiency of molecular descriptors must be concerned. Herein, we report a new classification method based on substructure pattern recognition, in which each molecule is represented as a substructure pattern fingerprint based on a predefined substructure dictionary, and then a support vector machine (SVM) algorithm is applied to build the prediction model. Therefore, a direct connection between substructures and molecular properties is built. The most important substructure patterns can be identified via the information gain analysis, which could help to interpret the models from a medicinal chemistry perspective. Afterward, this method was verified with two data sets, one for blood-brain barrier (BBB) penetration and the other for human intestinal absorption (HIA). The results demonstrated that the overall predictive accuracies of the best HIA model for the training and test sets were 98.5 and 98.8%, and the overall predictive accuracies of the best BBB model for the training and test sets were 98.8 and 98.4%, which confirmed the reliability of our method. In the additional validations, the predictive accuracies were 94 and 69.5% for the HIA and the BBB models, respectively. Moreover, some of the representative key substructure patterns which significantly correlated with the HIA and BBB penetration properties were also presented.
Background Coronavirus disease 2019 (COVID-19) is a new infectious disease caused by SARS-CoV-2, with epidemiological characteristics such as strong infectivity, high morbidity, multiple infection routes, and widespread infection [1, 2]. Since the epidemic began in Wuhan, China, in December 2019, from January 2020 to April 2020, COVID-19 has spread explosively in China. As of May 24, a total of 5,290,506 cases had been diagnosed worldwide, with a total of 342,448 deaths. A total of 84,525 cases had been diagnosed in China, with 4,645 deaths and 79,749 cured [3]. The spread of the COVID-19 epidemic occurred exceptionally quickly, and its range is extensive, covering almost all countries in the world. Since the outbreak in China in December, the outbreak has been effectively controlled through the development of the Chinese government's prevention and control measures. On February 25, 2020, there were no new cases in 26 provinces across the country. The Chinese government began to gradually ease the controls of the epidemic situation and initiate the national resumption of production and orderly recovery, and China's anti-epidemic efforts have achieved effective results. Health workers are among the most important people in every major public health challenge, as frontline anti-epidemic workers, have made great contributions to anti-epidemic work and have experienced great psychological pressure, which may increase the current baseline level of psychopathology [4], it can even lead to related disorders, stress, anger, and mood dysregulation [5], can not continue to put into work. Therefore, the mental health issues of healthcare workers cannot be ignored. On January 26, 2020, the National Health Commission of China issued the notification of principles for emergency psychological crisis intervention for the COVID-19 epidemic. Local governments responded actively, establishing
Background Appendicitis in elderly patients is associated with increased risk of postoperative complications. The choice between laparoscopy and open appendectomy remains controversial in treating elderly patients with appendicitis. Methods Comprehensive search of literature of MEDLINE, Embase, Cochrane Library and ClinicalTrials was done in January 2019. Studies compared laparoscopy and open appendectomy for elderly patients with appendicitis were screened and selected. Postoperative mortality, complications, wound infection, intra-abdominal abscess and operating time, length of hospital stay were extracted and analyzed. The Review Manage 5.3 was used for data analysis. Results Twelve studies with 126,237 patients in laparoscopy group and 213,201 patients in open group. Postoperative mortality was significantly lower following laparoscopy (OR, 0.33; 95% CI, 0.28 to 0.39). Postoperative complication and wound infection were reduced following laparoscopy ((OR, 0.65 95% CI, 0.62 to 0.67; OR,0.27, 95% CI, 0.22 to 0.32). Intra-abdominal abscess was similar between LA and OA (OR,0.44;95% CI, 0.19 to 1.03). Duration of surgery was longer following laparoscopy and length of hospital stay was shorter following laparoscopy (MD, 7.25, 95% CI, 3.13 to 11.36; MD,-2.72, 95% CI,-3.31 to − 2.13). Conclusions Not only laparoscopy is safe and feasible, but also it is related with decreased rates of mortality, post-operative morbidity and shorter hospitalization. Electronic supplementary material The online version of this article (10.1186/s12893-019-0515-7) contains supplementary material, which is available to authorized users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.