新型冠状病毒肺炎疫情期间外科患者围术期防控策略

目前新型冠状病毒肺炎疫情防控形势严峻,各级医疗机构在做好新型冠状病毒肺炎防控的同时,还需提供优质安全的常规医疗服务。笔者旨在通过严格患者分类管理以及围术期标准预防,为需行外科治疗的患者,尤其是需行急诊外科治疗的患者提供优质安全的医疗服务,减少院内感染,为新型冠状病毒肺炎防控贡献一份力量。     

Extended Synchronous Variational Integrators for Wave Propagations on Non-Uniform Meshes

This paper is concerned with the classical problem of wave propagation in discrete models of nonuniform resolution. We extend the traditional asynchronous variational integrators (AVIs) method to higher order and couple different spatial elements to adapt to nonuniform meshes. We show that the extension of AVIs method is stable, convergent and may reduce the spurious inter-grid reflection across meshes with different sizes. Numerical experiments are provided to verify the stability and convergence of the extended AVIs. The total energy is numerically conserved in our experiments.     

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study

BackgroundThe current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.MethodsTaking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR−), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.ResultsThe accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87–0.91, 0.89–0.92, 0.87–0.91, and 0.86–0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).ConclusionsThe CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registrationChictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.     

Factors associated with the publication outcomes of paediatric proceedings presented at 2010‐2016 the International Associations for Dental Research annual meetings

The International Associations for Dental Research (IADR) annual meeting is one of the most important dental meetings throughout the world, and researches about paediatric dentistry presented in this platform are often used to guide clinical work. To evaluate the publication outcomes of oral and poster paediatric proceedings, which were accepted by the International Associations for Dental Research (IADR), annual meetings from 2010 to 2016 and to analyse the possible factors influencing an abstract's progression to publication. Oral and poster abstracts were retrieved from the official website of IADR (2010‐2016). Searching for subsequent publications was conducted in PubMed and Google Scholar (up to March 2019). Two authors independently selected studies, collected, and analysed data. A total of 1396 abstracts were identified, including 275 oral presentations and 1121 poster presentations. Finally, 606 were published in peer‐reviewed journals, with a publication rate of 43.41%. Abstracts were published earlier if it is from Europe, well funded, presented orally, or with large sample size. The high publication rate of the IADR proceedings supported the impact of IADR annual meetings on paediatric dentistry in the last 10 years.