Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine.

Background: The Shexiang Baoxin Pill (MUSKARDIA) has been used for treating coronary artery disease (CAD) and angina for more than 30 years in China. Nevertheless, methodologically sound trials on the use of MUSKARDIA in CAD patients are scarce. The aim of the study is to determine the effects of MUSKARDIA as an add-on to optimal medical therapy (OMT) in patients with stable CAD. Methods: A total of 2674 participants with stable CAD from 97 hospitals in China were randomized 1:1 to a MUSKARDIA or placebo group for 24 months. Both groups received OMT according to local tertiary hospital protocols. The primary outcome was the occurrence of a major adverse cardiovascular event (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction (MI), or non-fatal stroke. Secondary outcomes included all-cause mortality, non-fatal MI, non-fatal stroke, hospitalization for unstable angina or heart failure, peripheral revascularization, angina stability and angina frequency. Results: In all, 99.7% of the patients were treated with aspirin and 93.0% with statin. After 2 years of treatment, the occurrence of MACEs was reduced by 26.9% in the MUSKARDIA group (MUSKARDIA: 1.9% vs. placebo: 2.6%; odds ratio = 0.80; 95% confidence interval: 0.45–1.07; P  = 0.2869). Angina frequency was significantly reduced in the MUSKARDIA group at 18 months (P = 0.0362). Other secondary endpoints were similar between the two groups. The rates of adverse events were also similar between the two groups (MUSKARDIA: 17.7% vs. placebo: 17.4%, P = 0.8785). Conclusions: As an add-on to OMT, MUSKARDIA is safe and significantly reduces angina frequency in patients with stable CAD. Moreover, the use of MUSKARDIA is associated with a trend toward reduced MACEs in patients with stable CAD. The results suggest that MUSKARDIA can be used to manage patients with CAD. Trial registration chictr.org.cn, No. ChiCTR-TRC-12003513

Our study expands the current knowledge on the genotype-correlated phenotypes with a defect of IL10 signaling. B-cell lymphoma was more frequent than would be expected in patients with IL10RB mutations. There may be a unique genetic architecture among Eastern Asia compared with other populations. Although allogeneic HSCT represents a causal therapeutic approach for IL10-and IL10R-deficient patients, a word of caution is warranted.

NAC attenuates lung damage, pulmonary emphysema and alveolar septal cell apoptosis by partly reversing the decrease in VEGF secretion and VEGFR2 protein expression in smoking-induced COPD in rats.

BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging. MATERIALS AND METHODS:This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (n ¼ 111), ependymoma (n ¼ 70), and pilocytic astrocytoma (n ¼ 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method x 2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P , .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review. ABBREVIATIONS: AUC ¼ area under the curve; AutoML ¼ automatic machine learning; CHSQ ¼ x 2 score; EP ¼ ependymoma; MB ¼ medulloblastoma; ML ¼ machine learning; PA ¼ pilocytic astrocytoma; TPOT ¼ Tree-Based Pipeline Optimization Tool A mong childhood malignancies, pediatric brain tumors are the second most common and the leading cause of death from solid tumors. 1,2 Posterior fossa tumors make up a disproportionate portion of brain tumors in the pediatric population, accounting for 54%-70% of tumors compared with ,20% in the adult population. 3 The most common subtypes of posterior fossa tumors among children are medulloblastoma (MB), pilocytic astrocytoma (PA), and ependymoma (EP). 4,5 Discrimination of these 3 malignancies is important due to the differing natural