Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

1. Download : Download high-res image (173KB)
2. Download : Download full-size image

     

新型冠状病毒核酸检测在眼科医院疫情防控中的实践与有效性研究

目的:探索以系统化、标准化的新型冠状病毒核酸检测为核心和重要组成部分,建立适合于眼科医院新冠疫情精准防控切实可行的防控体系。方法:系列病例研究。自2020年2月24日至3月2日期间,以标准化病毒核酸检测联合血常规、C反应蛋白(CRP)、血清淀粉样蛋白A(SAA)等检测作为所有眼科手术患者术前筛查常规检测项目,同时也为返岗员工提供病毒核酸检测。设计调查问卷了解受检者对鼻咽拭子采样的接受度以及核酸检测结果对其心理状态的影响。结果:99例患者血液学检测结果有一定的异常比例,其中SAA检测结果增高13例,淋巴细胞计数减低12例、增高5例,白细胞计数增高11例、减低1例,CRP增高2例,新型冠状病毒核酸检测均阴性。33例本院职工新型冠状病毒核酸检测均阴性。12%的受访者对鼻咽拭子采样接受度评分较低。受检者在核酸检测前后思想压力评分较高的比例分别为46.7%和6.7%。结论:该项举措一方面为术前新冠病毒肺炎筛查及鉴别诊断提供了客观依据,很大程度上排除了隐性无症状感染者传播的可能,有利于减少二次传播的风险,降低院内交叉感染的概率;另一方面,核酸检测阴性结果有利于调整患者术前的心理状态并缓解医护人员在疫情防控期间的心理压力。     

Fractal dimension in CT low attenuation areas is predictive of long-term oxygen therapy initiation in COPD patients: Results from two observational cohort studies

BackgroundSome chronic obstructive pulmonary disease (COPD) patients develop hypoxemia with disease progression, with some even requiring long-term oxygen therapy (LTOT). Lung function, especially diffusing capacity, and the annual decline in PaO₂, are reported to be predictive factors of chronic respiratory failure. However, the association between lung morphometry evaluated using computed tomography (CT) images and LTOT initiation is unknown.MethodsWe retrospectively evaluated the relationship between clinical indices, including pulmonary function, body mass index (BMI), and CT parameters, at baseline and LTOT initiation in two prospective COPD cohorts. In the Nara Medical University cohort (n = 76), the low attenuation area (LAA) and its fractal dimension (fractal D) were adapted as the indices for parenchymal destruction in CT images. The association between these CT measurements and LTOT initiation was replicated in the Kyoto University cohort (n = 130).ResultsIn the Nara Medical University cohort, lower BMI (hazard ratio [HR]:0.70, p = 0.006), lower % diffusing capacity (%DLCO) (HR: 0.92, p = 0.006), lower %DLCO/VA (HR, 0.90, p = 0.008), higher RV/TLC (HR, 1.26, p = 0.012), higher LAA% (HR: 1.18, p = 0.001), and lower fractal D (HR: 3.27 × 10^?8, p < 0.001) were associated with LTOT initiation. Multivariate analysis in the Kyoto University cohort confirmed that lower %DLCO and lower fractal D were independently associated with LTOT initiation, whereas LAA% was not.ConclusionFractal D, which is the index for morphometric complexity of LAA in CT analysis, is predictive of LTOT initiation in COPD patients.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

The oncogenic impact of RNF2 on cell proliferation,invasion and migration through EMT on mammary carcinoma

Mammary carcinoma (MC) is one of most common malignancy in women, and ring finger protein 2 (RNF2) possesses various roles in vast human tumors. In MC tissues as well as in cell lines RNF2 exhibited high expression, had significant association with tumor size, lymph node status, TNM stage, patients’ poor survival, and promoted cell proliferation, colony formation, cell migration and invasion of MC cell lines which was mediated by downregulation of E-cadherin protein. These data reveal that RNF2 protein plays a vital role in the development of MC and may be a potential therapy target of MC.     

Aminotransferases During Treatment Predict Long-Term Survival in Patients With Autoimmune Hepatitis Type 1: A Landmark Analysis

1. Download : Download high-res image (187KB)
2. Download : Download full-size image