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

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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%.     

基因间长链非编码RNA152靶向微小RNA?103a?3p对非小细胞肺癌细胞增殖和侵袭迁移的影响

目的探讨基因间长链非编码RNA 152(LINC00152)靶向调控微小RNA-103a-3p(miR-103a-3p)表达及对非小细胞肺癌(NSCLC)细胞增殖和侵袭迁移的影响。方法采用实时定量PCR(QPCR)检测正常肺上皮细胞BEAS-2B及NSCLC细胞(ANIP-973、NCI-H157、A549和NCI-H1975)的LINC00152水平。选取LINC00152水平最高的细胞分别转染LINC00152特异性小干扰RNA(si-LINC00152组)或无关序列(si-NC组),另设未转染细胞为对照组。QPCR检测LINC00152水平,活细胞计数CCK-8法、Transwell小室和划痕实验测定细胞增殖、侵袭和迁移能力,Western blotting检测基质金属蛋白酶(MMP)-2、MMP-9和第10号染色体缺失的磷酸酶及张力蛋白同源基因(PTEN)的水平;荧光素酶报告实验验证LINC00152靶向结合miR-103a-3p的能力。结果NSCLC细胞的LINC00152水平均高于BEAS-2B细胞(P<0.05),尤其是NCI-H1975细胞的最高。si-LINC00152组的LINC00152水平为0.352±0.087,低于对照组的1.058±0.219和si-NC组的1.126±0.139(P<0.05)。与si-NC组和对照组相比,si-LINC00152组NCI-H1975细胞转染48、72 h的增殖活力下降(P<0.05);si-LINC00152组的划痕愈合率和穿膜细胞数分别为(27.386±2.428)%和(78.840±5.031)个,低于si-NC组的(77.675±4.803)%和(179.208±13.264)个及对照组的(76.371±5.385)%和(174.003±15.678)个(P<0.05);与si-NC组和对照组相比,si-LINC00152组的MMP-2和MMP-9水平均降低,而PTEN水平升高(P<0.05)。对照组和si-NC组上述指标的差异无统计学意义(P>0.05)。双荧光素酶报告分析证实,miR-103a-3p模拟物降低了野生型LINC00152的荧光素酶活性(P<0.05),但对突变型无影响(P>0.05)。结论LINC00152在NSCLC细胞中高表达并发挥促癌作用,与NSCLC的迁移侵袭密切相关,LINC00152与miR-103a-3p间的相互作用在NSCLC靶向治疗中有一定潜能。     

Epigenetics explained: a topic “primer” for the epilepsy community by the ILAE Genetics/Epigenetics Task Force

Epigenetics refers broadly to processes that influence medium to long‐term gene expression by changing the readability and accessibility of the genetic code. The Neurobiology Commission of the International League Against Epilepsy (ILAE) recently convened a Task Force to explore and disseminate advances in epigenetics to better understand their role and intersection with genetics and the neurobiology of epilepsies and their co‐morbidities, and to accelerate translation of these findings into the development of better therapies. Here, we provide a topic primer on epigenetics, explaining the key processes and findings to date in experimental and human epilepsy. We review the growing list of genes with epigenetic functions that have been linked with epilepsy in humans. We consider potential practical applications, including using epigenetic signals as biomarkers for tissue‐ and biofluid‐based diagnostics and the prospects for developing epigenetic‐based treatments for epilepsy. We include a glossary of terms, FAQs and other supports to facilitate a broad understanding of the topic for the non‐expert. Last, we review the limitations, research gaps and the next challenges. In summary, epigenetic processes represent important mechanisms controlling the activity of genes, providing opportunities for insight into disease mechanisms, biomarkers and novel therapies for epilepsy.