Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

Preoperative comprehensive malignancy risk estimation for thyroid nodules: Development and verification of a network-based prediction model

BackgroundIn order to avoid excessive treatment of thyroid nodules in the clinic, it is necessary to find a simple and practical analysis method to comprehensively and accurately reflect benign or malignant thyroid nodules. This study aimed to construct and validate a comprehensive and reliable network-based predictive model using a variety of imaging and laboratory criteria for thyroid nodules to stratify the risk of malignancy prior to surgery.MethodsWe retrospectively analyzed data from patients who underwent surgical treatment for thyroid nodules at the Thyroid and Breast Diagnosis and Treatment Center of Weifang Hospital of Traditional Chinese Medicine between January 2018 and December 2020. Binary logical regression analysis was performed to predict whether nodules were malignant or benign. The developmental dataset included 457 patients (January 2018–December 2020). The validation set included separate data points (n = 225, January 2018–December 2020).ResultsIn this study, criteria that showed significant predictive value for malignant nodules included TI-RADS: 4b (p = 0.065); Bethesda IV, Bethesda V, Bethesda VI (P < 0.0001); BRAF^V600E mutation (P < 0.0001); Calcitonin>5 pg/ml (p = 0.0037); and FNA-Tg>30 ng/ml (p = 0.0003). A 10-grade risk scoring system was developed. The risk of malignancy risk ranged from 2.06% to 100% and was positively associated with increasing risk grade. The areas under the receiver-operating characteristic curve of the development and validation sets were 0.972 and 0.946, respectively.ConclusionA simple, comprehensive and reliable web-based predictive model was designed using a variety of imaging and laboratory criteria to stratify thyroid nodules by probability of malignancy.     

Delineating the geographic context of physical activities: A systematic search and scoping review of the methodological approaches used in social ecological research over two decades

BackgroundThe social ecological approach suggests that the spatial context among other factors influence physical activity behavior. Ample research documents physical environmental effects on physical activity. Yet, to date inconsistent associations remain, which might be explained by conceptual and methodological challenges in measuring the spatial dimensions of health behavior. We review methods applied to measure the spatial contexts in the social ecological physical activity literature.MethodsOnline databases and selected reviews were used to identify papers published between 1990 and 2020. A total of 2167 records were retrieved, from which 412 studies that used physical activity as a primary outcome variable, included measures of the physical environment and applied the main principles of the social ecological approach, were included.ResultsSubjective approaches were the dominant method to capture the spatial context of physical activities. These approaches were applied in 67% (n=279) of the studies. From the objective approaches an administrative unit was most prevalent and was applied in 29% (n=118) of the studies. The most comprehensive objective spatial methods that capture the true environmental exposure, were used only in 2% (n=10) of the studies.ConclusionsCurrent social ecological physical activity research applies simple conceptualizations and methods of the spatial context. While conceptual and methodological concerns have been repeatedly expressed, no substantive progress has been made in the use of spatial approaches. To further our understanding on place effects on health, future studies should carefully consider the choice of spatial approaches, and their effect on study results.     

基于LDA模型的中医药人工智能领域主题演化分析＊

目的 梳理总结中医药人工智能领域近40年的研究情况，分析该领域研究主题演化过程，发现热点主题和研究前沿。方法 本研究检索了CNKI和WOS中中医药人工智能领域的文献，根据生命周期理论划分不同时间片，将文献摘要作为研究对象，采用LDA主题模型挖掘文本主题，分析主题的演化过程，识别热点主题。结果 纳入中英文文献2750篇，根据生命周期理论划分中医药人工智能领域发展阶段，分别建立全局和各阶段的LDA模型，挖掘出全局热点主题有中医专家系统、中医辅助诊断系统、中医药数据挖掘等，发现主题演化路径并进行可视化展示。结论 中医药与人工智能主要的结合点在中医智能系统、中医药知识组织和中医智能诊疗机器人这3方面，这3个方向的研究均离不开神经网络技术的支持，同时也结合了关联规则发现、复杂网络研究等方法。     

蒙药安神补心六味丸预防给药对心肌缺血大鼠的保护作用及氧化应激水平的影响＊

目的 研究蒙药安神补心六味丸对大鼠心肌缺血损伤的保护作用和氧化应激机制。方法 将60只大鼠随机分为空白组、模型组、阳性对照组（丹参滴丸）及蒙药安神补心六味丸低、中、高剂量组，使用腹腔注射异丙肾上腺素方法制备大鼠急性心肌缺血模型，记录并观察大鼠心电图活动变化，然后进行腹主动脉取血，分离血清，测定乳酸脱氢酶（Lactic dehydrogenase，LDH）、肌酸激酶（Creatine kinase，CK）、磷酸肌酸激酶（Creatine kinase isoenzyme，CK-MB）、谷草转氨酶（Aspartate aminotransferase，AST）、超氧化物歧化酶（Superoxide dismutase，SOD）、丙二醛（Malondialdehyde，MDA）和谷胱甘肽过氧化物酶（Glutathione peroxidase，GSH-PX）含量变化。取心肌组织固定，对大鼠心肌组织进行HE染色，通过对心肌酶学和和心肌组织形态学两个方面来考察蒙药安神补心六味丸对大鼠心肌缺血模型的保护作用。结果 与空白组比较，模型组大鼠血清中LDH活性极显著升高（P<0.01），CK、CK-MB、AST、MDA活性均显著升高（P<0.05），SOD活性显著降低（P<0.05），GSH-PX活性高度显著降低（P<0.001），且差异均具有统计学意义；与模型组比较，蒙药安神补心六味丸组高剂量组大鼠血清中LDH、CK、CK-MB、MDA活性显著降低（P<0.05），SOD活性显著升高（P<0.05），GSH-PX活性极显著升高（P<0.01），且差异均具有统计学意义。从HE染色分析，蒙药安神补心六味丸能够改善因缺血而造成的心肌组织损伤。结论 蒙药安神补心六味丸能有效改善心肌组织病理状态，减轻大鼠心肌缺血损伤，以达到保护心肌作用，机制可能与氧化应激机制相关。     

The use of ultrasound,fibronectin and other parameters to predict the success of labour induction

Induction of labour is a common obstetrical procedure and is undertaken when the benefits of delivery are considered to outweigh the risks of continuation of pregnancy. However, more than one-fifth of induction cases fail to result in vaginal births and lead to unplanned caesarean deliveries, which compromise the birth experience and have negative clinical and resource implications. The need for accurate prediction of successful labour induction is increasingly recognised and many researchers have attempted to evaluate the potential predictability of different factors including maternal characteristics, Bishop score, various biochemical markers and ultrasound markers and derive predictive models to address this issue.     

1401例超长住院日患者分布与影响因素分析

目的分析超长住院患者分布及影响因素,探讨减少超长住院日的措施。方法从江苏省某三甲医院病案管理系统中调取2020年1月1日-2020年12月31日134016例出院患者的住院病案首页资料,对住院时间≥30天的1401例超长住院患者的分布特征进行统计描述,采用Logistic回归模型分析超长住院日的影响因素。结果2020年全院平均住院日为7.13天,其中超长住院患者平均住院日为41.85天。超长住院患者以60岁以上年龄组最多(39.61%);出院科室主要分布在血液科(42.18%)、普通外科(11.85%)、骨科(7.49%)等;疾病类别主要为肿瘤(47.32%)、影响健康状态和与保健机构接触的因素(10.56%)、循环系统疾病(7.07%)等;多因素Logistic回归结果显示,男性(OR=1.188)、离院方式为非医嘱离院或其他(OR=2.046)和死亡病例(OR=3.362)是超长住院的危险因素。结论控制超长住院日对平均住院日影响显著,医院应加强重点人群、重点科室和重点病种管理提高诊疗管理水平,缩短平均住院日。