Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting‐state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case–control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case–control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.     

Improving burn surgery education for medical students in China

IntroductionIn China, although burn treatment develops rapidly, and ranks in the forefront of the world, there is a relative shortage of burn specialists, which limits the development of burn education. In traditional curriculum of surgery education, burn surgery education accounts for few proportions, which results in the indifference to the burn surgery among medical students. To date, few research reported the application of Clinical pathway- Problem based Learning (CP-PBL) in burn surgery education. The objective of the study is to explore the teaching effect of this novel teaching method in burn surgery education.MethodsA pilot study was performed. One hundred and six students were randomly divided into a Lecture based Learning (LBL) only group (control group) and a LBL combined Clinical pathway- Problem based Learning group (observation group). A set of test was designed as evaluation criteria based on questions of burn surgery in National Medical Licensing Examination (NMLE) from 2011 to 2018.ResultsThe students with Clinical pathway- Problem based Learning had better academic performances in profession theory. Type A2 and Type A3/A4 scores in the observation group were higher than those in the control group (p < 0.05). The scores of the observation group were higher than those of control group in the domains of understanding and application (p < 0.05). They also have higher favorable impressions of learning experience.ConclusionsMore active approaches yield more learning and are viewed more favorable, which provides a vital message for the evolution of curriculum in Chinese medical schools.     

Effects of mild intrauterine hypoperfusion in the second trimester on memory and learning function in rat offspring

Mild intrauterine hypoperfusion(MIUH) is a serious pathological event that affects the growth and development of fetuses and offspring. MIUH can lead to growth restriction, low birth weight, neurodevelopmental disorders, and other adverse clinical outcomes. To study the effects of MIUH on learning and memory function in offspring, a model of MIUH was established by placing a coil(length 2.5 mm, diameter 0.24 mm) on the uterine artery and ovarian uterine artery of Sprague-Dawley rats in the second trimester of pregnancy(day 17). Next, 120 mg/kg lithium chloride(the MIUH + Li group) or normal saline(the MIUH group) was injected intraperitoneally into these rats. In addition, 120 mg/kg lithium chloride(the Li group) or normal saline(the SHAM group) was injected intraperitoneally into pregnant rats without coil placement. The Morris water maze was used to detect changes in learning and memory ability in the offspring at 4 weeks after birth. In the MIUH group, the escape latency and journey length before reaching the platform were both increased, and the number of times that the platform was crossed and the activity time in the target quadrant within 90 seconds were both decreased compared with the SHAM group. Immunofluorescence double staining and western blot assays demonstrated that hippocampal nestin and Ki67(both cell-proliferation-related proteins) expression was significantly downregulated in the MIUH group compared with the SHAM group. Furthermore, western blot assays were conducted to investigate changes in related signaling pathway proteins in the brains of offspring rats, and revealed that glycogen synthase kinase 3β(GSK3β) expression was upregulated and β-catenin expression was downregulated in the MIUH group compared with the SHAM group. In addition, compared with the MIUH group, the expression levels of p-GSK3β and β-catenin were upregulated in the MIUH + Li group. These results suggest that MIUH may affect learning and memory function in rat offspring by regulating the GSK3β signaling pathway. The experimental procedures were approved by Animal Ethics Committee of Shengjing Hospital of China Medical University(approval No. 2018 PS07 K) in June 2018.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

基于神经网络和支持向量机的中医体质辨识模型研究＊

目的 提出基于舌象和形体特征的中医体质辨识模型，探讨中医以舌辨质客观化、规范化方法。方法 提取客观化的舌象特征（舌质与舌苔的色度和饱和度、舌苔纹理的平均亮度、平滑度以及齿痕特征）和形体特征，建立基于神经网络和支持向量机的客观化舌象和形体特征的辅助中医体质识别模型。结果 对平和质、气虚质、阴虚质和气淤质四种体质进行模型的训练和测试，结果表明融合舌象特征和形体特征的中医体质辨识模型能有效地辅助中医体质识别，且支持向量机对四种体质辨识效果总体上优于神经网络。结论 基于客观化的舌象特征和形体特征辅助中医体质辨识有利于提高中医体质辨识的客观化水平，合理选择机器学习算法可以提高中医体质辨识的准确性。     

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

基于预测剂量引导的宫颈癌自动计划研究

目的:使用机器学习方法建立宫颈癌计划剂量预测回归模型，并将预测剂量引导生成Monaco 计划系统（TPS）可调用 的优化模板文件，实现宫颈癌的自动计划设计。方法:对50例宫颈癌术后调强治疗计划中的危及器官采集基于重叠体积直 方图的几何特征值和基于剂量直方图的剂量目标值，建模后将模型预测剂量结果自动生成Monaco TPS模板文件，进而由TPS 调用优化。使用该方法对另外10例未参与模型训练的测试病例进行自动计划设计，并和人工设计的计划进行对比分析。 结果:自动计划比手动计划的平均设计时间减少了40 min（P<0.05），且平均调优次数降低了3次（P<0.05），剂量学指标和计 划执行效率上两者无明显差异（P>0.05）。结论:基于预测剂量引导的宫颈癌自动计划可以达到临床要求，并且提高了计划 设计效率。     

基于病案的教学查房结合PACS在影像科医师培养中的应用

目的探讨在影像科医师培养中,基于病案(CBL)的教学查房结合医学影像存储与通讯系统(PACS)对医师影像诊断能力培养的价值。方法对2018年在北京大学第三医院放射科轮转CT的41名培训医师,应用PACS对其进行CBL教学查房,对照组为2017年轮转CT的39名培训医师(未进行CBL教学)。从PACS随机抽取每名医师书写的10份报告(报告时间为培训最后1个月),对开展CBL教学前后培训医师的报告书写质量评分并计算优良率。评分标准包括病灶征象描述全面准确、报告内容条理清晰规范、诊断结论逻辑清晰合理、能回答临床提出的问题、提出的下一步诊疗建议合理准确以及无漏诊。同时采用调查问卷的方式评价培训医师对CBL教学查房的主观感受,包括全面系统的认识疾病、提高临床思维能力、读片能力和学习兴趣、PACS对学习的帮助5方面,采用百分比表示。结果1)进行CBL教学查房后,培训医师报告质量评分提高(P<0.01)。2)培训医师对调查问卷5方面满意率分别为90.2%、68.3%、80.5%、75.6%和80.5%。结论应用PACS进行CBL教学查房是培养影像科医师诊断能力的有效方法。     

成果导向的教育理念在医学本科脑血管病教学中的实践

成果导向的教育(outcomes-based education,OBE)以学生的学习成果作为教育目标,教学过程围绕学习成果进行设计。本文主要探讨了OBE理念在医学本科脑血管病教学中的应用。以OBE理念为指导,将基于团队的学习、基于问题的学习、基于案例的学习等多种教学方法有机结合,由学生主动完成学习任务,教师起到引导和辅助的作用。教师根据学生的学习成果对教学设计进行持续改进,以保障其始终与毕业要求相符合。