高血压性心脏病生物标志物的筛选：基于中国重庆市7家医疗机构的数据分析研究*

目的: 筛选高血压性心脏病（HHD）的影响因素,建立HHD的预测模型,为HHD的发生提供预警。方法: 选取中国重庆市某医科院校数据研究院平台2016年1月1日至2019年12月31日主要诊断为高血压性心脏病和高血压患者。通过单因素分析、多因素分析筛选HHD的影响因素,采用R语言分别构建Logistics模型、随机森林（RF）模型和极限梯度上升（XGBoost）模型。结果: 单因素分析筛选出60项差异指标,多因素分析筛选出18项差异指标（P<0.05）。Logistics模型、RF模型、XGBoost模型曲线下面积（AUC）分别为0.979、0.983和0.990。结论: 本文建立的3种HHD预测模型结果稳定,其中XGBoost模型对于HHD的发生具有良好的诊断效应。     

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.     

基于深度学习与多层次信息融合的药物靶标亲和力预测*

药物研发是非常重要但也十分耗费人力物力的过程。利用计算机辅助预测药物与蛋白质亲和力的方法可以极大地加快药物研发过程。药物靶标亲和力预测的关键在于对药物和蛋白质进行准确详细地信息表征。提出一种基于深度学习与多层次信息融合的药物靶标亲和力的预测模型,试图通过综合药物与蛋白质的多层次信息,来获得更好的预测表现。首先将药物表述成分子图和扩展连接指纹两种形式,分别利用图卷积神经网络模块和全连接层进行学习;其次将蛋白质序列和蛋白质K-mer特征分别输入卷积神经网络模块和全连接层来学习蛋白质潜在特征;随后将4个通道学习到的特征进行融合,再利用全连接层进行预测。在两个基准药物靶标亲和力数据集上验证了所提方法的有效性,并与其他已有模型作对比研究。结果说明提出的模型相比基准模型能得到更好的预测性能,表明提出的综合药物与蛋白质多层次信息的药物靶标亲和力预测策略是有效的。     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.     

Comparison between touchscreen operant chambers and water maze to detect early prefrontal dysfunction in mice

The relative lack of sensitive and clinically valid tests of rodent behavior might be one of the reasons for the limited success of the clinical translation of preclinical Alzheimer's disease (AD) research findings. There is a general interest in innovative behavioral methodology, and protocols have been proposed for touchscreen operant chambers that might be superior to existing cognitive assessment methods. We assessed and analyzed touchscreen performance in several novel ways to examine the possible occurrence of early signs of prefrontal (PFC) functional decline in the APP/PS1 mouse model of AD. Touchscreen learning performance was compared between APP/PS1-21 mice and wildtype littermates on a C57BL/6J background at 3, 6 and 12 months of age in parallel to the assessment of spatial learning, memory and cognitive flexibility in the Morris water maze (MWM). We found that older mice generally needed more training sessions to complete the touchscreen protocol than younger ones. Older mice also displayed defects in MWM working memory performance, but touchscreen protocols detected functional changes beginning at 3 months of age. Histological changes in PFC of APP/PS1 mice indeed occurred as early as 3 months. Our results suggest that touchscreen operant protocols are more sensitive to PFC dysfunction, which is of relevance to the use of these tasks and devices in preclinical AD research and experimental pharmacology.     

Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PurposeTo predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).MethodsA data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.ResultsSuccessful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.ConclusionsMachine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.     

A novel fast kilovoltage switching dual-energy CT with deep learning: Accuracy of CT number on virtual monochromatic imaging and iodine quantification

PurposeA novel fast kilovoltage switching dual-energy CT with deep learning [Deep learning based-spectral CT (DL-Spectral CT)], which generates a complete sinogram for each kilovolt using deep learning views that complement the measured views at each energy, was commercialized in 2020. The purpose of this study was to evaluate the accuracy of CT numbers in virtual monochromatic images (VMIs) and iodine quantifications at various radiation doses using DL-Spectral CT.Materials and methodsTwo multi-energy phantoms (large and small) using several rods representing different materials (iodine, calcium, blood, and adipose) were scanned by DL-Spectral CT at varying radiation doses. Images were reconstructed using three reconstruction parameters (body, lung, bone). The absolute percentage errors (APEs) for CT numbers on VMIs at 50, 70, and 100 keV and iodine quantification were compared among different radiation dose protocols.ResultsThe APEs of the CT numbers on VMIs were <15% in both the large and small phantoms, except at the minimum dose in the large phantom. There were no significant differences among radiation dose protocols in computed tomography dose index volumes of 12.3 mGy or larger. The accuracy of iodine quantification provided by the body parameter was significantly better than those obtained with the lung and bone parameters. Increasing the radiation dose did not always improve the accuracy of iodine quantification, regardless of the reconstruction parameter and phantom size.ConclusionThe accuracy of iodine quantification and CT numbers on VMIs in DL-Spectral CT was not affected by the radiation dose, except for an extremely low radiation dose for body size.