联合检测血清NGAL、KIM-1和SCr可有效提高慢性肾病分期诊断

为了探讨中性粒细胞明胶酶相关脂质运载蛋白(neutrophil gelatinase-associated lipocalin, NGAL)和肾损伤分子-1 (kidney injury molecule-1, KIM-1)以及血肌酐(serum creatinine, SCr)联合检测对慢性肾病(chronic kidney disease, CKD)的早期诊断价值,本研究收集260例肾病患者和85例健康体检者,检测其血清NGAL、KIM-1和SCr水平。依据肾功能分级标准,CKD患者分为CKD 1期(53例),CKD 2期(68例),CKD 3期(71例),CKD 4期(46例)和CKD 5期(22例),并分析以上指标在各组间的含量差异,及其联合测定对CKD早期的敏感性。与健康对照组相比较,CKD 1期、CKD 2期、3期、4期和5期患者的NGAL、KIM-1水平均明显升高(p<0.001)。血清SCr含量在CKD 3期、4期和5期组较健康对照组显著增加(p<0.001)。以上3项指标均随着CKD严重程度增加而升高。各组指标阳性率分析显示,3项联合检测阳性率高于单项检测阳性率。ROC曲线分析NGAL、KIM-1、SCr对CKD诊断的AUC值F分别是为0.824、0.805、0.856。相关性分析结果显示,GFR和NGAL、KIM-1、SCr相关系数分别是r=-0.784、-0.756、-0.728 (p<0.05)。NGAL与KIM-1、SCr的相关系数分别是r=0.932、0.764 (p<0.05);KIM-1与SCr的相关系数r分别是0.791 (p<0.05)。本研究初步得出结论:血清NGAL、Kim-1可作为CKD早期诊断的重要指标,联合检测血清NGAL、Kim-1、SCr可有效提高CKD早期肾损伤诊断的敏感度,对CKD的分期诊断和治疗具有极其重要的临床价值。     

Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis,Blood Analysis and Disease History

Background

Chronic kidney disease (CKD) is a disorder associated with breakdown of kidney structure and function. CKD can be diagnosed in its early stage only by experienced nephrologists and urologists (medical experts) using the disease history, symptoms and laboratory tests. There are few studies related to the automatic diagnosis of CKD in the literature. However, these methods are not adequate to help the medical experts.

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

ObjectivesAlzheimer's Disease (AD) is the most general type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosis but existing has some limitations in terms of accuracy. So, main objective of this paper to include a preprocessing method before CNN model to increase the accuracy of classification.Materials and methodIn this paper, we present a deep learning-based approach for detection of Alzheimer's Disease from ADNI database of Alzheimer's disease patients, the dataset contains fMRI and PET images of Alzheimer's patients along with normal person's image. We have applied 3D to 2D conversion and resizing of images before applying VGG-16 architecture of Convolution neural network for feature extraction. Finally, for classification SVM, Linear Discriminate, K means clustering, and Decision tree classifiers are used.ResultsThe experimental result shows that the average accuracy of 99.95% is achieved for the classification of the fMRI dataset, while the average accuracy of 73.46% is achieved with the PET dataset. On comparing results on the basis of accuracy, specificity, sensitivity and on some other parameters we found that these results are better than existing methods.Conclusionsthis paper, suggested a unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction. We applied this method on ADNI database and on comparing the accuracies with other similar approaches it shows better results.     

马拉河流域植被生态需水特征及估算

生态需水是生态用水控制和区域生态环境恢复建设的基本依据。马拉河流域拥有世界著名的生态系统,植被生态需水占流域总需水量的很大一部分。基于1980—2020年ERA5气象数据、叶面积指数(LAI)与世界土壤数据库数据,采用Penman-Monteith法计算了马拉河流域四个季节(短旱季、长雨季、长旱季、短雨季)植被生态需水量的时空变化特征。在此基础上,使用支持向量机(SVM)、随机森林(RF)和卷积神经网络(CNN)3种机器学习方法与7个环境因子(气温、降水、10 m风速、LAI、太阳辐射、相对湿度、地形)建立了回归模型,分别估算了2011—2020年逐年不同季节的植被生态需水量,并与Penman-Monteith法计算结果进行时间序列拟合度和空间相似性的比较。结果表明：马拉河流域植被生态需水量在过去40年所有季节都呈现为波动变化,植被生态需水量长雨季>长旱季>短雨季>短旱季,长雨季的植被生态需水量约为短旱季的1.5倍。不同季节均呈现出上下游高、中游低的植被生态需水量空间分布格局。LAI为最大的正影响因子,风速为最大的负影响因子。就不同方法估算的植被生态需水量准确性而言,...     

Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine

Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen’s self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.     

Analysis and classification of speech imagery EEG for BCI

Electroencephalogram (EEG) is generally used in brain–computer interface (BCI), including motor imagery, mental task, steady-state evoked potentials (SSEPs) and P300. In order to complement existing motor-based control paradigms, this paper proposed a novel imagery mode: speech imagery. Chinese characters are monosyllabic and one Chinese character can express one meaning. Thus, eight Chinese subjects were required to read two Chinese characters in mind in this experiment. There were different shapes, pronunciations and meanings between two Chinese characters. Feature vectors of EEG signals were extracted by common spatial patterns (CSP), and then these vectors were classified by support vector machine (SVM). The accuracy between two characters was not superior. However, it was still effective to distinguish whether subjects were reading one character in mind, and the accuracies were between 73.65% and 95.76%. The results were better than vowel speech imagery, and they were suitable for asynchronous BCI. BCI systems will be also extended from motor imagery to combine motor imagery and speech imagery in the future.     

Di-codon usage for classification of genes

Genes are often classified into biologically related groups so that inferences on their functions can be made. This paper demonstrates that the di-codon usage is a useful feature for gene classification and gives better classification accuracy than the codon usage. Our experiments with different classifiers show that support vector machines performs better than other classifiers in classifying genes by using di-codon usage as features. The method is illustrated on 1841 HLA sequences which are classified into two major classes, HLA-I and HLA-II, and further classified into the subclasses of major classes. By using both codon and di-codon features, we show near perfect accuracies in the classification of HLA molecules into major classes and their sub-classes.     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Using a BP Neural Network for Rapid Assessment of Populations with Difficulties Accessing Drinking Water Because of Drought

Accurately predicting the populations with difficulties accessing drinking water because of drought and taking appropriate mitigation measures can minimize economic loss and personal injury. Taking the 2013 Guizhou extreme summer drought as an example, on the basis of collecting meteorological, basic geographic information, socioeconomic data, and disaster effect data of the study area, a rapid assessment model based on a backpropagation (BP) neural network was constructed. Six factors were chosen for the input of the network: the average monthly precipitation, Digital Elevation Model (DEM), river density, population density, road density, and gross domestic product (GDP). The population affected by drought was the model's output. Using samples from 50 drought-affected counties in Guizhou Province for network training, the model's parameters were optimized. Using the trained model, the populations in need were predicted using the other 74 drought-affected counties. The accuracy of the prediction model, represented by the coefficient of determination (R²) and the normalized root mean square error (N-RMSE), yielded 0.7736 for R² and 0.0070 for N-RMSE. The method may provide an effective reference for rapid assessment of the population in need and disaster effect verification.     

A Colorimetric Method for the Determination of Acetic Acid in Wood Hydrolyzate Containing a Large Amount of Sulfuric Acid

A method for the determination of acetic acid in presence of a large amount of sulfuric acid has been developed. The method consists of the following procedures. The sample is neutralyzed by barium carbonate. Barium sulfate and excess of barium carbonate are filtered off. On addition of sulfuric acid, acetic acid is extracted with n-butanol from the filtrate. By the reaction of acetic and sulfuric acids in butanol layer with aniline and furfural, a red color is produced. The color produced by sulfuric acid is bleached by treating with barium carbonate powder and the absorbancy of the color produced by acetic acid is measured in a photometer. Acetic acid determination by this method is disturbed by some other acids which give soluble barium salts but the acids which give insoluble barium salts do not disturb.     

Use of a Continuous Glucose Monitoring System in High-Risk Hospitalized Noncritically Ill Patients With Diabetes After Cardiac Surgery and During Their Transition of Care From the Intensive Care Unit During COVID-19: A Pilot Study

ObjectiveContinuous glucose monitoring (CGM) has demonstrated benefits in managing inpatient diabetes. We initiated this single-arm pilot feasibility study during the COVID-19 pandemic in 11 patients with diabetes to determine the feasibility and accuracy of real-time CGM in patients who underwent cardiac surgery and whose care was being transitioned from the intensive care unit.MethodsA Clarke error grid analysis was used to compare CGM and point-of-care measurements. The mean absolute relative difference (MARD) of the paired measurements was calculated to assess the accuracy of CGM for glucose measurements during the first 24 hours on CGM, the remaining time on CGM, and for different chronic kidney disease (CKD) strata.ResultsOverall MARD between point-of-care and CGM measurements was 14.80%. MARD for patients without CKD IV and V with an estimated glomerular filtration rate (eGFR) of ≥20 mL/min/1.73 m² was 12.13%. Overall, 97% of the CGM values were within the no-risk zone of the Clarke error grid analysis. For the first 24 hours, a sensitivity analysis of the overall MARD for all patients and those with an eGFR of ≥20 mL/min/1.73 m² was 15.42% ± 14.44% and 12.80% ± 7.85%, respectively. Beyond the first 24 hours, overall MARD for all patients and those with an eGFR of ≥20 mL/min/1.73 m² was 14.54% ± 13.21% and 11.86% ± 7.64%, respectively.ConclusionCGM has shown great promise in optimizing inpatient diabetes management in the noncritical care setting and after the transition of care from the intensive care unit with high clinical reliability and accuracy. More studies are needed to further assess CGM in patients with advanced CKD.