Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19 / 生物医学工程学杂志

The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently.

Training-Based Gradient LBP Feature Models for Multiresolution Texture Classification.

Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns. The feature mapping models of gLBP descriptors are constructed by the maximal relative-variation rate (mr2) of rotation-invariant patterns, and then prestored as mapping lookup files. By mapping, initial patterns can be transformed into low-dimensional ones. And then it generates multiresolution texture features via the joint and concatenation of gLBP descriptors on different sampling parameters. A trained nearest neighbor classifier with chi-square distance is applied to classify textures by feature histograms. The experimental results of simulation on five public texture databases show that the proposed method is reliable and efficient in texture classification. In comparison with nine other similar approaches, including two state-of-the-art ones, the proposed method runs faster than most of them and also outperforms all of them in terms of classification accuracy and noise robustness. It achieves higher accuracy and has also better robustness to the Salt&Pepper and Gaussian noise added artificially into texture images.

Development and clinical trial of fluorescence real time PCR to detect rubella virus / 生物医学工程学杂志

To establish a novel rapid, convenient, sensitive and specific method applicable to quantitative analysis of the rubella virus extensively, RV total RNA was extracted with Trizol. The envelope glycoprotein E1 gene was amplified from rubella virus by PCR, and the PCR products were cloned into the pMD18-T cloning vector and transfected into DH5alpha. After Amp selection and analysis of restriction enzyme, the clones carrying the E1 gene were identified. After quantitation and serial dilution, the quantitative analysis of E1 gene was made by real-time PCR with the use of FAM as indicator. Standard curve of the real-time PCR was plotted with starting cDNA concentration versus threshold cycle. Then the new method was used to measure 50 cases with suspectable RV infection. The results were compared with those obtained by ELISA assay. TaqMan(r)MGB real-time PCR could help evaluate the level of virus reliably. The correlation coefficient of the standard curve is 0.998, and the linear range of the system is from 10(3) copies/microl to 10(9) copies/microl in clinical samples. The CV value is 0.94% in batch assay and 3.36% in day to day assay. The new method is more sensitive and specific than ELISA assay. For its simplicity, sensitivity, specificity and digitized results, the real-time PCR for quantification of RV cDNA in clinical samples is available.