Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition

Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1H NMRS) can provide important information on tumor biology and metabolism.These metabolic fingerprints can then be used for tumor classification and grading,with great potential value for tumor diagnosis.We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies,including two astrocytomas (grade I),12 astrocytomas (grade II),eight anaplastic astrocytomas (grade III),three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS 1H NMRS.The results were correlated with pathological features using multivariate data analysis,including principal component analysis (PCA).There were significant differences in the levels of N-acetyl-aspartate (NAA),creatine,myo-inositol,glycine and lactate between tumors of different grades (P<0.05).There were also significant differences in the ratios of NAA/creatine,lactate/creatine,myo-inositol/creatine,glycine/creatine,scyllo-inositol/creatine and alanine/creatine (P<0.05).A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%.HRMAS 1H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt

To solve inefficient water stress classification of spinach seedlings under complex background,this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt(NCAM+MobileNetXt)network.Firstly,this study recon-structed the Sandglass Block to effectively increase the model accuracy;secondly,this study introduced the group convolution module and a two-dimensional adaptive average pool,which can significantly compress the model parameters and enhance the model robustness separately;finally,this study innovatively proposed the Normalization-based Channel Attention Module(NCAM)to enhance the image features obviously.The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%,and the number of parameters was decreased by 66%compared with the original MobileNetXt model.The N-MobileNetXt model was superior to other net-work models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification.It can provide a theoretical basis and technical support for automatic irrigation.

Regulating and tonifying spleen and kidney traditional Chinese medicine in the treatment of chronic uric acid nephropathy: A systematic review

Objective:To systematically evaluate the efficacy and safety of traditional Chinese medicine for regulating spleen and kidney.Methods:We developed a search strategy and then retrieved the database including CNKI,Wanfang data knowledge service platform,VIP journals resource integration service platform,PubMed,Embasefor randomized controlled trial of regulating spleen and kidney traditional Chinese medicine compared with conventional western medicine in the treatment of chronic uric acid nephropathy.The search deadline was set to June 30,2020.For the included literature,we applied the cochrane collaboration network risk bias assessment tool to evaluate the methodological quality,and evaluated the level of evidence according to GRADE standards.Quantitative data was analyzed by RevMan5.3 software,and trial sequential analysis method was used to analyze its efficiency.Results:A total of 709 cases in 10 articles were included.Compared with the control group,the related traditional Chinese medicine group improved the effective rate[RR=1.45,95%CI(1.32,1.58)],reduced the level of UA[MD=-36.24,95%CI(-41.03,-31.45)],BUN[SMD=-1.27,95%CI(-1.47,-1.07)]and SCR[MD=-36.33,95%CI(-55.79,-16.87),P=0.0003],the difference between the two groups was statistically significant(P<0.05).There was no evidence that a significant adverse reaction occurred.The results of TSA analysis showed that the Chinese medicine group had definite evidence for improving the efficiency.According to the GRADE evaluation criteria,the efficiency,UA,BUN and SCR outcome indicators were extremely low-quality evidence.Conclusions:Traditional Chinese medicine for regulating spleen and kidney in the treatment of chronic uric acid nephropathy improved efficiency,reduced the level of UA,BUN,SCR.Meanwhile,the therapy was proved to be safe.Nevertheless,the conclusions need further high-quality evidence to support.

Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities:A Survey

Cataracts are the leading cause of visual impairment and blindness globally.Over the years,researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading,aiming to prevent cataracts early and improve clinicians′diagnosis efficiency.This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images.We summarize existing literature from two research directions:conventional machine learning methods and deep learning methods.This survey also provides insights into existing works of both merits and limitations.In addition,we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.