Snakebite has become a serious public health problem with high mortality and disability rates.Ultrasound can provide imaging basis for diagnosis and treatment of snakebites and relative complications.The application p...Snakebite has become a serious public health problem with high mortality and disability rates.Ultrasound can provide imaging basis for diagnosis and treatment of snakebites and relative complications.The application progresses of ultrasound in snakebites and complications were reviewed in this article.展开更多
Exosomes,ubiquitously present in body fluids,serve as non-invasive biomarkers for disease diagnosis,monitoring,and treatment.As intercellular messengers,exosomes encapsulate a rich array of proteins,nucleic acids,and ...Exosomes,ubiquitously present in body fluids,serve as non-invasive biomarkers for disease diagnosis,monitoring,and treatment.As intercellular messengers,exosomes encapsulate a rich array of proteins,nucleic acids,and metabolites,although most studies have primarily focused on proteins and RNA.Recently,exosome metabolomics has demonstrated clinical value and potential advantages in disease detection and pathophysiology,despite significant challenges,particularly in exosome isolation and metabolite detection.This review discusses the significant technical challenges in exosome isolation and metabolite detection,highlighting the advancements in these areas that support the clinical application of exosome metabolomics,and illustrates the potential of exosomal metabolites from various body fluids as biomarkers for early disease diagnosis and treatment.展开更多
Objective To observe the value of intravoxel incoherent motion(IVIM)and dynamic contrast-enhanced MRI(DCE-MRI)for assessing abnormalities of brucellosis spondylitis(BS)without conventional MRI changes.Methods Data of ...Objective To observe the value of intravoxel incoherent motion(IVIM)and dynamic contrast-enhanced MRI(DCE-MRI)for assessing abnormalities of brucellosis spondylitis(BS)without conventional MRI changes.Methods Data of 36 brucellosis patients with definite spinal lesions displayed on conventional MRI(BS 1 group),14 cases without brucellosis infection nor abnormal spinal signals on MRI(control group)and 36 brucellosis patients without definite spinal lesions on conventional MRI(BS 2 group)were retrospectively analyzed.The values of IVIM parameters,including perfusion fraction(f),pure water diffusion coefficient(D)and pseudo-diffusion coefficient(D*),also of DCE-MRI parameters,including time-intensity curve(TIC)type,volume transport constant(K trans),the rate constant(K ep)and volume fraction of extravascular extracellular space per unit tissue volume(V e)were compared among groups.Univariate and multivariate logistic regression were used to screen independent factors for discriminating BS 1 and BS 2.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the efficiency of the above parameters for discriminating BS 1 and BS 2.Results Among IVIM parameters,compared with control group,D*values decreased but D values increased in BS 1 group,while D*values increased in BS 2 group(all adjusted P<0.05).Compared with BS 2 group,BS 1 group had higher values of f and D and lower D*(all adjusted P<0.05).In BS 1 group,the TIC types were predominantly typeⅠ(23/36,63.89%),which were wholly or predominantly typeⅢin BS 2 group and control group,and of the former was significantly different with latter 2(both adjusted P<0.05).Compared with control group,K trans increased progressively in both BS 1 and BS 2 groups(both adjusted P<0.05).BS 1 group had lower K ep and higher V e than BS 2 and control groups(all adjusted P<0.05).Among univariate logistic regression models,the model including only f had lower capability for discriminating BS 1 and BS 2(AUC=0.759)than those including D,K trans and V e(AUC=0.951,0.833,0.894,all P<0.05).No significant different was found among multivariate logistic regression model including f and D,model including K trans and V e nor model including all above parameters(all P>0.05).Conclusion Both IVIM and DCE-MRI could be used to evaluate BS abnormality without conventional MRI changes.展开更多
Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of s...Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.展开更多
Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.Methods From July 1;2020 to March 31;2022;clinical information of lung cancer ...Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.Methods From July 1;2020 to March 31;2022;clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hos-pital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Ex-amination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chi-nese Medicine;respectively.We obtained tongue images from patients with benign lung nod-ules and lung cancer using the TFDA-1 digital tongue diagnosis instrument;and analyzed these images with the TDAS V2.0 software.The extracted indicators included color space pa-rameters in the Lab system for both the tongue body(TB)and tongue coating(TC)(TB/TC-L;TB/TC-a;and TB/TC-b);textural parameters[TB/TC-contrast(CON);TB/TC-angular second moment(ASM);TB/TC-entropy(ENT);and TB/TC-MEAN];as well as TC parameters(perAll and perPart).The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis;and the overall correlation was analyzed using canonical correlation analysis(CCA).Results Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values.Simple correlation analysis indi-cated that the correlation of TB-L with TC-L;TB-b with TC-b;and TB-b with perAll in lung cancer group was higher than that in benign nodules group.Moreover;the correlation of TB-a with TC-a;TB-a with perAll;and the texture parameters of the TB(TB-CON;TB-ASM;TB-ENT;and TB-MEAN)with the texture parameters of the TC(TC-CON;TC-ASM;TC-ENT;and TC-MEAN)in benign nodules group was higher than lung cancer group.CCA further demon-strated a strong correlation between the TB and TC parameters in lung cancer group;with the first and second pairs of typical variables in benign nodules and lung cancer groups indicat-ing correlation coefficients of 0.918 and 0.817(P<0.05);and 0.940 and 0.822(P<0.05);re-spectively.Conclusion Benign lung nodules and lung cancer patients exhibited differences in correla-tion in the L;a;and b values of the TB and TC;as well as the perAll value of the TC;and the texture parameters(TB/TC-CON;TB/TC-ASM;TB/TC-ENT;and TB/TC-MEAN)between the TB and TC.Additionally;there were differences in the overall correlation of the TB and TC be-tween the two groups.Objective tongue diagnosis indicators can effectively assist in the diag-nosis of benign lung nodules and lung cancer;thereby providing a scientific basis for the ear-ly detection;diagnosis;and treatment of lung cancer.展开更多
Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve ...Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.展开更多
文摘Snakebite has become a serious public health problem with high mortality and disability rates.Ultrasound can provide imaging basis for diagnosis and treatment of snakebites and relative complications.The application progresses of ultrasound in snakebites and complications were reviewed in this article.
文摘Exosomes,ubiquitously present in body fluids,serve as non-invasive biomarkers for disease diagnosis,monitoring,and treatment.As intercellular messengers,exosomes encapsulate a rich array of proteins,nucleic acids,and metabolites,although most studies have primarily focused on proteins and RNA.Recently,exosome metabolomics has demonstrated clinical value and potential advantages in disease detection and pathophysiology,despite significant challenges,particularly in exosome isolation and metabolite detection.This review discusses the significant technical challenges in exosome isolation and metabolite detection,highlighting the advancements in these areas that support the clinical application of exosome metabolomics,and illustrates the potential of exosomal metabolites from various body fluids as biomarkers for early disease diagnosis and treatment.
文摘Objective To observe the value of intravoxel incoherent motion(IVIM)and dynamic contrast-enhanced MRI(DCE-MRI)for assessing abnormalities of brucellosis spondylitis(BS)without conventional MRI changes.Methods Data of 36 brucellosis patients with definite spinal lesions displayed on conventional MRI(BS 1 group),14 cases without brucellosis infection nor abnormal spinal signals on MRI(control group)and 36 brucellosis patients without definite spinal lesions on conventional MRI(BS 2 group)were retrospectively analyzed.The values of IVIM parameters,including perfusion fraction(f),pure water diffusion coefficient(D)and pseudo-diffusion coefficient(D*),also of DCE-MRI parameters,including time-intensity curve(TIC)type,volume transport constant(K trans),the rate constant(K ep)and volume fraction of extravascular extracellular space per unit tissue volume(V e)were compared among groups.Univariate and multivariate logistic regression were used to screen independent factors for discriminating BS 1 and BS 2.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the efficiency of the above parameters for discriminating BS 1 and BS 2.Results Among IVIM parameters,compared with control group,D*values decreased but D values increased in BS 1 group,while D*values increased in BS 2 group(all adjusted P<0.05).Compared with BS 2 group,BS 1 group had higher values of f and D and lower D*(all adjusted P<0.05).In BS 1 group,the TIC types were predominantly typeⅠ(23/36,63.89%),which were wholly or predominantly typeⅢin BS 2 group and control group,and of the former was significantly different with latter 2(both adjusted P<0.05).Compared with control group,K trans increased progressively in both BS 1 and BS 2 groups(both adjusted P<0.05).BS 1 group had lower K ep and higher V e than BS 2 and control groups(all adjusted P<0.05).Among univariate logistic regression models,the model including only f had lower capability for discriminating BS 1 and BS 2(AUC=0.759)than those including D,K trans and V e(AUC=0.951,0.833,0.894,all P<0.05).No significant different was found among multivariate logistic regression model including f and D,model including K trans and V e nor model including all above parameters(all P>0.05).Conclusion Both IVIM and DCE-MRI could be used to evaluate BS abnormality without conventional MRI changes.
基金National Natural Science Foundation of China(82274411)Science and Technology Innovation Program of Hunan Province(2022RC1021)Leading Research Project of Hunan University of Chinese Medicine(2022XJJB002).
文摘Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.
基金National Natural Science Foundation of China(82305090)Science and Technology Commission of Shanghai Municipality(22YF1448900)Shanghai Municipal Health Commission(20234Y0168).
文摘Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.Methods From July 1;2020 to March 31;2022;clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hos-pital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Ex-amination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chi-nese Medicine;respectively.We obtained tongue images from patients with benign lung nod-ules and lung cancer using the TFDA-1 digital tongue diagnosis instrument;and analyzed these images with the TDAS V2.0 software.The extracted indicators included color space pa-rameters in the Lab system for both the tongue body(TB)and tongue coating(TC)(TB/TC-L;TB/TC-a;and TB/TC-b);textural parameters[TB/TC-contrast(CON);TB/TC-angular second moment(ASM);TB/TC-entropy(ENT);and TB/TC-MEAN];as well as TC parameters(perAll and perPart).The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis;and the overall correlation was analyzed using canonical correlation analysis(CCA).Results Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values.Simple correlation analysis indi-cated that the correlation of TB-L with TC-L;TB-b with TC-b;and TB-b with perAll in lung cancer group was higher than that in benign nodules group.Moreover;the correlation of TB-a with TC-a;TB-a with perAll;and the texture parameters of the TB(TB-CON;TB-ASM;TB-ENT;and TB-MEAN)with the texture parameters of the TC(TC-CON;TC-ASM;TC-ENT;and TC-MEAN)in benign nodules group was higher than lung cancer group.CCA further demon-strated a strong correlation between the TB and TC parameters in lung cancer group;with the first and second pairs of typical variables in benign nodules and lung cancer groups indicat-ing correlation coefficients of 0.918 and 0.817(P<0.05);and 0.940 and 0.822(P<0.05);re-spectively.Conclusion Benign lung nodules and lung cancer patients exhibited differences in correla-tion in the L;a;and b values of the TB and TC;as well as the perAll value of the TC;and the texture parameters(TB/TC-CON;TB/TC-ASM;TB/TC-ENT;and TB/TC-MEAN)between the TB and TC.Additionally;there were differences in the overall correlation of the TB and TC be-tween the two groups.Objective tongue diagnosis indicators can effectively assist in the diag-nosis of benign lung nodules and lung cancer;thereby providing a scientific basis for the ear-ly detection;diagnosis;and treatment of lung cancer.
基金National Natural Science Foundation of China(82274265 and 82274588)Hunan University of Traditional Chinese Medicine Research Unveiled Marshal Programs(2022XJJB003).
文摘Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.
