Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

OBJECTIVE: This study aims to elucidate the therapeutic potential of osthole for the treatment of atopic dermatitis (AD), focusing on its ability to alleviate chronic pruritus (CP) and the underlying molecular mechanisms. METHODS: In this study, we investigated the anti-inflammatory effects of osthole in both a 2,4-dichloronitrobenzene (DNCB)-induced AD mouse model and tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) stimulated huma immortalized epidermal (HaCaT) cells. The anti-itch effect of osthole was specifically assessed in the AD mouse model. Using methods such as hematoxylin and eosin (HE) staining, enzyme-linked immunosorbent assay (ELISA), western blot (WB), quantitative real-time PCR (qRT-PCR), and immunofluorescence staining. RESULTS: Osthole improved skin damage and clinical dermatitis scores, reduced scratching bouts, and decreased epidermal thickness AD-like mice. It also reduced the levels of interleukin (IL)-31 and IL-31 receptor A (IL-31 RA) in both skin tissues and HaCaT cells. Furthermore, Osthole suppressed the protein expression levels of phosphor-p65 (p-p65) and phosphor-inhibitor of nuclear factor kappa-Bα (p-IκBα). Meanwhile, it increased the protein expression levels of peroxisome proliferator-activated receptor α (PPARα) and PPARγ in HaCaT cells. CONCLUSION These findings indicated that osthole effectively inhibited CP in AD by activating PPARα, PPARγ, repressing the NF-κB signaling pathway, as well as the expression of IL-31 and IL-31 RA.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

As an emerging discipline that combines traditional diagnostic methods with modern scientific technology,micro syndrome differentiation has good prospects for development,but there are some controversies in the research process.Based on ancient and modern literature,this article reviewed the origin and flow of research on micro syndrome differentiation,and summarized the problems to be improved in the process of research on micro syndrome differentiation from three aspects:application of disease type,guiding ideology and micro indicators.Based on this,the article further expounded the new thinking on"near-micro"syndrome differentiation from three aspects:connotation,scope of application,and links to traditional identification and micro-identification,and pointed out that the modern medical detection basis should be incorporated into the field of TCM syndrome differentiation,and at the same time,it should be based on the overall thinking mode of TCM,which would provide a new idea for the development of modern TCM diagnosis technology.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To predict microvascular invasion(MVI)in hepatocellular carcinoma(HCC)using a model based on multi-phase dynamic-enhanced CT(DCE-CT)radiomics feature and hierarchical fusion of multiple classifiers.Methods We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January,2016 and April,2020.The volume of interest was outlined in the early arterial phase,late arterial phase,portal venous phase and equilibrium phase,and radiomics features of these 4 phases were extracted.Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase.According to the hierarchical fusion strategy,a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model.The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve(AUC),accuracy,sensitivity,and specificity.The prediction model was also compared with the fusion models using a single phase or multiple phases,models based on a single phase with a single classifier,models with different base classifier diversities,and 8 classifier models based on other ensemble methods.Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers,with AUC,accuracy,sensitivity,and specificity of 0.828,0.766,0.877,and 0.648,respectively.Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To predict microvascular invasion(MVI)in hepatocellular carcinoma(HCC)using a model based on multi-phase dynamic-enhanced CT(DCE-CT)radiomics feature and hierarchical fusion of multiple classifiers.Methods We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January,2016 and April,2020.The volume of interest was outlined in the early arterial phase,late arterial phase,portal venous phase and equilibrium phase,and radiomics features of these 4 phases were extracted.Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase.According to the hierarchical fusion strategy,a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model.The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve(AUC),accuracy,sensitivity,and specificity.The prediction model was also compared with the fusion models using a single phase or multiple phases,models based on a single phase with a single classifier,models with different base classifier diversities,and 8 classifier models based on other ensemble methods.Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers,with AUC,accuracy,sensitivity,and specificity of 0.828,0.766,0.877,and 0.648,respectively.Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.