ObjectiveTo observe the effect and therapeutic effect of Kaixuan Jiedu core prescription （KXJD） on skin homing of Th17 cells in the mouse model of imiquimod （IMQ） combined with restraint stress-induced psoriasis-like skin damage， and to explore its potential mechanism from the perspective of neuro-immune axis. MethodsThirty male C57BL/6J mice were randomly allocated into five groups （n=6）： Control， model （IMQ）， restraint stress model （IMQ+RS）， KXJD， and methotrexate （MTX）. The mouse model of psoriasis-like skin damage was established by 5% IMQ combined with restraint stress. At the same time of modeling， each treatment group was treated with corresponding doses of drugs， and the control， IMQ， and IMQ+RS groups were treated with the same amount of normal saline by gavage once a day for 5 days. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in the skin tissue and Baker scoring was performed. Serum levels of interleukin-1β （IL-1β） and angiopoietin-2 （Ang-2） were measured by enzyme-linked immunosorbent assay （ELISA）. The levels of matrix metalloproteinase-9 （MMP-9）， tissue inhibitor of metalloproteinase-1 （TIMP-1）， C-C motif chemokine ligand 20 （CCL20）， and C-C motif chemokine receptor 6 （CCR6） in the skin tissue were determined. Immunohistochemistry （IHC） was employed to determine the protein expression of cutaneous lymphocyte-associated antigen （CLA）， integrin α_E （CD103）， cytokeratin 10 （CK10）， and nuclear factor-kappa B （NF-κB） in the skin. Immunofluorescence double staining （DIF） was adopted to detect the expression and co-localization of vascular endothelial cadherin （VE-cadherin） and platelet-endothelial cell adhesion molecule （CD31）， CCR6， CD103， substance P （SP）， calcitonin gene-related peptide （CGRP）， and protein gene product 9.5 （PGP9.5） in the skin tissue. Real-time PCR was employed to quantify the mRNA levels of IL-10， IL-17A， and IL-23. ResultsCompared with the control group， the IMQ group and IMQ+RS group showed significant inflammatory cell infiltration， abnormal proliferation of epidermal cells， keratinization and other pathological changes in the skin tissue， and a significant increase in Baker score， elevated levels of IL-1β and Ang-2 in the serum and MMP-9， CCL20 and CCR6 in the skin lesions， upregulated expression of CLA， CD103， CK10， NF-κB， IL-17A mRNA， and IL-23 mRNA in the skin lesions， and downregulated expression of TIMP-1 and IL-10 mRNA. In addition， the fluorescence intensities of VE-cadherin and CD31 co-localization， CCR6 and CD103 co-localization， SP and PGP9.5 co-localization， and CGRP and PGP9.5 co-localization were increased （P<0.05）. Compared with the IMQ group， the above indicators in the IMQ+RS group were further aggravated. Compared with the IMQ group， the above indicators in the IMQ+RS group were further aggravated. Compared with the IMQ+RS group， KXJD and MTX significantly alleviated the pathological damage of skin lesions， significantly decreased the Baker score， lowered the levels of IL-1β and Ang-2 in the serum and MMP-9， CCL20 and CCR6 in skin lesions， downregulated the expression of CLA， CD103， CK10， NF-κB， IL-17A mRNA and IL-23 mRNA in skin lesions， and upregulated the expression of TIMP-1 and IL-10 mRNA. Furthermore， KXJD and MTX reduced the fluorescence intensities of VE-cadherin and CD31 co-localization， CCR6 and CD103 co-localization， CGRP and PGP9.5 co-localization， and SP and PGP9.5 co-localization （P<0.05）. ConclusionKXJD can significantly ameliorate the psoriasis-like skin damage induced by IMQ combined with restraint stress in mice by regulating the neural-immune axis and inhibiting the skin homing of Th17 cells.

ObjectiveTo characterize the evidence distribution and methodological quality of randomized controlled trials （RCTs） on oral Chinese herbal medicine （CHM） for atopic dermatitis （AD） based on evidence mapping. MethodsSeven databases （CNKI， Wanfang Data， VIP， CBM， Cochrane Library， PubMed， and Embase） and the Chinese Clinical Trial Registry were searched for the RCTs in Chinese and English. Evidence distribution was presented graphically and textually， and methodological quality was assessed via the Cochrane Risk of Bias tool （ROB 1.0）. ResultsA total of 168 RCTs were included. The number of annual publications showing an increasing trend， and 72.6% RCTs had sample sizes of 51-100 participants. The studies evaluated 108 distinct CHM interventions categorized as decoctions， granules， Chinese patent medicines， and extracts. Compound Glycyrrhizin was the most frequently used， followed by Xiaofengsan and Chushi Weiling decoction. Among the RCTs， 57.1% had the treatment courses of 4-8 weeks. Outcome measures predominantly focused on clinical response rate， skin lesion severity scores， and adverse events， with less attention to TCM symptom scores， skin barrier function， and relapse rates. The overall risk of bias was generally high. ConclusionWhile CHM for AD is a research hotspot and demonstrates clinical advantages， the related studies have problems such as unclear clinical positioning， poor research standardization and methodological quality， and insufficient prominence of TCM clinical advantages. Large-sample， methodologically rigorous， and high-quality studies are needed to enhance the evidence base for CHM in treating AD.

ObjectiveTo characterize the evidence distribution and methodological quality of randomized controlled trials （RCTs） on oral Chinese herbal medicine （CHM） for atopic dermatitis （AD） based on evidence mapping. MethodsSeven databases （CNKI， Wanfang Data， VIP， CBM， Cochrane Library， PubMed， and Embase） and the Chinese Clinical Trial Registry were searched for the RCTs in Chinese and English. Evidence distribution was presented graphically and textually， and methodological quality was assessed via the Cochrane Risk of Bias tool （ROB 1.0）. ResultsA total of 168 RCTs were included. The number of annual publications showing an increasing trend， and 72.6% RCTs had sample sizes of 51-100 participants. The studies evaluated 108 distinct CHM interventions categorized as decoctions， granules， Chinese patent medicines， and extracts. Compound Glycyrrhizin was the most frequently used， followed by Xiaofengsan and Chushi Weiling decoction. Among the RCTs， 57.1% had the treatment courses of 4-8 weeks. Outcome measures predominantly focused on clinical response rate， skin lesion severity scores， and adverse events， with less attention to TCM symptom scores， skin barrier function， and relapse rates. The overall risk of bias was generally high. ConclusionWhile CHM for AD is a research hotspot and demonstrates clinical advantages， the related studies have problems such as unclear clinical positioning， poor research standardization and methodological quality， and insufficient prominence of TCM clinical advantages. Large-sample， methodologically rigorous， and high-quality studies are needed to enhance the evidence base for CHM in treating AD.

Objective To develop a convolution neural network (CNN) model to classify multi?sequence MR images of the prostate. Methods ResNet18 convolution neural network (CNN) model was developed to classify multi?sequence MR images of the prostate. A deep residual network was used to improve training accuracy and test accuracy. The dataset used in this experiment included 19 146 7?sequence prostate MR images (transverse T1WI, transverse T2WI, coronal T2WI, sagittal T2WI, transverse DWI, transverse ADC, transverse PWI), from which a total of 2 800 7?sequence MR images was selected as a training set. Three hundred and eighty eight 7?sequence MR images were selected as test sets. Accuracy was used to evaluate the effectiveness of ResNet18 CNN model. Results The classification accuracy of the model for transverse DWI, sagittal T2WI, transverse ADC, transverse T1WI, and transverse T2WI was as high as 100.0% (44/44,52/52), and the accuracy for transverse PWI was also as high as 96.7% (116/120). The accuracy for coronal T2WI was 77.5% (31/40). 0.8% (1/120) of transverse PWI was incorrectly assigned to transverse T2WI, and 2.5% (3/120) incorrectly assigned to sagittal T2WI. 15.0% (6/40) of coronal T2WI was incorrectly assigned to transverse T2WI, and 7.5% (3/40) to sagittal T2WI. Conclusion The experimental results show the effectiveness of our deep learning method regarding accuracy in the prostate multi?sequence MR images detection.

Objective To investigate the value of texture analysis based on MR ADC map of prostate in differentiating between low?grade and high?grade prostate cancer (PCa). Methods PCa confirmed by pathology after radical prostatectomy were analyzed retrospectively, all patients underwent multiparametric MRI before radical prostatectomy, including T1WI,T2WI and DWI. On the ADC map, ROI was drawn manually to encompass the whole tumor by ITK?SNAP software. The python?based pyradiomics package was used to extract 105 texture features. The intraclass correlation coefficient was used to evaluate the repeatability of the texture features. The independent sample t test or Mann?Whitney U test was used to exclude features that had no significant difference between low grade and high grade PCa. Lasso regression model and 5 fold cross validation method were used to obtain texture feature combination of the highest performance and develop a classification modelfor discriminating low from high grade PCa. ROC curve was used to evaluate the diagnostic efficiency of the model. Result Ninety patients with PCa confirmed by pathology after radical prostatectomywere analyzed retrospectively,including 36 patients with low?level PCa (GS≤3+4) and 54 patients with high?level PCa (GS≥4+3). The area under curve of the model was 0.841, with sensitivity 69.6% and specificity 91.2%, which was significantly higher than single texture feature or traditional mean ADC value. Conclusion Texture analysis based on MRI?ADC map of prostate could be used to discriminate low grade PCa from high grade PCa.

Objective: To develop a convolution neural network (CNN) model to classify multi-sequence MR images of the prostate. Methods: ResNet18 convolution neural network (CNN) model was developed to classify multi-sequence MR images of the prostate. A deep residual network was used to improve training accuracy and test accuracy. The dataset used in this experiment included 19 146 7-sequence prostate MR images (transverse T₁WI, transverse T₂WI, coronal T₂WI, sagittal T₂WI, transverse DWI, transverse ADC, transverse PWI), from which a total of 2 800 7-sequence MR images was selected as a training set. Three hundred and eighty eight 7-sequence MR images were selected as test sets. Accuracy was used to evaluate the effectiveness of ResNet18 CNN model. Results: The classification accuracy of the model for transverse DWI, sagittal T₂WI, transverse ADC, transverse T₁WI, and transverse T₂WI was as high as 100.0% (44/44,52/52), and the accuracy for transverse PWI was also as high as 96.7% (116/120). The accuracy for coronal T₂WI was 77.5% (31/40). 0.8% (1/120) of transverse PWI was incorrectly assigned to transverse T₂WI, and 2.5% (3/120) incorrectly assigned to sagittal T₂WI. 15.0% (6/40) of coronal T₂WI was incorrectly assigned to transverse T₂WI, and 7.5% (3/40) to sagittal T₂WI. Conclusion The experimental results show the effectiveness of our deep learning method regarding accuracy in the prostate multi-sequence MR images detection.

Objective: To investigate the value of texture analysis based on MR ADC map of prostate in differentiating between low-grade and high-grade prostate cancer (PCa). Methods: PCa confirmed by pathology after radical prostatectomy were analyzed retrospectively, all patients underwent multiparametric MRI before radical prostatectomy, including T₁WI,T₂WI and DWI. On the ADC map, ROI was drawn manually to encompass the whole tumor by ITK-SNAP software. The python-based pyradiomics package was used to extract 105 texture features. The intraclass correlation coefficient was used to evaluate the repeatability of the texture features. The independent sample t test or Mann-Whitney U test was used to exclude features that had no significant difference between low grade and high grade PCa. Lasso regression model and 5 fold cross validation method were used to obtain texture feature combination of the highest performance and develop a classification modelfor discriminating low from high grade PCa. ROC curve was used to evaluate the diagnostic efficiency of the model. Result: Ninety patients with PCa confirmed by pathology after radical prostatectomywere analyzed retrospectively,including 36 patients with low-level PCa (GS≤3+4) and 54 patients with high-level PCa (GS≥4+3). The area under curve of the model was 0.841, with sensitivity 69.6% and specificity 91.2%, which was significantly higher than single texture feature or traditional mean ADC value. Conclusion Texture analysis based on MRI-ADC map of prostate could be used to discriminate low grade PCa from high grade PCa.

The retinoblastoma-protein-interacting zinc finger proteinl (RIZ1),a methyltransferase,contains the characteristic PR zinc finger domain.RIZ1 can methylate H3K9 of the histone,acted as a transcription suppression factor of cancers.Increasing numbers of human cancers are reported to hold decreased or absent RIZ1 expression,which is closely related to the progression of cancer.RIZ1 is defined as a candidate anti-oncogene.The mechanisms of the suppression involved in both oncocytogenetics and epigenetic changes.

P-450-dependent epoxygenase pathway of arachidonic acid and the products of epoxyeicosatrienoic acids (EETs) have been demonstrated to be involved in angiogenesis and tumor progression.This study examined the expression of EETs and the role of the pathway in the angiogenesis of multiple myeloma (MM).MM cell lines of U266 and RPMI8226 were cultured,and the EETs levels (11,12-EET and 14,15-EET) in the supematant were determined by ELISA.Human umbilical vein endothelial cells (HUVECs) were cultured and used for analysis of the angiogenesis activity of the two MM cell lines,which was examined both in vitro and in vivo by employing MTT,chemotaxis,tube formation and matrigel plug assays.11,12-EET and 14,15-EET were found in the supematant of the cultured MM cells.The levels of the two EETs in the supernatant of U266 cells were significantly higher than those in the RPMI8226 cell supematant (P＜0.05),and the levels paralleled the respective angiogenesis activity of the two different MM cell lines.17-octadecynoic acid (17-ODYA),as a specific inhibitor of P450 enzyme,suppressed HUVECs proliferation and tube formation induced by MM cells.Furthermore,17-ODYA decreased the EET levels in the supernatant of MM cells.These results suggest that EETs may play an important role in the angiogenesis of MM,and the inhibitor 17-ODYA suppresses this effect.

P-450-dependent epoxygenase pathway of arachidonic acid and the products of epoxyeicosatrienoic acids (EETs) have been demonstrated to be involved in angiogenesis and tumor progression. This study examined the expression of EETs and the role of the pathway in the angiogenesis of multiple myeloma (MM). MM cell lines of U266 and RPMI8226 were cultured, and the EETs levels (11, 12-EET and 14, 15-EET) in the supernatant were determined by ELISA. Human umbilical vein endothelial cells (HUVECs) were cultured and used for analysis of the angiogenesis activity of the two MM cell lines, which was examined both in vitro and in vivo by employing MTT, chemotaxis, tube formation and matrigel plug assays. 11, 12-EET and 14, 15-EET were found in the supernatant of the cultured MM cells. The levels of the two EETs in the supernatant of U266 cells were significantly higher than those in the RPMI8226 cell supernatant (P<0.05), and the levels paralleled the respective angiogenesis activity of the two different MM cell lines. 17-octadecynoic acid (17-ODYA), as a specific inhibitor of P450 enzyme, suppressed HUVECs proliferation and tube formation induced by MM cells. Furthermore, 17-ODYA decreased the EET levels in the supernatant of MM cells. These results suggest that EETs may play an important role in the angiogenesis of MM, and the inhibitor 17-ODYA suppresses this effect.