In the field of dental aesthetics,digital aesthetic design plays a crucial role in helping dentists to predict treatment outcomes vis-ually,as well as in enhancing the consistency of knowledge and understanding of aesthetic goals between dentists and patients.It serves as the foundation for achieving ideal aesthetic effects.However,there is no clear standard for this digital process currently in China and abroad.Many dentists lack of systematic understanding of how to carry out digital aesthetic design for treatment.To establish standardized processes for dental aesthetic design and to improve the homogeneity of treatment outcomes,Chinese Society of Digital Dental Industry(CSD-DI)convened domestic experts in related field to compile this consensus.This article elaborates on the key aspects of digital aesthetic data collection,integration steps,and the digital aesthetic design process.It also formulates a decision tree for dental aesthetics at macro level and outlines corresponding workflows for various clinical scenarios,serving as a reference for clinicians.

Aim To observe the effects of Wenfei Jiangzhuo formula(WFJZF)on rats with vascular de-mentia and investigate its possible mechanism of ac-tion.Methods Thirty-six healthy male SD rats were randomly divided into the sham group,model group,donepezil group,and low-dose,medium-dose and high-dose groups of Wenfei Jiangzhuo formula,with six rats per group.Except for the sham group,the other groups were prepared as VaD models,and each group was gavaged with the corresponding drugs after suc-cessful modeling,and tests were performed after three weeks of treatment.Behavioral,hippocampal CA1 area morphology,neural dendrites and mitochondrial chan-ges were observed in all groups of rats,and phospho-glycerate mutase 5(PGAM5),dynamics-related pro-teins1(Drp1),opticatrophyprotein-1(OPA1),and other proteins were detected in each group.Results Compared with the sham group,rats in the model group and each intervention group had prolonged es-cape latency(P＜0.05),a shorter number of travers-als across the platforms(P＜0.05),a sparse morphol-ogy of hippocampal neurons,a reduction in the number of secondary dendritic spines,and a rupture of the out-er membrane of the mitochondria;the expression of the PGAM5 and Drp1 proteins in hippocampal tissues was elevated(P＜0.05),and the expression of the OPA1 and Mfn1/2 protein expression decreased(P＜0.05);compared with the model group,donepezil group and Wenfei Jiangzhuo formula high-dose group of rats had shorter evasion latency(P＜0.05),increased number of times to traverse the platform(P＜0.05),increased number of hippocampal neurons,tightly packed,more secondary dendritic structures,and reduced mitochon-drial damage;the expression of PGAM5 and Drp1 pro-teins was reduced(P＜0.05),and the expression of OPA1 and Mfn1/2 proteins was elevated(P＜0.05).Conclusions Wenfei Jiangzhuo formula can regulate the PGAM5-Drp1 signaling axis to improve the balance of mitochondrial homeostasis,thus improving the cog-nitive condition of the brain and exerting cerebroprotec-tive effects.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Establish a production line with controllable process and high intelligence, contribute to improve the quality and production efficiency of aconite processed by microwave, and promote the transformation and application of aconite processed by microwave. According to the principle of aconite detoxification and the characteristics of industrial microwave equipment, an industrial production line of aconite processed by microwave was established with diester alkaloids and monoester alkaloids as indicators, and pilot production was carried out. At the same time, the content of active constituents and efficacy were compared with that of the main processed products, such as Shengfupian, Baifupian and Heishunpian. The results showed that the industrial production of aconite processed by microwave can be divided into two stages: "Liquid seal to detoxification - drying and puffing". The content of monoester alkaloids in 10 batches of aconite processed by microwave was 0.071%-0.166% and the content of diester alkaloids was 0.004%-0.016%, which met the relevant requirements of the Chinese Pharmacopoeia in 2020. Compared with Heishunpian and Baifupian, the retention rate of the effective components of aconite processed by microwave was higher. Pharmacological experiments showed that aconite processed by microwave not only retained the anti-inflammatory and analgesic activities of Heishunpian and Baifupian, but also significantly increased the levels of leukocytes and lymphocytes in mice with liver cancer chemotherapy, enhanced the CD4/CD8 ratio in spleen cells of mice (P < 0.05), thus regulating the body's immunity. However, this effect of Baifupian was weak, while Heishunpian and Shengfupian had no such effect. Through the above research, this study established microwave processing line with controllable process and high intelligence, as well produced the aconite processed by microwave with low toxicity and stable quality. It laid a foundation for the industrialized continuous production and clinical positioning of aconite by microwave processed, and provided scientific support for the development and application of microwave technology in the field of traditional Chinese medicine. All animal experiments in this study were reviewed and approved by the Experimental Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine before being carried out (Approval No. 2020-28).

The aim of this study is to analyze the research hotspots and development trends in the field of pathogenesis of diabetic nephropathy in China from 2013 to 2022. Based on China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, China Science and Technology Journal Database, China Biology Medicine disc, Web of Science core collection and PubMed database, the related literatures in the field of pathogenesis of diabetic nephropathy in China from 2013 to 2022, were retrieved to establish the database, and the VOSviewer software was used for bibliometric analysis. A total of 1 664 Chinese and 2 149 English literatures are included in this study. The scientific research results from 2013 to 2022 have shown an overall increasing trend. The research hotspots in the field of pathogenesis of diabetic nephropathy in China are mainly concentrated in Podocytes, Oxidative stress, Inflammation, Renal fibrosis, Urine protein, etc. The frontier hotspots in this field include Biomarkers, Nrf2, Gut microbiota, NLRP3 inflammasome, Apoptosis, MicroRNA, etc. Through visual analysis, the research hotspots and frontier trends of the pathogenesis of diabetic nephropathy in China can be visually presented, and then provide new ideas and directions for the further in-depth research on the pathogenesis of diabetic nephropathy.
Humans ; Apoptosis ; Asian People ; China/epidemiology* ; Diabetes Mellitus ; Diabetic Nephropathies/etiology* ; MicroRNAs ; Biomedical Research/trends*