The inflammatory microenvironment is closely associated with the initiation and progression of osteoarthritis （OA）， specifically manifesting as macrophage activation， dysregulation of inflammatory cytokines， and redox imbalance. Following an overview of the pathological characteristics of the OA inflammatory microenvironment， this paper reviews the research progress on the mechanism of traditional Chinese medicine （TCM） intervening in OA by modulating the inflammatory microenvironment. It has been found that TCM monomers/active ingredients （such as total alkaloids from Strychnos nux-vomica ， quercetin， triptolide， etc.）， herb pairs （e.g. Angelica pubescens - Gentiana macrophylla ， Carthami Flos-Lycopodii Herba）， and TCM formulas （such as Zhuanggu jianxi formula， Duhuo jisheng decoction and Rongjin niantong formula， etc.） can inhibit macrophage activation， reduce the release of proinflammatory cytokines and the generation of reactive oxygen species by inhibiting multiple signaling pathways， including nuclear factor-κB， Wnt/ β -catenin， and mitogen-activated protein kinase， thereby alleviating the articular inflammatory microenvironment， restoring local joint homeostasis， and slowing the progression of OA.

The existing epilepsy seizure detection algorithms have problems such as overfitting and poor generalization ability due to high reliance on manual labeling of electroencephalogram's data and data imbalance between seizure and interictal periods. An unsupervised learning detection method for epileptic seizure that jointed graph attention network (GAT) and Transformer framework (GAT-T) was proposed. In this method, channel correlations were adaptively learned by GAT encoder. Temporal information was captured by one-dimensional convolution decoder. Combining outputs of the two mentioned above, predicted values for electroencephalogram were generated. The collective anomaly score was calculated and the detection threshold was determined. The results demonstrated that GAT-T achieved the average performance exceeding 90% (or 99%) with a 0.25 s (or 2 s) time segment length, which could effectively detect epileptic seizures. Moreover, the channel association probability matrix was expected to assist clinicians in the initial screening of the epileptogenic zone, and ablation experiments also reflected the significance of each module in GAT-T. This study may assist clinicians in making more accurate diagnostic and therapeutic decisions for epilepsy patients.
Humans ; Electroencephalography/methods* ; Epilepsy/physiopathology* ; Algorithms ; Seizures/physiopathology* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

A novel epileptic seizure prediction prediction model based on multichannel temporal and spatial feature extractions is presented.The model applies Stockwell transform to the original multichannel electroencephalogram(EEG)signals for extracting time-frequency components.To address the issue of insignificant difference between preseizure and interseizure time-frequency features,an adaptive feature module composing of ConvNeXt,SENet and pyramid pooling module is designed to enhance the ability of capturing key time-frequency features in each EEG channel.Meanwhile,a prediction model based on Bi-NLSTM is constructed to improve the spatiotemporal dependence between the time-frequency features of multichannel high-order EEG for further promoting the epilepsy classification performance.On the CHB-MIT dataset,the model has an accuracy,sensitivity,specificity and AUC of 96.0%,97.8%,96.8%and 0.987,respectively,and the false positive rate per hour decreased to 0.038,outperforming the existing mainstream methods.In addition,the effect of each component on the model performance is verified by ablation study.The proposed method improves the overall performance for seizure prediction effectively by optimizing local time-frequency feature extraction and enhancing multichannel spatiotemporal dependence.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To investigate the role of WW domain containing E3 ubiquitin protein ligase 1 (WWP1) in enamel development of mice.Methods:Single-cell RNA sequencing data of incisor tissues of postnatal day 7 (P7) mice and mandibular first molar tooth germs of P3.5 mice were used to analyze the expression of Wwp1 in dental epithelial cells. Immunohistochemistry was performed to observe the distribution and expression levels of WWP1 in the epithelium of mouse incisors and mandibular first molar tooth germs. Wwp1 knockout (Wwp1 KO) mice were generated and collected with their control littermates at P1, P7, three mice per group, as well as at P14, P28, 2 months (2M), and 3M, six mice per group. The enamel volumes of molars and incisors were analyzed using micro-CT. Scanning electron microscopy was employed to examine the enamel cross-sections of Wwp1 KO and control mice. Energy dispersive spectroscopy (EDS) was used to analyze the calcium and phosphorus content of the enamel rod of incisors. Immunofluorescence was performed to detect the expression of amelogenin (AMELX) in the ameloblasts of Wwp1 KO and control mice. Additionally, LS-8 ameloblast-like epithelial cells were cultured, and Wwp1 siRNA or overexpression plasmids were transfected to knock down or overexpress WWP1. The protein levels of AMELX were then assessed by Western blotting.Results:Single-cell sequencing result showed a high Wwp1 mRNA expression level in the epithelial cells of mouse incisors and mandibular molar tooth germs. Immunohistochemistry revealed the expression of WWP1 in presecretory, secretory, transitional, and mature ameloblasts. Wwp1 KO mice exhibited enamel developmental defects. The enamel volumes of molars and incisors in Wwp1 KO mice [(0.155±0.016), (0.300±0.017) μm 3] were reduced by 23.95% ( P<0.001) and 28.31% ( P<0.001) compared with the control group [(0.203±0.062), (0.418±0.023) μm 3] respectively. Scanning electron microscopy showed disorganized enamel structures in Wwp1 KO incisors and molars. EDS results showed the weight percent of calcium in the enamel rod of incisors decreased in Wwp1 KO mice [(20.74±0.91)%] compared with the control group [(30.30±3.83)%] ( P<0.001), and the calcium-to-phosphorus ratio decreased in Wwp1 KO mice (1.93±0.01) compared with the control group (2.02±0.01) ( P<0.001). Immunofluorescence showed weaker AMELX expression in ameloblasts of mandibular first molar tooth germs from P1 and P7 Wwp1 KO mice compared with the control group ( P<0.001, P<0.001). In LS-8 cells, Wwp1 knocked-down led to a decrease of AMELX protein expression, while WWP1 overexpression resulted in an increased AMELX protein level. Conclusions:WWP1 promotes ameloblast differentiation and enamel matrix mineralization, playing a critical role in enamel formation.

This study systematically collated and textual researched realgar from aspects such as name,origin,and quality by consulting ancient herbal texts,classic medical books,and modern literature.The textual research shows that current herbal works all take"realgar"as the official name,with aliases including"Huangshi shi","Shihuang","Tianyang shi","Jiguan shi","Xunhuang","Chouhuang",and"Mingxiong",etc.Ancient herbal records indicate that Xunhuang,Chouhuang,and Shuiku Realgar are all Realgar,while Orpiment is its associated mineral.In ancient times,the main production areas of Realgar were in Gansu,with outputs also seen in Shandong and Hunan.Nowadays,it is mainly produced in Hunan,Guizhou,Hubei,Gansu,Yunnan,Sichuan and other regions.Ancient herbal works mentioned characteristics like"cockscomb color","non-stinky","solid","red and bright"in their quality evaluation,while modern herbal works mostly evaluate its quality by color and texture,such as"red color","large blocks","brittle texture","glossy".The traditional efficacy of Realgar is to dry dampness,kill insects,detoxify various poisons,treat sores and activate blood.Modern studies have shown it also has anti-tumor,antibacterial and antiviral effects.Processing methods in past dynasties included water grinding,vinegar processing,refining,etc.,and currently,water grinding and acid water grinding are commonly used.This paper observed the properties of Realgar,detected the content of As?S?,and analyzed the microscopic characteristics and far-infrared spectral characteristics of qualified batches of Realgar.It was finally found that the As?S? content of qualified batches of Realgar was all more than 90%;under the scanning electron microscope,it showed massive shape,uniform distribution,obvious particles,and no agglomeration;a small amount of associated mineral Orpiment crystals were observed under the polarizing microscope;the characteristic peaks of Realgar(343-344 cm?1,224-225 cm?1,372-374 cm?1,367-369 cm?1,359 cm?1,207-208 cm?1,193-194 cm?1,168-170 cm?1)and Orpiment(390 cm?1,380 cm?1,347 cm?1,311 cm?1,300 cm?1,201 cm?1,182 cm?1,158 cm?1 and 139 cm?1)were determined."Red color and glossy"can be used as property references,"Realgar is a sulfide mineral of the Realgar family,with the main chemical component As?S?,associated with Orpiment"can be used as origin references,and"Shimen in Hunan,Wanshan in Guizhou,Yunnan,Gansu,Sichuan"can be used as production area references,which are consistent with the results of herbal textual research.This study provides a basis for the identification and analysis of Realgar,with a view to better guiding clinical medication and resource utilization.

Objective:To investigate the role of WW domain containing E3 ubiquitin protein ligase 1 (WWP1) in enamel development of mice.Methods:Single-cell RNA sequencing data of incisor tissues of postnatal day 7 (P7) mice and mandibular first molar tooth germs of P3.5 mice were used to analyze the expression of Wwp1 in dental epithelial cells. Immunohistochemistry was performed to observe the distribution and expression levels of WWP1 in the epithelium of mouse incisors and mandibular first molar tooth germs. Wwp1 knockout (Wwp1 KO) mice were generated and collected with their control littermates at P1, P7, three mice per group, as well as at P14, P28, 2 months (2M), and 3M, six mice per group. The enamel volumes of molars and incisors were analyzed using micro-CT. Scanning electron microscopy was employed to examine the enamel cross-sections of Wwp1 KO and control mice. Energy dispersive spectroscopy (EDS) was used to analyze the calcium and phosphorus content of the enamel rod of incisors. Immunofluorescence was performed to detect the expression of amelogenin (AMELX) in the ameloblasts of Wwp1 KO and control mice. Additionally, LS-8 ameloblast-like epithelial cells were cultured, and Wwp1 siRNA or overexpression plasmids were transfected to knock down or overexpress WWP1. The protein levels of AMELX were then assessed by Western blotting.Results:Single-cell sequencing result showed a high Wwp1 mRNA expression level in the epithelial cells of mouse incisors and mandibular molar tooth germs. Immunohistochemistry revealed the expression of WWP1 in presecretory, secretory, transitional, and mature ameloblasts. Wwp1 KO mice exhibited enamel developmental defects. The enamel volumes of molars and incisors in Wwp1 KO mice [(0.155±0.016), (0.300±0.017) μm 3] were reduced by 23.95% ( P<0.001) and 28.31% ( P<0.001) compared with the control group [(0.203±0.062), (0.418±0.023) μm 3] respectively. Scanning electron microscopy showed disorganized enamel structures in Wwp1 KO incisors and molars. EDS results showed the weight percent of calcium in the enamel rod of incisors decreased in Wwp1 KO mice [(20.74±0.91)%] compared with the control group [(30.30±3.83)%] ( P<0.001), and the calcium-to-phosphorus ratio decreased in Wwp1 KO mice (1.93±0.01) compared with the control group (2.02±0.01) ( P<0.001). Immunofluorescence showed weaker AMELX expression in ameloblasts of mandibular first molar tooth germs from P1 and P7 Wwp1 KO mice compared with the control group ( P<0.001, P<0.001). In LS-8 cells, Wwp1 knocked-down led to a decrease of AMELX protein expression, while WWP1 overexpression resulted in an increased AMELX protein level. Conclusions:WWP1 promotes ameloblast differentiation and enamel matrix mineralization, playing a critical role in enamel formation.

This study systematically collated and textual researched realgar from aspects such as name,origin,and quality by consulting ancient herbal texts,classic medical books,and modern literature.The textual research shows that current herbal works all take"realgar"as the official name,with aliases including"Huangshi shi","Shihuang","Tianyang shi","Jiguan shi","Xunhuang","Chouhuang",and"Mingxiong",etc.Ancient herbal records indicate that Xunhuang,Chouhuang,and Shuiku Realgar are all Realgar,while Orpiment is its associated mineral.In ancient times,the main production areas of Realgar were in Gansu,with outputs also seen in Shandong and Hunan.Nowadays,it is mainly produced in Hunan,Guizhou,Hubei,Gansu,Yunnan,Sichuan and other regions.Ancient herbal works mentioned characteristics like"cockscomb color","non-stinky","solid","red and bright"in their quality evaluation,while modern herbal works mostly evaluate its quality by color and texture,such as"red color","large blocks","brittle texture","glossy".The traditional efficacy of Realgar is to dry dampness,kill insects,detoxify various poisons,treat sores and activate blood.Modern studies have shown it also has anti-tumor,antibacterial and antiviral effects.Processing methods in past dynasties included water grinding,vinegar processing,refining,etc.,and currently,water grinding and acid water grinding are commonly used.This paper observed the properties of Realgar,detected the content of As?S?,and analyzed the microscopic characteristics and far-infrared spectral characteristics of qualified batches of Realgar.It was finally found that the As?S? content of qualified batches of Realgar was all more than 90%;under the scanning electron microscope,it showed massive shape,uniform distribution,obvious particles,and no agglomeration;a small amount of associated mineral Orpiment crystals were observed under the polarizing microscope;the characteristic peaks of Realgar(343-344 cm?1,224-225 cm?1,372-374 cm?1,367-369 cm?1,359 cm?1,207-208 cm?1,193-194 cm?1,168-170 cm?1)and Orpiment(390 cm?1,380 cm?1,347 cm?1,311 cm?1,300 cm?1,201 cm?1,182 cm?1,158 cm?1 and 139 cm?1)were determined."Red color and glossy"can be used as property references,"Realgar is a sulfide mineral of the Realgar family,with the main chemical component As?S?,associated with Orpiment"can be used as origin references,and"Shimen in Hunan,Wanshan in Guizhou,Yunnan,Gansu,Sichuan"can be used as production area references,which are consistent with the results of herbal textual research.This study provides a basis for the identification and analysis of Realgar,with a view to better guiding clinical medication and resource utilization.

A novel epileptic seizure prediction prediction model based on multichannel temporal and spatial feature extractions is presented.The model applies Stockwell transform to the original multichannel electroencephalogram(EEG)signals for extracting time-frequency components.To address the issue of insignificant difference between preseizure and interseizure time-frequency features,an adaptive feature module composing of ConvNeXt,SENet and pyramid pooling module is designed to enhance the ability of capturing key time-frequency features in each EEG channel.Meanwhile,a prediction model based on Bi-NLSTM is constructed to improve the spatiotemporal dependence between the time-frequency features of multichannel high-order EEG for further promoting the epilepsy classification performance.On the CHB-MIT dataset,the model has an accuracy,sensitivity,specificity and AUC of 96.0%,97.8%,96.8%and 0.987,respectively,and the false positive rate per hour decreased to 0.038,outperforming the existing mainstream methods.In addition,the effect of each component on the model performance is verified by ablation study.The proposed method improves the overall performance for seizure prediction effectively by optimizing local time-frequency feature extraction and enhancing multichannel spatiotemporal dependence.