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

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.

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.

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.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

Objective To investigate the prevalence of Cryptosporidium infection and the distribution of parasite species and genotypes among HIV-positive individuals in Jiangxi Province. Methods HIV-positive individuals' sociodemographic and clinical data were collected from three AIDS designated hospitals in Jiangxi Province from January 2022 to March 2023. Subjects' stool samples were collected, and genomic DNA was extracted from stool samples. Nested PCR assay was performed based on the small subunit ribosomal RNA (SSU rRNA) gene of Cryptosporidium, and Cryptosporidium gp60 gene was amplified in stool samples positive for the SSU rRNA gene. The second-round PCR amplification product was checked with 1.5% agarose gel electrophoresis, and the products of suspected positive amplifications were sequenced, followed by sequence alignment. The phylogenetic tree was created using the Neighbor-Joining method with the software MEGA 11.0, to characterize the species, genotypes and sub-genotypes of Cryptosporidium. Results A total of 382 HIV-positive individuals were enrolled, with two cases identified with Cryptosporidium infection (0.52% prevalence), and both cases had no abdominal pain or diarrhea. Following sequencing and sequence alignment, the gene sequences of these two Cryptosporidium isolates shared 99.76% and 99.88% similarity with the gene sequence of C. meleagridis isolates. Phylogenetic analysis based on the Cryptosporidium SSU rRNA gene sequence identified the species of these two Cryptosporidium-positive stool samples as C. meleagridis. Following nested PCR amplification of the Cryptosporidium gp60 gene, sequencing and sequence alignment, the two C. meleagridis isolates were characterized as III eA17G2R1 and III bA25G1R1a sub-genotypes, and the sub-genotype III bA25G1R1a was firstly described in humans. Conclusion The prevalence of Cryptosporidium is low among HIV-positive individuals in Jiangxi Province. The likelihood of Cryptosporidium infection cannot be neglected among HIV-positive individuals without diarrhea.

Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.

Objective:To investigate the value of dual-detector spectral CTA in distinguishing infarct core from penumbra in patients with acute ischemic stroke(AIS), and to further explore the risk factors associated with infarct core and their predictive value.Methods:The imaging and clinical data of 163 patients with AIS who met the inclusion criteria admitted to the Second Affiliated Hospital of Soochow University from March 2022 to May 2023 were retrospectively analyzed. Patients from March 2022 to December 2022 were used as the training group, and patients from January 2023 to May 2023 were used as the validation group for internal validation. The head and neck spectral CTA and brain CT perfusion imaging with dual-layer detector spectral CT were all carried out on all patients. Using CTP as reference, the patients were divided into infarct core group and non-infarct core group according to whether an infarct core occurred in the hypoperfusion regions of brain tissue. Multivariate logistic regression analysis was used to screen predictors related to the infarct core. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy.Results:A total of 163 patients were included in the study, including 112 in the training group and 51 in the validation group. There were significant differences in iodine density, effective atomic number, hypertension, triglyceride and neutrophils between the two groups ( P< 0.05). The cutoff values for iodine density values and effective atomic number values were 0.215 mg/mL and 7.405, respectively. Multivariate logistic regression analysis showed that iodine density and hypertension were independent risk factors for infarct core in AIS, and triglyceride was an independent protective factor. The area under the ROC curve (AUC) of iodine density value was the largest (0.859), with a sensitivity of 70.27%, and a specificity of 90.67%, which had a good predictive value. The ROC curve analysis results for the validation group were consistent with the training group. Conclusions:Spectral CT parameters iodine density values and effective atomic number values have the potential to distinguish the infarct core area from the penumbra area in patients with AIS. Iodine density and hypertension were independent risk factors of infarct core in AIS, triglyceride was an independent protective factor, and iodine density values obtained by dual-layer spectral detector CT had a high predictive value.