Technology and Health Care最新文献

BackgroundElectroencephalography (EEG), a noninvasive technique for recording the brain's electrical activity, has been widely utilized to investigate neurological disorders.ObjectiveEEG recordings can estimate scalp connectivity and select representative channels, which reveal network connectivity and associated brain regions. These details are considered essential for understanding the characteristics of neurological disorders.MethodsThis work proposes an explainable Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD) based EEG microstate sequence approach to achieve scalp connectivity estimation and channel selection. Epilepsy, one of the most frequently studied neurological disorders using EEG, has been selected for method validation. Receiver Operating Characteristic (ROC) curve analysis and consistency analysis with conventional techniques are performed to specify key parameters such as connection thresholds and time durations, ensuring the reliability of the outcomes.ResultsThe experimental results of the clinical Karunya dataset indicate that the proposed microstate sequence compressed from the EEG contains sufficient information to estimate scalp connectivity and select representative channels. The scalp connectivity results reveal differences between focal and generalized seizures, where focal seizures exhibit more localized connectivity and generalized seizures display a widespread distribution. Moreover, statistical results demonstrate that the F4, C4, T4, and P4 channels present a higher rate of being representative channels in this dataset.ConclusionsThe proposed approach offers valuable characteristics, indicating brain networks that assist in epilepsy analysis by focusing on the most informative scalp locations and reducing computational complexity. It lays the groundwork for investigating various neurological disorders through scalp behaviors from EEG, guiding personalized diagnostics and therapeutic strategies.

BackgroundSocial anxiety disorder (SAD) significantly impairs social functioning. Virtual reality exposure therapy (VRET) offers a promising treatment by providing a controlled, customizable environment. This study aimed to develop and evaluate the efficacy and user experience of a VRET program.MethodsThe study was conducted in two phases: Phase I with the general population and Phase II with individuals diagnosed with SAD at a university hospital. Social anxiety, depression, anxiety, and stress were measured using the Social Interaction Anxiety Scale and the Depression Anxiety and Stress Scale at three time points: before, immediately after, and two weeks post-VRET. The Virtual Reality Neuroscience Questionnaire assessed user experience, game mechanics, in-game assistance, and any VR-induced symptoms. Our VRET program utilized graded exposure techniques within culturally relevant social scenarios.ResultsBoth groups exhibited significant reductions in social anxiety levels following VRET sessions (ps < 0.01) compared to pre-VRET levels. However, individuals with SAD reported increased social anxiety at the two-week follow-up, while the general population maintained their improvement. The VR software received satisfactory ratings for usability, safety, and acceptability.ConclusionThis program demonstrates potential for reducing social anxiety and provides a satisfactory VR experience, supporting its feasibility for individuals with SAD in a developing country. Given the pilot nature and limited sample size, these findings should be interpreted cautiously. Future research with larger samples and repeated sessions is needed to enhance efficacy and ensure long-term benefits. Comprehensive treatment protocols, including tutorials, relaxation techniques, and stress monitoring, are recommended for optimal outcomes.

BackgroundThe invasion and metastasis of hepatocellular carcinoma (HCC) are closely associated with angiogenesis, positioning anti-angiogenic strategies as a promising approach for cancer treatment. This study aims to investigate the role of collagen triple helix repeat containing 1 (CTHRC1) in regulating angiogenesis in HCC.MethodsRelevant bioinformatics analysis was conducted by retrieving publicly available datasets of HCC patients to identify genes exhibiting significant expression patterns linked to vascular invasion. In vitro assays were performed using human liver cancer cell lines (Hep3B, HepG2) and human umbilical vein endothelial cells (HUVECs) to evaluate the effects of CTHRC1 on vascular endothelial growth factor (VEGF) levels and cellular behaviors, including proliferation, migration, and tube formation.ResultsElevated CTHRC1 expression was significantly associated with poor prognosis in HCC patients. Furthermore, CTHRC1 exhibited a positive correlation with VEGF-A, VEGF-B, and VEGF-C levels. Manipulating CTHRC1 expression directly impacted VEGF production and influenced the growth, migration, and tube formation capabilities of HUVECs, as well as the invasion potential of HCC cells.ConclusionCTHRC1 modulates HUVEC proliferation, motility, and tube formation by regulating VEGF expression,thereby influencing HCC progression.

BackgroundMycoplasma pneumoniae pneumonia (MPP) represents the predominant form of community-acquired pneumonia in children. Clinical challenges in identifying severe MPP (SMPP) critically threaten pediatric health.ObjectiveThis study aimed to evaluate the application of the computer-aided system for standardizing tongue image characteristics and diagnosing SMPP.MethodsWe enrolled hospitalized children with general MPP (GMPP, n = 243) and SMPP (n = 371) between 2023 and 2024. The SMF-III system was employed to quantify tongue image features. Univariate logistic regression analysis was performed to identify key independent risk factors for SMPP, followed by correlation analysis. ROC curve analysis was conducted to assess diagnostic efficacy.ResultsSignificant differences in tongue features were observed between the GMPP and SMPP groups. SMPP patients predominantly exhibited red/crimson tongue coloration, yellow-white/yellow coatings, thin-greasy/thick coating textures, and reduced or absent moisture with higher total tongue image scores. Logistic regression confirmed the scores, CRP, NLR, IL-6, and IFN-γ as independent risk factors for SMPP. The scores were positively correlated with CRP, NLR, IL-6, and IFN-γ. Notably, combining tongue image scores with CRP enhanced predictive accuracy for SMPP.ConclusionTongue image variations reflect pediatric MPP disease progression. The computer-aided tongue diagnostic system provides a rapid, cost-effective, and reliable tool for auxiliary SMPP diagnosis.

BackgroundWith the increasing incidence of malignant hematological neoplasms in the elderly population, debilitating issues have gradually become an important challenge for patients.ObjectivesTo construct a prediction model, draw a nomogram, and perform internal validation of the model.Methods505 elderly patients with hematological neoplasms were included in the study. The survey was conducted using research tools such as a general information questionnaire, the Chinese version of the Geriatric 8. A risk prediction model was established and a line chart was drawn to visualize the model after univariate and multivariate Logistic regression analysis. Internal validation of the model was performed using Boot strap bootstrap sampling, calibration curve, receiver operating characteristic curve and area under curve, decision curve analysis to internally validate the model.ResultsAfter constructing the model and resampling, it was shown that the calibration curve matched the ideal curve well, and the decision analysis curve showed good calibration, discrimination, and clinical benefit within the 0.0-1.0 threshold range.ConclusionThe prediction model constructed in this study has good predictive effects and can help clinical medical staff to identify the risk of frailty in geriatric hematologic neoplasms patients at an early stage.

BackgroundHepatic metastases are the most common malignant tumors in the liver. Conventional contrast-enhanced CT examinations face challenges in distinguishing between benign and malignant atypical metastatic liver lesions with a diameter <3 cm, and evaluating their therapeutic efficacy remains particularly difficult.ObjectiveTo assess the clinical value of quantitative iodine analysis and spectral curves for diagnosing and differentiating liver metastases using gemstone spectral CT.MethodsAmong 915 patients with suspected hepatic metastases, 140 cases (87 males, 53 females) were pathologically confirmed. Primary malignancies included colorectal cancer (41 cases), gastric cancer (21 cases), lung cancer (35 cases), pancreatic cancer (31 cases), and breast cancer (12 cases). A total of 425 small lesions (<3 cm) were detected. CT values at lesion centers and peripheries were measured and compared against normal liver parenchyma. Quantitative iodine concentrations and spectral curve slopes were analyzed to evaluate differences in small hepatic metastases originating from distinct primary malignanciesResultsIn the differentiation of hepatic metastatic subtypes, mean CT values demonstrated diagnostic utility in distinguishing colorectal cancer from gastric in AP (Arterial Phase, P = 0.001) in the Center of lesions.On the contrary, the quantitative analysis of focus edge iodine reliably distinguished the AP (P < 0.000), lung cancer (P = 0.023) and pancreatic cancer (P < 0.000) of colorectal cancer and gastric cancer. The results have statistical significance.ConclusionsGSI(Gemstone spectral Imaging)-derived spectral curve slope and quantitative iodine analysis may facilitate differential diagnosis of small hepatic metastatic lesions with diverse primary origins, especially the source of colorectal cancer.

BackgroundDiagnosing spinal tumors (ST) has always been challenging, especially when distinguishing between benign and malignant types. Incorrect diagnosis can lead to inappropriate treatment plans for patients. Previous studies have primarily focused on detecting and classifying these tumors using MRI scans. However, further research is needed to improve diagnostic accuracy.ObjectiveThis study proposes a novel approach that incorporates both MRI images and patient age data to enhance the detection and classification of spinal tumors. The approach utilizes Inception V3 for local feature extraction and the Vision Transformer (ViT) for global feature classification, addressing long-term dependencies in tumor data.MethodMRI images are pre-processed using an Average Filter (AF) and a Morphological Operator (MO) to smooth the images and convert them into binary format. The tumor detection is performed using a hybrid deep learning model, integrating age-related information to improve classification accuracy.ResultsThe pre-processed data is passed through a Self-Attention Fusion Mechanism (SAFM) to refine the results and enhance diagnostic accuracy by filtering out irrelevant information. The model's performance is evaluated through various metrics, including accuracy, sensitivity, and specificity, showing significant improvements over existing techniques.ConclusionThe proposed model demonstrates superior accuracy in diagnosing spinal tumors by combining MRI imaging and patient age data. It effectively differentiates benign from malignant tumors, providing a reliable tool for clinicians. The model achieved a specificity of 96%, accuracy of 93%, and computational delay of just 10.04 s, outperforming existing diagnostic models.

BackgroundIschemic stroke (IS) is a prevalent and serious neurological disorder, and inflammation and immune responses are crucial in the development of IS. O-GlcNAcylation is a form of post-translational modification that plays roles in numerous significant biological processes.ObjectiveThe major objective of the current study was to examine the involvement of O-GlcNAcylation associated genes in the pathogenesis of IS.MethodsWe downloaded two IS datasets from the GEO database, and subsequently the infiltration level of immune cells was quantified and compared. Differentially expressed O-GlcNAcylation genes were identified and machine learning algorithms were utilized to screen the hub genes. Subsequently, the IS samples were further classified based on hub genes through consensus clustering.ResultsOverall, nineteen O-GlcNAcylation related DEGs were identified. Through the machine learning algorithms, eight hub genes related to immune cell infiltration was identified. GSEA results showed that hub genes significantly correlated with immune system, RNA metabolism, and translation. Then two distinct subclusters mediated by O-GlcNAcylation were further defined, and functional analysis of cluster-specific DEGs demonstrated their participation in processes related to inflammation and immune response.ConclusionThe O-GlcNAcylation has a significant impact on the pathogenesis of IS, which is correlated with immunological response and metabolic activity. The findings of this research could serve as a valuable guide for exploring the molecular mechanisms of IS and offer insights into drug screening and immunotherapy for IS.

This study investigates the efficacy of two advanced magnetic resonance technologies, proton magnetic resonance spectroscopy (1H MRS) and chemical exchange saturation transfer (CEST), for the precise detection of epileptic foci through the quantification of glutamate in a patient with drug-resistant epilepsy. A 21-year old female patient with drug-resistant epilepsy was examined using magnetic resonance imaging (MRI), MRS, and CEST with a 3.0-T MRI scanner with an 8-channel phased array head coil. Despite the absence of identifiable lesions on conventional MRI scans, CEST identified regions of high glutamate concentration in the left hippocampus, consistent with an epileptic focus. These results were confirmed with MRS. The findings of this investigation indicate that CEST is an effective method for the detection of high levels of glutamate, which correspond with epileptic foci. We therefore propose that CEST and MRS be incorporated into the standard diagnostic protocol used for patients with drug-resistant, MRI-negative epilepsy.