Identification of Alzheimer's disease brain networks based on EEG phase synchronization.

Objective: Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer's disease (AD) and to assist in the early diagnosis of Alzheimer's disease.

Methods: In this paper, we selected outpatients (16 AD subjects, 20 mild cognitive impairment (MCI) subjects and 21 healthy control (HC) subjects) from the outpatient clinic of Yangpu Mental Health Center in Shanghai, China, from January 2023 to December 2023, and collected resting-state EEG data. To collect resting-state EEG data, each patient was asked to sit down with eyes closed for 5 min.

A Multi-objective transfer learning framework for time series forecasting with Concept Echo State Networks.

This paper introduces a novel transfer learning framework for time series forecasting that uses Concept Echo State Network (CESN) and a multi-objective optimization strategy. Our approach addresses the challenges of feature extraction and knowledge transfer in heterogeneous data environments. By optimizing CESN for each data source, we extract targeted features that capture the unique characteristics of individual datasets.

Estradiol Promotes Endometriosis Progression Via the ERβ/QKI /circSMAD2 Axis.

Aims: The present study aimed to examine the roles of circRNA-circSMAD2 and its regulatory mechanisms in endometriosis (EMs).

Background: Evidence has confirmed that circRNAs play multiple roles in regulating the occurrence and development of EMs, but the regulatory mechanisms of circRNAs in EMs remain largely unknown.

Objective: The roles and regulatory mechanisms of circSMAD2 in EMs.

Identification of Urinary Metabolic Biomarkers for H3K27M Mutation Diagnosis in Brainstem Gliomas.

Background: Brainstem gliomas (BSGs) harboring a histone 3 lysine27-to-methionine (H3K27M) mutation represent one of the deadliest brain tumors with a dismal prognosis, as they exhibit a much worse response to therapy compared to the wildtype BSGs. Early non-invasive recognition of the H3K27M mutation is paramount for clinical decision-making in treating BSGs.

Methods: Plasma and urine samples were prospectively collected from BSG patients before biopsy or surgical resection and were chronologically divided into discovery, test, and validation cohorts.

Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.

Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.

Elucidating the Molecular Mechanisms and Comprehensive Effects of Sludge-Derived Plant Biostimulants on Crop Growth: Insights from Metabolomic Analysis.

The utilization of urban waste for land management plays a crucial role in reshaping material flows between human activities and the environment. Sewage sludge alkaline thermal hydrolysis (ATH) produces sludge-derived plant biostimulants (SPB), which have garnered attention due to the presence of indole-3-acetic acid. However, there remains a gap in understanding SPB's molecular-level effects and its comprehensive impact on crops throughout their growth cycle.

Multisource representation learning for pediatric knowledge extraction from electronic health records.

Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts.

Identification of drug use degree by integrating multi-modal features with dual-input deep learning method.

Most of studies on drug use degree are based on subjective judgments without objective quantitative assessment, in this paper, a dual-input bimodal fusion algorithm is proposed to study drug use degree by using electroencephalogram (EEG) and near-infrared spectroscopy (NIRS). Firstly, this paper uses the optimized dual-input multi-modal TiCBnet for extracting the deep encoding features of the bimodal signal, then fuses and screens the features using different methods, and finally fused deep encoding features are classified. The classification accuracy of bimodal is found to be higher than that of single modal, and the classification accuracy is up to 89.

The role and mechanism of NRG1/ErbB4 in inducing the differentiation of induced pluripotent stem cells into cardiomyocytes.

Background: We aimed to investigate the effect and potential mechanism of enhancing Neuregulin1 (NRG1)/v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 4 (ErbB4) expression on the differentiation of induced pluripotent stem cells (iPSCs) into cardiomyocytes.

Methods: We utilized CRISPR-CAS9 technology to knock in ErbB4 and obtained a single-cell clone IPSN-AAVS1-CMV-ErbB4 (iPSCs-ErbB4). Subsequently, we induced the differentiation of iPSCs into cardiomyocytes and quantified the number of beating embryoid bodies.

Clinical Value of Impulse Oscillometry in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.

Introduction: Impulse oscillometry (IOS) is an effortless test compared to spirometry. Numerous studies explored the role of IOS in spirometry-based chronic obstructive pulmonary disease (COPD), but most of them had limited sample sizes with poor statistical power. This systematic review and meta-analysis aimed to pool the individual data and quantitatively analyze the clinical value of IOS in COPD.

EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.

Background: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is vital due to the variability among individuals and temporal states.

Methods: This study introduces a novel network architecture, 3D-convolutional neural network-generative adversarial network (3D-CNN-GAN), for decoding both within-session and cross-session motor imagery.

Efficiency of ear molding for treating constricted ears of different severity.

Purpose: To investigate the treatment time and efficiency of constricted ears of different severity after correction.

Materials And Methods: We included the patients with constricted ear presented to our hospital for treatment between December 2021 and December 2023 in this retrospective analysis. The patients were divided into class I, II and III groups based on the severity of the constriction.

Differential expression profiles and functional prediction of circular RNAs in lung cancer patients with chronic obstructive pulmonary disease: a pilot study.

Chronic obstructive pulmonary disease (COPD), characterized by clinical sub-phenotypes such as emphysema (E) and chronic bronchitis (CB), is associated with a greater risk of lung cancer (LC). This study aimed to assess the expression patterns of circRNA and their potential functional involvement in LC patients with COPD. A circRNA microarray was used to characterize differentially expressed circRNAs (DEcircRNAs) profiles.

Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.

Background: Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required.

New Method: In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN).

DNA of neutrophil extracellular traps promote NF-κB-dependent autoimmunity via cGAS/TLR9 in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is characterised by persistent airway inflammation even after cigarette smoking cessation. Neutrophil extracellular traps (NETs) have been implicated in COPD severity and acute airway inflammation induced by short-term cigarette smoke (CS). However, whether and how NETs contribute to sustained airway inflammation in COPD remain unclear.

CircFOXO3 mediates hypoxia-induced autophagy of endometrial stromal cells in endometriosis.

Endometriosis is a benign gynecological disease that shares some common features of malignancy. Autophagy plays vital roles in endometriosis and influences endometrial cell metastasis, and hypoxia was identified as the initiator of this pathological process through hypoxia inducible factor 1 alpha (HIF-1α). A newly discovered circular RNA FOXO3 (circFOXO3) is critical in cell autophagy, migration, and invasion of various diseases and is reported to be related to hypoxia, although its role in endometriosis remains to be elucidated up to now.

Brain network analysis of working memory in schizophrenia based on multi graph attention network.

The cognitive impairment in schizophrenia (SZ) is characterized by significant deficits in working memory task. In order to explore the brain changes of SZ during a working memory task, we performed time-domain and time-frequency analysis of event related potentials (ERP) of SZ during a 0-back task. The P3 wave amplitude was found to be significantly lower in SZ patients than in healthy controls (HC) (p < 0.

Exploring wireless device-free localization technique to assist home-based neuro-rehabilitation.

Home-based movement neuro-rehabilitation is quite necessary when the patient goes back home from hospital. Due to lack of supervision from doctors, rehabilitation at home is often forgotten. As an alternate to doctor-supervision, in this research, we explore the wireless device-free localization technique to assist the rehabilitation procedure.

Transcriptome-wide N6-methyladenosine (m6A) methylation profiling of long non-coding RNAs in ovarian endometriosis.

N6-methyladenosine (m6A) methylation is the most prevalent internal epigenetic posttranscriptional mechanism for regulating mammalian RNA. Despite recent advances in determining the biological functions of m6A methylation, its association with the pathology of ovarian endometriosis remains uncertain. Herein, we performed m6A transcriptome-wide profiling to identify key lncRNAs with m6A modification involved in ovarian endometriosis development by bioinformatics analysis.

Blood Eosinophil Count and Its Determinants in a Chinese Population-Based Cohort.

Introduction: Blood eosinophil count has been shown markedly variable across different populations. However, its distribution in Chinese general population remains unclear. We aimed to investigate blood eosinophil count and its determinants in a Chinese general population.

Expression of Interleukin-13 Receptor Alpha 2 in Brainstem Gliomas.

The objective of this study was to investigate IL13Ra2 expression in brainstem glioma (BSG) and its correlation with key markers, functions, and prognostic implications, evaluating its therapeutic potential. A total of 80 tumor samples from BSG patients were analyzed. Multiplex immunofluorescence was used to examine six markers-IL13Ra2, H3.

Electrochemical biosensor based on PAMAM functionalized MXene nanoplatform for detection of folate receptor.

The level of folate receptor (FR) has become one of the independent factors for measuring human tumor diseases. The precise quantification of FR is helpful for the early diagnosis and subsequent treatment of tumors. The modification of electrodes is a key issue in ensuring and enhancing the electrochemical biosensing ability.

The diagnosis and treatment of pediatric clinical myocarditis in China: a multicenter retrospective study.

Unlabelled: This study aims to examine the clinical characteristics and outcomes of clinical myocarditis in pediatric patients in China. This is a multicenter retrospective study. Children diagnosed with clinical myocarditis from 20 hospitals in China and admitted between January 1, 2015, and December 30, 2021, were enrolled.