The Initial Screening of Laryngeal Tumors via Voice Acoustic Analysis Based on Siamese Network Under Small Samples.

Objective: The initial screening of laryngeal tumors via voice acoustic analysis is based on the clinician's experience that is subjective. This article introduces a Siamese network with an auxiliary gender classifier for automated, accurate, and objective initial screening of laryngeal tumors based on voice signals.

Methods: The study involved 71 tumor patients and 293 non-tumor subjects of Chinese Mandarin.

Single-View Contrastive Learning for Laryngeal Leukoplakia Classification With NBI Laryngoscopy Images.

Background: Laryngeal cancer is the second most common upper respiratory tract cancer. Early and accurate diagnosis can improve the cure rate of patients. Laryngoscopy with NBI is a commonly used tool that can help endoscopists diagnose laryngeal diseases.

Endovascular treatment for cerebral venous thrombosis: a multicenter study in China.

Background: Endovascular treatment (EVT) is gaining popularity for the management of severe forms of cerebral venous thrombosis (CVT), but the evidence supporting its efficacy and safety is limited.

Methods: This multicenter study included patients with CVT admitted to 104 hospitals in 31 provinces/cities in China between January 2018 and June 2022. Propensity score weighting models were used to adjust baseline confounding variables to determine the association of EVT on the primary outcome of good functional status, defined as score 0 - 1 on the modified Rankin Scale after hospital discharge.

Exploring the role of alternative splicing in the pathogenesis of Sjögren's syndrome: identification of novel isoforms associated with salivary gland immune infiltration.

Introduction: Sjögren's syndrome (SS) is an autoimmune disorder affecting exocrine glands, causing dry mouth and eyes, with no effective treatment. While high-throughput sequencing has provided insights into its mechanisms, the role of alternative splicing (AS) in SS remains underexplored.

Objective: To investigate the relationship between immune infiltration in the salivary glands and AS events at the transcriptomic level, and to identify potential biomarkers that may be linked to the diagnosis and prognosis of SS.

Integrating transcriptome and metabolomics revealed the key metabolic pathway response of Amaranthus retroflexus L. to the resistance to fomesafen.

Background: Amaranthus retroflexus L. is one of the main broad-leaved weeds in soybean fields in Heilongjiang Province and is an important factor affecting soybean yield. It is becoming increasingly resistant to herbicides.

Deep Learning for Obstructive Sleep Apnea Detection and Severity Assessment: A Multimodal Signals Fusion Multiscale Transformer Model.

Purpose: To develop a deep learning (DL) model for obstructive sleep apnea (OSA) detection and severity assessment and provide a new approach for convenient, economical, and accurate disease detection.

Methods: Considering medical reliability and acquisition simplicity, we used electrocardiogram (ECG) and oxygen saturation (SpO) signals to develop a multimodal signal fusion multiscale Transformer model for OSA detection and severity assessment. The proposed model comprises signal preprocessing, feature extraction, cross-modal interaction, and classification modules.

Deep self-supervised spatial-variant image deblurring.

Most existing model-based and learning-based image deblurring methods usually use synthetic blur-sharp training pairs to remove blur. However, these approaches do not perform well in real-world applications as the blur-sharp training pairs are difficult to be obtained and the blur in real-world scenarios is spatial-variant. In this paper, we propose a self-supervised learning-based image deblurring method that can deal with both uniform and spatial-variant blur distributions.

Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments.

Metabolomics analysis of the effects of chelerythrine on .

Rice false smut (RFS) caused by a virens is widely distributed in major rice-producing regions. Previous studies have shown that treating RFS with chelerythrine can decrease the germination of fungus spores by 86.7% and induce fungal cell apoptosis.

Porous Waterborne Polyurethane Films Templated from Pickering Foams for Fabrication of Synthetic Leather.

Waterborne polyurethane (WPU) latex nanoparticles with proven interfacial activity were utilized to stabilize air-water interfaces of Pickering foams through interfacial interaction with hydrophobic fumed silica particles (SPs). The rheological properties of the Pickering foam were tailored through adjustment of their SP content, which influenced their formability and stability. A Pickering foam stabilized with WPU and SPs was used as a template to prepare a WPU-SP composite porous film.

Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis.

Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean graph data, have shown advantages in learning representations of brain networks. However, considering the complexity, multilayeredness, and spatio-temporal dynamics of brain activities, we have identified two limitations in current GCN-based research on brain networks: 1) Most studies have focused on unidirectional information transmission across brain network levels, neglecting joint learning or bidirectional information exchange among networks. 2) Most of the existing models determine node neighborhoods by thresholding or simply binarizing the brain network, which leads to the loss of edge weight information and weakens the model's sensitivity to important information in the brain network.

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis.

Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio.

Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images.

Objective: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification.

Study Design: A study of a classification network based on a retrospective database.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

Background: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification.

A shallow mirror transformer for subject-independent motor imagery BCI.

Objective: Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.

Background: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized.

Salvianolic acid B exerts an anti-hepatocellular carcinoma effect by regulating the Hippo/YAP pathway and promoting pSmad3L to pSmad3C simultaneously.

Salvianolic acid B (Sal B) is a component obtained from Salvia miltiorrhiza and is empirically used for liver diseases. The TGF-β/Smad and Hippo/YAP pathways may interact with each other in hepatocellular carcinoma (HCC). Previously, we found that Sal B mediates the TGF-β/Smad pathway in mice and delays liver fibrosis-carcinoma progression by promoting the conversion of pSmad3L to pSmad3C, but the effect of Sal B on the Hippo/YAP pathway has not been determined.

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain.

Effects of imidacloprid and thiamethoxam on the development and reproduction of the soybean aphid Aphis glycines.

The soybean aphid Aphis glycines Matsumura (Hemiptera: Aphididae) is a primary pest of soybeans and poses a serious threat to soybean production. Our studies were conducted to understand the effects of different concentrations of insecticides (imidacloprid and thiamethoxam) on A. glycines and provided critical information for its effective management.

Smad3 gene C-terminal phosphorylation site mutation exacerbates CCl-induced hepatic fibrogenesis by promoting pSmad2L/C-mediated signaling transduction.

Current researches have confirmed that Smads, mediators of TGF-β signaling, are strictly controlled by domain-specific site phosphorylation in the process of hepatic disease. Usually, Smad3 phospho-isoform pSmad3L and pSmad3C are reversible and antagonistic; pSmad2L/C could act together with pSmad3L by stimulating PAI-1 expression and ECM synthesis to transmit fibrogenic signals. Our recent study found that pSmad3C mutation is supposed to perform a vigorous role on the early phase of liver injury and abates salvianolic acid B's anti-hepatic fibrotic-carcinogenesis.

Astragaloside IV inhibits hepatocellular carcinoma by continually suppressing the development of fibrosis and regulating pSmad3C/3L and Nrf2/HO-1 pathways.

Ethnopharmacological Relevance: Astragalus is a medicinal herb used in China for the prevention and treatment of diseases such as diabetes and cancer. As one of the main active ingredients of astragalus, Astragaloside IV (AS-IV) has a wide range of pharmacological effects, including anti-inflammation and anti-cancer effects.

Aim Of The Study: Different phosphorylated forms of Smad3 differentially regulate the progression of hepatic carcinoma.

Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images.

Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions.

Effect of rotenone-induced stress on physiologically active substances in adult Aphis glycines.

The aim of this study was to determine the effect of rotenone stress on Aphis glycines Matsumura (Hemiptera: Aphididae) populations in different habitats of Northeast China. The changes in kinase expression activity of endogenous substances (proteins, total sugars, trehalose, cholesterol, and free amino acids), detoxifying enzymes (cytochrome P450 and glutathione S-transferase), and metabolic enzymes (proteases and phosphofructokinases) in specimens from three populations were compared before and after stress with rotenone at median lethal concentration (LC50) and their response mechanisms were analyzed. Following a 24 h treatment with rotenone, the average LC50 rotenone values in A.

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images.

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper.