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.

Beyond Borders: Investigating the Impact of COVID-19 Anxiety and Eating Attitudes on Psychological Well-Being and Physical Activity Objectives in Poland and China.

The mechanisms linking eating attitudes to well-being and physical activity objectives have increasingly attracted the attention of researchers in recent years. This research is particularly significant in the context of the COVID-19 pandemic, which has profoundly disrupted eating habits, exercise routines, and psychosocial well-being across the globe. Additionally, these variables are influenced by cultural dimensions, such as individualism in Poland and collectivism in China.

Identification of the Metabolic Characteristic of Organ Fibrosis Using Microbial Analysis on RNA-seq Data.

Background: Fibrosis refers to abnormal deposition of extracellular matrix, which leads to organ dysfunction. Metabolic alterations, especially enhanced glycolysis and suppressed fatty acid oxidation, are recognized as an essential pathogenic process of fibrosis. Recently, several reports indicate that the changes in microbiota composition are associated with metabolic disorders, suggesting microbes may contribute to organ fibrosis by regulating metabolic processes.

Doxazosin Attenuates Development of Testosterone Propionate-induced Prostate Growth by regulating TGF-β/Smad Signaling Pathway, Prostate-specific Antigen Expression and Reversing Epithelial-mesenchymal Transition in Mice and Stroma Cells.

Background: Finasteride and doxazosin are used for the treatment of benign prostatic hyperplasia (BPH) and lower urinary tract symptoms (LUTS). Epithelial-mesenchymal transition (EMT) plays an important role in BPH, little is known about the growth inhibition and anti-fibrosis effects of doxazosin on the regulation of EMT and morphology in the prostate.

Objectives: The present study examined the effects of doxazosin on testosterone propionate (TP)-induced prostate growth in vivo and in vitro and its impact on the EMT and TGF-β/Smad signaling pathway.

Organoids in skin wound healing.

Stem cells (SCs) can self-replicate and differentiate into multiple lineages. Organoids, 3D cultures derived from SCs, can replicate the spatial structure and physiological characteristics of organs . Skin organoids can effectively simulate the physiological structure and function of skin tissue, reliably restoring the natural skin ecology in various environments.

Early psilocybin intervention alleviates behavioral despair and cognitive impairment in stressed Wistar rats.

Chronic stress exerts profound effects on mental health, contributing to disorders such as depression, anxiety, and cognitive impairment. This study examines the potential of psilocybin to alleviate behavioral despair and cognitive deficits in a rodent model of chronic stress, focusing on the interplay between the Hypothalamic-Pituitary-Adrenal (HPA) axis and the Endocannabinoid System (ECS). Twenty-two male Wistar rats were divided into control and stress groups.

Autoantibodies in hospitalised patients with COVID-19.

Objectives: CD209L and its homologous protein CD209 act as alternative entry receptors for the SARS-CoV-2 virus and are highly expressed in the virally targeted tissues. We tested for the presence and clinical features of autoantibodies targeting these receptors and compared these with autoantibodies known to be associated with COVID-19.

Methods: Using banked samples ( = 118) from Johns Hopkins patients hospitalised with COVID-19, we defined autoantibodies against CD209 and CD209L by enzyme-linked immunosorbent assay (ELISA).

Craniofacial Development Characteristics in Children with Obstructive Sleep Apnea for Establishment and External Validation of the Prediction Model.

Purpose: Aimed to analyze the developmental characteristics of craniofacial structures and soft tissues in children with obstructive sleep apnea (OSA) and to establish and evaluate prediction model.

Methods: It's a retrospective study comprising 747 children aged 2-12 years (337 patients and 410 controls) visited the Department of Otolaryngology-Head and Neck Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University (July 2017 to March 2024). Lateral head radiographs were obtained to compare the cephalometric measurements.

Omnipotent antibacterial cotton fabrics with superhydrophobic and photothermal properties.

Due to the outbreak of global public health emergency, antibacterial fabrics such as face masks are in great demand. However, common antibacterial fabrics cannot kill bacteria in minutes and they are easy to be contaminated and lost biological activity. In this work, omnipotent antibacterial cotton fabrics with superhydrophobic and photothermal properties are developed by the combination of dopamine with copper sulfide (CuS) and silver nanoparticles on cotton fabrics, and post-modification with PDMS.

Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.

Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framework integrating pretreatment CT scans and H&E-stained biopsy images, for improved decision-making regarding neoadjuvant chemotherapy. We have constructed and tested iSCLM using retrospective data from 2,387 patients across 10 medical centers and evaluated its discriminative ability in a prospective cohort (132 patients; ChiCTR2300068917).

Development and external validation of a Nomogram to predict obstructive sleep apnea in Children.

Unlabelled: To develop a nomogram model to predict obstructive sleep apnea in children and perform an external validation. 864 children who underwent polysomnography at our hospital were randomly assigned to a training cohort and an internal validation cohort (7:3) and 292 children at another hospital were enrolled for external validation. Logistic regression analyses were performed to explore the risk factors for obstructive sleep apnea.

Cationic Metal-Organic Layer Delivers siRNAs to Overcome Radioresistance and Potentiate Cancer Radiotherapy.

Radiotherapy plays an important role in modern oncology, but its treatment efficacy is limited by the radioresistance of tumor cells. As a member of the inhibitor of apoptosis protein family, survivin plays a key role in developing radioresistance by mediating apoptosis evasion, promoting epithelial-mesenchymal transition, and modulating cell cycle dynamics. Efficient downregulation of survivin expression presents a promising strategy to enhance the antitumor effects of radiotherapy.

[Construction of OSA-related hypertension prediction model based on nomogram].

This study aimed to construct a risk prediction model for obstructive sleep apnea（OSA） related hypertension based on the nomogram, and to explore the independent risk factors for OSA-related hypertension, so as to provide reference for clinical treatment decision-making. The clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 were collected retrospectively and randomly divided into training sets and validation sets. A total of 1 493 OSA patients with 27 variables were included.

Organometallic Compound Stabilizes All-Inorganic Tin-Based Perovskite Nanocrystals Against Antisolvent Post-Treatment.

The isolation and purification of all-inorganic Sn-based perovskite nanocrystals (PNCs) remain troublesome, as common antisolvents accelerate the collapse of the optically active perovskite structure. Here, we mitigate such instabilities and endow strong resistance to antisolvent by incorporating the organometallic compound zinc diethyldithiocarbamate, Zn(DDTC), during the solution-based synthesis of all-inorganic CsSnI nanocrystals. Thiourea is shown to form through the thermal-driven conversion of Zn(DDTC) during synthesis, which binds to un-passivated Sn sites on the crystal surface and shields it from irreversible oxidation reactions.

Resistance risk and resistance-associated point mutations in the target protein PcVHA-a of fluopimomide in Phytophthora capsici.

Fluopimomide, developed by Shandong Sino-Agri United Biotechnology Co., Ltd., is a pyridinylmethyl-benzamide fungicide with good activity against plant diseases caused by phytopathogenic oomycetes.

A case of neoadjuvant targeted therapy with pralsetinib for locally advanced lung adenocarcinoma with RET fusion mutation.

This case report details the successful treatment of a 68-year-old male patient with locally advanced RET-rearranged lung adenocarcinoma using neoadjuvant pralsetinib. The patient initially presented with a suspicious right upper lobe nodule, which was later diagnosed as lung adenocarcinoma following genetic testing that revealed a RET exon 12 fusion. After 2 months of neoadjuvant treatment with pralsetinib, a significant radiological response was observed, with a reduction in tumor size and metabolic activity.

Optically accessible long-lived electronic biexcitons at room temperature in strongly coupled H- aggregates.

Photon absorption is the first process in light harvesting. Upon absorption, the photon redistributes electrons in the materials to create a Coulombically bound electron-hole pair called an exciton. The exciton subsequently separates into free charges to conclude light harvesting.

Dronedarone inhibits the proliferation of esophageal squamous cell carcinoma through the CDK4/CDK6-RB1 axis in vitro and in vivo.

Treatment options for patients with esophageal squamous cell carcinoma (ESCC) often result in poor prognosis and declining health-related quality of life. Screening FDA-approved drugs for cancer chemoprevention is a promising and cost-efficient strategy. Here, we found that dronedarone, an antiarrhythmic drug, could inhibit the proliferation of ESCC cells.

Vitamin E Regulates the Collagen Contents in the Body Wall of Sea Cucumber () via Its Antioxidant Effects and the TGF-β/Smads Pathway.

A 70-day feeding experiment was performed to investigate the effects of dietary vitamin E at different addition levels (0, 100, 200, and 400 mg/kg) on the growth, collagen content, antioxidant capacity, and expressions of genes related to the transforming growth factor beta (TGF-β)/Sma- and Mad-related protein (SMAD) signaling pathway in sea cucumbers (). The results showed that the in the group with 200 mg/kg vitamin E exhibited significantly higher growth rates, hydroxyproline (Hyp) and type III collagen contents, and superoxide dismutase (SOD) activity, as well as the upregulation of genes related to Tenascin, SMAD1, and TGF-β. Additionally, the in the group with 100 mg/kg vitamin E exhibited significantly higher body-wall indexes, denser collagen arrangements, improved texture quality, higher activities of glutathione peroxidase (GSH-Px) and peroxidase (POD), as well as the upregulation of genes related to collagen type I alpha 2 chain (COL1A2), collagen type III alpha 1 chain (COL3A1), and Sp-Smad2/3 (SMAD2/3).

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.