Excessive Initial Renal Function Decline Following Sodium-Glucose Cotransporter-2 Inhibitor Treatment Predicts Major Adverse Cardiorenal Outcomes.

Objective: To investigate how estimated glomerular filtration rate (eGFR) decline following sodium-glucose cotransporter-2 inhibitors (SGLT2i) initiation predicts long-term cardiorenal outcomes.

Methods: From 2016 to 2020, a longitudinal cohort of 4942 diabetic patients treated with SGLT2i were enrolled and followed until December 2021. Patients were categorized into mild (≤30%), moderate (>30%∼≤40%) and severe (>40%) decline groups by the maximal eGFR change between 2 to 12 weeks after SGLT2i treatment.

Atomic Gap-State Engineering of MoS for Alkaline Water and Seawater Splitting.

Transition-metal dichalcogenides (TMDs), such as molybdenum disulfide (MoS), have emerged as a generation of nonprecious catalysts for the hydrogen evolution reaction (HER), largely due to their theoretical hydrogen adsorption energy close to that of platinum. However, efforts to activate the basal planes of TMDs have primarily centered around strategies such as introducing numerous atomic vacancies, creating vacancy-heteroatom complexes, or applying significant strain, especially for acidic media. These approaches, while potentially effective, present substantial challenges in practical large-scale deployment.

Effects of adjuvant hyperbaric oxygen therapy and real-time fluorescent imaging on deep sternal wound infection: a retrospective study.

Objective: Deep sternal wound infection (DSWI) is a rare but devastating complication that is estimated to occur in 1-2% of patients after median sternotomy. Current standard of care (SoC) comprises antibiotics, debridement and negative pressure wound therapy (NPWT). Hyperbaric oxygen therapy (HBOT) appears to be an effective adjuvant therapy for osteomyelitis.

Unlocking Vision Care Accessibility: Evaluating Caregivers' Willingness to Pay in Specialised Eye Clinics for Family Members With Intellectual Disabilities.

Background: People with intellectual disabilities (IDs) require more vision care but encounter considerable challenges during eye examinations. Specialised clinics established specifically for people with IDs are generally limited. This study aims to evaluate primary family caregivers' willingness to pay (WTP) for specialised ophthalmology services designed for people with IDs.

Identifying Distinct Developmental Patterns of Brain Complexity in Autism: A Cross-Sectional Cohort Analysis Using the Autism Brain Imaging Data Exchange.

Aim: Autistic traits exhibit neurodiversity with varying behaviors across developmental stages. Brain complexity theory, illustrating the dynamics of neural activity, may elucidate the evolution of autistic traits over time. Our study explored the patterns of brain complexity in autistic individuals from childhood to adulthood.

Resistance reduction and cross-resistance of spinosad-resistant Rhyzopertha dominica (Coleoptera: Bostrichidae) and the association between spinosad resistance and maltase activity.

Background: The lesser grain borer, Rhyzopertha dominica, is a serious stored-products pest mainly controlled by insecticides. Spinosad, an environmentally friendly biological insecticide with low mammalian toxicity, is considered a suitable candidate for R. dominica management.

Efficiency and Safety of Repeated Vertebroplasty for Adjacent Segment Fractures.

To review the outcomes of patients who underwent repeated vertebroplasty (VP) surgery for adjacent segment fractures (ASF), defined as new osteoporotic vertebral fractures occurring at levels immediately above or below a previously treated vertebra. From 1 January 2018, to 31 December 2020, forty-one patients who developed ASF following initial VP and underwent repeated VP were enrolled in our study. Radiographic measurements included single and two-segment kyphotic angles (SKA and TKA), and anterior and mid-vertebral body height (AVH and MVH).

Adverse Pregnancy Outcomes in Patients with Polycystic Ovary Syndrome with Pre-Conceptional Hyperandrogenism: A Multi-Institutional Registry-Based Retrospective Cohort Study.

: Women with polycystic ovarian syndrome (PCOS) are at higher risk for pregnancy complications. The PCOS population is heterogeneous, with different phenotypes linked to varying risks of adverse outcomes. However, literature on pre-conceptional hyperandrogenism is limited and based on small sample sizes.

Nutritional Status, Frailty, Oral Health, and Oral Motor Functions in Long-Term Care Residents with Swallowing Dysfunction.

: Swallowing dysfunction is a prevalent but often overlooked problem in long-term care facilities. This study investigated the relationships between nutritional deficits, frailty, oral health, and swallowing dysfunction. Additionally, we explored the associations between oral muscle weakness, frailty markers, and swallowing dysfunction.

The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing.

This paper addresses the increasing demand for efficient and scalable streaming service applications within the context of edge computing, utilizing NVIDIA Jetson Xavier NX hardware and Docker. The study evaluates the performance of DeepStream and Simple Realtime Server, demonstrating that containerized applications can achieve performance levels comparable to traditional physical machines. The results indicate that WebRTC provides superior low-latency capabilities, achieving delays of around 5 s, while HLS typically experiences delays exceeding 10 s.

Progressive Bounded Error Piecewise Linear Approximation with Resolution Reduction for Time Series Data Compression.

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy.

Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.

The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.

Monitoring a Railway Bridge with Distributed Fiber Optic Sensing Using Specially Installed Fibers.

This article explores the use of distributed fiber optic sensing (DFOS) technology in monitoring civil infrastructure, with a concrete example of an elevated railway bridge in Taiwan. The field test utilized multiple strain-sensing fibers attached to a 1 km span of a bullet train railway bridge, which were combined to calculate the 3-dimensional bridge deformation. The installed sensing system and continuous measurements enabled quick safety confirmation after earthquakes of Richter scale 6.

Time-Series Forecasting of PM and PM Concentrations Based on the Integration of Surveillance Images.

Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images.

Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks.

This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.

Intragroup and Intergroup Pairwise Key Predistribution for Wireless Sensor Networks.

The major task of a wireless sensor network (WSN) is data collection. Key predistribution (KP) is to establish pairwise keys for secure communication in a WSN, such that all collected data could be securely sent to a backend database. Most research on KP-like schemes is dedicated to enhancing resiliency against node capture attack (NA) and retaining the link connectivity in the meantime.

Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation.

Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps.

Using Infrared Raman Spectroscopy with Machine Learning and Deep Learning as an Automatic Textile-Sorting Technology for Waste Textiles.

With the fast-fashion trend, an increasing number of discarded clothing items are being eliminated at the stages of both pre-consumer and post-consumer each year. The linear economy produces large volumes of waste, which harm environmental sustainability. This study addresses the pressing need for efficient textile recycling in the circular economy (CE).

Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment.

Distributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often involve high complexity and significant computational demands.

An Energy Modulation Interrogation Technique for Monitoring the Adhesive Joint Integrity Using the Full Spectral Response of Fiber Bragg Grating Sensors.

Adhesive joining has the severe limitation that damages/defects developed in the bondline are difficult to assess. Conventional non-destructive examination (NDE) techniques are adequate to reveal disbonding defects in fabrication and delamination near the end of service life but are not helpful in detecting and monitoring in-service degradation of the joint. Several techniques suitable for long-term joint integrity monitoring are proposed.

A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System.

Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures.

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AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data.

Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions.