Usefulness of Magnifying Endoscopy With Narrow-Band Imaging for Diagnosis of Ulcerative Colitis-Associated Neoplasia.

Background And Aim: Qualitative diagnosis of ulcerative colitis-associated neoplasia (UCAN) is crucial for surveillance colonoscopy in patients with ulcerative colitis (UC). Although the utility of magnifying endoscopy with narrow-band imaging (ME-NBI) in sporadic neoplasia diagnosis has been reported, its efficacy in UCAN remains unclear. This study aimed to evaluate the usefulness of ME-NBI for qualitative diagnosis of UCAN.

Anisotropic Plasmon Resonance in TiCT MXene Enables Site-Selective Plasmonic Catalysis.

The ever-growing interest in MXenes has been driven by their distinct electrical, thermal, mechanical, and optical properties. In this context, further revealing their physicochemical attributes remains the key frontier of MXene materials. Herein, we report the anisotropic localized surface plasmon resonance (LSPR) features in TiCT MXene as well as site-selective photocatalysis enabled by the photophysical anisotropy.

CsMPDB 1.0: An interactive web application for visualizing and exploring the microRNAs and phasiRNAs of tea plant (Camellia sinensis var. sinensis 'Shuchazao').

The microRNAs and phasiRNAs of plant are small non-coding RNAs with important functions through regulating gene expression at the post-transcriptional level. However, identifying miRNAs, phasiRNAs and their target genes from numerous sequencing raw data requires multiple software and command-line operations, which are time-consuming and labor-intensive for non-model plants. Therefore, we present CsMPDB (miRNAs and phasiRNAs database of Camellia sinensis), an interactive web application with multiple analysis modules developed to visualize and explore miRNA and phasiRNA in tea plants based on 259 sRNA-seq samples and 24 degradome-seq samples in NCBI.

Spatiotemporal distribution prediction for PM based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China.

The increasing demand for air pollution control has driven the application of low-cost sensors (LCS) in air quality monitoring, enabling higher observation density and improved air quality predictions. However, the inherent limitations in data quality from LCS necessitate the development of effective methodologies to optimize their application. This study established a hybrid framework to enhance the accuracy of spatiotemporal predictions of PM, standard instrument measurements were employed as reference data for the remote calibration of LCS.

An Analysis of the Influence of DSC Parameters on the Measurement of the Thermal Properties of Phase-Change Material.

A differential scanning calorimeter (DSC) is widely used for measuring the thermal properties of phase-change materials (PCMs). Optimizing test conditions based on material characteristics is essential for accurate results. This study investigates the effects of experimental parameters, including sample mass, heating rate, measurement modes, and atmosphere flow rate, on the phase-change enthalpy and phase-change temperature results.

Energy insufficiency induced by high purine diet: Catalysts for renal impairment in hyperuricemia nephropathy rat model.

A high purine diet emerges as a significant risk factor for hyperuricemia, and this diet may potentiate hyperuricemia nephropathy. Despite this, the mechanistic underpinnings of kidney damage precipitated by a high purine diet warrant further research. In the current investigation, a hyperuricemia nephropathy rat model was developed through induction via a high purine diet.

Transient Leakage Electroluminescence of Quantum-Dot Light-Emitting Diodes.

Parasitic emission or leakage emission caused by electron leakage to a hole transport layer in quantum-dot light-emitting diodes (QLEDs) critically impacts device efficiency and operational stability. The buildup dynamics of such emission channels, however, was insufficiently researched. Herein, we investigate transient electroluminescence dynamics of leakage emission in red/green/blue (R/G/B) QLEDs and reveal notable contrast for R and G.

PPGV: a comprehensive database of peach population genome variation.

Peach tree is one of the most important fruit trees in the world, and it has been cultivated for more than 7,500 years. In recent years, the genome and population resequencing of peach trees have been published continuously, which has effectively promoted the research of peach tree genetics and breeding. In order to promote the further mining and utilization of these data, we integrated and constructed a comprehensive peach genome and variation database (PPGV, http://peachtree.

ICG-labeled PD-L1-antagonistic affibody dimer for tumor imaging and enhancement of tumor photothermal-immunotherapy.

In clinical practice, tumor-targeting diagnosis and immunotherapy against programmed death ligand 1 (PD-L1) have a significant impact. In this research, a PD-L1-antagonistic affibody dimer (Z) was successfully prepared through Escherichia coli expression system, and conjugated with the photosensitizer of ICG via N-hydroxysuccinimide (NHS) ester to develop a novel tumor-targeting agent (ICG-Z) for both tumor imaging diagnosis and photothermal-immunotherapy simultaneously. In vitro, Z could specifically bind to PD-L1-positive LLC and MC38 tumor cells, and ICG-Z-mediated photothermal therapy (PTT) also showed excellent phototoxicity to these tumor cells.

HybMED: A Hybrid Neural Network Training Processor With Multi-Sparsity Exploitation for Internet of Medical Things.

Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical Things (AIoMT) applications suffer from accuracy degradation due to physiological signal variations among patients. On-chip learning can overcome this issue by online adaptation of neural network parameters for user-specific tasks. However, existing on-chip learning processors have limitations in terms of versatility, resource utilization, and energy efficiency.

Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.

Implantable neuromodulation devices have significantly advanced treatments for neurological disorders such as Parkinson's disease, epilepsy, and depression. Traditional open-loop devices like deep brain stimulation (DBS) and spinal cord stimulators (SCS) often lead to overstimulation and lack adaptive precision, raising safety and side-effect concerns. Next-generation closed-loop systems offer real-time monitoring and on-device diagnostics for responsive stimulation, presenting a significant advancement for treating a range of brain diseases.

ILD-GAP combined with the monocyte ratio could be a better prognostic prediction model than ILD-GAP in patients with interstitial lung diseases.

Background: The ILD-GAP scoring system is known to be useful in predicting prognosis in patients with interstitial lung disease (ILD). An elevated monocyte count was associated with increased risks of IPF poor prognosis. We examined whether the ILD-GAP scoring system combined with the monocyte ratio (ILD-GAPM) is superior to the conventional ILD-GAP model in predicting ILD prognosis.

Integrated analysis of bulk RNA-seq and single-cell RNA-seq reveals the function of pyrocytosis in the pathogenesis of abdominal aortic aneurysm.

Pyrocytosis is involved in the development of abdominal aortic aneurysm (AAA), we explored the pyrocytosis-related hub genes in AAA and conducted a diagnostic model based on the pyrocytosis-related genes score (PRGs). A total of 2 bulk RNA-seq (GSE57691 and GSE47472) datasets and pyrocytosis-related genes were integrated to obtain 24 pyrocytosis-related different expression genes (DEGs). The LASSO Cox regression analysis was conducted to filter out 7 genes and further establish the nomogram signature based on the PRGs that exhibited a good diagnosis value.

Individual-Level Health Care Costs Attributable to Noncommunicable Diseases: A Longitudinal Study Based on the Older Adults in China.

Noncommunicable chronic diseases among the elderly population represent a significant economic burden in China. However, previous disease-related health cost studies lacked representation of older adults and comparability of the burden of multiple chronic diseases. The objective of this study was to determine the fraction of health care costs attributable to the 6 most prevalent chronic diseases and comorbidities in the sample of older adults.

Healthcare costs attributable to abnormal weight in China: evidence based on a longitudinal study.

Background: The prevalence of abnormal weight is on the rise, presenting serious health risks and socioeconomic problems. Nonetheless, there is a lack of studies on the medical cost savings that can be attained through the mitigation of abnormal weight. The aim of this study was to estimate the impact of abnormal weight on healthcare costs in China.

A 510 μW 0.738-mm 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor With Unified Computation Engine in 40-nm CMOS.

Implementing neural networks (NN) on edge devices enables AI to be applied in many daily scenarios. The stringent area and power budget on edge devices impose challenges on conventional NNs with massive energy-consuming Multiply Accumulation (MAC) operations and offer an opportunity for Spiking Neural Networks (SNN), which can be implemented within sub-mW power budget. However, mainstream SNN topologies varies from Spiking Feedforward Neural Network (SFNN), Spiking Recurrent Neural Network (SRNN), to Spiking Convolutional Neural Network (SCNN), and it is challenging for the edge SNN processor to adapt to different topologies.

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency.

Software-Hardware Co-Design for Energy-Efficient Continuous Health Monitoring via Task-Aware Compression.

Low power consumption associated with data transmission and processing of wearable/implantable devices is crucial to ensure the usability of continuous health monitoring systems. In this paper, we propose a novel health monitoring framework where the signal acquired is compressed in a task-aware manner to preserve task-relevant information at the sensor end with a low computation cost. The resulting compressed signals can be transmitted with significantly lower bandwidth, analyzed directly without a dedicated reconstruction process, or reconstructed with high fidelity.

NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications.

Highly accurate classification methods for multi-task biomedical signal processing are reported, including neural networks. However, reported works are computationally expensive and power-hungry. Such bottlenecks make it hard to deploy existing approaches on edge platforms such as mobile and wearable devices.

Empirical validation of the information-motivation-behavioral skills model of gestational weight management behavior: a framework for intervention.

Background: Unhealthy gestational weight gain is a modifiable risk factor for adverse maternal and child health. Appropriate and effective intervention strategies that focus on behavioral change or maintenance are critical in weight management during pregnancy. Our aim was to uncover the influencing factors and psychosocial mechanisms of gestational weight control behavior, and to construct a behavioral model suitable for intervention based on Information-Motivation-Behavioral skills (IMB) model.

Tiny CNN for Seizure Prediction in Wearable Biomedical Devices.

Epilepsy is a life-threatening disease affecting millions of people all over the world. Artificial intelligence epileptic predictors offer excellent potential to improve epilepsy therapy. Particularly, deep learning models such as convolutional neural networks (CNN) can be used to accurately detect ictogenesis through deep structured learning representations.

Akkermansia muciniphila prevents cold-related atrial fibrillation in rats by modulation of TMAO induced cardiac pyroptosis.

Background: Cold exposure is one of the most important risk factors for atrial fibrillation (AF), and closely related to the poor prognosis of AF patients. However, the mechanisms underlying cold-related AF are poorly understood.

Methods: Various techniques including 16S rRNA gene sequencing, fecal microbiota transplantation, and electrophysiological examination were used to determine whether gut microbiota dysbiosis promotes cold-related AF.