Construction of bimetallic oxy-hydroxides based on Ni(OH) nanosheets for sensitive non-enzymatic glucose detection electrochemical oxidation and incorporation.

Due to their ease of synthesis and large specific surface area, Ni(OH) nanosheets have emerged as promising electrochemical sensing materials, attracting significant attention in recent years. Herein, a series of oxy-hydroxides based on Ni(OH) nanosheets, including NiO/Ni(OH)@NF and (MNi)O/Ni(OH)@NF (M = Co, Fe, or Cr), are successfully synthesized the electrochemical oxidation and incorporation strategies. Electrochemical tests demonstrate that these Ni(OH)-based oxy-hydroxides exhibit excellent electrochemical oxidation activity for glucose in alkaline electrolyte.

Carboxymethyl-β-1,3-D-glucan (CMG)-gemcitabine conjugate improves the anticancer efficacy and alleviate the toxicity of gemcitabine (GEM).

Gemcitabine (GEM) is an antitumor drug approved by the US FDA in 1996. It is used to treat cancer and solid tumours, but its effectiveness is limited by toxicity. Carboxymethyl-β-1,3-D-glucan (CMG) is a derivative of β-glucan with improved solubility.

Study on High-Temperature Constitutive Model and Plasticity of the Novel Cr-Mo-V Hot-Work Die Steel Forging.

In response to the increasingly strict performance requirements of large molds, a novel Cr-Mo-V hot-work die steel has been developed. In order to study the high-temperature hot deformation behavior and plasticity of the novel steel, hot compression tests were conducted on the Gleeble-1500D thermal simulation testing machine at a deformation temperature of 950~1200 °C and a strain rate of 0.001~5 s.

A foundation model with weak experiential guidance in detecting muscle invasive bladder cancer on MRI.

Preoperative detection of muscle-invasive bladder cancer (MIBC) remains a great challenge in practice. We aimed to develop and validate a deep Vesical Imaging Network (ViNet) model for the detection of MIBC using high-resolution Tweighted MR imaging (hrTWI) in a multicenter cohort. ViNet was designed using a modified 3D ResNet, in which, the encoder layers were pretrained using a self-supervised foundation model on over 40,000 cross-modal imaging datasets for transfer learning, and the classification modules were weakly supervised by an experiential knowledge-domain mask indicated by a nnUNet segmentation model.

Delta corticomedullary apparent diffusion coefficient on MRI as a biomarker for prognosis in IgA nephropathy.

Objectives: To explore the association of the cortico-medullary difference in apparent diffusion coefficient (ΔADC) with clinicopathological parameters of disease activity at the time of biopsy, and with the prognositic risk stratification in IgA nephropathy (IgAN) patients.

Methods: We included 112 patients with biopsy-proven IgAN who measured ΔADC. Patients underwent a kidney biopsy and diffusion-weighted magnetic resonance imaging within one week of the biopsy.

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

Unlabelled: Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. Vaginal fluid samples were classified as BV positive or BV negative using the BVBlue Test and clinical microscopy, followed by SERS spectral acquisition to construct the data set.

Thermodynamically Dependent Behavior in Gas Transport in Two-Dimensional Graphene Nanochannels.

Gas transport through nanochannels has aroused significant interest in many fields. Recently, "ballistic transport" of gas was observed through a two-dimensional graphene nanochannel, and it causes a peculiar enhancement compared to the predictions of the Knudson theory. Many studies attributed this effect to the specular reflection caused by the atomically smooth surface of the channel.

Boosting the electrochemical performance of the Ni-rich LiNiCoMnO cathode by high-valence Zr/Mo dual-doping.

Ni-rich oxides are attractive high-energy cathodes for lithium-ion batteries but suffer from inherent instability. Herein, we employed an ectopic Zr/Mo dual-doping strategy to reinforce the layered structure through robust M-O bonds and the pillar effect. This strategy mitigates lattice distortion during cycling and inhibits the interface side reactions.

Fishing net-inspired PVA-chitosan-CNT hydrogels with high stretchability, sensitivity, and environmentally stability for textile strain sensors.

Soft electronic products are being extensively investigated in diverse applications including sensors and devices, due to their superior softness, responsiveness, and biocompatibility. One-dimensional (1-D) fiber electronic devices are recognized for their lightweight, wearable, and stretchable qualities, thus emerging as critical constituents for seamless integration with the human body and attire, exhibiting great potential in wearable applications. However, wearable conductive hydrogel fibers usually face challenges in combining stretchability and excellent stability, notably in high-temperature environment.

Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals.

It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing methods, especially the multi-classification problem. We proposed a multi-modal feature fusion (MMFF) method for epileptic EEG signals.

PIAA: Pre-imaging all-round assistant for digital radiography.

Background: In radiography procedures, radiographers' suboptimal positioning and exposure parameter settings may necessitate image retakes, subjecting patients to unnecessary ionizing radiation exposure. Reducing retakes is crucial to minimize patient X-ray exposure and conserve medical resources.

Objective: We propose a Digital Radiography (DR) Pre-imaging All-round Assistant (PIAA) that leverages Artificial Intelligence (AI) technology to enhance traditional DR.

The role of inflammatory proteins in regulating the impact of lipid specifications on deep venous thrombosis: a two sample and mediated Mendelian randomization study.

Objective: To investigate the potential mediating role of inflammatory proteins in the association between lipid species and Deep Venous Thrombosis (DVT).

Methods: A comprehensive analysis was conducted using pooled data from genome-wide association studies (GWAS), incorporating double-sample and reverse Mendelian randomization (MR) techniques, to identify the specific inflammatory proteins that act as intermediaries among 91 screened proteins in relation to deep vein thrombosis (DVT). Furthermore, a two-step MR approach was employed to quantify the proportion of DVT risk attributed to lipid effects mediated by these inflammatory proteins.

Analysis of Rock Mechanical Characteristics and Establishment of an Evaluation System for Coal Measures in the Longtan Formation, Guizhou Province.

The mechanical properties of coal measure rocks and their evaluation significantly impact the process and efficacy of coal measure exploration and development. This study focuses on the Guizhou Longtan Formation coal measure. The mechanical and fracturing characteristics of coal measure rock samples are analyzed via well coring, geophysical logging, and indoor experiments.

Revealing the Impact of Dual Site Modification on the Phase Transformation and Ion Transport Mechanism of Ni-Rich Cathode Materials.

Ni-rich cathode materials have garnered significant attention attributable to the high reversible capacity and superior rate performance, particularly in the electric vehicle industry. However, the structural degradation experienced during cycling results in rapid capacity decay and deterioration of the rate performance, thereby impeding the widespread application of Ni-rich cathodes. Herein, a Mg/Ti co-doping strategy was developed to boost the structure stability and Li-ion transport kinetics of the Ni-rich cathode material LiNiCoMnO (NCM9055) under long cycle.

DeepDate: A deep fusion model based on whale optimization and artificial neural network for Arabian date classification.

Purpose: As agricultural technology continues to develop, the scale of planting and production of date fruit is increasing, which brings higher yields. However, the increasing yields also put a lot of pressure on the classification step afterward. Image recognition based on deep learning algorithms can help to identify and classify the date fruit species, even in natural light.

Molecular exploration of the diurnal alteration of glycogen structural fragility and stability in time-restricted-feeding mouse liver.

The structure of glycogen α particles in healthy mouse liver has two states: stability and fragility. In contrast, glycogen α particles in diabetic liver present consistent fragility, which may exacerbate hyperglycemia. Currently, the molecular mechanism behind glycogen structural alteration is still unclear.

EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification.

Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance.

Large Foundation Model for Cancer Segmentation.

Recently, large language models such as ChatGPT have made huge strides in understanding and generating human-like text and have demonstrated considerable success in natural language processing. These foundation models also perform well in computer vision. However, there is a growing need to use these technologies for specific medical tasks, especially for identifying cancer in images.

Intellectual assessment of amyotrophic lateral sclerosis using deep resemble forward neural network.

ALS (Amyotrophic Lateral Sclerosis) is a neurodegenerative disorder causing profound physical disability that severely impairs a patient's life expectancy and quality of life. It also leads to muscular atrophy and progressive weakness of muscles due to insufficient nutrition in the body. At present, there are no disease-modifying therapies to cure ALS, and there is a lack of preventive tools.