Multimodal Metaverse Healthcare: A Collaborative Representation and Adaptive Fusion Approach for Generative Artificial-Intelligence-Driven Diagnosis.

The metaverse enables immersive virtual healthcare environments, presenting opportunities for enhanced care delivery. A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications, which is a problem that needs to be addressed. This paper proposes a novel multimodal learning framework for metaverse healthcare, MMLMH, based on collaborative intra- and intersample representation and adaptive fusion.

Sensorless Control of Permanent magnet in-wheel motor for EVs Using Global Fast Terminal Sliding Mode Observer.

This article aims to provide a high-precision sensorless controller for permanent magnet in-wheel motors (PMIWMs) used in electric vehicles (EVs) when an unexpected failure of the position sensor occurs. To address this, we propose a position sensorless control method that combines a global fast terminal sliding mode observer (GFTSMO) with a phase-locked loop (PLL) estimation scheme. First, the GFTSMO is introduced to reduce the significant chattering that typically occurs in traditional sliding mode observers (SMO).

CardiOT: Towards Interpretable Drug Cardiotoxicity Prediction Using Optimal Transport and Kolmogorov--Arnold Networks.

Investigating the inhibitory effects of compounds on cardiac ion channels is essential for assessing cardiac drug safety. Consequently, researchers have developed computational models to evaluate combined cardiotoxicity (CCT) on cardiac ion channels. However, limitations in experimental data often cause issues like uneven data distribution and scarcity.

Activation of sphingosine-1-phosphate receptor 2 (S1PR2) upregulates dihydropyrimidine dehydrogenase (DPD) expression in colon cancer cells.

Introduction: Dihydropyrimidine dehydrogenase (DPD) is a major determinant of cancer 5-fluorouracyl (5-FU) resistance via its direct degradation. However, the mechanisms of tumoral DPD upregulation have not been fully understood.

Objectives: This study aimed to explore the role of S1PR2 in the regulation of tumoral DPD expression, identifying S1PR2 as the potential target for reversing 5-FU resistance.

BloodPatrol: Revolutionizing Blood Cancer Diagnosis - Advanced Real-Time Detection Leveraging Deep Learning & Cloud Technologies.

Cloud computing and Internet of Things (IoT) technologies are gradually becoming the technological changemakers in cancer diagnosis. Blood cancer is an aggressive disease affecting the blood, bone marrow, and lymphatic system, and its early detection is crucial for subsequent treatment. Flow cytometry has been widely studied as a commonly used method for detecting blood cancer.

Warming acupuncture-moxibustion combined with Bushen Qianggu Recipe effectively improves bone metabolism in osteoporosis patients.

Background: Osteoporosis (OP) increases the risk of fractures in older adults, with no effective treatment options at present.

Objectives: To analyze the effects of warming acupuncture-moxibustion (WAM) combined with Bushen Qianggu Recipe on bone metabolism, bone mineral density (BMD), and pain intensity in OP patients.

Methods: This retrospective study involved 103 patients with OP who were admitted to Wuhan Hospital of Traditional Chinese Medicine between July 2021 and December 2023.

GenoM7GNet: An Efficient N-Methylguanosine Site Prediction Approach Based on a Nucleotide Language Model.

N-methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanwhile, the existing machine learning methods extract limited hidden information from RNA sequences, thus making it difficult to improve the accuracy.

XAI Driven Intelligent IoMT Secure Data Management Framework.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO.

DER-GCN: Dialog and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialog Emotion Recognition.

With the continuous development of deep learning (DL), the task of multimodal dialog emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL. The MDER aims to identify the emotional information contained in different modalities, e.g.

Can pluripotent/multipotent stem cells reverse Parkinson's disease progression?

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by continuous and selective degeneration or death of dopamine neurons in the midbrain, leading to dysfunction of the nigrostriatal neural circuits. Current clinical treatments for PD include drug treatment and surgery, which provide short-term relief of symptoms but are associated with many side effects and cannot reverse the progression of PD. Pluripotent/multipotent stem cells possess a self-renewal capacity and the potential to differentiate into dopaminergic neurons.

Enhancing drug-food interaction prediction with precision representations through multilevel self-supervised learning.

Drug-food interactions (DFIs) crucially impact patient safety and drug efficacy by modifying absorption, distribution, metabolism, and excretion. The application of deep learning for predicting DFIs is promising, yet the development of computational models remains in its early stages. This is mainly due to the complexity of food compounds, challenging dataset developers in acquiring comprehensive ingredient data, often resulting in incomplete or vague food component descriptions.

Dual-View Learning Based on Images and Sequences for Molecular Property Prediction.

The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images. Molecular images, as a novel representation, have proven to be competitive, yet they lack explicit information and detailed semantic richness.

Translocation of IGF-1R in endoplasmic reticulum enhances SERCA2 activity to trigger Ca perturbation in hepatocellular carcinoma.

The well-known insulin-like growth factor 1 (IGF1)/IGF-1 receptor (IGF-1R) signaling pathway is overexpressed in many tumors, and is thus an attractive target for cancer treatment. However, results have often been disappointing due to crosstalk with other signals. Here, we report that IGF-1R signaling stimulates the growth of hepatocellular carcinoma (HCC) cells through the translocation of IGF-1R into the ER to enhance sarco-endoplasmic reticulum calcium ATPase 2 (SERCA2) activity.

Activation of insulin-like growth factor-1 receptor (IGF-1R) promotes growth of colorectal cancer through triggering the MEX3A-mediated degradation of RIG-I.

Insulin-like growth factor-1 receptor (IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers. However, targeting it has often produced the disappointing results as the role played by cross talk with numerous downstream signalings. Here, we report a disobliging IGF-1R signaling which promotes growth of cancer through triggering the E3 ubiquitin ligase MEX3A-mediated degradation of RIG-I.

Traffic forecasting with graph spatial-temporal position recurrent network.

With the development of social economy and smart technology, the explosive growth of vehicles has caused traffic forecasting to become a daunting challenge, especially for smart cities. Recent methods exploit graph spatial-temporal characteristics, including constructing the shared patterns of traffic data, and modeling the topological space of traffic data. However, existing methods fail to consider the spatial position information and only utilize little spatial neighborhood information.

The antifibrotic effects of the novel compound gorse isoflavone alkaloid on chemical liver injury in rats.

Objective: Liver fibrosis is a frequently occurring liver injury which lacks of effective treatment clinically. Here, we investigated the protective effects of a novel compound Gorse isoflavone alkaloid (GIA) against liver fibrosis.

Methods: Totally forty rats were randomly divided into four groups.

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors.