A smart drug delivery microgel system with phased intervention capabilities and dual physical state of use promotes healing of diabetic infected wounds.

Effectively managing infected diabetic wounds involves the elimination of bacteria, neutralization of reactive oxygen species (ROS), suppression of inflammation, and induction of angiogenesis. This study describes the development of a multifunctional hyaluronic acid (HA)-based microgel system capable of serving as either an injectable wet microgel or dry microspheres (MSs). After initially engineering Fe/tea polyphenol (TP) metal-polyphenol network (MPN)-functionalized HAMA MS, these particles were found to suppress inflammation and facilitate ROS scavenging.

Microenvironment Remodeling Microgel Repairs Degenerated Intervertebral Disc via Programmed Delivery of MicroRNA-155.

The progression of intervertebral disc degeneration (IVDD) is associated with increased cell apoptosis and reduced extracellular matrix (ECM) production, both of which are driven by ongoing inflammation. Thus, alleviating the acidic inflammatory microenvironment and mitigating the apoptosis of nucleus pulposus cells (NPCs) are essential for intervertebral disc (IVD) regeneration. Regulating pH levels in the local environment can reduce inflammation and promote tissue recovery.

Genetics causal analysis of oral microbiome on type 2 diabetes in East Asian populations: a bidirectional two-sample Mendelian randomized study.

Introduction: The dysbiosis of the oral microbiome is associated with the progression of various systemic diseases, including diabetes. However, the precise causal relationships remain elusive. This study aims to investigate the potential causal associations between oral microbiome and type 2 diabetes (T2D) using Mendelian randomization (MR) analyses.

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance.

Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance.

Hepatitis C virus infection is associated with high risk of breast cancer: a pooled analysis of 68,014 participants.

Introduction: Breast cancer is the most common malignancy among women. Previous studies had shown that hepatitis C virus (HCV) infection might serve as a risk factor for breast cancer, while some studies failed to find such an association.

Methods: In this study, we presented a first attempt to capture and clarify this clinical debate via a cumulative analysis (registration ID: CRD42023445888).

Hsa_circ_0000585 promotes chemoresistance to cis-platin in epithelial cells of ovarian cancer by modulating autophagy.

Background: Chemoresistance, i.e., resistance to cisplatin (DDP), has been a major obstacle to ovarian cancer treatment.

A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding.

Individual differences among different subjects pose a great challenge to motor imagery (MI) decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce individual differences, which can utilize rich information and align the data distribution among different subjects. However, most MSTL methods in MI-BCI combine all data in the source subjects into a single mixed domain, which will ignore the effect of important samples and the large differences in multiple source subjects.

Supervised Feature Selection With Orthogonal Regression and Feature Weighting.

Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection.

Functional Magnetic Resonance Imaging Study of Apathy in Alzheimer's Disease.

An fMRI study was launched to understand mechanisms of Alzheimer's disease (AD) patients with apathy. The authors reviewed 7 AD patients with apathy and 6 AD patients without apathy. The block method was adopted, and 24 pictures representing positive, negative, and neutral emotional stimuli were viewed when patients were given brain fMRI.

Role of magnetic resonance diffusion-weighted imaging in evaluating response after chemoembolization of hepatocellular carcinoma.

Objective: To investigate the value of hepatocellular carcinoma pretreatment apparent diffusion coefficients (ADCs) and its ADCs changes after treatment in predicting and early monitoring the response after chemoembolization.

Materials And Methods: Twenty-five responding and nine nonresponding hepatocellular carcinoma lesions were prospectively evaluated with magnetic resonance diffusion-weighted imaging in 24 h before and in 48 h after chemoembolization. Quantitative ADC maps were calculated with images with b values of 0 and 500 s/mm(2).