Cyclic Heptapeptide FZ1 Acts as an Integrin αvβ3 Agonist to Facilitate Diabetic Skin Wound Healing by Enhancing Angiogenesis.

Diabetic wound healing remains a persistent clinical challenge, necessitating the development of effective therapeutic agents and a deeper understanding of regulatory mechanisms. The cyclic heptapeptide FZ1, characterized by favorable biocompatibility, exhibited significantly greater efficacy than rh-bFGF and Cy in promoting cell proliferation and migration. In diabetic wound models, FZ1 markedly accelerated tissue regeneration and stimulated angiogenesis, as indicated by the upregulation of CD31 and α-SMA.

Associations of Rare Variants in the Gene with Bipolar Disorder in Chinese Population.

Purpose: This pioneering study aimed to explore the associations between the A-kinase anchoring protein 11 () gene and bipolar disorder (BD) in a Chinese population. We sought to replicate findings from European populations regarding ultra-rare protein-truncating variants (PTVs) within exon 8 of and identify any novel rare mutations linked to Chinese BD patients.

Methods: We conducted a case-control association study, including a cohort of 284 Chinese BD patients, with the control group comprising 10,588 individuals from the China Metabolic Analytics Project (ChinaMAP) database.

Application analysis of an internet platform tracking management mode combined with progressive resistance training in the rehabilitation of patients with breast cancer-related lymphedema.

Objective: To observe the influence of internet platform tracking management mode combined with progressive resistance training (PRT) on the rehabilitation of patients with breast cancer-related lymphedema (BCRL).

Methods: A total of 100 patients with BCRL admitted to Hainan general hospital from January 2023 to March 2024 were selected. After the shedding cases were excluded, they were divided into group A (n=47) where PRT + out-of-hospital follow-up were given, and group B (n=48) where PRT + Internet platform tracking management mode was given.

Developing an anticoagulant microfibrous vascular graft with enhanced kink resistance and self-sealing capabilities.

For clinical treatment of end-stage renal disease (ESRD) patients, the development of vascular grafts possessing both puncture resistance and anticoagulant properties remains crucial for arteriovenous fistula establishment. In this study, small-diameter vascular conduits were engineered through electrospinning of polyurethane (PU) microfibers, incorporating polyethylene coil reinforcement within the graft wall architecture to confer kink resistance. The microporous structure of the grafts demonstrated effective self-sealing capabilities following needle perforation.

Altered resting-state brain activity in patients with major depression disorder and bipolar disorder: A regional homogeneity analysis.

Background: Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy.

Exploring Self-Conditioning Co-Sample Strategy of Diffusion Models in Dermoscopic Images.

Generative dataset expansion methods can effectively alleviate the scarcity of data in dermoscopic image segmentation but commonly employ a two-stage synthesis strategy that contains additional learnable components and complex design, which results in high computational resource costs. Diffusion models utilizing a self-conditioning strategy have shown strong potential for efficiently reusing priors in the pipeline without relying on excessively complicated conditioning designs. Inspired by this, we propose a dataset expansion method called SCCS-Diff.

Dual Breaking of Electron Cloud and Space Structure Symmetry Induced Microscopic Split-phase Interface for High-rate and Long-term Aqueous Zinc Batteries.

Weak dipole interactions between highly symmetric HO molecules and SO species are the root cause of unstable electric double layer (EDL), which triggers the hydrogen evolution reaction and Zn dendrite formation, significantly impeding the commercialization of aqueous zinc-ion batteries. Herein, we designed a microscopic split-phase interface (MSPI) by dual breaking of electron cloud and space structure symmetry to suppress interfacial side reactions and achieve uniform Zn deposition. The structurally asymmetric methylurea (MU) molecules possess both hydrophobic methyl and hydrophilic amino groups, which disrupt the continuity of H-bonding network and the aggregation state of HO molecules, resulting in peculiar nanoscale core-shell-like clusters.

BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.

Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities and facilitating the discovery of novel biocatalysts. However, the limited availability of data for specific enzyme functions, such as conversion efficiency and stereoselectivity, presents challenges for prediction accuracy.

Orientation Determination of Cryo-EM Projection Images Using Reliable Common Lines and Spherical Embeddings.

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a critical technique for recovering and studying the fine 3D structure of proteins and other biological macromolecules, where the primary issue is to determine the orientations of projection images with high levels of noise. This paper proposes a method to determine the orientations of cryo-EM projection images using reliable common lines and spherical embeddings. First, the reliability of common lines between projection images is evaluated using a weighted voting algorithm based on an iterative improvement technique and binarized weighting.

LTBP2 regulates cisplatin resistance in GC cells via activation of the NF-κB2/BCL3 pathway.

Gastric cancer (GC) often develops resistance to cisplatin treatment, but while latent transforming growth factor β-binding protein (LTBP2) is recognized as a potential regulator in GC, its specific role in cisplatin resistance is not fully understood. This study investigated LTBP2's impact on cisplatin resistance in GC. LTBP2 expression was assessed in various GC cell lines, and its correlation with cisplatin sensitivity was determined through cell viability assays.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes.

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts.

Crowding Effect-Induced Zinc-Enriched/Water-Lean Polymer Interfacial Layer Toward Practical Zn-Iodine Batteries.

Although the meticulous design of functional diversity within the polymer interfacial layer holds paramount significance in mitigating the challenges associated with hydrogen evolution reactions and dendrite growth in zinc anodes, this pursuit remains a formidable task. Here, a large-scale producible zinc-enriched/water-lean polymer interfacial layer, derived from carboxymethyl chitosan (CCS), is constructed on zinc anodes by integration of electrodeposition and a targeted complexation strategy for highly reversible Zn plating/stripping chemistry. Zinc ions-induced crowding effect between CCS skeleton creates a strong hydrogen bonding environment and squeezes the moving space for water/anion counterparts, therefore greatly reducing the number of active water molecules and alleviating cathodic I attack.

Metabolic reprogramming, autophagy, and ferroptosis: Novel arsenals to overcome immunotherapy resistance in gastrointestinal cancer.

Background: Gastrointestinal cancer poses a serious health threat owing to its high morbidity and mortality. Although immune checkpoint blockade (ICB) therapies have achieved meaningful success in most solid tumors, the improvement in survival in gastrointestinal cancers is modest, owing to sparse immune response and widespread resistance. Metabolic reprogramming, autophagy, and ferroptosis are key regulators of tumor progression.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications.

STDP-based adaptive graph convolutional networks for automatic sleep staging.

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN.

A Multicenter Cohort Study on Children Suffering from Acute Lymphoblastic Leukemia: Effects of Obesity on Mortality.

Background: Overweight and obesity have been reported in specific patients and disease survivors compared to other types of childhood cancer. This study is aimed at determining the effect of children's obesity on the mortality of acute lymphoblastic leukemia.

Method: Children admitted to Inner Mongolia International Mongolian Hospital from 1 January 2017 to 31 December 2020 participated in this study.

Two Different Transplant Preconditioning Regimens Combined with Irradiation and Chemotherapy in the Treatment of Childhood Leukemia: Systematic Review and Meta-Analysis.

Objective: To observe the therapeutic effect and the incidence of adverse reactions of total body irradiation plus cyclophosphamide (TBI/CY) and busulfan plus cyclophosphamide (BU/CY) in the treatment of pediatric hematopoietic stem cell transplantation.

Methods: By searching the Cochrane Library, PubMed, Web of Knowledge, Embase, Chinese Biomedical Literature Database (CBM), and screening randomized controlled trials (RCTs), quality evaluation and data extraction were performed for the included literature, and meta-analysis was performed for RCTs included at using Review Manager 5.2 software.

Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures.

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task.

Bridging atomistic simulations and thermodynamic hydration models of aqueous electrolyte solutions.

Chemical thermodynamic models of solvent and solute activities predict the equilibrium behavior of aqueous solutions. However, these models are semi-empirical. They represent micro-scale ion and solvent behaviors controlling the macroscopic properties using small numbers of parameters whose values are obtained by fitting to activities and other partial derivatives of the Gibbs energy measured for the bulk solutions.

Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers.

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering.

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy.

Bcl2-associated athanogene 4 promotes the invasion and metastasis of gastric cancer cells by activating the PI3K/AKT/NF-κB/ZEB1 axis.

Bcl2-associated athanogene 4 (BAG4) has been found to be aberrantly expressed in several types of human cancers. However, little is known about its expression, role, and clinical significance in gastric cancer (GC). In this study, we aimed to address these issues and to explore the underlying mechanisms.