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Immunotherapies have revolutionized intervention strategies for many primary cancers, but have not improved the outcomes of glioblastoma multiforme (GBM), which remains one of the most lethal malignant cerebral tumours. Here we present an injectable hydrogel system that stimulates tumoricidal immunity after GBM surgical resection, which mitigates its relapse. The hydrogel comprises a tumour-homing immune nanoregulator, which induces immunogenic cell death and suppression of indoleamine 2,3-dioxygenase-1, and chemotactic CXC chemokine ligand 10, for a sustained T-cell infiltration. When delivered in the resected tumour cavity, the hydrogel system mimics a ‘hot’ tumour-immunity niche for attacking residual tumour cells and significantly suppresses postoperative GBM recurrence. Our work provides an alternative strategy for conferring effective tumoricidal immunity in GBM patients, which may have a broad impact in the immunotherapy of ‘cold’ tumours after surgical intervention. Tumour relapse after resection undermines the efficacy of surgical treatment for glioblastoma multiforme. Here the authors present a hydrogel that can be injected in the tumour cavity after resection and that promotes antitumour immunity, reducing postoperative cancer growth in animal models.

Chitosan is a linear polyelectrolyte with active hydroxyl and amino groups that can be made into chitosan-based hydrogels by different cross-linking methods. Chitosan-based hydrogels also have a three-dimensional network of hydrogels, which can accommodate a large number of aqueous solvents and biofluids. CS, as an ideal drug-carrying material, can effectively encapsulate and protect drugs and has the advantages of being nontoxic, biocompatible, and biodegradable. These advantages make it an ideal material for the preparation of functional hydrogels that can act as wound dressings for skin injuries. This review reports the role of chitosan-based hydrogels in promoting skin repair in the context of the mechanisms involved in skin injury repair. Chitosan-based hydrogels were found to promote skin repair at different process stages. Various functional chitosan-based hydrogels are also discussed.

Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network’s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model’s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model’s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.

Neural disruptions during emotion regulation are common of generalized anxiety disorder (GAD). Identifying distinct functional and effective connectivity patterns in GAD may provide biomarkers for their diagnoses. This study aims to investigate the differences of features of brain network connectivity between GAD patients and healthy controls (HC), and to assess whether those differences can serve as biomarkers to distinguish GAD from controls. Independent component analysis (ICA) with hierarchical partner matching (HPM-ICA) was conducted on resting-state functional magnetic resonance imaging data collected from 20 GAD patients with medicine-free and 20 matched HC, identifying nine highly reproducible and significantly different functional brain connectivity patterns across diagnostic groups. We then utilized Granger causality (GC) to study the effective connectivity between the regions that identified by HPM-ICA. The linear discriminant analysis was finally used to distinguish GAD from controls with these measures of neural connectivity. The GAD patients showed stronger functional connectivity in amygdala, insula, putamen, thalamus, and posterior cingulate cortex, but weaker in frontal and temporal cortex compared with controls. Besides, the effective connectivity in GAD was decreased from the cortex to amygdala and basal ganglia. Applying the ICA and GC features to the classifier led to a classification accuracy of 87.5%, with a sensitivity of 90.0% and a specificity of 85.0%. These findings suggest that the presence of emotion dysregulation circuits may contribute to the pathophysiology of GAD, and these aberrant brain features may serve as robust brain biomarkers for GAD.

Background Muscle and adipose tissue are the most critical indicators of beef quality, and their development and function are regulated by noncoding RNAs (ncRNAs). However, the differential regulatory mechanisms of ncRNAs in muscle and adipose tissue remain unclear. Results In this study, 2,343 differentially expressed mRNAs (DEMs), 235 differentially expressed lncRNAs (DELs), 95 differentially expressed circRNAs (DECs) and 54 differentially expressed miRNAs (DEmiRs) were identified in longissimus dorsi muscle (LD), subcutaneous fat (SF) and perirenal fat (VF) in Qinchuan beef cattle. The results of functional enrichment analysis showed that DEMs, DELs, DECs and DEmiRs were enriched in biological processes related to development and function of muscle and fat deposition, including skeletal muscle contraction, muscle organ development, PPAR signaling pathway, fatty acid metabolism and MAPK signaling pathway. Based on the competing endogenous RNA (ceRNA) regulatory mechanism, we constructed a lncRNA/circRNA-miRNA-mRNA network consisting of 6 circRNAs, 5 lncRNAs, 6 miRNAs and 27 mRNAs. Among them, 55 ceRNA axes were involved, including circRNA12990 - bta-miR-133a_L-1R + 1 - MYO6 / ZEB2 , circRNA2893 / MSTRG.28538.1 / MSTRG.11613.4 - pma-miR-145-5p_R + 2 - EYA4 and MSTRG.26982.1 - bta-let-7e_R + 1 - RBM40 . Conclusions This study identified a group of differentially expressed mRNAs, lncRNAs, circRNAs and miRNAs between muscle and adipose tissue and constructed a potential ceRNA regulatory network, which may serve as a foundation for studying the differential regulatory roles of ncRNAs in the development and function of muscle and adipose tissue.

Anti-LGI1 encephalitis is associated with disruptions in large-scale brain network functionality. Although hippocampal atrophy has been structurally characterized, the morphometric patterns of subcortical structures and their surface deformations remain poorly understood. We therefore investigated the shape abnormalities of subcortical structures and their morphological correlations in patients with anti-LGI1 encephalitis. This study included 31 patients diagnosed with anti-LGI1 encephalitis and 31 group-matched healthy controls. The mesh-based shape method was performed on the fifteen segmented subcortical structures for vertex-wise analyses. Permutation method based on general linear model was applied for statistical group comparison. Associations with disease severity and cognitive impairment were assessed in the patients. The volumetric representations of these subcortical structures were also estimated. Correlations between subcortical shape alterations and disease severity were explored. Significant inward shape deformations were observed in the limbic system and basal ganglia in patients with anti-LGI1 encephalitis compared to healthy controls. Moreover, correlation analyses revealed that greater inward shape indices in the hippocampus and thalamus were associated with increased disease severity and poorer cognitive functioning, underscoring the pathological significance of these morphological alterations. These findings indicate that precisely localized subcortical shape deformations are associated with disease severity and cognitive impairment, suggesting widespread damage of limbic system and basal ganglia in anti-LGI1 encephalitis.

Background Intramuscular fat (IMF) accumulation plays a crucial role in determining beef quality. However, the molecular mechanisms regulating adipogenesis within bovine tissue remain unclear. Results This study conducted a comprehensive reanalysis of circular RNAs (circRNAs) transcriptomes using Temporal Clustering Analysis (TCA) to characterize their dynamic expression during the differentiation of bovine intramuscular preadipocytes across four time points. TCA revealed eight distinct clusters. Clusters 2 and 6 were selected due to exhibiting inverse expression trajectories that suggest stage-specific regulatory functions during early differentiation and late adipocyte maturation, respectively. Differential expression analysis identified 40 circRNAs which were dynamically modulated during the early stages of differentiation, including 19 core circRNAs shared among clusters, which may play essential roles in adipogenic development. Functional enrichment analysis of the host genes associated with these circRNAs indicated pathways related to extracellular matrix remodeling, focal adhesion, glycosylation, and DNA repair. This highlights the complex regulatory landscape mediated by circRNAs in adipogenesis. Regulatory network analysis of miRNA–mRNA interactions has identified a competing endogenous RNA (ceRNA) network consisting of 19 circRNAs, 54 miRNAs, and 209 mRNAs. This network exhibits significant enrichment in the PI3K-Akt signaling pathway and includes key adipogenic regulators such as PPARGC1A , AKT2 , and STK11 . Notably, the circLMO7 –bta-miR-2442– STK11 axis has been identified as a critical regulatory module. Experimental validation confirmed the circular conformation, temporal expression dynamics, and tissue-specific enrichment of circLMO7 , particularly within intramuscular fat. Functional knockdown of circLMO7 in bovine preadipocytes resulted in the significant downregulation of adipogenic markers such as FABP4 , SREBP1 , C/EBPβ ), indicating that circLMO7 acts as a positive regulator of adipogenesis. Conclusions This study identified a novel circLMO7 regulatory mechanism underlying the development of intramuscular fat in bovines and suggests promising molecular targets for genetic strategies aimed at enhancing meat quality. This study may serve as a foundation for studying the differential regulatory roles of ncRNAs in the development and function of adipose tissue.

Skin is a barrier to maintaining the stability of the human environment and preventing the invasion of pathogens. When skin tissue is exposed to the external environment, it will inevitably develop defects due to trauma, injury, burns, ulcers, surgery, and chronic diseases. Rapid skin repair is the key to reducing infection, relieving pain, and improving quality of life. Dihydroquercetin is a kind of flavonoid that has a wide range of pharmacological activities and can improve skin repair, skin inflammation, skin cancer, and so on. In this paper, the application of dihydroquercetin in medical dressings and the research progress in the treatment of skin-related diseases are reviewed, so as to provide reference for further developing dihydroquercetin as a drug for the treatment of skin diseases.

To address the issues of intense image noise interference and computational intensity faced by traditional vision-based weld tracking systems, we propose WeldLight, a lightweight and noise-resistant convolutional neural network for precise classification and positioning of welding seam feature points using single-line structured light vision. Our approach includes (1) an online data augmentation method to enhance training samples and improve noise adaptability; (2) a one-stage lightweight network for simultaneous positioning and classification; and (3) an attention module to filter features corrupted by intense noise, thereby improving stability. Experiments show that WeldLight achieves an F1-score of 0.9668 for seam classification on an adjusted test set, with mean absolute positioning errors of 1.639 pixels and 1.736 pixels on low-noise and high-noise test sets, respectively. With an inference time of 29.32 ms on a CPU platform, it meets real-time seam tracking requirements.

Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer's disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.