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Sleep staging plays an important role in the diagnosis and treatment of clinical sleep disorders. The sleep staging standard defines every 30 seconds as a sleep period, which may mean that there exist similar brain activity patterns during the same sleep period. Thus, in this work, we propose a novel time-related synchronization analysis framework named time-related multimodal sleep scoring model (TRMSC) to explore the potential time-related patterns of sleeping. In the proposed TRMSC, the time-related synchronization analysis is first conducted on the single channel electrophysiological signal, i.e., Electroencephalogram (EEG) and Electrooculogram (EOG), to explore the time-related patterns, and the spectral activation features are also extracted by spectrum analysis to obtain the multimodal features. With the extracted multimodal features, the feature fusion and selection strategy is utilized to obtain the optimal feature set and achieve robust sleep staging. To verify the effectiveness of the proposed TRMSC, sleep staging experiments were conducted on the Sleep-EDF dataset, and the experimental results indicate that the proposed TRMSC has achieved better performance than other existing strategies, which proves that the time-related synchronization features can make up for the shortcomings of traditional spectrum-based strategies and achieve a higher classification accuracy. The proposed TRMSC model may be helpful for portable sleep analyzers and provide a new analytical method for clinical sleeping research.

During tumor development, the tumor itself must continuously generate new blood vessels to meet their growth needs while also allowing for tumor invasion and metastasis. One of the most common features of tumors is hypoxia, which drives the process of tumor angiogenesis by regulating the tumor microenvironment, thus adversely affecting the prognosis of patients. In addition, to overcome unsuitable environments for growth, such as hypoxia, nutrient deficiency, hyperacidity, and immunosuppression, the tumor microenvironment (TME) coordinates angiogenesis in several ways to restore the supply of oxygen and nutrients and to remove metabolic wastes. A growing body of research suggests that tumor angiogenesis and hypoxia interact through a complex interplay of crosstalk, which is inextricably linked to the TME. Here, we review the TME's positive contribution to angiogenesis from an angiogenesis-centric perspective while considering the objective impact of hypoxic phenotypes and the status and limitations of current angiogenic therapies.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

Abstract Background Previous studies have identified the carbohydrate epitope Galα1–3Galβ1–4GlcNAc-R (termed the α-galactosyl epitope), known as the α-Gal antigen as the primary xenoantigen recognized by the human immune system. The α-Gal antigen is regulated by galactosyltransferase (GGTA1), and α-Gal antigen-deficient mice have been widely used in xenoimmunological studies, as well as for the immunogenic risk evaluation of animal-derived medical devices. The objective of this study was to develop α-Gal antigen-deficient rabbits by GGTA1 gene editing with the CRISPR/Cas9 system. Results The mutation efficiency of GGTA1 gene-editing in rabbits was as high as 92.3% in F0 pups. Phenotype analysis showed that the α-Gal antigen expression in the major organs of F0 rabbits was decreased by more than 99.96% compared with that in wild-type (WT) rabbits, and the specific anti-Gal IgG and IgM antibody levels in F1 rabbits increased with increasing age, peaking at approximately 5 or 6 months. Further study showed that GGTA1 gene expression in F2-edited rabbits was dramatically reduced compared to that in WT rabbits. Conclusions α-Gal antigen-deficient rabbits were successfully generated by GGTA1 gene editing via the CRISPR/Cas9 system in this study. The feasibility of using these α-Gal antigen-deficient rabbits for the in situ implantation and residual immunogenic risk evaluation of animal tissue-derived medical devices was also preliminarily confirmed.

Wild type (WT) animals cannot be used to objectively assess the immunogenicity of animal tissue-derived biomaterials when used as recipients due to difference with human in α-Gal expression. The purpose of this study is to compare the differences of immunological responses between the GGTA1 gene-knockout (GTKO) rabbits and WT rabbits after implantation with animal tissue-derived biomaterials. The porcine-derived decellularized bone matrix (natural bone material, NBM) and fresh porcine cancellous bone (PCB) were implanted in GTKO rabbits and WT rabbits, respectively, and sham operation was used as control (Con). At 2- and 6-week post-implantation, the related immunological items including antibody levels, serum-mediated cell lysis, cytokines, lymphocyte subtypes, and histopathological changes were assessed.GTKO rabbits exhibited more sensitive immune responses than WT rabbits after PCB implantation, resulted from a significant increase of antibodies (except total antibodies) and cytokines levels, cell lysis ratios, CD4/CD8 proportions, and inflammatory cells infiltration. Immunological factors and inflammatory cells infiltrate in GTKO rabbits after NBM implantation were significantly lower than those in the PCB group. Among the three groups, the NBM group showed the highest contents of new bone formation elements.In conclusion, the GTKO rabbit is a more sensitive alternative model than WT rabbit for preclinical study of xenografts via in situ implantation. Studies on multiple gene-edited animals are also necessary for more comprehensively evaluating xenoimmunologen risks of animal tissue-derived biomaterials in the future. Additionally, the immunogenicity of NBM was remarkably decreased compared to PCB.