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Objective: This study employs bibliometric and visual analysis to elucidate global research trends in Autism Spectrum Disorder (ASD) biomarkers, identify critical research focal points, and discuss the potential integration of diverse biomarker modalities for precise ASD assessment. Methods: A comprehensive bibliometric analysis was conducted using data from the Web of Science Core Collection database until December 31, 2022. Visualization tools, including R, VOSviewer, CiteSpace, and gCLUTO, were utilized to examine collaborative networks, co-citation patterns, and keyword associations among countries, institutions, authors, journals, documents, and keywords. Results: ASD biomarker research emerged in 2004, accumulating a corpus of 4348 documents by December 31, 2022. The United States, with 1574 publications and an H-index of 213, emerged as the most prolific and influential country. The University of California, Davis, contributed significantly with 346 publications and an H-index of 69, making it the leading institution. Concerning journals, the Journal of Autism and Developmental Disorders, Autism Research, and PLOS ONE were the top three publishers of ASD biomarker-related articles among a total of 1140 academic journals. Co-citation and keyword analyses revealed research hotspots in genetics, imaging, oxidative stress, neuroinflammation, gut microbiota, and eye tracking. Emerging topics included ''DNA methylation,'' ''eye tracking,'' ''metabolomics,'' and ''resting-state fMRI.'' Conclusion: The field of ASD biomarker research is dynamically evolving. Future endeavors should prioritize individual stratification, methodological standardization, the harmonious integration of biomarker modalities, and longitudinal studies to advance the precision of ASD diagnosis and treatment.

Mining the core value of Industrial Internet of Things (IIoT) data safely and reducing the risk of malicious attacks are the inherent requirements of industrial data visualization. Visualization technology has become the main tool for data aggregation, mining and analysis of IIoT data through graphical representation. However, visualization technology still has two shortcomings in big data calculation and analysis scenarios. On the one hand, visual results will lead to the disclosure of sensitive privacy. On the other hand, most visualization tools can't provide an interactive framework for users to select the suitable solutions. To address these problems, we present an open accessible Visual framework based on Differential Privacy theory (VisDP), which provides Multi-index Quantitative comprehensive Evaluation technology (MQE) for data mining results. Considering the advantages of interactive mechanism, VisDP provides rich optional schemes, including the operating web, calling API and the downloading SDK. Finally, we verify the availability and privacy of MQE through mathematical proofs, analyze the hospital medical waste detection system that actually applies the framework, and the experimental results have showed the effectiveness and practicality of the proposed platform.

Abstract Exosomes mediate intercellular communication by transmitting active molecules. The function of long noncoding RNA (lncRNA) H19 in autoimmune liver injury is unclear. Concanavalin A (ConA)‐induced liver injury is well‐characterized immune‐mediated hepatitis. Here, we showed that lncRNA H19 expression was increased in the liver after ConA treatment, accompanied by increased exosome secretion. Moreover, injection of AAV‐H19 aggravated ConA‐induced hepatitis, with an increase in hepatocyte apoptosis. However, GW4869, an exosome inhibitor, alleviated ConA‐induced liver injury and inhibited the upregulation of lncRNA H19. Intriguingly, lncRNA H19 expression in the liver was significantly downregulated, after macrophage depletion. Importantly, the lncRNA H19 was primarily expressed in type I macrophage (M1) and encapsulated in M1‐derived exosomes. Furthermore, H19 was transported from M1 to hepatocytes via exosomes, and exosomal H19 dramatically induced hepatocytes apoptosis both in vitro and vivo. Mechanistically, H19 upregulated the transcription of hypoxia‐inducible factor‐1 alpha (HIF‐1α), which accumulated in the cytoplasm and mediated hepatocyte apoptosis by upregulating p53. M1‐derived exosomal lncRNA H19 plays a pivotal role in ConA‐induced hepatitis through the HIF‐1α–p53 signaling pathway. These findings identify M1 macrophage‐derived exosomal H19 as a novel target for the treatment of autoimmune liver diseases.

Gastrointestinal dysfunction is a common symptom of acute mountain sickness (AMS). The gut microbiota and γδ T cells play critical roles in intestinal disease. However, the mechanistic link between the microbiota and γδ T cells in hypoxia-induced intestinal injury remains unclear. Here, we show that hypoxia-induced intestinal damage was significantly alleviated after microbiota depletion with antibiotics. Hypoxia modulated gut microbiota composition by promoting antimicrobial peptides angiogenin-4 secretions. The abundance of Clostridium in the gut of mice after hypoxia significantly decreased, while the abundance of Desulfovibrio significantly increased. Furthermore, Desulfovibrio-derived phosphatidylethanolamine and phosphatidylcholine promoted γδ T cell activation. In CD1d-deficient mice, the levels of intraepithelial IL-17A and γδ T cells and intestinal damage were significantly decreased compared with those in wild-type mice under hypoxia. Mechanistically, phospholipid metabolites from Desulfovibrio are presented by intestinal epithelial CD1d to induce the proliferation of IL-17A-producing γδ T cells, which aggravates intestinal injury. Gut microbiota-derived metabolites promote hypoxia-induced intestinal injury via CD1d-dependent γδ T cells, suggesting that phospholipid metabolites and γδ T cells can be targets for AMS therapy.

Background: Coronavirus disease 2019 (COVID-19) is a worldwide emergency, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Long non-coding RNAs (lncRNAs) do not encode proteins but could participate in immune response.Methods: In our study, 39 COVID-19 patients were enrolled. The microarray of peripheral blood mononuclear cells from healthy and COVID-19 patients was applied to identify the expression profiles of lncRNAs and mRNAs. Identified differentially expressed (DE) lncRNAs were validated by qRT-PCR. Then, the lncRNA–mRNA network was constructed and visualized using Cytoscape (3.6.1) based on the Pearson correlation coefficient. The enrichment of DE mRNAs was analyzed using Metascape. The difference in frequencies of immune cells and cytokines was detected using CIBERSORT and ImmPort based on DE mRNAs.Results: All patients with COVID-19 displayed lymphopenia, especially in T cells, and hyper-inflammatory responses, including IL-6 and TNF-α. Four immune-related lncRNAs in COVID-19 were found and further validated, including AC136475.9, CATG00000032642.1, G004246, and XLOC_013290. Functional analysis enriched in downregulation of the T-cell receptor and the antigen processing and presentation as well as increased apoptotic proteins, which could lead to T-cell cytopenia. In addition, they participated in monocyte remodeling, which contributed to releasing cytokines and chemokines and then recruiting more monocytes and aggravating the clinical severity of COVID-19 patients.Conclusion: Taken together, four lncRNAs were in part of immune response in COVID-19, which was involved in the T-cell cytopenia by downregulating the antigen processing and presentation, the T-cell receptor, and an increased proportion of monocytes, with a distinct change in cytokines and chemokines.

Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate personalized tutorials according to learners' preferences, nor can they adjust tutorial content as moods or levels of knowledge change. Therefore, this study develops an intelligent tutorial-generating system (Self-GT) for self-aid learning, integrating cognitive computing and generative learning to capture learners' dynamic preferences. The critical components of Self-GT are the tutorial-generating model based on cyclic deep reinforcement learning (RL) and the multimodal knowledge graph containing complex relationships. Specifically, the proposed RL model dynamically explores learners' preferences from the temporal dimension, enabling RL agents to express learning behavior characteristics accurately and generate personalized tutorials. Then, relying on the internal self-developed education base and external Internet sources, a multimodal knowledge graph with multiple self-defined relationships is designed to enhance the precision of tutorial generation. Finally, the experimental results indicate that the Self-GT performs well in generating tutorials and has been successfully applied in the generating tutorial for "Hospital Network Architecture Planning and Design."