Exploration of Programmed Cell Death-Associated Characteristics and Immune infiltration in Neonatal Sepsis: New insights From Bioinformatics analysis and Machine Learning

BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. to further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000)

Would You Accept Virtual Tourism? The Impact of COVID-19 Risk Perception on Technology Acceptance from a Comparative Perspective

Due to the COVID-19 pandemic, the tourism industry and its stakeholders have tried to develop a new virtual tourism market, but its effectiveness remains to be tested. We proposed and tested a new measurement scale composed of ease of use, usefulness, autonomy, enjoyment, perceived risk of COVID-19, and attitude. In total, 274 questionnaires were collected by the purposive sampling method and 239 of them were valid, with 57 potential virtual tourists (who knew of but had not used VR in tourism) and 182 actual virtual tourists (who had experienced virtual tourism). Then, we used path analysis to test the hypothetical model and compared the results of two groups. The results show that (1) the popularity of virtual tourism is limited, (2) ease of use significantly affects usefulness and enjoyment for the two groups, (3) usefulness significantly affects autonomy and enjoyment for the two groups, (4) perceived risk of COVID-19 has a direct impact on the attitude towards virtual tourism for the two groups rather than a moderating role, and (5) expected ease of use has a significant effect on autonomy, and autonomy further influences enjoyment for potential tourists. This paper is an explorative attempt to explore virtual technology applied in tourism during the COVID-19 pandemic. The results provide theoretical contributions and practical implications for technology improvement, tourism marketing, and virtual tourism development

A review of the tools and techniques used in the digital preservation of architectural heritage within disaster cycles

Abstract Architectural heritage is vulnerable to disasters. Digital technologies can fight destruction and can ensure integrity by monitoring, managing and protecting architectural heritage from disasters. In this paper, we clarify the relationship between disasters, digitalization and architectural heritage conservation for the sustainability of cultural heritage. This study used the PRISMA process, and bibliometric tools VOSviewer and Citespace to explore the potential of digital technologies in the protection of architectural heritage—especially during disaster cycles, from the perspectives of both universal and typicality; the results revealed that digital twins, deep learning, and preventive conservation are currently hot topics in digital preservation research (especially that research which relates to disaster cycles). On this basis, this paper summarizes the relevant technologies involved in architectural heritage preservation from the perspective of the disaster cycle and the digital phase, and proposes three future research directions: accurate prediction of multi-disasters, automatic early warning of structural damages, and intelligent monitoring of human–computer interaction. This paper constructs a new research frame for digital preservation of architectural heritage during disasters, providing theoretical reference and practical guidance for architectural heritage conversation

Profiling, clinicopathological correlation and functional validation of specific long non-coding RNAs for hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignancies worldwide. Studies seeking to advance the overall understanding of lncRNA profiling in HCC remain rare. Methods The transcriptomic profiling of 12 HCC tissues and paired adjacent normal tissues was determined using high-throughput RNA sequencing. Fifty differentially expressed mRNAs (DEGs) and lncRNAs (DELs) were validated in 21 paired HCC tissues via quantitative real-time PCR. The correlation between the expression of DELs and various clinicopathological characteristics was analyzed using Student’s t-test or linear regression. Co-expression networks between DEGs and DELs were constructed through Pearson correlation co-efficient and enrichment analysis. Validation of DELs’ functions including proliferation and migration was performed via loss-of-function RNAi assays. Results In this study, we identified 439 DEGs and 214 DELs, respectively, in HCC. Furthermore, we revealed that multiple DELs, including NONHSAT003823, NONHSAT056213, NONHSAT015386 and especially NONHSAT122051, were remarkably correlated with tumor cell differentiation, portal vein tumor thrombosis, and serum or tissue alpha fetoprotein levels. In addition, the co-expression network analysis between DEGs and DELs showed that DELs were involved with metabolic, cell cycle, chemical carcinogenesis, and complement and coagulation cascade-related pathways. The silencing of the endogenous level of NONHSAT122051 or NONHSAT003826 could significantly attenuate the mobility of both SK-HEP-1 and SMMC-7721 HCC cells. Conclusion These findings not only add knowledge to the understanding of genome-wide transcriptional evaluation of HCC but also provide promising targets for the future diagnosis and treatment of HCC

Additional file 1 of Exploration of programmed cell death-associated characteristics and immune infiltration in neonatal sepsis: new insights from bioinformatics analysis and machine learning

Supplementary Material 1: Table S1. Baseline characteristics of the patients of GSE25504 (GPL6947 platform