ABSTRACT
[This corrects the article DOI: 10.3389/fimmu.2023.1247131.].
ABSTRACT
In [1], this paper was submitted for the Special Issue on Flexible Biomedical Sensors for Healthcare Applications. The paper was instead published in Volume 16, Issue 6, 2022.
ABSTRACT
Background: The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated. Methods: We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosis-based prognostic model was developed. In vitro experiments were performed to verify distinct PANoptosis-related genes. An external scRNA-seq dataset was used to verify cellular localization. Results: Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore. Conclusion: We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis.
Subject(s)
Sepsis , Single-Cell Gene Expression Analysis , Humans , Sepsis/genetics , Algorithms , B-Lymphocytes , Cell Transformation, NeoplasticABSTRACT
This paper provides a special flexible graphene film based capacitive wireless power transfer (FGCPT) system for powering biomedical sensors of smart wearable devices. The graphene conductive material is flexible, transparent, highly conductive, and impermeable to most gases and liquids. Generally, the coupling structure of capacitive wireless power transfer (CPT) system is consisted of metal plates. However, it is hard to use for the biomedical sensors as the low power density and big volume. The shape of graphene conductive material could be easily built and changed according to the application requirements. In this paper, the power supply of biomedical sensing system could be accomplished by a single graphene film which is acted as the receiver of FGCPT system. The 200 mW power level is achieved with the maximum 9 V output voltage. The theory and calculation are verified by the simulated and experimental results.
