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Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a 'data train' and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data. [ABSTRACT FROM AUTHOR]

Myocardial injury is associated with inflammation and fibrosis. Cardiac myosin-binding protein-C (cMyBP-C) is cleaved by µ-calpain upon myocardial injury, releasing C0-C1f, an N-terminal peptide of cMyBP-C. Previously, we reported that the presence of C0-C1f is pathogenic within cardiac tissue and is able to activate macrophages. Fibroblasts also play a crucial role in cardiac remodeling arising from ischemic events, as they contribute to both inflammation and scar formation. To understand whether C0-C1f directly modulates fibroblast phenotype, we analyzed the impact of C0-C1f on a human fibroblast cell line in vitro by performing mRNA microarray screening, immunofluorescence staining, and quantitative real-time PCR. The underlying signaling pathways were investigated by KEGG analysis and determined more precisely by targeted inhibition of the potential signaling cascades in vitro. C0-C1f induced pro-inflammatory responses that might delay TGFβ-mediated myofibroblast conversion. TGFβ also counteracted C0-C1f-mediated fibroblast activation. Inhibition of TLR4 or NFκB as well as the delivery of miR-146 significantly reduced C0-C1f-mediated effects. In conclusion, C0-C1f induces inflammatory responses in human fibroblasts that are mediated via TRL4 signaling, which is decreased in the presence of TGFβ. Specific targeting of TLR4 signaling could be an innovative strategy to modulate C0-C1f-mediated inflammation. [ABSTRACT FROM AUTHOR]

Extracellular vesicles are released by numerous cell types of the human body under physiological but also under pathophysiological conditions. They are important for cell–cell communication and carry specific signatures of peptides and RNAs. In this study, we aimed to determine whether extracellular vesicles isolated from patients with pulmonary hypertension show a disease specific signature of small non-coding RNAs and thus have the potential to serve as diagnostic and prognostic biomarkers. Extracellular vesicles were isolated from the serum of 23 patients with chronic thromboembolic pulmonary hypertension (CTEPH) and 23 controls using two individual methods: a column-based method or by precipitation. Extracellular vesicle- associated RNAs were analyzed by next-generation sequencing applying molecular barcoding, and differentially expressed small non-coding RNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR). We identified 18 microRNAs and 21 P-element induced wimpy testis (PIWI)-interacting RNAs (piRNAs) or piRNA clusters that were differentially expressed in CTEPH patients compared with controls. Bioinformatic analysis predicted a contribution of these piRNAs to the progression of cardiac and vascular remodeling. Expression levels of DQ593039 correlated with clinically meaningful parameters such as mean pulmonary arterial pressure, pulmonary vascular resistance, right ventricular systolic pressure, and levels of N-terminal pro-brain natriuretic peptide. Thus, we identified the extracellular vesicle- derived piRNA, DQ593039, as a potential biomarker for pulmonary hypertension and right heart disease. [ABSTRACT FROM AUTHOR]

Small extracellular vesicles (sEV) hold enormous potential as biomarkers, drug carriers, and therapeutic agents. However, due to previous limitations in the phenotypic characterization of sEV at the single vesicle level, knowledge of cell type-specific sEV signatures remains sparse. With the introduction of next-generation sEV analysis devices, such as the single-particle interferometric reflectance imaging sensor (SP-IRIS)-based ExoView R100 platform, single sEV analyses are now possible. While the tetraspanins CD9, CD63, and CD81 were generally considered pan-sEV markers, it became clear that sEV of different cell types contain several combinations and amounts of these proteins on their surfaces. To gain better insight into the complexity and heterogeneity of sEV, we used the ExoView R100 platform to analyze the CD9/CD63/CD81 phenotype of sEV released by different cell types at a single sEV level. We demonstrated that these surface markers are sufficient to distinguish cell-type-specific sEV phenotypes. Furthermore, we recognized that tetraspanin composition in some sEV populations does not follow a random pattern. Notably, the tetraspanin distribution of sEV derived from mesenchymal stem cells (MSCs) alters depending on cell culture conditions. Overall, our data provide an overview of the cell-specific characteristics of sEV populations, which will increase the understanding of sEV physiology and improve the development of new sEV-based therapeutic approaches.