Effect of cryopreservation on CD4+ T cell subsets in foreskin tissue.

Voluntary medical male circumcision (VMMC) reduces HIV acquisition by at least 60%, but the determinants of HIV susceptibility in foreskin tissues are incompletely understood. Flow cytometry is a powerful tool that helps us understand tissue immune defenses in mucosal tissue like the inner foreskin, but foreskin flow cytometry has only been validated using fresh tissue samples. This restricts immune analyses to timepoints immediately after surgical acquisition and hinders research in this area. We compared fresh analysis with whole tissue cryopreservation and later thawing and digestion to analyze CD4+ T cell populations relevant to HIV susceptibility (CCR5, CD25, CD127, CCR4, CXCR3, CCR6, CCR10, HLA-DR, and CD38). Eight foreskin samples from HIV-negative males aged >18 years were collected after VMMC. For each sample, half the foreskin was immediately cryopreserved for later digestion and flow cytometry analysis, while the remaining tissues were analyzed fresh. We demonstrate no significant impact of cryopreservation on CD4+ T cell expression of CD25, CCR4, CCR6, HLA-DR, CCR10, or CD127. Although expression levels of CCR5, CD38, and CXCR3 were increased after cryopreservation, the relative ranking of participants was retained. In conclusion, cryopreserved foreskin tissues may be suitable for subsequent digestion and flow cytometry phenotyping of HIV-susceptible T cell populations.

A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS.

Digital healthcare is a composite infrastructure of networking entities that includes the Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS), base stations, services provider, and other concerned components. In the recent decade, it has been noted that the demand for this emerging technology is gradually increased with cost-effective results. Although this technology offers extraordinary results, but at the same time, it also offers multifarious security perils that need to be handled effectively to preserve the trust among all engaged stakeholders. For this, the literature proposes several authentications and data preservation schemes, but somehow they fail to tackle this issue with effectual results. Keeping in view, these constraints, in this paper, we proposed a lightweight authentication and data preservation scheme for IoT based-CPS utilizing deep learning (DL) to facilitate decentralized authentication among legal devices. With decentralized authentication, we have depreciated the validation latency among pairing devices followed by improved communication statistics. Moreover, the experimental results were compared with the benchmark models to acknowledge the significance of our model. During the evaluation phase, the proposed model reveals incredible advancement in terms of comparative parameters in comparison with benchmark models.

An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement.