Visualizing lipid nanoparticle trafficking for mRNA vaccine delivery in non-human primates.

mRNA delivered using lipid nanoparticles (LNPs) has become an important subunit vaccine modality, but mechanisms of action for mRNA vaccines remain incompletely understood. Here, we synthesized a metal chelator-lipid conjugate enabling positron emission tomography (PET) tracer labeling of LNP/mRNA vaccines for quantitative visualization of vaccine trafficking in live non-human primates (NHPs). Following i.m. injection, we observed LNPs distributing through injected muscle tissue, simultaneous with rapid trafficking to draining lymph nodes (dLNs). Deltoid injection of LNPs mimicking human vaccine administration led to stochastic LNP delivery to 3 different sets of dLNs. LNP uptake in dLNs was confirmed by histology, and cellular analysis of tissues via flow cytometry identified antigen-presenting cells as the primary cell type responsible for early LNP uptake and mRNA translation. These results provide insights into the biodistribution of mRNA vaccines administered at clinically relevant doses, injection volumes, and injection sites in an important large animal model for vaccine development.

Local delivery of cell surface-targeted immunocytokines programs systemic anti-tumor immunity.

Cytokine therapies are potent immunotherapy agents but exhibit severe dose-limiting toxicities. One strategy to overcome this involves engineering cytokines for intratumoral retention following local delivery. Here, we develop a localized cytokine therapy that elicits profound anti-tumor immunity by engineered targeting to the ubiquitous leukocyte receptor CD45. We designed CD45-targeted immunocytokines (αCD45-Cyt) that, upon injection, decorated the surface of leukocytes in the tumor and tumor-draining lymph node (TDLN) without systemic exposure. αCD45-Cyt therapy eradicated both directly treated tumors and untreated distal lesions in multiple syngeneic mouse tumor models. Mechanistically, αCD45-Cyt triggered prolonged pSTAT signaling and reprogrammed tumor-specific CD8+ T cells in the TDLN to exhibit an anti-viral transcriptional signature. CD45 anchoring represents a broad platform for protein retention by host immune cells for use in immunotherapy.

Internet of Things enables smart solid waste bin management system for a sustainable environment.

Solid waste management (SWM) is a pressing concern and significant research topic that requires attention from citizens and government stakeholders. Most of the responsibility of waste management is on the municipal sector for its collection, reallocation, and reuse of other resources. The daily solid waste production is more than 54,850 tonnes in urban areas and is difficult to manage due to limited resources and different administrative and service issues. New technologies are playing their role in this area but how to integrate the technologies is still a question, especially for developing countries. This paper is divided into two main phases including a detailed investigation and a technological solution. In the first phase, the data is collected by using the qualitative method to investigate and identify the issues related to waste management. After a detailed investigation and results, the gap is identified by using statistical analysis and proposed a technological solution in the second phase. The technology-based solution is used to control and manage waste with a low-cost, fast, and manageable solution. The new sensor-based technologies, cellular networks, and social media are utilized to monitor the trash in the areas. The trash management department receives notification via cellular services to locate the dustbin when the dustbin reaches a maximum level so the department may send a waste collector vehicle to the relevant spot to collect waste. The smart and fast solution will connect all stakeholders in the community and reduce the cost and time and make the collection process faster. The experiment results indicated the issues and effectiveness of the proposed solution.

Vaccine-boosted CAR T crosstalk with host immunity to reject tumors with antigen heterogeneity.

Chimeric antigen receptor (CAR) T cell therapy effectively treats human cancer, but the loss of the antigen recognized by the CAR poses a major obstacle. We found that in vivo vaccine boosting of CAR T cells triggers the engagement of the endogenous immune system to circumvent antigen-negative tumor escape. Vaccine-boosted CAR T promoted dendritic cell (DC) recruitment to tumors, increased tumor antigen uptake by DCs, and elicited the priming of endogenous anti-tumor T cells. This process was accompanied by shifts in CAR T metabolism toward oxidative phosphorylation (OXPHOS) and was critically dependent on CAR-T-derived IFN-γ. Antigen spreading (AS) induced by vaccine-boosted CAR T enabled a proportion of complete responses even when the initial tumor was 50% CAR antigen negative, and heterogeneous tumor control was further enhanced by the genetic amplification of CAR T IFN-γ expression. Thus, CAR-T-cell-derived IFN-γ plays a critical role in promoting AS, and vaccine boosting provides a clinically translatable strategy to drive such responses against solid tumors.

Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks.

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.

Data Analytics, Self-Organization, and Security Provisioning for Smart Monitoring Systems.

Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user's security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms.

Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks.

Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes' air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.

Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare.

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread.

Nature-inspired solution for coronavirus disease detection and its impact on existing healthcare systems.

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing.  Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy.

Health-ID: A Blockchain-Based Decentralized Identity Management for Remote Healthcare.

COVID-19 has made eHealth an imperative. The pandemic has been a true catalyst for remote eHealth solutions such as teleHealth. Telehealth facilitates care, diagnoses, and treatment remotely, making them more efficient, accessible, and economical. However, they have a centralized identity management system that restricts the interoperability of patient and healthcare provider identification. Thus, creating silos of users that are unable to authenticate themselves beyond their eHealth application's domain. Furthermore, the consumers of remote eHealth applications are forced to trust their service providers completely. They cannot check whether their eHealth service providers adhere to the regulations to ensure the security and privacy of their identity information. Therefore, we present a blockchain-based decentralized identity management system that allows patients and healthcare providers to identify and authenticate themselves transparently and securely across different eHealth domains. Patients and healthcare providers are uniquely identified by their health identifiers (healthIDs). The identity attributes are attested by a healthcare regulator, indexed on the blockchain, and stored by the identity owner. We implemented smart contracts on an Ethereum consortium blockchain to facilities identification and authentication procedures. We further analyze the performance using different metrics, including transaction gas cost, transaction per second, number of blocks lost, and block propagation time. Parameters including block-time, gas-limit, and sealers are adjusted to achieve the optimal performance of our consortium blockchain.

Fully Integrated Data Communication Framework by Using Visualization Augmented Reality for Internet of Things Networks.

The new and integrated area called Internet of Things (IoT) has gained popularity due to its smart, objects, services and affordability. These networks are based on data communication, augmented reality (AR), and wired and wireless infrastructures. The basic objective of these network is data communication, environment monitoring, tracking, and sensing by using smart devices and sensor nodes. The dAR is one of the attractive and advanced areas that is integrated in IoT networks in smart homes and smart industries to convert the objects into 3D to visualize information and provide interactive reality-based control. With attraction, this idea has suffered with complex and heavy processes, computation complexities, network communication degradation, and network delay. This article presents a detailed overview of these technologies and proposes a more convenient and fast data communication model by using edge computing and Fifth-Generation platforms. The article also introduces a Visualization Augmented Reality framework for IoT (VAR-IoT) networks fully integrated by communication, sensing, and actuating features with a better interface to control the objects. The proposed network model is evaluated in simulation in terms of applications response time and network delay and it observes the better performance of the proposed framework.

Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach.

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Teleophthalmology with optical coherence tomography imaging in community optometry. Evaluation of a quality improvement for macular patients.

PURPOSE: To describe a quality improvement for referral of National Health Service patients with macular disorders from a community optometry setting in an urban area. METHODS: Service evaluation of teleophthalmology consultation based on spectral domain optical coherence tomography images acquired by the community optometrist and transmitted to hospital eye services. RESULTS: Fifty patients with suspected macular conditions were managed via telemedicine consultation over 1 year. Responses were provided by hospital eye service-based ophthalmologists to the community optometrist or patient within the next day in 48 cases (96%) and in 34 (68%) patients on the same day. In the consensus opinion of the optometrist and ophthalmologist, 33 (66%) patients required further "face-to-face" medical examination and were triaged on clinical urgency. Seventeen cases (34%) were managed in the community and are a potential cost improvement. Specialty trainees were supervised in telemedicine consultations. CONCLUSION: Innovation and quality improvement were demonstrated in both optometry to ophthalmology referrals and in primary optometric care by use of telemedicine with spectral domain optical coherence tomography images. E-referral of spectral domain optical coherence tomography images assists triage of macular patients and swifter care of urgent cases. Teleophthalmology is also, in the authors' opinion, a tool to improve interdisciplinary professional working with community optometrists. Implications for progress are discussed.

Intracameral bevacizumab for rubeotic glaucoma secondary to retinal vein occlusion.

Two patients with ischaemic central retinal vein occlusion were treated with intracameral bevacizumab in order to affect the outcome of their disease. In addition, both patients underwent episodes of cycloablative and pan-retinal laser treatment. The patients were followed for more than 6 months and achieved some stability with a combination of these modalities.

An anti-adenoma antibody, Adnab-9, may reflect the risk for neoplastic progression in familial hamartomatous polyposis syndromes.

Patients with the hamartomatous polyposis Peutz-Jeghers and familial juvenile polyposis syndromes are predisposed to colorectal cancer but lack early genetic alterations found in adenomatous premalignant lesions. We studied hamartomatous polyps for the expression of an early preneoplastic colorectal neoplasia risk marker also found in familial adenomatous polyposis patients. Retrospective, genetic, and hospital archival tissue immunohistochemistry using Adnab-9, a premalignant marker often found in Paneth-like cells (PCs), was performed on sections of polyps from eight patients with Peutz-Jeghers syndrome, eight patients with familial juvenile polyposis, and 36 hyperplastic polyp control sections. Anti-alpha-defensin 5 (AD5), a universal PC marker, was also used to label a subgroup of sections. Hamartomatous polyposis patients also underwent specific genetic analysis. Eighty-nine percent of Peutz-Jeghers syndrome polyps labeled with Adnab-9 compared with 63% for AD5; 88% of familial juvenile polyposis sections also labeled with Adnab-9. Of the 36 hyperplastic polyp sections, only four (11%) labeled with Adnab-9 and one (3%) with AD5. Adnab-9 labeling of PCs in the epithelial elements of hamartomatous colonic lesions of hereditary hamartomatous syndrome patients reflects the predisposition to colorectal cancer, further justifying early intervention strategies.

Inhibition of CYP1A enzymes by alpha-naphthoflavone causes both synergism and antagonism of retene toxicity to rainbow trout (Oncorhynchus mykiss).

Retene (7-isopropyl-1-methyl phenanthrene) is a polycyclic aromatic hydrocarbon (PAH), that causes dioxin-like toxicity to early life stages of fish, including increased rates of mortality, developmental defects characterized as blue sac disease (BSD), and induction of CYP1A enzymes. This study determined whether toxicity is associated with retene, or with its metabolism by CYP1A enzymes to hydroxylated derivatives. Larval rainbow trout (Oncorhynchus mykiss) were co-exposed to four concentrations of waterborne retene and four concentrations of waterborne alpha-naphthoflavone (ANF), a compound that antagonizes CYP1A induction and inhibits oxygenation reactions. The prevalence of mortality and BSD increased in an exposure-dependent manner for larvae exposed to retene alone. Tissue concentrations of CYP1A protein and retene metabolites also increased, but no un-metabolized retene (i.e., the parent compound) was measurable. At low concentrations of ANF, toxicity increased dramatically, while tissue concentrations of polar hydroxylated metabolites of retene decreased, and concentrations of less polar metabolites, and of parent retene, increased. At the highest concentration of ANF, retene toxicity was eliminated, and parent retene was the predominant form in tissue; no concentration of ANF was toxic by itself. The inhibition of retene hydroxylation and toxicity by ANF suggests that toxicity was caused by specific retene metabolites, and not by parent retene. The potentiation of retene toxicity at low concentrations of ANF, and the antagonism at high concentrations is a unique, non-linear interaction based on modulating CYP1A enzyme activity and retene metabolism. It demonstrates that effects on fish of different complex mixtures of hydrocarbons may not be easily predicted.