Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies.

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities.

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

Votucalis, a Novel Centrally Sparing Histamine-Binding Protein, Attenuates Histaminergic Itch and Neuropathic Pain in Mice.

Votucalis is a biologically active protein in tick (R. appendiculatus) saliva, which specifically binds histamine with high affinity and, therefore, has the potential to inhibit the host's immunological responses at the feeding site. We hypothesized that scavenging of peripherally released endogenous histamine by Votucalis results in both anti-itch and anti-nociceptive effects. To test this hypothesis, adult male mice were subjected to histaminergic itch, as well as peripheral nerve injury that resulted in neuropathic pain. Thus, we selected models where peripherally released histamine was shown to be a key regulator. In these models, the animals received systemic (intraperitoneal, i.p.) or peripheral transdermal (subcutaneous, s.c. or intraplantar, i.pl.) administrations of Votucalis and itch behavior, as well as mechanical and thermal hypersensitivity, were evaluated. Selective histamine receptor antagonists were used to determine the involvement of histamine receptors in the effects produced by Votucalis. We also used the spontaneous object recognition test to confirm the centrally sparing properties of Votucalis. Our main finding shows that in histamine-dependent itch and neuropathic pain models peripheral (s.c. or i.pl.) administration of Votucalis displayed a longer duration of action for a lower dose range, when compared with Votucalis systemic (i.p.) effects. Stronger anti-itch effect was observed after co-administration of Votucalis (s.c.) and antagonists that inhibited peripheral histamine H1 and H2 receptors as well as central histamine H4 receptors indicating the importance of these histamine receptors in itch. In neuropathic mice, Votucalis produced a potent and complete anti-nociceptive effect on mechanical hypersensitivity, while thermal (heat) hypersensitivity was largely unaffected. Overall, our findings further emphasize the key role for histamine in the regulation of histaminergic itch and chronic neuropathic pain. Given the effectiveness of Votucalis after peripheral transdermal administration, with a lack of central effects, we provide here the first evidence that scavenging of peripherally released histamine by Votucalis may represent a novel therapeutically effective and safe long-term strategy for the management of these refractory health conditions.

Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities.

Nowadays, technology has been evolving rapidly. Due to the consequent impact of smart technologies, it becomes a ubiquitous part of life. These technologies have led to the emergence of smart cities that are geographic areas driven by advanced information and communication technologies. In the context of smart cities, IoT, blockchain, and fog computing have been found as the significant drivers of smart initiates. In this recognition, the present study is focused on delineating the impact and potential of blockchain, IoT, and fog computing on healthcare services in the context of smart cities. In pursuit of this objective, the study has conducted a systematic review of literature that is most relevant to the topic of the paper. In order to select the most relevant and credible articles, the researcher has used PRISMA and AMSTAR that have culminated in the 10 most relevant articles for the present study. The findings revealed that IoT, blockchain, and fog computing had become drivers of efficiency in the healthcare services in smart cities. Among the three technologies, IoT has been found to be widely incorporated. However, it is found to be lacking in terms of cost efficiency, data privacy, and interoperability of data. In this recognition, blockchain technology and fog computing have been found to be more relevant to the healthcare sector in smart cities. Blockchain has been presented as a promising technology for ensuring the protection of private data, creating a decentralized database, and improving the interoperability of data while fog computing has been presented as the promising technology for low-cost remote monitoring, reducing latency and increasing efficiency.

Edge Detection-Based Feature Extraction for the Systems of Activity Recognition.

Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup.

Elucidating the Neuroprotective Role of PPARs in Parkinson's Disease: A Neoteric and Prospective Target.

One of the utmost frequently emerging neurodegenerative diseases, Parkinson's disease (PD) must be comprehended through the forfeit of dopamine (DA)-generating nerve cells in the substantia nigra pars compacta (SN-PC). The etiology and pathogenesis underlying the emergence of PD is still obscure. However, expanding corroboration encourages the involvement of genetic and environmental factors in the etiology of PD. The destruction of numerous cellular components, namely oxidative stress, ubiquitin-proteasome system (UPS) dysfunction, autophagy-lysosome system dysfunction, neuroinflammation and programmed cell death, and mitochondrial dysfunction partake in the pathogenesis of PD. Present-day pharmacotherapy can alleviate the manifestations, but no therapy has been demonstrated to cease disease progression. Peroxisome proliferator-activated receptors (PPARs) are ligand-directed transcription factors pertaining to the class of nuclear hormone receptors (NHR), and are implicated in the modulation of mitochondrial operation, inflammation, wound healing, redox equilibrium, and metabolism of blood sugar and lipids. Numerous PPAR agonists have been recognized to safeguard nerve cells from oxidative destruction, inflammation, and programmed cell death in PD and other neurodegenerative diseases. Additionally, various investigations suggest that regular administration of PPAR-activating non-steroidal anti-inflammatory drugs (NSAIDs) (ibuprofen, indomethacin), and leukotriene receptor antagonists (montelukast) were related to the de-escalated evolution of neurodegenerative diseases. The present review elucidates the emerging evidence enlightening the neuroprotective outcomes of PPAR agonists in in vivo and in vitro models experiencing PD. Existing articles up to the present were procured through PubMed, MEDLINE, etc., utilizing specific keywords spotlighted in this review. Furthermore, the authors aim to provide insight into the neuroprotective actions of PPAR agonists by outlining the pharmacological mechanism. As a conclusion, PPAR agonists exhibit neuroprotection through modulating the expression of a group of genes implicated in cellular survival pathways, and may be a propitious target in the therapy of incapacitating neurodegenerative diseases like PD.

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques.

Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of heart vessels and then classified. Segmentation and classification of heart angiography provides significant information for the physician as well as patient. Contradictorily, in the mention domain of heart angiography, the charge is prone to error, phase overwhelming, and thought-provoking task for the physician (heart specialist). An automatic segmentation and classification of heart blood vessels descriptions can improve the truthfulness and speed up the finding of heart illnesses. In this work, we recommend a computer-assisted conclusion arrangement for the localization of human heart blood vessels within heart angiographic imageries by using multiclass ensemble classification mechanism. In the proposed work, the heart blood vessels will be first segmented, and the various features according to accuracy have been extracted. Low-level features such as texture, statistical, and geometrical features were extracted in human heart blood vessels. At last, in the proposed framework, heart blood vessels have been categorized in their four respective classes including normal, block, narrow, and blood flow-reduced vessels. The proposed approach has achieved best result which provides very useful, easy, accurate, and time-saving environment to cardiologists for the diagnosis of heart-related diseases.

Histamine, histamine receptors, and neuropathic pain relief.

Histamine, acting via distinct histamine H1 , H2 , H3 , and H4 receptors, regulates various physiological and pathological processes, including pain. In the last two decades, there has been a particular increase in evidence to support the involvement of H3 receptor and H4 receptor in the modulation of neuropathic pain, which remains challenging in terms of management. However, recent data show contrasting effects on neuropathic pain due to multiple factors that determine the pharmacological responses of histamine receptors and their underlying signal transduction properties (e.g., localization on either the presynaptic or postsynaptic neuronal membranes). This review summarizes the most recent findings on the role of histamine and the effects mediated by the four histamine receptors in response to the various stimuli associated with and promoting neuropathic pain. We particularly focus on mechanisms underlying histamine-mediated analgesia, as we aim to clarify the analgesic potential of histamine receptor ligands in neuropathic pain. LINKED ARTICLES: This article is part of a themed section on New Uses for 21st Century. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.3/issuetoc.