Role of Quercetin in DNA Repair: Possible Target to Combat Drug Resistance in Diabetes.

Diabetes Mellitus (DM) is referred to as hyperglycemia in either fasting or postprandial phases. Oxidative stress, which is defined by an excessive amount of reactive oxygen species (ROS) production, increased exposure to external stress, and an excessive amount of the cellular defense system against them, results in cellular damage. Increased DNA damage is one of the main causes of genomic instability, and genetic changes are an underlying factor in the emergence of cancer. Through covalent connections with DNA and proteins, quercetin has been demonstrated to offer protection against the creation of oxidative DNA damage. It has been found that quercetin shields DNA from possible oxidative stress-related harm by reducing the production of ROS. Therefore, Quercetin helps to lessen DNA damage and improve the ability of DNA repair mechanisms. This review mainly focuses on the role of quercetin in repairing DNA damage and compensating for drug resistance in diabetic patients. Data on the target topic was obtained from major scientific databases, including SpringerLink, Web of Science, Google Scholar, Medline Plus, PubMed, Science Direct, and Elsevier. In preclinical studies, quercetin guards against DNA deterioration by regulating the degree of lipid peroxidation and enhancing the antioxidant defense system. By reactivating antioxidant enzymes, decreasing ROS levels, and decreasing the levels of 8-hydroxydeoxyguanosine, Quercetin protects DNA from oxidative damage. In clinical studies, it was found that quercetin supplementation was related to increased antioxidant capacity and decreased risk of type 2 diabetes mellitus in the experimental group as compared to the placebo group. It is concluded that quercetin has a significant role in DNA repair in order to overcome drug resistance in diabetes.

Prediction of soil seed bank of piedmont and alluvial environments of Dera Ghazi Khan, Punjab, Pakistan

Abstract The current focus is on the overall pattern of seed storage present in the fragments of the soil of piedmont and alluvial landscapes of the environment. The present study predicted the seed banks of both soils of alluvial and piedmont zones in different ecological conditions and evaluate the potential of seeds in the restoration of both environments. The composition of the seed bank of soil is mainly affected by the alluvial environment and the structure of cleared area shows that more species of germinating annual grasses and growable seeds with the higher total number. Extant vegetation structures have an important role in the diversity of soil seed reservoirs, whose composition corresponded with the openness of the site. When in situ soil seed bank is recruited, it helps to restore only some components of the plant community in an alluvial environment. In our current research, it was confirmed that seed richness was higher in number at lower elevation (alluvial) than that at high elevation (piedmont). Seed richness showed a significant negative correlation with anions, cations, while significantly positive with altitude that suggests the richness pattern of the overall seed bank of the area is influenced by various environmental factors.

Resumo O foco atual está no padrão geral de armazenamento de sementes presente nos fragmentos de solo do Piemonte e nas paisagens aluviais do meio ambiente. O presente estudo previu os bancos de sementes de ambos os solos das zonas aluvial e piemontesa em diferentes condições ecológicas e avaliou o potencial das sementes na restauração de ambos os ambientes. A composição do banco de sementes do solo é afetada principalmente pelo ambiente aluvial e a estrutura da área desmatada mostra que mais espécies de gramíneas anuais em germinação e sementes cultiváveis apresentam o maior número total. As estruturas da vegetação existente têm um papel importante na diversidade dos reservatórios de sementes do solo, cuja composição correspondeu à abertura do local. Quando o banco de sementes do solo in situ é recrutado, o mesmo ajuda a restaurar apenas alguns componentes da comunidade de plantas em um ambiente aluvial. Em nossa pesquisa atual, foi confirmado que a riqueza de sementes era maior em número em altitudes mais baixas (aluviais) do que em altitudes elevadas (Piemonte). A riqueza de sementes mostrou uma correlação negativa significativa com ânions, cátions, enquanto significativamente positiva com a altitude, o que sugere que o padrão de riqueza do banco geral de sementes da área é influenciado por vários fatores ambientais.

Prediction of soil seed bank of piedmont and alluvial environments of Dera Ghazi Khan, Punjab, Pakistan / Predição do banco de sementes do solo do piemonte e ambientes aluviais de Dera Ghazi Khan, Punjab, Paquistão

Abstract The current focus is on the overall pattern of seed storage present in the fragments of the soil of piedmont and alluvial landscapes of the environment. The present study predicted the seed banks of both soils of alluvial and piedmont zones in different ecological conditions and evaluate the potential of seeds in the restoration of both environments. The composition of the seed bank of soil is mainly affected by the alluvial environment and the structure of cleared area shows that more species of germinating annual grasses and growable seeds with the higher total number. Extant vegetation structures have an important role in the diversity of soil seed reservoirs, whose composition corresponded with the openness of the site. When in situ soil seed bank is recruited, it helps to restore only some components of the plant community in an alluvial environment. In our current research, it was confirmed that seed richness was higher in number at lower elevation (alluvial) than that at high elevation (piedmont). Seed richness showed a significant negative correlation with anions, cations, while significantly positive with altitude that suggests the richness pattern of the overall seed bank of the area is influenced by various environmental factors.

Resumo O foco atual está no padrão geral de armazenamento de sementes presente nos fragmentos de solo do Piemonte e nas paisagens aluviais do meio ambiente. O presente estudo previu os bancos de sementes de ambos os solos das zonas aluvial e piemontesa em diferentes condições ecológicas e avaliou o potencial das sementes na restauração de ambos os ambientes. A composição do banco de sementes do solo é afetada principalmente pelo ambiente aluvial e a estrutura da área desmatada mostra que mais espécies de gramíneas anuais em germinação e sementes cultiváveis ​​apresentam o maior número total. As estruturas da vegetação existente têm um papel importante na diversidade dos reservatórios de sementes do solo, cuja composição correspondeu à abertura do local. Quando o banco de sementes do solo in situ é recrutado, o mesmo ajuda a restaurar apenas alguns componentes da comunidade de plantas em um ambiente aluvial. Em nossa pesquisa atual, foi confirmado que a riqueza de sementes era maior em número em altitudes mais baixas (aluviais) do que em altitudes elevadas (Piemonte). A riqueza de sementes mostrou uma correlação negativa significativa com ânions, cátions, enquanto significativamente positiva com a altitude, o que sugere que o padrão de riqueza do banco geral de sementes da área é influenciado por vários fatores ambientais.

Amyloid-ß: A potential mediator of aging-related vascular pathologies.

Aging is one of the most promising risk factors for vascular diseases, however, the precise mechanisms mediating aging-related pathologies are not fully understood. Amyloid beta (Aß), a peptide produced by the proteolytic processing of amyloid precursor protein (APP), is known as a key mediator of brain damage involved in the pathogenesis of Alzheimer's disease (AD). Recently, it was found that the accumulation of Aß in the vascular wall is linked to a range of aging-related vascular pathologies, indicating a potential role of Aß in the pathogenesis of aging-associated vascular diseases. In the present review, we have updated the molecular regulation of Aß in vascular cells and tissues, summarized the relevance of the Aß deposition with vascular aging and diseases, and the role of Aß dysregulation in aging-associated vascular pathologies, including the impaired vascular response, endothelial dysfunction, oxidative stress, and inflammation. This review will provide advanced information in understanding aging-related vascular pathologies and a new avenue to explore therapeutic targets.

Trustworthy and Reliable Deep Learning-based Cyberattack Detection in Industrial IoT.

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics such as trust, privacy, security, reliability, resilience and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this paper, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep learning-based Pyramidal Recurrent Units (PRU) and Decision Tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The non-linear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on fifteen datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

An Adaptive Distributed Denial of Service Attack Prevention Technique in a Distributed Environment.

Cyberattacks in the modern world are sophisticated and can be undetected in a dispersed setting. In a distributed setting, DoS and DDoS attacks cause resource unavailability. This has motivated the scientific community to suggest effective approaches in distributed contexts as a means of mitigating such attacks. Syn Flood is the most common sort of DDoS assault, up from 76% to 81% in Q2, according to Kaspersky's Q3 report. Direct and indirect approaches are also available for launching DDoS attacks. While in a DDoS attack, controlled traffic is transmitted indirectly through zombies to reflectors to compromise the target host, in a direct attack, controlled traffic is sent directly to zombies in order to assault the victim host. Reflectors are uncompromised systems that only send replies in response to a request. To mitigate such assaults, traffic shaping and pushback methods are utilised. The SYN Flood Attack Detection and Mitigation Technique (SFaDMT) is an adaptive heuristic-based method we employ to identify DDoS SYN flood assaults. This study suggested an effective strategy to identify and resist the SYN assault. A decision support mechanism served as the foundation for the suggested (SFaDMT) approach. The suggested model was simulated, analysed, and compared to the most recent method using the OMNET simulator. The outcome demonstrates how the suggested fix improved detection.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems.

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

Regulatory Effects of Curcumin on Platelets: An Update and Future Directions.

The rhizomatous plant turmeric, which is frequently used as a spice and coloring ingredient, yields curcumin, a bioactive compound. Curcumin inhibits platelet activation and aggregation and improves platelet count. Platelets dysfunction results in several disorders, including inflammation, atherothrombosis, and thromboembolism. Several studies have proved the beneficial role of curcumin on platelets and hence proved it is an important candidate for the treatment of the aforementioned diseases. Moreover, curcumin is also frequently employed as an anti-inflammatory agent in conventional medicine. In arthritic patients, it has been shown to reduce the generation of pro-inflammatory eicosanoids and to reduce edema, morning stiffness, and other symptoms. Curcumin taken orally also reduced rats' acute inflammation brought on by carrageenan. Curcumin has also been proven to prevent atherosclerosis and platelet aggregation, as well as to reduce angiogenesis in adipose tissue. In the cerebral microcirculation, curcumin significantly lowered platelet and leukocyte adhesion. It largely modulated the endothelium to reduce platelet adhesion. Additionally, P-selectin expression and mice survival after cecal ligation and puncture were improved by curcumin, which also altered platelet and leukocyte adhesion and blood-brain barrier dysfunction. Through regulating many processes involved in platelet aggregation, curcuminoids collectively demonstrated detectable antiplatelet activity. Curcuminoids may therefore be able to prevent disorders linked to platelet activation as possible therapeutic agents. This review article proposes to highlight and discuss the regulatory effects of curcumin on platelets.

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

How Curcumin Targets Inflammatory Mediators in Diabetes: Therapeutic Insights and Possible Solutions.

Diabetes mellitus is a multifactorial chronic metabolic disorder, characterized by altered metabolism of macro-nutrients, such as fats, proteins, and carbohydrates. Diabetic retinopathy, diabetic cardiomyopathy, diabetic encephalopathy, diabetic periodontitis, and diabetic nephropathy are the prominent complications of diabetes. Inflammatory mediators are primarily responsible for these complications. Curcumin, a polyphenol derived from turmeric, is well known for its anti-oxidant, anti-inflammatory, and anti-apoptotic properties. The regulation of several signaling pathways effectively targets inflammatory mediators in diabetes. Curcumin's anti-inflammatory and anti-oxidative activities against a wide range of molecular targets have been shown to have therapeutic potential for a variety of chronic inflammatory disorders, including diabetes. Curcumin's biological examination has shown that it is a powerful anti-oxidant that stops cells from growing by releasing active free thiol groups at the target location. Curcumin is a powerful anti-inflammatory agent that targets inflammatory mediators in diabetes, and its resistant form leads to better therapeutic outcomes in diabetes complications. Moreover, Curcumin is an anti-oxidant and NF-B inhibitor that may be useful in treating diabetes. Curcumin has been shown to inhibit diabetes-related enzymes, such as a-glucosidase, aldose reductase and aldose reductase inhibitors. Through its anti-oxidant and anti-inflammatory effects, and its suppression of vascular endothelial development and nuclear transcription factors, curcumin has the ability to prevent, or reduce, the course of diabetic retinopathy. Curcumin improves insulin sensitivity by suppressing phosphorylation of ERK/JNK in HG-induced insulin-resistant cells and strengthening the PI3K-AKT-GSK3B signaling pathway. In the present article, we aimed to discuss the anti-inflammatory mechanisms of curcumin in diabetes regulated by various molecular signaling pathways.

Therapeutic Role of Carotenoids in Blood Cancer: Mechanistic Insights and Therapeutic Potential.

Blood cancers are characterized by pathological disorders causing uncontrolled hematological cell division. Various strategies were previously explored for the treatment of blood cancers, including chemotherapy, Car-T therapy, targeting chimeric antigen receptors, and platelets therapy. However, all these therapies pose serious challenges that limit their use in blood cancer therapy, such as poor metabolism. Furthermore, the solubility and stability of anticancer drugs limit efficacy and bio-distribution and cause toxicity. The isolation and purification of natural killer cells during Car-T cell therapy is a major challenge. To cope with these challenges, treatment strategies from phyto-medicine scaffolds have been evaluated for blood cancer treatments. Carotenoids represent a versatile class of phytochemical that offer therapeutic efficacy in the treatment of cancer, and specifically blood cancer. Carotenoids, through various signaling pathways and mechanisms, such as the activation of AMPK, expression of autophagy biochemical markers (p62/LC3-II), activation of Keap1-Nrf2/EpRE/ARE signaaling pathway, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), increased level of reactive oxygen species, cleaved poly (ADP-ribose) polymerase (c-PARP), c-caspase-3, -7, decreased level of Bcl-xL, cycle arrest at the G0/G1 phase, and decreasing STAT3 expression results in apoptosis induction and inhibition of cancer cell proliferation. This review article focuses the therapeutic potential of carotenoids in blood cancers, addressing various mechanisms and signaling pathways that mediate their therapeutic efficacy.

Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface.

Little is known about the rising impacts of Coriolis force and volume fraction of nanoparticles in industrial, mechanical, and biological domains, with an emphasis on water conveying 47 nm nanoparticles of alumina nanoparticles. We explored the impact of the volume fraction and rotation parameter on water conveying 47 nm of alumina nanoparticles across a uniform surface in this study. The Levenberg-Marquardt backpropagated neural network (LMB-NN) architecture was used to examine the transport phenomena of 47 nm conveying nanoparticles. The partial differential equations (PDEs) are converted into a system of Ordinary Differential Equations (ODEs). To assess our soft-computing process, we used the RK4 method to acquire reference solutions. The problem is investigated using two situations, each with three sub-cases for the change of the rotation parameter K and the volume fraction ϕ. Our simulation results are compared to the reference solutions. It has been proven that our technique is superior to the current state-of-the-art. For further explanation, error histograms, regression graphs, and fitness values are graphically displayed.

Prediction of soil seed bank of piedmont and alluvial environments of Dera Ghazi Khan, Punjab, Pakistan.

The current focus is on the overall pattern of seed storage present in the fragments of the soil of piedmont and alluvial landscapes of the environment. The present study predicted the seed banks of both soils of alluvial and piedmont zones in different ecological conditions and evaluate the potential of seeds in the restoration of both environments. The composition of the seed bank of soil is mainly affected by the alluvial environment and the structure of cleared area shows that more species of germinating annual grasses and growable seeds with the higher total number. Extant vegetation structures have an important role in the diversity of soil seed reservoirs, whose composition corresponded with the openness of the site. When in situ soil seed bank is recruited, it helps to restore only some components of the plant community in an alluvial environment. In our current research, it was confirmed that seed richness was higher in number at lower elevation (alluvial) than that at high elevation (piedmont). Seed richness showed a significant negative correlation with anions, cations, while significantly positive with altitude that suggests the richness pattern of the overall seed bank of the area is influenced by various environmental factors.

Security and Blockchain Convergence with Internet of Multimedia Things: Current Trends, Research Challenges and Future Directions.

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications.

Future snow projections in a small basin of the Western Himalaya.

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981-2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21st century (2071-2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10-18% reduction in annual snow cover area is projected in the cold-wet models, a 22-36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices.

A Secured and Intelligent Communication Scheme for IIoT-enabled Pervasive Edge Computing.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

A Lightweight Mutual Authentication and Privacy-preservation Scheme for Intelligent Wearable Devices in Industrial-CPS.

Industry 5.0 is the digitalization, automation and data exchange of industrial processes that involve artificial intelligence, Industrial Internet of Things (IIoT), and Industrial Cyber-Physical Systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled Hidden Markov Model (HMM). We analyzed the robustness and security of our scheme using BurrowsAbadiNeedham (BAN) logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.

Targeting epigenetics in cancer: therapeutic potential of flavonoids.

Irrespective of sex and age, cancer is the leading cause of mortality around the globe. Therapeutic incompliance, unwanted effects, and economic burdens imparted by cancer treatments, are primary health challenges. The heritable features in gene expression that are propagated through cell division and contribute to cellular identity without a change in DNA sequence are considered epigenetic characteristics and agents that could interfere with these features and are regarded as potential therapeutic targets. The genetic modification accounts for the recurrence and uncontrolled changes in the physiology of cancer cells. This review focuses on plant-derived flavonoids as a therapeutic tool for cancer, attributed to their ability for epigenetic regulation of cancer pathogenesis. The epigenetic mechanisms of various classes of flavonoids including flavonols, flavones, isoflavones, flavanones, flavan-3-ols, and anthocyanidins, such as cyanidin, delphinidin, and pelargonidin, are discussed. The outstanding results of preclinical studies encourage researchers to design several clinical trials on various flavonoids to ascertain their clinical strength in the treatment of different cancers. The results of such studies will define the clinical fate of these agents in future.

Developing Novel Anticancer Drugs for Targeted Populations: An Update.

BACKGROUND: Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish. OBJECTIVES: This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population. METHODS: Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms. RESULTS: The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented. CONCLUSION: Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.