Discovery Mechanism Research Articles

A Systematic Review on Drug-to-Drug Interaction Prediction and Cryptographic Mechanism for Secure Drug Discovery Using AI Techniques

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug–drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)–deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.

Abstract 4119052: Redefining the phenotypic continuum of cardiovascular disease using machine learning

Background: Current approaches that define cardiovascular disease rely on assignment of traditional diagnostic labels which may not reflect the natural continuum of phenotypic expression, influenced by genetic and environmental modifiers, or adequately identify groups with shared molecular mechanisms. Machine learning can leverage advances in genetic and imaging characterisation to reclassify phenotypic diversity along a continuum, from health to disease. Research Aims: Our study aims to build a tree-like classification of cardiovascular phenotypes in the community where branches represent subjects with shared features, ordered by their severity. Using dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) as exemplar conditions with opposing phenotypes, we aim to demonstrate the interaction between genetic and environmental modifiers of disease expression. Methods: We analysed cardiovascular magnetic resonance (CMR) imaging, electrocardiogram (ECG), biomarkers and clinical data from participants in the UK Biobank. Using unsupervised learning of multiparametric data, we built a tree of phenotypic expression. We projected the risk of DCM and HCM cases, and associated rare and common variant risk, onto the tree structure. Results: Ten main branches were discovered, using data from 41,525 participants (51.7 % female, median age 65 yrs [IQR: 58, 70]). The extremities of branches 1 and 6 were enriched for DCM cases (p <0.05) and participants with reduced ejection fraction and high left ventricular volumes (p <0.05). Compared with subjects at the tree centre, distal participants in branch 1 had increased blood pressure (BP) and body mass index (BMI), whilst those in branch 6 were more likely to have high HbA1c levels and carry a pathogenic DCM variant (p <0.05). The extremity of branch 5 was enriched for HCM cases, rare sarcomeric variants (p = 0.004) and high polygenic risk (p <0.001). Whilst the ends of branches 3 and 9 had high polygenic risk for HCM, they were less likely to have elevated BP and BMI, and had reduced likelihood of HCM expression, compared with branch 5. A phenomapping model was trained to accurately project unseen subjects onto the tree (R 2 0.98). Conclusions: We present a tree-like continuum of cardiovascular phenotypes, providing a novel framework for mechanistic discovery and exploration of genotype-phenotype associations. Phenomapping of unseen subjects enables personalised estimation of genetic and cardiovascular risk.

Beyond genomic studies of congenital heart defects through systematic modelling and phenotyping.

Congenital heart defects (CHDs), the most common congenital anomalies, are considered to have a significant genetic component. However, despite considerable efforts to identify pathogenic genes in patients with CHDs, few gene variants have been proven as causal. The complexity of the genetic architecture underlying human CHDs likely contributes to this poor genetic discovery rate. However, several other factors are likely to contribute. For example, the level of patient phenotyping required for clinical care may be insufficient for research studies focused on mechanistic discovery. Although several hundred mouse gene knockouts have been described with CHDs, these are generally not phenotyped and described in the same way as CHDs in patients, and thus are not readily comparable. Moreover, most patients with CHDs carry variants of uncertain significance of crucial cardiac genes, further complicating comparisons between humans and mouse mutants. In spite of major advances in cardiac developmental biology over the past 25 years, these advances have not been well communicated to geneticists and cardiologists. As a consequence, the latest data from developmental biology are not always used in the design and interpretation of studies aimed at discovering the genetic causes of CHDs. In this Special Article, while considering other in vitro and in vivo models, we create a coherent framework for accurately modelling and phenotyping human CHDs in mice, thereby enhancing the translation of genetic and genomic studies into the causes of CHDs in patients.

Service similarity measurement integrating Bi-LSTM contextual representation and attention mechanism for web service discovery

With the rapid growth and wide adoption of web services, service discovery is becoming commonly used to locate the optimal services that can meet the requirements of users. This paper proposes a bidirectional long short-term memory (Bi-LSTM) service discovery method that integrates contextual representation and attention mechanisms for web service discovery. To conduct the study, FastText word embedding with the N-gram feature is primarily used as the input of the Bi-LSTM model to obtain the context feature vector of the service function description. Moreover, the attention mechanism is employed to calculate the similarity of the service function description to gain the semantic feature vector of the correlation degree between services. Then, the multilayer perceptron network is used to mine the mapping relationship between semantic feature vectors and matching levels among services. Finally, for a given query, target services are retrieved by ranking candidate services according to the prediction cores through the matching method. The proposed method is examined against multiple evaluation metrics, including accuracy and error, and compared with the state-of-the-art searching approaches. The experimental results show that the proposed method is more effective and attains better relevant web services, which helps the request service find more accurate results. Furthermore, this study provides new insights for researchers into web service discovery.

Identification of ANXA1 as a Novel Upstream Negative Regulator of Notch1 Function in AML.

Abnormal Notch1 expression has an important role in tumorigenesis. However, upstream control mechanisms for Notch1 are still insufficiently understood. Acute myeloid leukemia (AML) is one of the most common and lethal blood malignancies with limited possibilities for treatment. Thus, new therapeutic targets are urgently needed to improve current ineffective therapies. Herein, high Annexin A1 (ANXA1) expression is found correlated with hyperproliferation of AML cells, and then ANXA1 is identified as a novel negative regulator of Notch1 function in AML. Mechanistically, ANXA1 directly bound to the intracellular domain of Notch1 (NICD) to target this tumor suppressor for degradation. Furthermore, NICD executed its tumor suppressive function through activation of the p15 promoter. Thus, ablation of the Notch1-p15-mediated tumor suppression by ANXA1 provided a novel mechanism of AML proliferation. In human AML patients, a mutual exclusive relation is discovered between ANXA1 and Notch1/p15, corroborating mechanistic discovery. On the basis of these results, it is reasonably speculated that targeting ANXA1 would provide an effective approach for treatment of AML. In support of this new therapeutic paradigm, provided proof-of-concept data by antagonizing ANXA1 using NICD inhibitory peptides.

A trust model for VANETs using malicious-aware multiple routing

Vehicular ad hoc networks (VANETs) enable multi-hop communication among vehicles, promoting information sharing and smarter collaborative driving. However, VANETs are facing several challenges due to the open wireless communication environment. Attackers may maliciously drop or alter packets so that the receiver cannot obtain correct information. In addition, the high mobility of vehicles may lead to link failures, consequently resulting in packet loss. In this paper, we propose a multipath-based trust model (MPTM), in which the reliability of packet transmission is guaranteed by data redundancy and the detection of potential attackers is achieved by trust evaluation. Specifically, we present a route discovery mechanism to find multiple routes that avoid potential attackers, which reduces the risk of attacks on redundant packets. The receivers identify correct information based on two factors including content consistency and route information. An attacker detection module is presented to evaluate the trustworthiness of vehicles involved in packet transmission and vehicles with trust value below a threshold are detected as attackers. We conducted extensive experiments using OMNeT++ simulation platform, considering various attack scenarios. Experiment results show that MPTM can reach 90% packet delivery ratio and effectively detect attackers in terms of 90% detection precision.

Illuminating IT Landscapes: A Comparative Analysis of Discovery in ServiceNow and BMC Helix

Abstract: This research paper presents a comprehensive comparison of the Discovery applications offered by ServiceNow and BMC Helix. The study examines key features, discovery mechanisms, data collection capabilities, integration with CMDB, automation features, and analytics tools of both solutions. By evaluating their strengths and weaknesses in discovering and mapping IT assets and relationships, this paper aims to provide insights for organizations seeking to enhance their visibility into complex IT environments and improve overall IT service management.

DNA methylation in mammalian development and disease.

The DNA methylation field has matured from a phase of discovery and genomic characterization to one seeking deeper functional understanding of how this modification contributes to development, ageing and disease. In particular, the past decade has seen many exciting mechanistic discoveries that have substantially expanded our appreciation for how this generic, evolutionarily ancient modification can be incorporated into robust epigenetic codes. Here, we summarize the current understanding of the distinct DNA methylation landscapes that emerge over the mammalian lifespanand discuss how they interact with other regulatory layers to support diverse genomic functions. We thenreview the rising interest in alternative patterns foundduring senescence and the somatic transition to cancer. Alongside advancements in single-cell and long-read sequencing technologies, the collective insights made across these fields offer new opportunities to connect the biochemical and genetic features of DNA methylation to cell physiology, developmental potential and phenotype.

Price Discovery Function of Government Bond Futures: Evidence from Chinese 10-Year Treasury Note Markets

Utilizing the data of the futures and spot prices of Chinese 10-year Treasury notes from 2015 to 2022, this paper examines the price discovery mechanism on Chinese Treasury bond markets. We find that there is a significant cointegrating relationship between the futures price and the spot price of Chinese 10-year Treasury notes, and that in this long-run equilibrium the futures price has a one-way predicting effect on the spot price. We also find that the proportions of the variations in the spot price and the futures price explained by a shock or impulse in the futures market is higher in a later period than in a comparable earlier period, indicating that the contribution of the futures market to the price discovery has been increasing gradually. These results suggest that the price discovery function of futures markets is significant and has become more important to market efficiency. Furthermore, the disparities of our 10-year-note results from the previous 5-year-note results indicate that the price discovery function varies across underlying assets.

Chloride deregulation and GABA depolarization in MTOR related malformations of cortical development.

Focal Cortical Dysplasia, Hemimegalencephaly and Cortical Tuber are pediatric epileptogenic malformations of cortical development (MCDs) frequently pharmaco-resistant and mostly surgically treated by the resection of epileptic cortex. Availability of cortical resection samples allowed significant mechanistic discoveries directly from human material. Causal brain somatic or germline mutations in the AKT/PI3K/DEPDC5/MTOR genes were identified. GABAa mediated paradoxical depolarization, related to altered chloride (Cl-) homeostasis, was shown to participate to ictogenesis in human pediatric MCDs. However, the link between genomic alterations and neuronal hyperexcitability is still unclear. Here we studied the post translational interactions between the mTOR pathway and the regulation of cation-chloride cotransporters (CCC), KCC2 and NKCC1, that are largely responsible for controlling intracellular Cl- and ultimately GABAergic transmission. For this study, 35 children (25 MTORopathies and 10 pseudo controls, diagnosed by histology plus genetic profiling) were operated for drug resistant epilepsy. Postoperative cortical tissues were recorded on multielectrode array (MEA) to map epileptic activities. CCC expression level and phosphorylation status of the WNK1/SPAK-OSR1 pathway was measured during basal conditions and after pharmacological modulation. Direct interactions between mTOR and WNK1 pathway components were investigated by immunoprecipitation. Membranous incorporation of MCD samples in Xenopus laevis oocytes enabled Cl- conductance and equilibrium potential (EGABA) for GABA measurement. Of the 25 clinical cases, half harbored a somatic mutation in the mTOR pathway, while pS6 expression was increased in all MCD samples. Spontaneous interictal discharges were recorded in 65% of the slices. CCC expression was altered in MCDs, with a reduced KCC2/NKCC1 ratio and decreased KCC2 membranous expression. CCC expression was regulated by the WNK1/SPAK-OSR1 kinases through direct phosphorylation of Thr906 on KCC2, that was reversed by WNK1 and SPAK antagonists (NEM and Staurosporine). mSIN1 subunit of MTORC2 was found to interact with SPAK-OSR1 and WNK1. Interactions between these key epileptogenic pathways could be reversed by the mTOR specific antagonist Rapamycin, leading to a dephosphorylation of CCCs and recovery of the KCC2/NKCC1 ratio. The functional effect of such recovery was validated by the restoration of the depolarizing shift in EGABA by rapamycin, measured after incorporation of MCD membranes to X. laevis oocytes, in line with a reestablishment of normal ECl-. Our study deciphers a protein interaction network through a phosphorylation cascade between MTOR and WNK1/SPAK-OSR1 leading to chloride cotransporters deregulation, increased neuronal chloride levels and GABAa dysfunction in malformations of Cortical Development, linking genomic defects and functional effects and paving the way to target epilepsy therapy.

Design of a Context-Aware Distributed Service Registry and Discovery Mechanism for IoT

Design of a Context-Aware Distributed Service Registry and Discovery Mechanism for IoT

Vulnerabilities in SDN Topology Discovery Mechanism: Novel Attacks and Countermeasures

Vulnerabilities in SDN Topology Discovery Mechanism: Novel Attacks and Countermeasures

Unlocking fast‐charging capabilities of lithium‐ion batteries through liquid electrolyte engineering

AbstractGlobal trends toward green energy have empowered the extensive application of high‐performance energy storage systems. With the worldwide spread of electric vehicles (EVs), lithium‐ion batteries (LIBs) capable of fast‐charging have become increasingly important. Nonetheless, state‐of‐the‐art LIBs have failed to satisfy the demands of prospective customers, including rapid charging, extended cycle life, and high energy density. Addressing these challenges through innovations in material science and other advanced battery technologies is essential for meeting the growing demands of prospective customers. Besides the choice of active materials, electrolyte formulation has a significant impact on the fast‐charging performance and cycle life of LIBs over a wide range of temperatures. The liquid electrolyte is typically composed of lithium salts to provide an ion source, solvents to carry Li+ ions, and functional additives to build a stable solid electrolyte interphase (SEI). To enable the fast movement of Li+ ions, the liquid electrolytes should have low viscosity and high ionic conductivity. Meanwhile, SEI layers must be thin, uniform and ionically conductive. Furthermore, the low binding energy of the solvent facilitates desolvation of the solvation sheath, enabling fast Li+ ion transport to the anode during fast charging. This review provides the latest insights into rapid Li+ ion transport during fast charging, focusing on ensuring a deeper understanding of liquid electrolyte chemistry. The involvement of existing electrolyte mechanisms in materials discovery will develop electrolyte engineering techniques to improve the fast‐charging performance of batteries over a wide temperature range and will also facilitate the development of EV‐adoptable advanced electrodes.image

Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

Representing a paradigm shift in cellular communication, Device-to-Device (D2D) Communication is an emerging technology that enables direct communication between two User Equipments(UEs) without going through the Base Station. It enables decentralization of cellular networks, enhances network capacity, increases spectral efficiency, and offloads network traffic. In order to proceed with direct D2D communication, the devices must have knowledge of the proximal devices and should work in all coverage scenarios such as in-coverage, partial-coverage and out-of-coverage. Most of the existing works on Device Discovery are based on 3GPP Model A and Model B. However, these schemes suffer from overhead of discovery message exchanges with the increase in the number of devices in a mobile environment. To address these issues, we propose a distributed and adaptive device discovery mechanism that can discover the proximal UEs in all possible scenarios. To achieve this, we propose a Hybrid Discovery Model ‘H’, which uses the concept of ‘Who are there?’. In Model ‘H’, the discoverer sends a single request message to discover all proximal devices. The responses of the proximal UEs are staggered based on the estimated number of neighbors to avoid collisions. In addition to that, we adapt the discovery mechanism to the relative mobility of the neighborhood using our proposed Adaptive Probing Algorithm named ‘ReMAP’. The relative mobility is determined based on the current and previous neighbor lists of each UE. The key advantage of our proposed mechanism is that we do not require the knowledge of position and mobility pattern of the UEs. We implement and evaluate our proposed work in D2D Module of the NS3 simulator. From the simulation results, we observe that our proposed model shows better performance in terms of the number of UEs discovered and reduced number of discovery message exchanges compared to the state-of-the-art. We also compare the performance of ReMAP with a constant probing mechanism and show the improvement in terms of adaptability to the mobility of the neighborhood.

An interference-conscious reduced routing overhead protocol for Device-to-Device (D2D) Networks

The Device-to-Device (D2D) communication has attracted much popularity in the recent years. D2D communication requires a multi-hop route to establish the communication between D2D users which are not in the communication range of each other. D2D users communicate at lower power to keep the interference to a minimum level which necessitates the need of an interference aware routing scheme. The existing interference-aware routing schemes for D2D communication either consider observed average interference or Signal-to-Interference-and-Noise-Ratio (SINR). However, to the best of our knowledge, there is no scheme in the literature that satisfies the network requirements of having better SINR and minimum interference simultaneously. In this paper, we propose a novel routing metric and novel route discovery mechanisms for D2D communication. The novel routing metric, MIIS (Metric for Interference Impact and SINR) selects routes with higher SINR and lower interference. The novel route discovery mechanism, reactive centralized routing, takes advantage of the presence of BS to establish D2D routes which reduces routing overhead as compared to distributed routing scheme. We also extend the reactive centralized routing to proactive centralized routing. The performance evaluation in OMNeT++ network simulator verifies that our proposed schemes outperform other schemes in terms of average hop count, routing overhead, packet loss ratio and end-to-end delay.

Proteogenomic profiling of clonal hematopoiesis in the solid tumor microenvironment.

2567 Background: Clonal hematopoiesis (CH) is caused by somatic mutations that provide a fitness advantage in hematopoietic stem cells, contributing to inflammation and disease. CH is common in solid tumor patients, and has shown context-dependent associations with survival; however, its contribution to the tumor microenvironment (TME) remains unclear. Here, we employ proteogenomic methods to define CH-associated alterations in the TME. Methods: We tested 1,550 patients across 10 primary, treatment-naïve cancers in the Clinical Proteomic Tumor Analysis Consortium cohort. CH calls were derived from peripheral blood and tumour whole exome sequencing (WES) data, and CH was defined as the presence of a somatic driver mutation at variant allele frequency (VAF) ≥2% in blood. Overall survival (OS) analysis was conducted using Cox proportional hazard models, controlled for age, sex, tumor type, metastatic status, and smoking. Tumor bulk RNA-sequencing and mass spectrometry proteomics data were processed for differential expression and gene set enrichment analyses. Abundance of immune cell populations was estimated with CibersortX. Results: 349 CH mutations were identified in 283 patients (18.3%). CH was strongly associated with age and mutations were mostly found in the epigenetic regulators DNMT3A(37.8%, n=132) and TET2(20.6%, n=72). CH was most prevalent in ovarian cancer (30%, n=27/90) and colorectal cancer (CRC; 28.3%, n=30/106). 103 blood CH mutations were also detected in tumor WES (CHTum), with presence in the tumor associated with higher tumor immune infiltration and peripheral blood VAF ≥10%. CHTum, but not CH, was associated with worse OS (CHTum HR = 1.74 [1.13-2.69]; CH HR = 1.12 [0.83-1.50]). CHTum was also associated with a reduced likelihood of patients being classified as tumor free at follow up (OR = 0.39 [0.19-0.82]). We did not identify a pan-cancer proteogenomic signature of CH in the TME. At the tumor-specific level, we consistently observed associations between CH and its subtypes with dysregulated inflammation, with high transcriptomic-proteomic concordance. In CRC, TET2-mutant CH was associated with greater infiltration of CD4+ T cells, monocyte/macrophages, NK cells, and B cells, alongside an inflammatory response characterized by IL6/JAK/STAT3 signalling, TNF signalling via NFκB, and IL2/STAT5 signalling. Conclusions: CH is common, even prior to therapy, in solid tumor patients and the infiltration of CH-mutant immune clones into the TME is linked with poor outcomes. Beyond confounding molecular tumor diagnostics, CH in the TME also dysregulates the anti-tumor immune response, highlighting the value of a blood reference in precision oncology. The lack of a pan-cancer CH signature in the TME supports a tumor-specific influence of CH. Further study is needed for mechanistic discovery and biomarker development to realize the potential of CH in immuno-oncology and improve patient outcomes.

Optimal Node Routing Layouts in Mobile Adhoc Network

Mobile Adhoc Network (MANET) is a self-organizing network of various network devices responsible for communication. In MANET, new layouts are adopted for energy optimization, security implementation, traffic minimization and overhead reduction. MANET are aggressive in nature. This requires modification in network routing protocols for performance enhancement. This paper presents the detailed survey of different layouts involved in the implementation of network routing protocols in MANET. The survey is classified into four phases involved in MANET routing applications namely- routing protocols, neighborhood discovery, distributed or centralized hash tables, and bloom filter. Firstly, the classification of routing protocols based on various traffic models presents the MANET configuration. Secondly, a neighborhood discovery mechanism conveys the enhancement in the routing configurations. Thirdly, application of hash table operation (distributed/ centralized) to MANET to improve the matching efficiencies and computational speed. Finally, the application of bloom filter concept to improve service discovery rate and reduce the fake identities. Traditionally, the unpredictability occurs during the prediction of optimal node routing due to the dynamic nature of the MANET. Moreover, the presence of ejected malicious node in another cluster disgraced the link stability. This survey conveys the problems such as storage overhead, mobility nature, malicious nodes, and false positive rates in the traditional methods. The detailed survey leads to the stone of integration of Distributed Hash Table (DHT) with a bloom filter to reduce the problems in traditional research works. Index Terms – Performance Optimization, Bloom Filter, Centralized Hash Table (CHT), Distributed Hash Table (DHT), Energy Consumption, Mobile Ad- hoc Network (MANET), Quality of Service (QoS), Routing.

A novel thin plate spline methodology to model tissue surfaces and quantify tumor cell invasion in organ-on-chip models

Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method – a generalized cubic spline that can be used to model coordinate transformations – to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool – the Chip Invasion and Contour Analysis (ChICA) – to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.

Abstract 134: Lesion-specific Suppression Of YAP/TAZ By Biomimetic Nanoparticles Ameliorates Atherosclerosis

Introduction: Recent evidence highlights the potential therapeutic benefit of targeting YAP/TAZ in mitigating atherosclerosis. However, challenges associated with systemic delivery hinder the translation of the mechanistic discovery into an effective treatment. This study aimed to address the challenges by developing a lesion-specific nanodrug to achieve site-specific inhibition of YAP/TAZ for atherosclerosis therapy. Hypothesis: We have formulated a novel monocyte membrane-coated nanoparticle (MoNP) platform designed to facilitate precise delivery of payloads to atherosclerotic arteries. Our hypothesis is that leveraging MoNP for the targeted delivery of verteporfin (VP), a potent YAP/TAZ inhibitor, will significantly enhance the efficacy in mitigating atherosclerosis. Methods: Polymeric nanoparticles containing VP were prepared using an emulsion approach, followed by enclosing them with plasma membranes isolated from monocytes, yielding MoNP-VP. The pharmacological effects of MoNP-VP were assessed on cultured vascular and immune cells, as well as in the atherosclerotic mouse model. Results: Our in vivo findings demonstrated that the administration of MoNP-VP, as opposed to free VP, led to a substantial reduction in arterial inflammation and plaque development, suggesting that VP delivered by MoNP is a more effective route than its free drug counterpart. To dissect the mechanisms of actions, we analyzed the arterial tissues post-treatment using scRNA-seq analysis. Our results identified 9 distinct cell clusters within the affected arteries. Specifically, MoNP-VP treatment led to a decrease in macrophage (MΦ) clusters and an increase in the cluster of smooth muscle cells (SMC). Furthermore, our in vitro assays confirmed the efficacies of MoNP-VP in reducing the atheroprone phenotypes. These include diminished endothelial cell activation, less proliferation and migration of SMC, and a reduction in foam cell formation from MΦ. Conclusion: Together, these findings support the notion that targeting YAP/TAZ dysregulation is a feasible strategy for reducing plaque development and progression and demonstrate that MoNP-VP is a highly effective nanodrug for treating atherosclerosis.

An efficient safest route prediction-based route discovery mechanism for drivers using improved golden tortoise beetle optimizer

ABSTRACT Several commercialised route recommendation systems only consider the metrics like cost, time, and distance. The essential metric ‘safety’ is neglected by the existent systems. It suggests only the short way and doesn’t include any safety information, such as crime awareness, road availability. This paper describes an inventive ideology to discover the safest route with minimal risk score for security of the road travellers. Hence, a new safety route navigation mechanism is developed to solve the challenges in the traditional route discovery approaches using deep learning. In the developed route discovery mechanism, the examination of the safest roads is done by the developed deep learning network, where the network is trained with the inputs obtained from the roads, such as road surface conditions, Road users, weather conditions, traffic conditions, accidental cases, and crime areas. The availability of the safest route will be determined by the ‘Long Short-Term Memory with Attention Mechanism’ (LSTM-AM). The route discovery is done with the help of a developed ‘Fitness-based Golden Tortoise Beetle Optimizer’ (FGTBO) with multi-objective constraints like distance, time, and road availability. The implementation outcome of the developed route discovery scheme will be validated with the traditional route discovery approaches concerning various measures.