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Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of −1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for high-frequency features—crucial for the nuanced details in medical diagnostics—while streamlining the network structure for enhanced computational efficiency. DRFDCAN’s architecture adopts a residual-within-residual design to facilitate faster inference and reduce memory demands without compromising the integrity of the image reconstruction. This design strategy, combined with an innovative feature extraction method that emphasizes the utility of the initial layer features, allows for improved image clarity and is particularly effective in optimizing the peak signal-to-noise ratio (PSNR). The proposed work redefines efficiency in SR models, outperforming established frameworks like RFDN by improving model compactness and accelerating inference. The meticulous crafting of a feature extractor that effectively captures edge and texture information exemplifies the model’s capacity to render detailed images, necessary for accurate medical analysis. The implications of this study are two-fold: it presents a viable solution for deploying SR technology in real-time medical applications, and it sets a precedent for future models that address the delicate balance between computational efficiency and high-fidelity image reconstruction. This balance is paramount in medical applications where the clarity of images can significantly influence diagnostic outcomes. The DRFDCAN model thus stands as a transformative contribution to the field of medical image super-resolution.

Introduction: Type 2 diabetes mellitus (T2DM) patients are at high risk of dyslipidemia, which in turn is associated with macrovascular diseases, such as heart diseases and stroke, and microvascular diseases, such as neuropathy and nephropathy. There are contradictory findings in the literature regarding the relationship between glycated hemoglobin (HbA1c) and the lipid profile among T2DM patients. This study was performed to investigate the association between HbA1c level and the lipid profile in elderly T2DM patients at a primary care hospital in Jeddah City, Saudi Arabia. Methods: This study is a retrospective cross-sectional study conducted at the Prince Abdul Majeed Healthcare Center (PAMHC) in Jeddah, Saudi Arabia. The sociodemographic and clinical data of the T2DM patients who had visited the PAMHC from 1 January 2020 to 31 December 2021, were collected from the data registry of the PAMHC and analyzed for publication. Results: The study included a total of 988 T2DM patients (53.3% male). Of the participants, 42.9% were aged between 55 and 64 years. Dyslipidemia parameters were presented as high LDL-c (in 60.3% cases), low HDL-c (in 39.8% cases), high triglycerides (in 34.9% cases), and high total cholesterol (in 34.8% cases). The correlation of HbA1c with total cholesterol (TC) and triglycerides (TGs) was positively significant, thereby highlighting the important link between glycemic control and dyslipidemia. A mean increase of 4.88 mg/dL and 3.33 mmHg in TG level and diastolic blood pressure, respectively, was associated with the male gender, in comparison to the female gender. However, the male gender was significantly associated with the reduction in the mean cholesterol level, BMI, HbA1c, HDL-c, and LDL-c by 11.49 mg/dL, 1.39 kg/m2, 0.31%, 7.47 mg/dL, and 5.6 mg/dL, respectively, in comparison to the female gender. Conclusions: The results of this study show that HbA1c was significantly associated with cholesterol and triglyceride levels in the T2DM patients included in the study. Our findings highlight the important relationship between glycemic control and dyslipidemia.

Let R be a commutative ring with identity, and Z(R) be the set of zero-divisors of R. The weakly zero-divisor graph of R denoted by WΓ(R) is an undirected (simple) graph with vertex set Z(R)*, and two distinct vertices x and y are adjacent, if and only if there exist r∈ann(x) and s∈ann(y), such that rs=0. Importantly, it is worth noting that WΓ(R) contains the zero-divisor graph Γ(R) as a subgraph. It is known that graph theory applications play crucial roles in different areas one of which is chemical graph theory that deals with the applications of graph theory to solve molecular problems. Analyzing Zagreb indices in chemical graph theory provides numerical descriptors for molecular structures, aiding in property prediction and drug design. These indices find applications in QSAR modeling and chemical informatics, contributing to efficient compound screening and optimization. They are essential tools for advancing pharmaceutical and material science research. This research article focuses on the basic properties of the weakly zero-divisor graph of the ring Zp×Zt×Zs, denoted by WΓ(Zp×Zt×Zs), where p, t, and s are prime numbers that may not necessarily be distinct and greater than 2. Moreover, this study includes the examination of various indices and coindices such as the first and second Zagreb indices and coindices, as well as the first and second multiplicative Zagreb indices and coindices of WΓ(Zp×Zt×Zs).

In this manuscript, the nonlinear Burgers equations are studied via a fractal fractional (FF) Caputo operator. The results of coupled fixed point theorems in cone metric space are used to discuss the uniqueness of solution to the proposed coupled equations. The solution of the proposed equation is computed via Natural transform associated with the Adomian decomposition method (NADM). The acquired results are graphically presented for some values of fractional order and fractal dimensions. The accuracy and consistency of the applied method is verified through error analysis.

In this study, we analyze the transmission of the COVID-19 model by using a piecewise operator in the classical Caputo sense. The existence along with the uniqueness of the solution of the COVID-19 model under a piecewise derivative is presented. The numerical scheme with Newton polynomials is used to obtain a numerical solution to the model under consideration. The graphical illustrations for the suggested model are demonstrated with various fractional orders. The crossover behavior of the considered system is observed in the graphical analysis. Furthermore, the comparison of simulations with real data for three different countries is presented, where best-fitted dynamics are observed.

The biosynthesis of algal-based zinc oxide (ZnO) nanoparticles has shown several advantages over traditional physico-chemical methods, such as lower cost, less toxicity, and greater sustainability. In the current study, bioactive molecules present in Spirogyra hyalina extract were exploited for the biofabrication and capping of ZnO NPs, using zinc acetate dihydrate and zinc nitrate hexahydrate as precursors. The newly biosynthesized ZnO NPs were characterized for structural and optical changes through UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX). A color change in the reaction mixture from light yellow to white indicated the successful biofabrication of ZnO NPs. The UV-Vis absorption spectrum peaks at 358 nm (from zinc acetate) and 363 nm (from zinc nitrate) of ZnO NPs confirmed that optical changes were caused by a blue shift near the band edges. The extremely crystalline and hexagonal Wurtzite structure of ZnO NPs was confirmed by XRD. The involvement of bioactive metabolites from algae in the bioreduction and capping of NPs was demonstrated by FTIR investigation. The SEM results revealed spherical-shaped ZnO NPs. In addition to this, the antibacterial and antioxidant activity of the ZnO NPs was investigated. ZnO NPs showed remarkable antibacterial efficacy against both Gram-positive and Gram-negative bacteria. The DPPH test revealed the strong antioxidant activity of ZnO NPs.

Chronic lymphocytic leukemia (CLL) is an incurable neoplasm of B-lymphocytes, which accounts for about one-third of all leukemias. Ocimum sanctum, an herbaceous perennial, is considered as one of the important sources of drugs for the treatment of various diseases, including cancers and autoimmune diseases. The present study was designed to screen various phytochemicals of O. sanctum for discovering their potential to inhibit Bruton’s tyrosine kinase (BTK), a well-known drug target of CLL. Various phytochemicals of O. sanctum were screened for their potential to inhibit BTK using several in silico protocols. First, the molecular docking approach was used to calculate the docking scores of the selected phytochemicals. Then, the selected top-ranked phytochemicals were screened for their physicochemical characteristics using ADME analysis. Finally, the stability of the selected compounds in their corresponding docking complexes with BTK was analysed using molecular dynamics simulations. Primarily, our observations revealed that, out of the 46 phytochemicals of O. sanctum, six compounds possessed significantly better docking scores (ranging from −9.2 kcal/mol to −10 kcal/mol). Their docking scores were comparable to those of the control inhibitors, acalabrutinib (−10.3 kcal/mol), and ibrutinib (−11.3 kcal/mol). However, after ADME analysis of these top-ranked six compounds, only three compounds (Molludistin, Rosmarinic acid, and Vitexin) possessed drug likeliness characteristics. During the MD analysis, the three compounds Molludistin, Rosmarinic acid, and Vitexin were found to remain stable in the binding pocket in their corresponding docking complexes with BTK. Therefore, among the 46 phytochemicals of O. sanctum tested in this study, the three compounds, Molludistin, Rosmarinic acid, and Vitexin are the best inhibitors of BTK. However, these findings need to be confirmed by biological experiments in the laboratory.

Amoxicillin is the most widely used antibiotic in human medicine for treating bacterial infections. However, in the present research, Micromeria biflora’s flavonoids extract mediated gold nanoparticles (AuNPs) were conjugated with amoxicillin (Au-amoxi) to study their efficacy against the inflammation and pain caused by bacterial infections. The formation of AuNPs and Au-amoxi conjugates were confirmed by UV–visible surface plasmon peaks at 535 nm and 545 nm, respectively. The scanning electron microscopy (SEM), zeta potential (ZP), and X-ray diffraction (XRD) studies reveal that the size of AuNPs and Au-amoxi are found to be 42 nm and 45 nm, respectively. Fourier-transform infrared spectroscopy (FT-IR) absorption bands at 3200 cm−1, 1000 cm−1, 1500 cm−1, and 1650 cm−1 reveal the possible involvement of different moieties for the formation of AuNPs and Au-amoxi. The pH studies show that AuNPs and Au-amoxi conjugates are stable at lower pH. The carrageenan-induced paw edema test, writhing test, and hot plate test were used to conduct in vivo anti-inflammatory and antinociceptive studies, respectively. According to in vivo anti-inflammatory activity, Au-amoxi compounds have higher efficiency (70%) after 3 h at a dose of 10 mg/kg body weight as compared to standard diclofenac (60%) at 20 mg/kg, amoxicillin (30%) at 100 mg/kg, and flavonoids extract (35%) at 100 mg/kg. Similarly, for antinociceptive activities, writhing test results show that Au-amoxi conjugates produced the same number of writhes (15) but at a lower dose (10 mg/kg) compared to standard diclofenac (20 mg/kg). The hot plate test results demonstrate that the Au-amoxi has a better latency time of 25 s at 10 mg/kg dose when compared to standard Tramadol of 22 s at 30 mg/ kg, amoxicillin of 14 s at 100 mg/kg, and extract of 14 s at 100 mg/kg after placing the mice on the hot plate for 30, 60, and 90 min with a significance of (p ≤ 0.001). These findings show that the conjugation of AuNPs with amoxicillin to form Au-amoxi can boost its anti-inflammatory and antinociceptive potential caused by bacterial infections.

Symmetry can play an important role in the study of boundary value problems, which are a type of problem in mathematics that involves finding the solutions to differential equations subject to given boundary conditions. Integral transforms play a crucial role in solving ordinary differential equations (ODEs), partial differential equations (PDEs), and integral equations. This article focuses on extending a single-valued Sawi transform to a double-valued ST, which we call the double Sawi (DS) transform. We derive some fundamental features and theorems for the proposed transform. Finally, we study the applications of the proposed transform by solving some boundary value problems such as the Fourier heat equation and the D’Alembert wave equation.

Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a neural network. It has many applications in the field of banking, automobile industry, agriculture, and healthcare industry. Deep learning has played a significant role in solving complex tasks related to computer vision, such as image classification, natural language processing, and object detection. On the other hand, optimizers also play an intrinsic role in training the deep learning model. Recent studies have proposed many deep learning models, such as VGG, ResNet, DenseNet, and ImageNet. In addition, there are many optimizers such as stochastic gradient descent (SGD), Adam, AdaDelta, Adabelief, and AdaMax. In this study, we have selected those models that require lower hardware requirements and shorter training times, which facilitates the overall training process. We have modified the Adam based optimizers and minimized the cyclic path. We have removed an additional hyper-parameter from RMSProp and observed that the optimizer works with various models. The learning rate is set to minimum and constant. The initial weights are updated after each epoch, which helps to improve the accuracy of the model. We also changed the position of the epsilon in the default Adam optimizer. By changing the position of the epsilon, it accumulates the updating process. We used various models with SGD, Adam, RMSProp, and the proposed optimization technique. The results indicate that the proposed method is effective in achieving the accuracy and works well with the state-of-the-art architectures.

The Delta and Omicron variants’ system was used in this research study to replicate the complex process of the SARS-CoV-2 outbreak. The generalised fractional system was designed and rigorously analysed in order to gain a comprehensive understanding of the transmission dynamics of both variants. The proposed dynamical system has heredity and memory effects, which greatly improved our ability to perceive the disease propagation dynamics. The non-singular Atangana–Baleanu fractional operator was used to forecast the current pandemic in order to meet this challenge. The Picard recursions approach can be used to ensure that the designed fractional system has at least one solution occupying the growth condition and memory function regardless of the initial conditions. The Hyers–Ulam–Rassias stability criteria were used to carry out the stability analysis of the fractional governing system of equations, and the fixed-point theory ensured the uniqueness of the solution. Additionally, the model exhibited global asymptotically stable behaviour in some conditions. The approximate behaviour of the fatal virus was investigated using an efficient and reliable fractional numerical Adams–Bashforth approach. The outcome demonstrated that there will be a significant decline in the population of those infected with the Omicron and Delta SARS-CoV-2 variants if the vaccination rate is increased (in both the symptomatic and symptomatic stages).

In the current study, a macro fungus was collected and identified by using morphological and molecular tools to study the ITS region, which has been described as a universal barcode marker during molecular investigation for the identification of fungi. Based on morphology and molecular evidence, the collected fungus was identified as Daedalea Mushroom. The identified fungus was used for the synthesis of Iron and ZnO nanoparticles as an eco-friendly agent for nanoparticle synthesis. The synthesized nanoparticles were confirmed by, Fourier transfer infrared spectroscopy analysis (FTIR), X-ray diffraction analysis (XRD), energy dispersive X-ray analysis (EDX), and scanning electron microscopy analysis (SEM). All these characterizations revealed the synthesis of Iron and ZnO NPs with an irregular shape and a size of 16.8 nm. The zinc oxide nanoparticles had a size in the range of 18.53 nm. Daedalea Mushroom was used for the first time to synthesize Iron and zinc nanoparticles. The mycosynthesized Iron and ZnO NPs were assessed as control agents at various dosage rates against the pathogenic fungus Aspergillus niger, which was isolated from an apple and identified using its morphology. At higher concentrations (0.75 mg/mL), the iron nanoparticles inhibited fungal growth by 72%, whereas at lower concentrations (0.25 mg/mL), they inhibited fungal growth by 60%. ZnO NPs showed good antifungal activity at different concentrations including growth inhibition at 0.25 mg/mL (88%), 1.0 mg/mL (68%), 0.75 mg/mL (75%), and 0.5 mg/mL (70%) concentrations of ZnO NPs. However, the maximum growth inhibition of ZnO NPs was observed at 0.25 mg/mL (88%) concentration and minimum growth inhibition at 0.1 mg/mL (22%). The current study concludes that Daedalea Mushroom works as a novel and eco-friendly source for the synthesis of Iron and ZnO NPs with prominent antifungal activities that can be further applied in different fields.

In the recent past, a number of research articles have explored the stability, existence, and uniqueness of the solutions and controllability of dynamical systems with a fractional order (FO). Nevertheless, aside from the controllability and other dynamical aspects, very little attention has been given to the observability of FO dynamical systems. This paper formulates a novel type of FO delay system of the Pantograph type in the Caputo sense and explores its controllability and observability results. This research endeavor begins with the conversion of the proposed dynamical system into a fixed-point problem by utilizing Laplace transforms, the convolution of Laplace functions, and the Mittag–Leffler function (MLF). We then set out Gramian matrices for both the controllability and observability of the linear parts of our proposed dynamical system and prove that both the Gramian matrices are invertible, thus confirming the controllability and observability in a given domain. Considering the controllability and observability results of the linear part along with some other assumptions, we investigate the controllability and observability results related to the nonlinear system. The Banach contraction result, the fixed-point result of Schaefer, the MLF, and the Caputo FO derivative are used as the main tools for establishing these results. To establish the authenticity of the established results, we add two examples at the end of the manuscript.

KdV equations have a lot of applications of in fluid mechanics. The exact solutions of the KdV equations play a vital role in the wave dynamics of fluids. In this paper, some new exact solutions of a generalized geophysical KdV equation are computed with the aid of tanh-coth method. To implement the tanh-coth procedure, we first convert the PDEs to ODEs with the help of wave transformation. Then, using a system of algebraic equations, we obtain several soliton solutions. To verify and clearly illustrate the exact solutions, several graphic presentations are developed by giving the parameter values, which are then thoroughly discussed in the relevant components.

Palaeoflora in Pakistan in the Miocene is characterized by its high biodiversity. The present study investigated the pollen of fossil plants from the Murree Formation of Pakistan. Shales and mudstones were collected from the Murree section located at the foothills of the Margalla hills and analyzed by palynofacies and palynostratigraphic analyses. In this paleopalynological study of the Miocene Murree Formations of Pakistan, 31 samples were analyzed using microscopic techniques containing 48 pollen types from 12 families. The quantitative and qualitative morphological features of pollen were determined using light and scanning electron microscopy to help identify the pollen grains. Exine ornamentations and spines were the most important diagnostic features for distinguishing one pollen grain from another. The maximum exine thickness was observed in Ocimum basilicum of 4.25 μm, whereas the maximum pollen diameter was recorded for Pinus of 69.5 μm. Lamiaceae, Asteraceae, and Poaceae were the dominant families. The results showed that the preservation of floral records was not optimal. The presented results provide data on the dominant fossil plant taxa that existed in Pakistan (23.03–5.33 Ma). The evolution and phytogeographical histories of fossil plants can be unraveled using rock sediments to preserve biodiversity.

Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences.

Many diseases are caused by viruses of different symmetrical shapes. Rotavirus particles are approximately 75 nm in diameter. They have icosahedral symmetry and particles that possess two concentric protein shells, or capsids. In this research, using a piecewise derivative framework with singular and non-singular kernels, we investigate the evolution of rotavirus with regard to the effect of vaccination. For the considered model, the existence of a solution of the piecewise rotavirus model is investigated via fixed-point results. The Adam–Bashforth numerical method along with the Newton polynomial is implemented to deduce the numerical solution of the considered model. Various versions of the stability of the solution of the piecewise rotavirus model are presented using the Ulam–Hyres concept and nonlinear analysis. We use MATLAB to perform the numerical simulation for a few fractional orders to study the crossover dynamics and evolution and effect of vaccination on rotavirus disease. To check the validity of the proposed approach, we compared our simulated results with real data from various countries.

Recently, several research articles have investigated the existence of solutions for dynamical systems with fractional order and their controllability. Nevertheless, very little attention has been given to the observability of such dynamical systems. In the present work, we explore the outcomes of controllability and observability regarding a differential system of fractional order with input delay. Laplace and inverse Laplace transforms, along with the Mittage–Leffler matrix function, are applied to the proposed dynamical system in Caputo’s sense, and a general solution is obtained in the form of an integral equation. Then, we set out conditions for the controllability of the underlying model, regarding the linear case. We then expound controllability conditions for the nonlinear case by utilizing the fixed point result of Schaefer and the Arzola–Ascoli theorem. Using the fixed point concept, we prove the observability of the linear case using the observability Grammian matrix. The necessary and sufficient conditions for the nonlinear case are investigated with the help of the Banach contraction mapping theorem. Finally, we add some examples to elaborate on our work.

The KdV equation has many applications in mechanics and wave dynamics. Therefore, researchers are carrying out work to develop and analyze modified and generalized forms of the standard KdV equation. In this paper, we inspect the KdV-mKdV equation, which is a modified and generalized form of the ordinary KdV equation. We use the fractional operator in the Caputo sense to analyze the equation. We examine some theoretical results concerned with the solution’s existence, uniqueness, and stability. We employ a modified Laplace method to extract the numerical results of the considered equation. We use MATLAB-2020 to simulate the results in a few fractional orders. We report the effects of the fractional order on the wave dynamics of the proposed equation.

In this manuscript, we investigate the nonlinear Boussinesq equation (BEQ) under fractal-fractional derivatives in the sense of the Caputo–Fabrizio and Atangana–Baleanu operators. We use the double modified Laplace transform (LT) method to determine the general series solution of the Boussinesq equation. We study the convergence, existence, uniqueness, boundedness, and stability of the solution of the considered good BEQ under the aforementioned derivatives. The obtained solutions are presented with numerical illustrations considering a particular example by two cases based on both derivatives with suitable initial conditions. The results are illustrated graphically where good agreements are obtained. Our results show that fractal-fractional derivatives are a very effective tool for studying nonlinear systems. Furthermore, when t increases, the solitary waves of the system oscillate. As the fractional order α or fractal dimension β increases, the soliton solutions become coherently close to the exact solution. For compactness, an error analysis is performed. The absolute error reveals an approximate linear evolution in the soliton solutions as time increases and that the system does not blow up nonlinearly.

Introduction: The ongoing coronavirus disease 2019 (COVID-19), which emerged in December 2019, is a serious health concern throughout the world. Despite massive COVID-19 vaccination on a global scale, there is a rising need to develop more effective vaccines and drugs to curb the spread of coronavirus. Methodology: In this study, we screened the amino acid sequence of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 (the causative agent of COVID-19) for the identification of B and T cell epitopes using various immunoinformatic tools. These identified potent B and T cell epitopes with high antigenicity scores were linked together to design the multi-epitope vaccine construct. The physicochemical properties, overall quality, and stability of the designed vaccine construct were confirmed by suitable bioinformatic tools. Results: After proper in silico prediction and screening, we identified 3 B cell, 18 CTL, and 10 HTL epitopes from the RdRp protein sequence. The screened epitopes were non-toxic, non-allergenic, and highly antigenic in nature as revealed by appropriate servers. Molecular docking revealed stable interactions of the designed multi-epitope vaccine with human TLR3. Moreover, in silico immune simulations showed a substantial immunogenic response of the designed vaccine. Conclusions: These findings suggest that our designed multi-epitope vaccine possessing intrinsic T cell and B cell epitopes with high antigenicity scores could be considered for the ongoing development of peptide-based novel vaccines against COVID-19. However, further in vitro and in vivo studies need to be performed to confirm our in silico observations.

Nanocomposites of polyvinylidene fluoride (PVDF) with dimensional (1D) cobalt oxide (Co3O4) and f-MWCNTs were prepared successfully by the solution casting method. The impact of 1D Co3O4 filler and 1D Co3O4/f-MWCNTs co-fillers on the structural, thermal, and electrical behavior of PVDF were studied. The crystal structural properties of pure PVDF and its nanocomposite films were studied by XRD, which revealed a significant enhancement of β-phase PVDF in the resulting nanocomposite films. The increase in β-phase was further revealed by the FTIR spectroscopic analysis of the samples. TG, DTA, and DSC analyses confirmed an increase in thermal stability of PVDF with the addition of nano-fillers as well as their increasing wt.%. From impedance spectroscopic studies, it was found that the DC conductivity of PVDF increases insignificantly initially (up to 0.1 wt.% of nano-fillers addition), but a significant improvement in DC conductivity was found at higher concentrations of the nano-fillers. Furthermore, it was observed that the DC conductivity decreases with frequency. The increase in DC conductivity corresponded to the strong interactions of nano-fillers with PVDF polymer chains.

Purpose: This study aims to analyze and characterize anthrax vaccine-related research, key developments, global research trends, and mapping of published scientific research articles during the last three decades (1991–2021). Methods: A bibliometric and visualized study was conducted. The Web of Science Core Collection database (WoSCC) was searched using relevant keywords (“Anthrax” OR “Anthrax bacterium” OR “Bacillus anthracis” OR “Bacteridium anthracis” OR “Bacillus cereus var. Anthracis” (Topic)) AND (“Vaccine” OR “Vaccines” OR “Immunization” OR “Immunisation” OR “Immunizations” OR “Immunisations” (Topic)) with specific restrictions. The data was analyzed and plotted by using different bibliometric software and tools (HistCiteTM software, version 12.3.17, Bibliometrix: An R-tool version 3.2.1, and VOSviewer software, version 1.6.17). Results: The initial search yielded 1750 documents. After screening the titles and abstracts of the published studies, a total of 1090 articles published from 1991 to 2021 were included in the final analysis. These articles were published in 334 journals and were authored by 4567 authors from 64 countries with a collaboration index of 4.32. The annual scientific production growth rate was found to be 9.68%. The analyzed articles were cited 31335 times. The most productive year was 2006 (n = 77, 7.06%), while the most cited year was 2007 (2561 citations). The leading authors and journals in anthrax research were Rakesh Bhatnagar from Jawaharlal Nehru University, India (n = 35, 3.21%), and Vaccine (n = 1830, 16.51%), while the most cited author and journal were Arthur M. Friedlander from the United States Department of Defense (n = 2762), and Vaccine (n = 5696), respectively. The most studied recent research trend topics were lethal, double-blind, epidemiology, B surface antigen, disease, and toxin. The United States of America (USA) was the most dominant country in terms of publications, citations, corresponding author country, and global collaboration in anthrax vaccine research. The USA had the strongest collaboration with the United Kingdom (UK), China, Canada, Germany, and France. Conclusion: This is the first bibliometric study that provides a comprehensive historical overview of scientific studies. From 2006 to 2008, more than 20% of the total articles were published; however, a decrease was observed since 2013 in anthrax vaccine research. The developed countries made significant contributions to anthrax vaccine-related research, especially the USA. Among the top 10 leading authors, six authors are from the USA. The majority of the top leading institutions are also from the USA. About 90% of the total studies were funded by the United States Department of Health and Human Services (HHS), National Institutes of Health (NIH), USA, and the National Institute of Allergy and Infectious Diseases (NIAID), USA.

(1) Background: Inflammation is one of the primary responses of the immune system and plays a key role in the pathophysiology of various diseases. Recent reports suggest that various phytochemicals exhibit promising anti-inflammatory and immunomodulation activities with relatively few undesirable effects, thus offering a viable option to deal with inflammation and associated diseases. The current study evaluates the anti-inflammatory and immunomodulatory effects of withaferin A (WA) in immune cells extracted from BALB/c mice. (2) Methods: MTT assays were performed to assess the cell viability of splenocytes and anti-inflammatory doses of WA. Under aseptic conditions, the isolation of macrophages and splenocytes from BALB/c mice was performed to investigate the anti-inflammatory effects of WA. Analysis of the expression of proinflammatory cytokines and associated signaling mediators was performed using proinflammatory assay kits, real-time polymerase chain reaction (RT-PCR), and immunoblotting, while the quantification of B and T cells was performed by flow cytometry. (3) Results: Our results demonstrated that WA exhibits anti-inflammatory and immunomodulatory effects in LPS-stimulated macrophages and splenocytes derived from BALB/c mice, respectively. Mechanistically, we found that WA promotes an anti-inflammatory effect on LPS-stimulated macrophages by attenuating the secretion and expression of proinflammatory cytokines TNF-α, IL-1β, IL-6, and the inflammation modulator NO, both at the transcriptional and translational level, respectively. Further, WA inhibits LPS-stimulated inflammatory signaling by dephosphorylation of p-Akt-Ser473 and p-ERK1/2. This dephosphorylation does not allow IĸB-kinase activation to disrupt IĸB–NF-ĸB interaction. The consistent interaction of IĸB with NF-ĸB in WA-treated cells attenuates the activation of downstream inflammatory signaling mediators Cox-2 and iNOS expression, which play crucial roles in inflammatory signaling. Additionally, we observed significant immunomodulation of LPS-stimulated spleen-derived lymphocytes by suppression of B (CD19) and T (CD4+/CD8+) cell populations after treatment with WA. (4) Conclusion: WA exhibits anti-inflammatory and immunomodulatory activity by modulating Akt/ERK/NF-kB-mediated inflammatory signaling in macrophages and immunosuppression of B (CD19) and T cell (CD4+/CD8+) populations in splenocytes after LPS stimulation. These results suggest that WA could act as a potential anti-inflammatory/immunomodulatory molecule and support its use in the field of immunopharmacology to modulate immune system cells.

Excitotoxicity is a type of neurodegenerative disorder. It caused by excessive glutamate receptor activation, which leads to neuronal malfunction and fatality. The N-methyl-D-aspartate (NMDA) receptors are found in glutamatergic neurons, and their excessive activation is primarily responsible for excitotoxicity. They are activated by both glutamate binding and postsynaptic depolarization, facilitating Ca2+ entry upon activation. Therefore, they are now widely acknowledged as being essential targets for excitotoxicity issues. Molecular docking and molecular dynamics (MD) simulation analyses have demonstrated that nobiletin efficiently targets the binding pocket of the NMDA receptor protein and exhibits stable dynamic behavior at the binding site. In this study, five potential neuroprotectants, nobiletin, silibinin, ononin, ginkgolide B, and epigallocatechin gallate (EGCG), were screened against the glutamate NMDA receptors in humans via computational methods. An in silico ADMET study was also performed, to predict the pharmacokinetics and toxicity profile for the expression of good drug-like behavior and a non-toxic nature. It was revealed that nobiletin fulfills the criteria for all of the drug-likeness rules (Veber, Lipinski, Ghose, Muegge, and Egan) and has neither PAINS nor structural alerts (Brenks). In conclusion, nobiletin demonstrated a possible promising neuroprotectant activities compared to other selected phytochemicals. Further, it can be evaluated in the laboratory for promising therapeutic approaches for in vitro and in vivo studies.

The current manuscript describes the dynamics of a fractional mathematical model of serial killing under the Mittag–Leffler kernel. Using the fixed point theory approach, we present a qualitative analysis of the problem and establish a result that ensures the existence of at least one solution. Ulam’s stability of the given model is presented by using nonlinear concepts. The iterative fractional-order Adams–Bashforth approach is being used to find the approximate solution. The suggested method is numerically simulated at various fractional orders. The simulation is carried out for various control strategies. Over time, all of the compartments demonstrate convergence and stability. Different fractional orders have produced an excellent comparison outcome, with low fractional orders achieving stability sooner.

The main metabolism of yeasts produces bioethanol. Bioethanol, which is produced from biomass and bioenergy crops, has been promoted as one of the most viable alternatives to fossil fuels. The following reaction represents all of the knowledge we have regarding intracellular reactions and their regulatory mechanisms: biomass+substrates→ethanol+biomass(morecells). Atangana has suggested new operators based on a combination of fractional and fractal calculus. Fractal-fractional operators (FFOs) have frequently been utilized to investigate the dynamics of a physical problem. In this paper, FFOs are used to investigate a nonlinear mathematical model for ethanol production with three different kernels. Famous fixed point results are employed to show the existence and uniqueness of the solution of the FFO ethanol model under the Mittag–Leffler kernel. The concept of non-linear analysis is utilized to demonstrate the model’s Ulam–Hyres stability. The Adams—Bashforth numerical technique, which is based on the Lagrangian interpolation method, is utilized to find the solution of the model under fractal-fractional operators with three different kernels. The numerical results are simulated with MATLAB-17 for several sets of fractional orders and fractal dimensions to show the relationship between components of ethanol production under new operators in various senses.

Copper oxide nanoparticles (CuO NPs) were synthesized through the coprecipitation method and used as nanocarriers for etoricoxib (selective COX-2 inhibitor drug) and montelukast (leukotriene product inhibitor drug) in combination therapy. The CuO NPs, free drugs, and nanoformulations were investigated through UV/Vis spectroscopy, FTIR spectroscopy, XRD, SEM, and DLS. SEM imaging showed agglomerated nanorods of CuO NPs of about 87 nm size. The CE1, CE2, and CE6 nanoformulations were investigated through DLS, and their particle sizes were 271, 258, and 254 nm, respectively. The nanoformulations were evaluated through in vitro anti-inflammatory activity, in vivo anti-inflammatory activity, in vivo analgesic activity, in vivo anti-pyretic activity, and in vivo acute toxicity activity. In vivo activities were performed on albino mice. BSA denaturation was highly inhibited by CE1, CE2, and CE6 as compared to other nanoformulations in the in vitro anti-inflammatory activity. The in vivo bioactivities showed that low doses (5 mg/kg) of nanoformulations were more potent than high doses (10 and 20 mg/kg) of free drugs in the inhibition of pain, fever, and inflammation. Lastly, CE2 was more potent than that of other nanoformulations.

Cancer is one of the leading diseases, causing deaths worldwide. Nearly 10 million deaths were reported in 2020 due to cancer alone. Several factors are involved in cancer progressions, such as lifestyle and genetic characteristics. According to a recent report, extracellular vesicles (EVs) are involved in cancer initiation, progression, and therapy failure. EVs can play a major role in intracellular communication, the maintenance of tissue homeostasis, and pathogenesis in several types of diseases. In a healthy person, EVs carry different cargoes, such as miRNA, lncRNA etc., to help other body functions. On the other hand, the same EV in a tumor microenvironment carries cargoes such as miRNA, lncRNA, etc., to initiate or help cancer progression at various stages. These stages may include the proliferation of cells and escape from apoptosis, angiogenesis, cell invasion, and metastasis, reprogramming energy metabolism, evasion of the immune response, and transfer of mutations. Tumor-derived EVs manipulate by altering normal functions of the body and affect the epigenetics of normal cells by limiting the genetic makeup through transferring mutations, histone modifications, etc. Tumor-derived EVs also pose therapy resistance through transferring drug efflux pumps and posing multiple drug resistances. Such EVs can also help as biomarkers for different cancer types and stages, which ultimately help with cancer diagnosis at early stages. In this review, we will shed light on EVs’ role in performing normal functions of the body and their position in different hallmarks of cancer, in altering the genetics of a normal cell in a tumor microenvironment, and their role in therapy resistance, as well as the importance of EVs as diagnostic tools.

The immune system of human beings plays a pivotal role in guarding against different types of diseases. During the COVID-19 pandemic, people with weak immune systems were more likely to die. Regular physical activities and healthy food intake can significantly improve the immune system; however, people with a sedentary lifestyle and a busy job schedule find it challenging and tedious to maintain regularity. Different approaches have been used over the years to engage people in various physical activities and improve their mental and physical health. The concept of employing serious games (games whose primary purpose is not fun or entertainment, but a serious goal) to effectuate better results has become one of the popular choices among healthcare professionals and research communities. Internet of things (IoT) has enabled digital transformation with smart cities, smart infrastructure, and the fourth industrial revolution. There have been some relevant studies on the encouragement of serious games in healthcare in the past few years. However, few research studies encourage IoT-enabled serious games played with IoT devices (sensors and actuators) by making the game experience more ubiquitous and pervasive. Consequently, the adaptation of the IoT in serious games for healthcare applications is a massive gap despite its growing need in an era significantly affected by COVID-19. This paper discusses the possibilities of integrating serious games with IoT and discusses the standard architecture, core technologies, and possible challenges. Finally, we present a prototype architecture and its various components and a qualitative analysis with recent studies.

In wireless sensor networks (WSN), flooding increases the reliability in terms of successful transmission of a packet with higher overhead. The flooding consumes the resources of the network quickly, especially in sensor networks, mobile ad-hoc networks, and vehicular ad-hoc networks in terms of the lifetime of the node, lifetime of the network, and battery lifetime, etc. This paper aims to develop an efficient and reliable protocol by using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Unicasting is used when the desired destination is at a minimum distance to avoid an extra overhead and increases the efficiency of the network in terms of overhead and energy because unicasting is favorable where the distance is minimum. Similarly, multicasting is used when the desired destination is at maximum distance and increases the network’s reliability in terms of throughput. The results are implemented in the Department of Computer Science, Bacha Khan University Charsadda (BKUC), Pakistan, as well as in the Network Simulator-2 (NS-2). The results are compared with benchmark schemes such as PUMA and ERASCA, and based on the results, the performance of the proposed approach is improved in terms of overhead, throughput, and packet delivery fraction by avoiding flooding.

The Internet of Things (IoT) is a new paradigm that connects objects to provide seamless communication and contextual information to anyone, anywhere, at any time (AAA). These Internet-of-Things-enabled automated objects interact with visitors to present a variety of information during museum navigation and exploration. In this article, a smart navigation and information system (SNIS) prototype for museum navigation and exploration is developed, which delivers an interactive and more exciting museum exploration experience based on the visitor’s personal presence. The objects inside a museum share the information that assist and navigate the visitors about the different sections and objects of the museum. The system was deployed inside Chakdara Museum and experimented with 381 users to achieve the results. For results, different users marked the proposed system in terms of parameters such as interesting, reality, ease of use, satisfaction, usefulness, and user friendly. Of these 381 users, 201 marked the system as most interesting, 138 marked most realistic, 121 marked it as easy-in-use, 219 marked it useful, and 210 marked it as user friendly. These statistics prove the efficiency of SNIS and its usefulness in smart cultural heritage, including smart museums, exhibitions and cultural sites.

The emergence of drug-resistant lice, acari, and their associated pathogens (APs) is associated with economic losses; thus, it is essential to find new appropriate therapeutic approaches. In the present study, a subtractive proteomics approach was used to predict suitable therapeutics against these vectors and their infectious agents. We found 9701 proteins in the lice (Pediculus humanus var. corporis) and acari (Ixodes scapularis, Leptotrombidium deliense), and 4822 proteins in the proteomes of their APs (Babesia microti, Borreliella mayonii, Borrelia miyamotoi, Borrelia recurrentis, Rickettsia prowazekii, Orientia tsutsugamushi str. Boryong) that were non-homologous to host proteins. Among these non-homologous proteins, 365 proteins of lice and acari, and 630 proteins of APs, were predicted as essential proteins. Twelve unique essential proteins were predicted to be involved in four unique metabolic pathways of lice and acari, and 103 unique proteins were found to be involved in 75 unique metabolic pathways of APs. The sub cellular localization analysis of 115 unique essential proteins of lice and acari and their APs revealed that 61 proteins were cytoplasmic, 42 as membrane-bound proteins and 12 proteins with multiple localization. The druggability analysis of the identified 73 cytoplasmic and multiple localization essential proteins revealed 22 druggable targets and 51 novel drug targets that participate in unique pathways of lice and acari and their APs. Further, the predicted 42 membrane bound proteins could be potential vaccine candidates. Screening of useful inhibitors against these novel targets may result in finding novel compounds efficient for the control of these parasites.

To lower the concentration of organic pollutants in the effluent stream, wastewater must be treated before being discharged into the environment. The question of whether wastewater treatment facilities can successfully reduce the concentration of micropollutants found in their influent streams is becoming increasingly pressing. The removal of micropollutants in treatment plants is investigated using a model that incorporates biodegradation and sorption as the key processes of micropollutant removal. This article provides the mathematical analysis of the wastewater model that describes the removal of micropollutant in treatment plants under a non-local operator in Caputo sense. The positivity of the solution is presented for the Caputo fractional model. The steady state’s solution of model and their stability is presented. The fixed point theorems of Leray–Schauder and Banach are used to deduce results regarding the existence of the solution of the model. Ulam–Hyers (UH) types of stabilities are presented via functional analysis. The fractional Euler method is used to find the numerical results of the proposed model. The numerical results are illustrated via graphs to show the effects of recycle ratio and the impact of fractional order on the evolution of the model.

Internet of Vehicles (IoV) has emerged as an advancement over the traditional Vehicular Ad-hoc Networks (VANETs) towards achieving a more efficient intelligent transportation system that is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. In order for the IoV and VANETs environments to be able to offer such beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities in these vehicular networks wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, we target the authentication part of the security system while highlighting the efficiency of blockchains in the IoV and VANETs environments. First, a detailed background on IoV and blockchain is provided, followed by a wide range of security requirements, challenges, and possible attacks in vehicular networks. Then, a more focused review is provided on the recent blockchain-based authentication schemes in IoV and VANETs with a detailed comparative study in terms of techniques used, network models, evaluation tools, and attacks counteracted. Lastly, some future challenges for IoV security are discussed that are necessary to be addressed in the upcoming research.

Beta vulgaris extract contains water-soluble red pigment betanin and is used as a food colorant. In this study, the biogenic Ag-Cu bimetallic nanoparticles were synthesized and characterized by different spectroscopic and microscopic techniques, including UV–Visible, FTIR, TEM. SEM-EDX, XRD, and TGA. Further, Ag-Cu bimetallic nanoparticles capped with Beta vulgaris biomolecules were evaluated for their antifungal activity against Candida albicans via targeting its major virulence factors, including adherence, yeast to hyphae transition, extracellular enzyme secretion, biofilm formation, and the expression of genes related to these pathogenic traits by using standard methods. C. albicans is an opportunistic human fungal pathogen that causes significant morbidity and mortality, mainly in immunocompromised patients. The current antifungal therapy is limited with various shortcomings such as host toxicity and developing multidrug resistance. Therefore, the development of novel antifungal agents is urgently required. Furthermore, NPs were screened for cell viability and cytotoxicity effect. Antifungal susceptibility testing showed potent antifungal activity of the Ag-Cu bimetallic NPs with a significant inhibitory effect on adherence, yeast to hyphae transition, extracellular enzymes secretion, and formation of biofilms in C. albicans at sub-inhibitory and inhibitory concentrations. The RT-qPCR results at an MIC value of the NPs exhibited a varying degree of downregulation in expression levels of virulence genes. Results also revealed the dose-dependent effect of NPs on cellular viability (up to 100%) using MUSE cell analyzer. Moreover, the low cytotoxicity effect of bimetallic NPs has been observed using haemolytic assay. The overall results indicated that the newly synthesized Ag-Cu bimetallic NPs capped with Beta vulgaris are proven to possess a potent anticandidal activity, by affecting the vital pathogenic factors of C. albicans.

The nonlinear fractional-order model of bioethanol production under a generalized nonlocal operator in the Caputo sense is investigated in this work. Theoretical and computational aspects of the considered model are discussed. We prove that the model has at least one solution and a unique solution using the Leray–Schauder and Banach contraction theorems. Using functional analysis, we investigate several types of Ulam–Hyres model stability. We use the predictor–corrector (P–C) method to construct a broad numerical scheme for the model’s solution. The proposed numerical method’s stability is demonstrated. Finally, we depict the numerical findings geometrically to demonstrate the model’s dynamics.

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.

Background: Hepatitis B and C viral infections, which are the most common cause of liver infection worldwide, are major health issues around the globe. People with chronic hepatitis infections remain at risk of liver cirrhosis and hepatic carcinoma, while also being a risk to other diseases. These infections are highly contagious in nature, and the prevention of hepatitis B and C transmission during blood transfusion is a major challenge for healthcare workers. Although epidemiological characteristics of hepatitis B and C infections in blood donors in Saudi Arabia have been previously investigated in multiple studies, due to targeted cohorts and the vast geographical distribution of Saudi Arabia, there are a lot of missing data points, which necessitates further investigations. Aim of the study: This study aimed to determine the prevalence of hepatitis B and hepatitis C viral infections among blood donors in the northern region of Riyadh, Saudi Arabia. Methods: To determine the given objectives, a retrospective study was performed which included data gathered from serological as well as nucleic acid test (NAT) screening of blood donors. Clinical data of 3733 blood donors were collected for a period of 2 years (from January 2019 to December 2020) at the blood bank of King Khalid General Hospital and the associated blood banks and donation camps in the region. Statistical analysis of the clinical data was performed using SPSS. Results: The blood samples of 3733 donors were analyzed to determine the seroprevalence of hepatitis B and C among the blood donors in the northern region of Riyadh, Saudi Arabia. Among the total of 3733 blood donors, 3645 (97.65%) were men and 88 (2.36%) were women. Most of the donors were younger than 27 years of age (n = 1494). The most frequent blood group in our study was O-positive (n = 1534), and the least frequent was AB-negative (n = 29). After statistically analyzing the clinical data, we observed that 7 (0.19%), 203 (5.44%) and 260 (6.96%) donor blood samples were positive for the HBV serological markers HBsAgs, HBsAbs and HBcAbs, respectively, and 12 (0.32%) blood samples reacted positively to anti-HCV antibodies. Moreover, 10 (0.27%) and 1 (0.027%) samples were NAT-HBV positive and NAT-HCV positive, respectively. Conclusion: In the current study, low prevalence rates of HBV and HCV were observed in the blood donors. Statistical correlations indicated that both serological tests and NATs are highly effective in screening potential blood donors for HBV and HCV, which, in turn, prevents potential transfusion-transmitted hepatitis.

The ageing population’s problems directly impact countries’ socio-economic structure, as more resources are required to monitor the aged population’s health. The growth in human life expectancy is increasing due to medical technologies and nutritional science innovations. The Internet of Things (IoT) is the connectivity of physical objects called things to the Internet. IoT has a wide range of health monitoring applications based on biomedical sensing devices to monitor health conditions. This paper proposes elderly patients’ health monitoring architecture based on an intelligent task mapping approach for a closed-loop IoT healthcare environment. As a case study, a health monitoring system was developed based on the proposed architecture for elderly patients’ health monitoring in the home, ambulance, and hospital environment. The system detects and notifies deteriorating conditions to the authorities based on biomedical sensors for faster interventions. Wearable biomedical sensors are used for monitoring body temperature, heart rate, blood glucose level, and patient body position. Threshold and machine learning-based approaches were used to detect anomalies in the health sensing data. The proposed architecture’s performance analysis is evaluated in terms of round trip time, reliability, task drop rate, and latency performance metrics. Performance results show that the proposed architecture of the elderly patient health monitoring can provide reliable solutions for critical tasks in IoT environments.

A novel temperature stable alkaline protease yielding bacteria was isolated from the soils of Dachigam National Park, which is known to be inhabited by a wide variety of endemic plant and animal species of Western Himalaya. This high-potential protease producing isolate was characterized and identified as Bacillus amyloliquefaciens strain HM48 by morphological, Gram’s staining and biochemical techniques followed by molecular characterization using 16S rRNA approach. The extracellular protease of B. amyloliquefaciens HM48 was purified by precipitating with ammonium sulfate (80%), followed by dialysis and Gel filtration chromatography increasing its purity by 5.8-fold. The SDS–PAGE analysis of the purified enzyme confirmed a molecular weight of about ≈25 kDa. The enzyme displayed exceptional activity in a broad temperature range (10–90 °C) at pH 8.0, retaining its maximum at 70 °C, being the highest reported for this proteolytic Bacillus sp., with KM and Vmax of 11.71 mg/mL and 357.14 µmol/mL/min, respectively. The enzyme exhibited remarkable activity and stability against various metal ions, surfactants, oxidizing agent (H2O2), organic solvents and displayed outstanding compatibility with widely used detergents. This protease showed effective wash performance by exemplifying complete blood and egg-yolk stains removal at 70 °C and efficiently disintegrated chicken feathers making it of vital importance for laundry purpose and waste management. For functional analysis, protease gene amplification of strain HM48 yielded a nucleotide sequence of about 700 bp, which, when checked against the available sequences in NCBI, displayed similarity with subtilisin-like serine protease of B. amyloliquefaciens. The structure of this protease and its highest-priority substrate β-casein was generated through protein modeling. These protein models were validated through futuristic algorithms following which protein–protein (protease from HM48 and β-casein) docking was performed. The interaction profile of these proteins in the docked state with each other was also generated, shedding light on their finer details. Such attributes make this thermally stable protease novel and suitable for high-temperature industrial and environmental applications.

The authors wish to make the following corrections to this paper [...]

Renewable energy sources are environmentally friendly and cost-efficient. However, the problem with these renewable resources is their heavy reliance on weather conditions. Thus, at times, these solutions are not guaranteed to meet the required demand all the time. For this, hybrid microgrids are introduced, which have a combination of both renewable energy sources and non-renewable energy resources. In this paper, a cost-efficient optimization algorithm is proposed that minimizes the use of non-renewable energy sources. It maximizes the use of renewable energy resources by meeting the demand for utility grids. Real data based on the load and demand of the utility grids in Italy is used, and a system that determines the optimal sizing of the microgrid and a daily plan is introduced to optimize the renewable resources operations. As part of the proposal, the objective function for the operation and planning of the microgrid in such a way to minimize cost is formulated. Moreover, a variant of the PSO algorithm named recurrent PSO is implemented. The recurrent PSO algorithm solves the proposed optimization objective function by minimizing the cost for the installation and working of the microgrid. Afterwards, the energy management system algorithm lays out a plan for the daily operation of the microgrid. The performance of the system is evaluated using different state-of-the-art optimization methods. The proposed work can help minimize the use of diesel generators, which not only saves financial resources but also contributes toward a green environment.

Ticks and tick-borne pathogens (TBPs) continuously causing substantial losses to the public and veterinary health sectors. The identification of putative drug targets and vaccine candidates is crucial to control TBPs. No information has been recorded on designing novel drug targets and vaccine candidates based on proteins. Subtractive proteomics is an in silico approach that utilizes extensive screening for the identification of novel drug targets or vaccine candidates based on the determination of potential target proteins available in a pathogen proteome that may be used effectively to control diseases caused by these infectious agents. The present study aimed to investigate novel drug targets and vaccine candidates by utilizing subtractive proteomics to scan the available proteomes of TBPs and predict essential and non-host homologous proteins required for the survival of these diseases causing agents. Subtractive proteome analysis revealed a list of fifteen essential, non-host homologous, and unique metabolic proteins in the complete proteome of selected pathogens. Among these therapeutic target proteins, three were excluded due to the presence in host gut metagenome, eleven were found to be highly potential drug targets, while only one was found as a potential vaccine candidate against TBPs. The present study may provide a foundation to design potential drug targets and vaccine candidates for the effective control of infections caused by TBPs.

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has improved and secured the human healthcare system. The increase in portable health devices has enhanced the quality of health-monitoring status both at an activity/fitness level for self-health tracking and at a medical level, providing more data to clinicians with potential for earlier diagnosis and guidance of treatment. When sharing personal medical information, data security and comfort are essential requirements for interaction with and collection of electronic medical records. However, it is hard for current systems to meet these requirements because they have inconsistent security policies and access control structures. The new solutions should be directed towards improving data access, and should be managed by the government in terms of privacy and security requirements to ensure the reliability of data for medical purposes. Blockchain paves the way for a revolution in the traditional pharmaceutical industry and benefits from unique features such as privacy and transparency of data. In this paper, we propose a novel platform for monitoring patient vital signs using smart contracts based on blockchain. The proposed system is designed and developed using hyperledger fabric, which is an enterprise-distributed ledger framework for developing blockchain-based applications. This approach provides several benefits to the patients, such as an extensive, immutable history log, and global access to medical information from anywhere at any time. The Libelium e-Health toolkit is used to acquire physiological data. The performance of the designed and developed system is evaluated in terms of transaction per second, transaction latency, and resource utilization using a standard benchmark tool known as Hyperledger Caliper. It is found that the proposed system outperforms the traditional health care system for monitoring patient data.

As the world population is increasing rapidly, food and water demands are the most crucial problem for humanity. In some areas of the world, water or environment is unsuitable for plant growth; hydroponic systems can provide a suitable environment for crop production with effective management of natural resources. Internet of Things paradigm based automated systems has been creating an excellent opportunity for monitoring and controlling agriculture by minimizing the cost and maximizing the profit significantly over the past decade. The reduction of the cost can be achieved by sufficient usage of resources and setting up optimum operational parameters for agricultural devices. This paper presents an optimization scheme with novel objective function for hydroponics environment parameters management with efficient energy consumption. The proposed approach provides optimal energy and resource utilization in the hydroponics system with setting up a working level and operational duration to the actuators. We have developed an optimization scheme with objective function for optimal humidity and water level control based on fuzzy logic, which can support the optimal measurement for crop growth with energy efficiency. Fuzzy logic control is applied for the compromise between actuators working level and operational duration. A real hydroponics environment has been implemented and presented to evaluate the effectiveness of the proposed approach. It can be assessed through the simulation results that the optimization module achieves a signification reduction (18%) in energy consumption as compared to the other scheme.

Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life.

Inula britannica L., family Asteraceae, is used in traditional Chinese and Kampo Medicines for various diseases. Flowers or the aerial parts are a rich source of secondary metabolites. These consist mainly of terpenoids (sesquiterpene lactones and dimmers, diterpenes and triterpenoids) and flavonoids. The isolated compounds have shown diverse biological activities: anticancer, antioxidant, anti-inflammatory, neuroprotective and hepatoprotective activities. This review provides information on isolated bioactive phytochemicals and pharmacological potentials of I. britannica.