Blocking the major inflammatory pathways by newly synthesized thiadiazine derivatives via in-vivo, in-vitro and in-silico mechanism.

A series of new thiadiazine derivatives including 2-(5-alkyl/aryl-6-thioxo-1,3,5-thiadiazinan-3-yl) propanoic acids (a) and 4-methyl-2-(5-alkyl/aryl-6-thioxo-1,3,5-thiadiazinan-3-yl) pentanoic acids (b) were synthesized by reacting primary alkyl/aryl amines with CS2, followed by reaction with formaldehyde and amino acids. The chemical structures of synthesized compounds were confirmed by 13C- NMR and 1H- NMR techniques. The inhibitory potential of major inflammatory enzymes, COX-2 and 5-LOX was examined. Moreover, anti-nociceptive and anti-inflammatory activities were evaluated in the in vivo thermally induced nociceptive, and carrageenan induced paw edema models in mice. The in-vitro results reflect that these compounds exhibited concentration dependent inhibition of COX-2 and 5-LOX. The tested compounds at 50 mg/kg showed significant effect on thermally induced pain, and reduced latency time (seconds) as compared to the vehicle treated animals. Moreover, tested compounds exhibited percent inhibition of paw edema in the carrageenan induced paw edema model in mice. Furthermore, the binding modes of the most active COX-2 and 5-LOX inhibitors were determined through computational methods. The computational study reflects that the docked compounds have high binding affinities for COX-2 and 5-LOX enzymes, which leads to inhibition of these enzymes.

Assessing the Role of AI-Based Smart Sensors in Smart Cities Using AHP and MOORA.

We know that in today's advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance.

A Privacy and Energy-Aware Federated Framework for Human Activity Recognition.

Human activity recognition (HAR) using wearable sensors enables continuous monitoring for healthcare applications. However, the conventional centralised training of deep learning models on sensor data poses challenges related to privacy, communication costs, and on-device efficiency. This paper proposes a federated learning framework integrating spiking neural networks (SNNs) with long short-term memory (LSTM) networks for energy-efficient and privacy-preserving HAR. The hybrid spiking-LSTM (S-LSTM) model synergistically combines the event-driven efficiency of SNNs and the sequence modelling capability of LSTMs. The model is trained using surrogate gradient learning and backpropagation through time, enabling fully supervised end-to-end learning. Extensive evaluations of two public datasets demonstrate that the proposed approach outperforms LSTM, CNN, and S-CNN models in accuracy and energy efficiency. For instance, the proposed S-LSTM achieved an accuracy of 97.36% and 89.69% for indoor and outdoor scenarios, respectively. Furthermore, the results also showed a significant improvement in energy efficiency of 32.30%, compared to simple LSTM. Additionally, we highlight the significance of personalisation in HAR, where fine-tuning with local data enhances model accuracy by up to 9% for individual users.

FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks.

Increased demand for fast edge computation and privacy concerns have shifted researchers' focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the high diversity in different clients. Our research shows that using highly diverse data sets in FL can lead to low accuracy of some local models, which can be categorised as anomalous behaviour. In this paper, we present FedBranched, a clustering-based framework that uses probabilistic methods to create branches of clients and assigns their respective global models. Branching is performed using hidden Markov model clustering (HMM), and a round of branching depends on the diversity of the data. Clustering is performed on Euclidean distances of mean absolute percentage errors (MAPE) obtained from each client at the end of pre-defined communication rounds. The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. The results show a substantial increase in the average MAPE of all clients; the biggest improvement of 11.36% was observed in one client.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan.

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems.

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.

Association of ITGAX and ITGAM gene polymorphisms with susceptibility to IgA nephropathy.

Previous genome-wide association studies have discovered significant association at ITGAX-ITGAM on 16p11.2 for IgA nephropathy (IgAN). In this study, we performed a two-stage association study that enrolled 1700 IgAN cases and 2400 controls to further investigate the relationship of ITGAX and ITGAM gene polymorphisms with IgAN. Seven single-nucleotide polymorphisms (SNPs) were selected for genotyping in 1000 IgAN cases and 1000 healthy controls in the discovery stage, and the significant SNP was further validated in additional 700 IgAN cases and 1400 healthy controls. We found that four SNPs (rs11150619, rs11150614, rs7190997, and rs4597342) showed potential associations with IgAN susceptibility in the discovery stage, but only SNP rs11150619 was further genotyped in the validation stage after multiple testing. The results indicated that rs11150619 was significantly associated with IgAN in the combined samples (OR = 0.81, 95%CI = 0.71-0.91, and dominant P = 6.68 × 10-4). Moreover, patients with TT genotype of rs11150619 exhibited increased estimated glomerular filtration rate levels and a reduced proportion of global sclerosis compared with those with TC and CC genotypes. Our results suggested that ITGAX and ITGAM gene polymorphisms were associated with IgAN in a Chinese Han population, and the rs11150619-T allele showed a potential protective role for IgAN.

Single versus double burr-hole drainage for chronic subdural hematoma: A study of relevant prognostic factors conducted in Pakistan.

OBJECTIVE: To compare the efficacy of single versus double burr-hole for drainage of chronic subdural hematoma, keeping in consideration pertinent demographic, pre and postoperative associations. METHODS: A prospective cohort study carried out in Combined Military Hospital, Multan, (December 2016-August 2018), on adults with diagnosed chronic subdural hematoma (CSDH); being segregated by randomized control trial, non-probability purposive sampling into Group-A and Group-B (who underwent single and double burr-holes for CSDH-drainage respectively). Utilizing SPSS-21, data expressed as frequencies/percentages and mean± standard deviation (SD) and cross-tabulated; p-value <0.05 was taken as significant. RESULTS: Age and GCS scores were 62±13.694 (range 38-94) and 11.00±3.350 (range 3-15) respectively, males being 40(66.7). Post-operative fatality was Nil, while 8(13.3%) and 14(23.3%) had post-operative seizures and recurrence of hematoma respectively. There was no significant association between type of burr-hole and hospital stay (p 0-884), seizures (p 0.448) or recurrence (p 0.542). Hospital stay (p<0.000) and seizures (p-0.005) were inversely proportional to GCS scores on presentation. Recurrence rates were not affected by age (p-0 .175) or gender (p-0 .281). CONCLUSION: There was no significant difference between outcomes of single and double burr-hole surgeries; the former must be preferred because of lesser iatrogenic trauma. GCS-score on presentation was validated as a negative association to anticipate post-operative outcomes.

Do doctors have hidden distress; a study conducted at tertiary care hospital at Lahore.

OBJECTIVE: Toanalyse the prevalence of distress in doctors serving in a tertiary care hospital and studying the factors having significant impact on the subject. METHODS: The cross-sectional study was conducted at the Combined Military Hospital, Lahore, from February to December 2014, and comprised doctors serving for at least six months who volunteered to fill out the standardised General Health Questionnaire-12Demographic features and level of job satisfaction were taken as independent variables. Outcome variable was the questionnaire score. SPSS 20 was used for data analysis. RESULTS: The mean questionnaire score of the 97 respondents in the study was 12.27±6.397. Of them, 19(19.6%) and 11(11.3%) had distress and severe distress respectively. Marital status (p=0.006), age (p=0.029), income per month (p=0.010) and levels of job satisfaction (p=0.001) had significant impact on the scores. Variables having insignificant impact were gender (p=0.529), number of children (p=0.220), education (p=0.816), service years (p=0.155), current employment (p=0.504), nature of job (p=0.531), working hours (p=0.632), additional duties (p=0.663), and socioeconomic class (p=0.935). CONCLUSIONS: Almost one-third of the doctors had distress under the significant impact of multiple factors.

Prevalence of anxiety and depression among doctors; the unscreened and undiagnosed clientele in Lahore, Pakistan.

OBJECTIVES: To analyze prevalence of anxiety and depression among doctors serving in a tertiary care hospital in Lahore, with a study of impact of relevant demographic features. METHODS: A cross sectional study was conducted at Combined Military Hospital, Lahore, from February 2014 to Jan 2015. Participants were doctors serving in subject hospital for at least six months duration. Standardized Hospital Anxiety Depression Score (HADS) inventory was selected as inventory. Formal approval from hospital ethical committee and written informed consent from participants were obtained. Demographic characteristics of participants were recorded as independent variables; anxiety and depression scores being outcome variables. Data analysis was done via descriptive statistics (SPSS-20), qualitative data expressed as frequencies, percentages; quantitative as mean ± standard deviation(SD). Cross tabulation was done via chi-square, p-value < 0.05 considered as significant. RESULTS: Out of 203 volunteers, 97(47.78%) responded. Score of anxiety was 7.04±4.470, maximum being 19, scores of depression was 4.94±3.605, maximum score being 15. Mild to moderate anxiety and depression were revealed in 33(34%) and 24(24.8%) respectively, while 7(7.2%) and 1(1.0%) had severe anxiety and depression respectively. There was strong positive relation between anxiety and depression (p<0.001). There was significant impact of service years on depression (p-0.011) and gender on anxiety (p-0.002), 9(17.31%) males and 24(53.33%) females had mild to moderate anxiety while 4(7.69%) males and 3(6.66%) females revealed severe anxiety and other variables did not reveal significant impact on HADS scores. CONCLUSION: Doctors showed high grades of anxiety and depression. They must be promptly screened and managed at all medical institutions.

PERSISTENT ELEVATION OF NEUTROPHIL/LYMPHOCYTE RATIO ASSOCIATED WITH NEW ONSET ATRIAL FIBRILLATION FOLLOWING PERCUTANEOUS CORONARY INTERVENTION FOR ACUTE ST SEGMENT ELEVATION MYOCARDIAL INFARCTION.

BACKGROUND: Increasing evidence suggests that inflammation plays an important role in initiation and maintaining of atrial fibrillation (AF). The Neutrophil to Lymphocyte (N/L) Ratio is an easily derived and readily available parameter that has emerged as marker of inflammation with predictive and prognostic value. We investigated the association between N/L ratio and incidence of atrial fibrillation in patients undergoing cardiac catheterization for acute ST-segment elevation myocardial infarction (STEMI. METHODS: This cross sectional descriptive study was carried out at New York Hospital Queens. We retrospectively analysed clinical, hematologic and angiographic data of 290 patients who underwent coronary angiography with stent placement for acute ST-segment elevation myocardial infarction between 2008-2011. RESULTS: Study cohort of 290 patients had mean age 63.3 +/- 13.0 years consisting of 81.4% male. The N/L ratio was measured at time points: <6 hours precatheterization, <12, 48 and 96 hours post catheterization. Patients who developed AF (n=40, 13.8%), had higher post catheterization N/L ratios at 48 hours (median 5.23 vs. 3.00, p=0.05) and 96 hours (median 4.67 vs. 3.56, p=0.03), with no differences in the immediate pre and post procedural measurements, <6 hours pre catheterization (median 2.49 vs. 2.82, p=0.467) and <12 hours post catheterization (median 5.93 vs. 5.03, p=0.741) respectively. CONCLUSION: In conclusion, these findings support an inflammatory aetiology contributing to new onset AF following percutaneous coronary intervention for acute STEMI. Further studies are warranted to elucidate these findings.

Job satisfaction among doctors, a multi-faceted subject studied at a tertiary care hospital in Lahore.

OBJECTIVE: To study the level of job satisfaction among doctors serving in a tertiary care hospital in Lahore and ascertain its co-relation with multiple demographic variables which had a profound impact. METHODS: This cross sectional study with non-probability purposive sampling was conducted at Combined Military Hospital, Lahore, from February 2014 to November 2014. Subjects were doctors serving in that hospital for minimum six months duration. Pre-formed questionnaires were distributed to volunteers (average filling time was 3 ½ to 7 minutes). Multiple demographic features were independent variables. Outcome variable was job satisfaction. Statistical analysis was done via descriptive statistics (SPSS 20), data expressed as mean ± standard deviation (SD). RESULTS: Out of 263 doctors serving in hospital, 203 (77.91%) volunteered to participate; response rate by depositing the filled forms was 47.78% (97 doctors). Amongst the respondents, 10 (10.3%) doctors had below average job satisfaction, 32(33.0%), 21(21.6%), 21(21.6%) and 13(13.3%) had average, above average, well above average and outstanding job satisfaction respectively. There was significant relation between job satisfaction and age group of the doctors (p 0.025), education (p 0.015), service years (p 0.013) income per month (p<0.001). There was no significant impact of gender (p 0.540), marital status (p 0.087), number of children (p 0.153), current employment (p 0.71), nature of job (p 0.204), working hours (p 0.089), additional duties p 0.421) and socioeconomic class (p 0.104) on outcome variable. CONCLUSION: A significant number of doctors was found discontented with their job, which may consequently impact their yield/performance. The job satisfaction can be substantially improved if these contributory factors are aptly addressed at all tiers.

Bandwidth enhancement of a dual band planar monopole antenna using meandered microstrip feeding.

A meandered-microstrip fed circular shaped monopole antenna loaded with vertical slots on a high dielectric material substrate (ε r = 15) is proposed in this paper. The performance criteria of the proposed antenna have been experimentally verified by fabricating a printed prototype. The experimental results show that the proposed antenna has achieved wider bandwidth with satisfactory gain by introducing meandered-microstrip feeding in assistant of partial ground plane. It is observed that, the -10 dB impedance bandwidth of the proposed antenna at lower band is 44.4% (600 MHz-1 GHz) and at upper band is 28% (2.25 GHz-2.95 GHz). The measured maximum gains of -1.18 dBi and 4.87 dBi with maximum radiation efficiencies have been observed at lower band and upper band, respectively. The antenna configuration and parametric study have been carried out with the help of commercially available computer-aided EM simulator, and a good accordance is perceived in between the simulated and measured results. The analysis of performance criteria and almost consistent radiation pattern make the proposed antenna a suitable candidate for UHF RFID, WiMAX, and WLAN applications.

Low-cost dielectric substrate for designing low profile multiband monopole microstrip antenna.

This paper proposes a small sized, low-cost multiband monopole antenna which can cover the WiMAX bands and C-band. The proposed antenna of 20 × 20 mm(2) radiating patch is printed on cost effective 1.6 mm thick fiberglass polymer resin dielectric material substrate and fed by 4 mm long microstrip line. The finite element method based, full wave electromagnetic simulator HFSS is efficiently utilized for designing and analyzing the proposed antenna and the antenna parameters are measured in a standard far-field anechoic chamber. The experimental results show that the prototype of the antenna has achieved operating bandwidths (voltage stand wave ratio (VSWR) less than 2) 360 MHz (2.53-2.89 GHz) and 440 MHz (3.47-3.91 GHz) for WiMAX and 1550 MHz (6.28-7.83 GHz) for C-band. The simulated and measured results for VSWR, radiation patterns, and gain are well matched. Nearly omnidirectional radiation patterns are achieved and the peak gains are of 3.62 dBi, 3.67 dBi, and 5.7 dBi at 2.66 GHz, 3.65 GHz, and 6.58 GHz, respectively.

Compact double-p slotted inset-fed microstrip patch antenna on high dielectric substrate.

This paper presents a compact sized inset-fed rectangular microstrip patch antenna embedded with double-P slots. The proposed antenna has been designed and fabricated on ceramic-PTFE composite material substrate of high dielectric constant value. The measurement results from the fabricated prototype of the antenna show -10 dB reflection coefficient bandwidths of 200 MHz and 300 MHz with center resonant frequency of 1.5 GHz and 4 GHz, respectively. The fabricated antenna has attained gains of 3.52 dBi with 81% radiation efficiency and 5.72 dBi with 87% radiation efficiency for lower band and upper band, respectively. The measured E- and H-plane radiation patterns are also presented for better understanding. Good agreement between the simulation and measurement results and consistent radiation patterns make the proposed antenna suitable for GPS and C-band applications.

Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology.

The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts' groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems.

A comprehensive study on unraveling the advances of immersive technologies (VR/AR/MR/XR) in the healthcare sector during the COVID-19: Challenges and solutions.

The current COVID-19 pandemic has affected almost every aspect of life but its impact on the healthcare landscape is conspicuously adverse. However, digital technologies played a significant contribution in coping with the challenges spawned by this pandemic. In this list of applied digital technologies, the role of immersive technologies in battling COVID-19 is notice-worthy. Immersive technologies consisting of virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), metaverse, gamification, etc. have shown enormous market growth within the healthcare system, particularly with the emergence of pandemics. These technologies supplemented interactivity, immersive experience, 3D modeling, touching sensory elements, simulation, and feedback mechanisms to tackle the COVID-19 disease in healthcare systems. Keeping in view the applicability and significance of immersive technological advancement, the major aim of this study is to identify and highlight the role of immersive technologies concerning handling COVID-19 in the healthcare setup. The contribution of immersive technologies in the healthcare domain for the different purposes such as medical education, medical training, proctoring, online surgeries, stress management, social distancing, physical fitness, drug manufacturing and designing, and cognitive rehabilitation is highlighted. A comprehensive and in-depth analysis of the collected studies has been performed to understand the current research work and future research directions. A state-of-the-artwork is presented to identify and discuss the various issues involving the adoption of immersive technologies in the healthcare area. Furthermore, the solutions to these emerging challenges and issues have been provided based on an extensive literature study. The results of this study show that immersive technologies have the considerable potential to provide massive support to stakeholders in the healthcare system during current COVID-19 situation and future pandemics.

A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis.

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

Designing novel multiepitope mRNA vaccine targeting Hendra virus (HeV): An integrative approach utilizing immunoinformatics, reverse vaccinology, and molecular dynamics simulation.

Human and animal health is threatened by Hendra virus (HeV), which has few treatments. This in-silico vaccine design study focuses on HeV G (glycoprotein), F (fusion protein), and M (matrix protein). These proteins were computationally assessed for B and T-cell epitopes after considering HeV strain conservation, immunogenicity, and antigenicity. To improve vaccination immunogenicity, these epitopes were selectively ligated into a multiepitope construct. To improve vaccination longevity and immunological response, adjuvants and linkers were ligated. G, F, and M epitopes were used to create an mRNA HeV vaccine. Cytotoxic, helper, and linear B-lymphocytes' epitopes are targeted by this vaccine. The population coverage analysis demonstrates that multi-epitope vaccination covers 91.81 percent of CTL and 98.55 percent of HTL epitopes worldwide. GRAVY evaluated the vaccine's well-characterized physicochemical properties -0.503, indicating solubility and functional stability. Structure analysis showed well-stabilized 2° and 3° structures in the vaccine, with alpha helix, beta sheet, and coil structures (Ramachandran score of 88.5% and Z score of -3.44). There was a strong affinity as shown by docking tests with TLR-4 (central score of -1139.4 KJ/mol) and TLR-2 (center score of -1277.9 KJ/mol). The coupled V-apo, V-TLR2, and V-TLR4 complexes were tested for binding using molecular dynamics simulation where extremely stable complexes were found. The predicted mRNA structures provided significant stability. Codon optimization for Escherichia. coli synthesis allowed the vaccine to attain a GC content of 46.83% and a CAI score of 1.0, which supports its significant expression. Immunological simulations indicated vaccine-induced innate and adaptive immune reactions. Finally, this potential HeV vaccine needs more studies to prove its efficacy and safety.

Finite Element Approach for Rheological Behavior in Colloidal Electrolytes in Lithium-Ion Battery Performance.

The main implication of articulating electrolyte performance is studying the energy density, charging aspects, formation of precipitates, thermal fluctuations during charging-discharging, and safety of batteries against fire or spark. One of the most significant aspects is the ability to design colloidal electrolytes that can enhance the overall performance of batteries along with dealing with all internal problems within a battery system. Through this optimization progression, the general performance and efficiency of Li-ion batteries can be improved. This work is presented in the study of the boundary value problem for rheological properties of colloidal electrolytes as a fourth grade fluid for lithium ion (Li-ion) batteries down a vertical cylinder. They have exceptional characteristics, such as low volatility and high thermal stability. The practical usage of the exact flow is restricted, as it involves very complicated integrals. The nonlinear problem that arises is solved by Galerkin's finite element approach based on the weighted-residual formulation, which is used to find the approximate solutions of the fourth-grade problem. This approach utilizes a piecewise linear approximation using linear Lagrange polynomials. Convergence of the solutions, which briefly describes the flow characteristics, includes the effects of the emerging parameters. The results obtained after implementation are not restrictive to small values of the flow parameters. Numerical studies have shown the superior accuracy and lesser computational cost of this scheme in comparison to collocation, the homotopy analysis method, and the homotopy perturbation method. The impact of the relevant parameters is examined through graphical results after implementation of a number of iterations.