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Polymer nanocomposites are emerging hybrid materials for the production of energy storage electrodes, biomedical sensors, and building construction materials. However, experimentation cost and time can be unfavorable to their performance investigation. Therefore, using a modeling approach to predict the electrical conductivity of polymer nanocomposite is an effective approach in mitigating experimentation cost and time. Since the polymer nanocomposites’ electrical conductivity depends on several factors, the engagement of efficient analytical models for predicting their properties, cannot be overemphasized. Herein, this study developed a series-parallel model, which incorporates the connection between the polymer and the nanofillers for the prediction of the electrical conductivity of graphene-polypyrrole (Gr-PPy) and reduced graphene oxide/polyvinyl alcohol/polypyrrole (RGO/PVA/PPy) nanocomposites. In addition to explicit modelling, an artificial intelligence approach (neural network) was also explored for the prediction tasks. The results of the models in an entity and when compared to an existing model, show flexibility and accuracy for the polymer nanocomposites electrical conductivity prediction. It can be inferred that the model can be suitable to predict the electrical conductivity of polymer nanocomposites.

The article aims to determine the relationship between the Indonesian state and the Islamic economy related to the influence of the Indonesian government's Islamic political economy in the development of sharia economic practices in society, especially in the reform era. The research method used in this study is a qualitative approach with the type of library research. The data sources used are secondary data sources, obtained from the collection of documentation related to the development of Islamic economic politics in Islamic banking in the reform era in Indonesia. The data that has been collected is analyzed through content analysis and the validity of the data is checked through triangulation of secondary data sources. This article uses the political theory of Islamic economics in the reform era and the theory of the development of sharia banking regulations by Dr. Itang, M.Ag. This article finds that the influence of the State of Indonesia with its political economy oriented to the Islamic economic system in developing Islamic economic practices is very significant. This also shows that there is a rapid progress in the practice of Islamic economics, especially in the field of Islamic banking, which is increasingly attracting the whole community to invest in Islamic banking because the formal regulations are very clearly stated in the Act. In addition, the development of Islamic political economy has also greatly influenced the development of other non-banking Islamic financial institutions, such as sharia insurance, sharia capital markets, sharia bonds, sharia mutual funds, and Islamic microfinance institutions, and Islamic public finance after the Indonesian state supported them in many things, especially through formal regulation.

Abstract Background Heart failure with preserved ejection fraction (HFpEF)is challenging. Patients usually have normal LV size and ejection fraction. This clinical syndrome develops from a complex interaction of several risk factors that cause organ dysfunction and clinical symptoms. There’s evidence that testosterone deficiency is associated with a worse cardiometabolic profile and increased inflammatory markers. We thought that these changes might have an impact on heart failure pathogenesis. We aimed to study the relationship between testosterone level and symptoms in HFpEF. Methods We studied 120 male patients with HFpEF. According to New York Heart Association (NYHA), patients were classified into I, II and III classes; class IV patients were excluded. All patients were subjected to clinical and echocardiographic examinations. In addition, we measured serum testosterone, cardio-metabolic profile, intracellular adhesive molecule-1(ICAM-1), P-selectin and nitric oxide (NO) levels. Results Patients with testosterone deficiency had worse NYHA class and higher BNP P = (0.001). Additionally, they had a significantly worse metabolic profile; higher total cholesterol, triglycerides, LDL cholesterol, fasting insulin and HOMA-IR P = (0.005, 0.001, 0.001, 0.001), respectively. Also, they had higher inflammatory markers and worse endothelial functional parameters; (ICAM-1, NO and P- selectin) P = (0.001). Age, BNP and testosterone deficiency can be used as independent predictors of NYHA class III symptoms with a Testosterone cutoff value of 2.7 ng/ml. Conclusion Testosterone deficiency could be used as an independent predictor of symptom severity in HFpEF, and it aggravates systemic inflammation and endothelial dysfunction in these patients.

Objective To investigate the renoprotective, the antioxidant, and the anti-inflammatory impact of a combination of SPL and ZnO-NPs to combat against chronic kidney disease (CKD).Methods In total, 50 males of rats were distributed into 5 groups (10 rats each); normal group, adenine sulfate (0.25% in diet for 10 days) (CKD) group. After the last dose of adenine sulfate, rats were divided into three groups: SPL + Adenine sulfate group; rats were treated orally by mixing SPL (20 mg/kg/day) into chow for 8 weeks, ZnO-NPs + Adenine sulfate group; rats were injected intraperitoneally with ZnO-NPs (5 mg/kg) three times weekly for 8 weeks, ZnO-NPs + SPL + Adenine sulfate group; rats were injected with the same previous doses for 8 weeks.Results Each of SPL and ZnO-NPs up-regulated antioxidant genes (Nrf2 and HO-1), down-regulated fibrotic and inflammatory genes (TGF-β1, Wnt7a, β-catenin, fibronectin, collagen IV, α-SMA, TNF-α, and IL-6) compared to CKD. Furthermore, a combination of SPL and ZnO-NPs resulted in a greater improvement in the measured parameters than a single treatment.Conclusion The therapeutic role of SPL was enhanced by the antioxidant and the anti-inflammatory role of ZnO-NPs, which presented a great renoprotective effect against CKD.

Abstract Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

Abstract Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image‐based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego‐image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego‐image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state‐of‐the‐art schemes.

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks.

Nowadays, the demand for healthcare to transform from traditional hospital and disease-centered services to smart healthcare and patient-centered services, including the health management, biomedical diagnosis, and remote monitoring of patients with chronic diseases, is growing tremendously [...]

Dextrocardia is a rare anatomical anomaly in which the heart is located in the patient’s right hemithorax with its apex directed to the right. Although it usually does not pose any serious health risks, patients with undiagnosed dextrocardia present a diagnostic challenge especially in those presenting with chest pain. Traditional left-sided electrocardiograms (ECG) inadequately capture the electrical activity of a heart positioned in the right hemithorax, which if unnoticed could delay or even miss an acute coronary syndrome diagnosis. Here, we present a case of a patient with dextrocardia presenting with chest pain and diagnosed with ST-elevation myocardial infarction using a right-sided ECG.

Abstract Background and objective More than five million individuals died because of problems connected to COVID-19. SARS-Cov-2 poses a particular challenge to expectant mothers, who comprise one of the most vulnerable segments of the population. Our aim is to demonstrate the maternal and neonatal safety of the COVID-19 vaccine during pregnancy. Methods We searched PubMed, Cochrane Library, Scopus, Web of Science (WOS), Embase, Ovid, MedRxiv, and BioRxiv databases from inception till December 2021 and then updated it in April 2022. Additionally, we searched ClinicalTrials.gov, Research Square and grey literature. Cohort, case–control studies, and randomized controlled trials detecting the safety of the Covid-19 vaccine during pregnancy were included. We used the Cochrane tool and Newcastle–Ottawa Scale to assess the risk of bias of the included studies and the GRADE scale to assess the quality of evidence. A meta-analysis was conducted using review manager 5.4. Results We included 13 studies with a total number of 56,428 patients. Our analysis showed no statistically significant difference in the following outcomes: miscarriage (1.56% vs 0.3%. RR 1.23; 95%CI 0.54 to 2.78); length of maternal hospitalization (MD 0.00; 95%CI -0.08 to 0.08); puerperal fever (1.71% vs 1.1%. RR 1.04; 95%CI 0.67 to 1.61); postpartum hemorrhage (4.27% vs 3.52%. RR 0.84; 95%CI 0.65 to 1.09); instrumental or vacuum-assisted delivery (4.16% vs 4.54%. RR 0.94; 95%CI 0.57 to 1.56); incidence of Apgar score ≤ 7 at 5 min (1.47% vs 1.48%. RR 0.86; 95%CI 0.54 to 1.37); and birthweight (MD -7.14; 95%CI -34.26 to 19.99). Conclusion In pregnancy, the current meta-analysis shows no effect of SAR-CoV-2 vaccination on the risk of miscarriage, length of stay in the hospital, puerperal fever, postpartum hemorrhage, birth weight, or the incidence of an Apgar score of ≤ 7 at 5 min.

Abstract A magneto couple stress nanofluid flow along with double diffusive convection is presented for peristaltic induce flow through symmetric nonuniform channel. A comprehensive mathematical model is scrutinized for couple stress nanofluid magneto nanofluids and corresponding equations of motions are tackled by applying small Reynolds and long wavelength approximation in viewing the scenario of the biological flow. Computational solution is exhibited with the help of graphical illustration for nanoparticle volume fraction, solutal concentration and temperature profiles in MATHEMTICA software. Stream function is also computed numerically by utilizing the analytical expression for nanoparticle volume fraction, solutal concentration and temperature profiles. Whereas pressure gradient profiles are investigated analytically. Impact of various crucial flow parameter on the pressure gradient, pressure rise per wavelength, nanoparticle volume fraction, solutal concentration, temperature and the velocity distribution are exhibited graphically. It has been deduced that temperature profile is significantly rise with Brownian motion, thermophoresis, Dufour effect, also it is revealed that velocity distribution really effected with strong magnetic field and with increasing non-uniformity of the micro channel. The information of current investigation will be instrumental in the development of smart magneto-peristaltic pumps in certain thermal and drug delivery phenomenon.

Up to 30% of ischemic stroke cases are due to large vessel occlusion (LVO), causing significant morbidity. Studies have shown that the collateral circulation of patients with acute ischemic stroke (AIS) secondary to LVO can predict their clinical and radiological outcomes. The aim of this study is to identify baseline patient characteristics that can help predict the collateral status of these patients for improved triage. In this IRB approved retrospective study, consecutive patients presenting with AIS secondary to anterior circulation LVO were identified between September 2019 and August 2021. The baseline patient characteristics, laboratory values, imaging features and outcomes were collected using a manual chart review. From the 181 consecutive patients initially reviewed, 54 were confirmed with a clinical diagnosis of AIS and anterior circulation LVO. In patients with poor collateral status, the body mass index (BMI) was found to be significantly lower compared to those with good collateral status (26.4 ± 5.6 vs. 31.7 ± 12.3; p = 0.045). BMI of >35 kg/m2 was found to predict the presence of good collateral status. Age was found to be significantly higher (70.5 ± 9.6 vs. 58.9 ± 15.6; p = 0.034) in patients with poor collateral status and M1 strokes associated with older age and BMI.

Background: Cardiopulmonary resuscitation (CPR) skills are simple and can be mastered easily. Knowledge about these interventions and their potential benefits can aid resuscitative efforts and improve survival. These facts mandate that all healthcare providers, irrespective of their specialty, level of training, or work setting, be competent in initiating and performing CPR. Aim: To assess the level of knowledge of healthcare providers about which CPR modalities, interventions, and medications for nontraumatic cardiac arrest have been proven beneficial in terms of return of spontaneous circulation, intact neurological function, and mortality. Methods: This was a cross-sectional study. A standardized electronic questionnaire was formed based on a literature review to assess the knowledge of healthcare providers working in Saudi Arabia about CPR interventions and medications. Responses of the participants were collected via Google Forms and SurveyMonkey. The data were analyzed in Statistical Package for the Social Science V27 to answer the questions of interest. Results: Only 16% of the participants had good knowledge (score >80%) of which CPR modalities have benefit. Physicians ranked the highest, followed by technicians, while nurses and emergency medical services professionals ranked the lowest among the study participants. Being certified in life support courses was not associated with a higher level of knowledge. Conclusion: The overall level of knowledge of healthcare providers of different CPR modalities was average (score = 60%-80%) which is below expectation. Efforts should be made to improve this knowledge among healthcare providers to understand which modalities benefit patients in cardiopulmonary arrest better. [SJEMed 2022; 3(1.000): 063-074]

Abstract Ethylene glycol is commonly used as a cooling agent in the engine, therefore the study associated with EG has great importance in engineering and mechanical fields. The hybrid nanofluid has been synthesized by adding copper and graphene nanoparticles into the Ethylene glycol, which obeys the power-law rheological model and exhibits shear rate-dependent viscosity. As a result of these features, the power-law model is utilized in conjunction with thermophysical characteristics and basic rules of heat transport in the fluid to simulate the physical situations under consideration. The Darcy Forchhemier hybrid nanofluid flow has been studied under the influence of heat source and magnetic field over a two-dimensionally stretchable moving permeable surface. The phenomena are characterized as a nonlinear system of PDEs. Using resemblance replacement, the modeled equations are simplified to a nondimensional set of ODEs. The Parametric Continuation Method has been used to simulate the resulting sets of nonlinear differential equations. Figures and tables depict the effects of physical constraints on energy, velocity and concentration profiles. It has been noted that the dispersion of copper and graphene nanoparticulate to the base fluid ethylene glycol significantly improves velocity and heat conduction rate over a stretching surface.

BackgroundDyslipidemia is common among patients with heart failure, and it negatively impacts clinical outcomes. Limited data regarding the factors associated with poor lipid control in patients with HF patients. Therefore, this study aimed to evaluate lipid control and to explore the factors associated with poor lipid control in patients with HF.MethodsThe current cross-sectional study was conducted at outpatient cardiology clinics at two major hospitals in Jordan. Variables including socio-demographics, biomedical variables, in addition to disease and medication characteristics were collected using medical records and custom-designed questionnaire. Medication adherence was assessed using the validated 4-item Medication Adherence Scale. Binary logistic regression analysis was conducted to explore significant and independent predictors of poor lipid control among the study participants.ResultsA total of 428 HF patients participated in the study. Results showed that 78% of the participants had poor lipid control. The predictors that were associated with poor lipid control included uncontrolled BP (OR = 0.552; 95% CI: 0.330–0.923; P