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Background: Diabetes is not uncommon in patients requiring cardiac surgery. These patients have a higher incidence of morbidity and mortality. Subsequently, diabetes represents a major medico-economic problem in both developed and developing countries. This study was designed to observe the association between glycemic control and outcome of patients after open heart surgery in adult population. Materials and Methods: Data was collected retrospectively in all patients who underwent open cardiac surgery (coronary artery bypass grafting, valve, or bypass grafting with valve surgery) and survived 72 hours postoperatively and had diabetes. The study was conducted from January 2015 to December 2016. Results: Of the 129 patients included in the study, male dominated 101 (78.3%). Most frequent surgery was coronary artery bypass grafting (CABG) 123 (95.3%), CABG plus aortic valve replacement 4 (3.1%), and CABG plus mitral valve replacement 2 (1.6%). Considering diabetes, only 3 (2.3%) were on diet control, 112 (86.8%) on oral hypoglycemic agents (OHA), whereas 9 (7%) had control on both insulin and OHA. Only 5 (3.9%) had type I diabetes. The mean fasting blood sugar (FBS) was 154.58 g/dl, and the mean duration of diabetic mellitus was observed 12.32 years. Microvascular and macrovascular complications were 26/129 (20.16%) and 17/129 (13.17%), respectively. Total 75 (58.1%) patients did not require insulin and 54 (41.9%) were treated with insulin intraoperatively to keep the blood glucose level less than 200 g/dl. Cardiac arrhythmias were frequent in the insulin group (P < 0.05), which was also associated with increased stay in the cardiac intensive care unit. Conclusion: Inadequate glycemic control during open cardiac surgery can possibly lead to increased perioperative morbidity and mortality and with decreased long-term survival and recurrent ischemic events. Therefore, aiming for blood glucose levels around 140 mg/dl appears reasonable. Further studies are required to define specific glucose ranges for a clearer definition of recommended blood glucose goals in postoperative cardiac patients for the best outcomes in patients with diabetes mellitus.

Abstract Soil heavy metals (HMs) pollution is a growing global concern, mainly in regions with rapid industrial growth. This study assessed the concentrations, potential sources, and health risks of HMs in agricultural soils near marble processing plants in Malakand, Pakistan. A total of 21 soil samples were analyzed for essential and toxic HMs via inductively coupled plasma‒optical emission spectrometry (ICP‒OES), and probabilistic health risks were evaluated via Monte Carlo simulation. The concentrations (mg/kg) of Ca (29,250), P (805.5) and Cd (4.5) exceeded the average shale limits of 22,100, 700, and 3.0 mg/kg, respectively, and indices such as Nemerow’s synthetic contamination index (NSCI) and the geoaccumulation index (Igeo) categorized the soil sites as moderately polluted. The potential ecological risk index (PERI) indicated considerable to high ecological risk for As and Cd. The deterministic analysis indicated non-carcinogenic risks for children (HI > 1), whereas the probabilistic analysis suggested no significant risk (HI

Abstract Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver condition globally and the leading cause of liver-related death and morbidity. The goal of this study was to collect current data in order to calculate the pooled prevalence of NAFLD in Pakistan. We conducted a comprehensive literature search on four electronic databases until March 2024 to find studies on the prevalence of NAFLD in Pakistan. Pooled prevalence estimates of NAFLD were obtained using random-effects meta-analytic models. The chi-square test was used to account for study heterogeneity, whereas the I2 statistic was used to assess inconsistency. The data were stratified by the general population (average risk) and individuals with metabolic diseases (high risk). Two reviewers thoroughly and independently screened, reviewed, and assessed all studies. In total, 468 studies were reviewed, and 34 were included. The pooled NAFLD prevalence in the general population was 29.82% (95% CI 21.39–39.01%; prediction interval: 2.98–68.92%) based on 13 studies. In individuals with metabolic disorders, the prevalence of NAFLD in patients with diabetes, hypertension, and obesity, was 58.47% (95% CI 54.23–62.64%; prediction interval: 38.16–77.40%), 74.08% (95% CI 60.50–85.70%), and 47.43% (95% CI 30.49–64.66%), respectively. There was no evidence of publication bias, although a statistically significant level of heterogeneity was seen among the studies (I 2 ranged from 57.5 to 98.69%). The findings of this study indicate a substantial prevalence of NAFLD in the population of Pakistan. The Pakistani government must formulate a comprehensive approach and plan aimed at augmenting awareness, control, prevention, and treatment of fatty liver disease. Prospero Registration no: CRD42022356607.

Background: Vigna unguiculata, belonging to the Fabaceae family, commonly known as cowpea is an important edible legume, distributed mainly across the African and Asian countries. Traditionally, the plant has an outstanding background for the management of multiple diseases, animal feeding and human consumption. Objective: This review aims to mainly focus on the traditional applications, pharmacological activities, phytochemistry as well as nutritious composition of the V. unguiculata. Methods: Data present in the literature on the V. unguiculata, were collected from major scientific databases including Science Direct, SpringerLink, Google Scholar, Medline Plus, Web of Science, PubMed and Elsevier. Results: Number of compounds have been isolated including flavonoids, steroids, alkaloids, phenolic compounds, saponins, fatty acids, tannins, carbohydrates, vitamins, amino acids, carotenoids and fibers from various parts of plant. These compounds exhibit widespread pharmacological potentials both in-vitro and in-vivo including anthelmintic, antibacterial, antinociceptive, thrombolytic, antidiabetic, hypocholestrolemic and antiatherogenic effect, antimicrobial, anti-sickling, antioxidant, anti-covid activity, anticancer and neurobehavioral activities. These compounds have strong pharmacological background and might be responsible for the traditional uses of this plant that are not investigated. Conclusion: It is concluded that V. unguiculata possessed strong pharmacological, nutritious and phytochemical potential, therefore, it is strongly recommended for additional comprehensive investigations in order to determine its clinical utility.

Entropy generation(EG) has attracted the attention of scientists and researchers worldwide due to its applications in the industrial and engineering sectors. Applications of entropy generation can be seen in chillers, air conditioners, air separates, air coolers, nuclear reactors, and vehicle engines. In the current investigation, we have analyzed entropy generation in Maxwell hybrid nanofluid(HNF) flow over a porous stretching sheet. Darcy Forchheimer(DF) relation is considered. Due to stretching of sheet flow generates. The thermal expression is reported taking influences of dissipation, radiation, and heat source. The second law of thermodynamics is utilized to obtain EG for considered flow. Boundary layer assumptions are considered in the modeling. Partial differential equations(PDEs) governing the flow are changed into dimensionless ordinary differential equations(ODEs) through transformations. The solution of reduced ODEs is constructed via NDSolve code in Mathematica software. Different influential flow variables impact on velocity, temperature, irreversibility, and Bejan number are discussed graphically. Physical quantities are examined numerically. The results show that velocity diminished for rising porosity and Forchheimer variables. Temperature field upsurges for an upturn in radiation and Eckert numbers, an opposite impact is noticed for higher Prandtl number. EG escalates for greater radiation, temperature, and Brinkman variables.

Entropy generation in nanofluid flows is a critical parameter that influences the performance, efficiency, and sustainability of thermal systems. Understanding and optimizing entropy creation can lead to noteworthy advancements in numerous engineering applications, from renewable energy systems to industrial processes and biomedical equipments. The purpose of this article is to examine the entropy produced in the bioconvection flow of Eyring-Powell nanomaterial with gyrotactic microorganisms. The mathematical equations representing the flow are modeled considering the flow towards a porous surface of a cylinder. Together with the effects of the governing parameters, the mass and heat transfer components of the problem are discussed. The thermal effects of different natures are used in a new way. Overall, entropy creation is designed with regard to the second law of thermodynamics. Activation energy, chemical reaction, thermal radiation, and internal friction force effects are accounted for the development of the mathematical model. Coupled non-linear dimensional equations have been altered into ordinary differential equations (ODEs) and then treated using the MATLAB Finite Difference Method (FDM). Consequence of diverse sundry parameters on entropy production, thermal field, mass concentration profile, Bejan quantity, and motile density of microorganisms is deliberated via plots. Through tables, engineering quantities are analyzed. According to the findings, the velocity profile rises as the curvature and Eyring-Powell fluid parameters rise, while it falls when the magnetic parameter is enhanced. Additionally, it is noted that as the curvature variable enhances, the rate of heat transfer and skin friction coefficient decays.

Introduction: Despite extensive studies of the impact of COVID-19 on patients with cancer, there is a dearth of information from the Middle East and North Africa (MENA) region. Our study aimed to report pertinent MENA COVID-19 and Cancer Registry (MCCR) findingson patient management and outcomes. Methods: MCCR was adapted from the American Society of Clinical Oncology COVID-19 Registry to collect data specifically from patients with cancer and SARS-CoV-2 infection from 12 centers in eight countries including Saudi Arabia, Jordan, Lebanon, Turkey, Egypt, Algeria, United Arab Emirates, and Morocco. The Registry included data on patients and disease characteristics, treatment, and patient outcomes. Logistic regression was used to assess associations with mortality. Results: Between November 29, 2020, and June 8, 2021, data were captured on 2008 patients diagnosed with COVID-19 from the beginning of the pandemic. Median age was 56 years (16–98), 56.4% were females, and 26% were current or ex-smokers. Breast cancer (28.5%) was the leading diagnosis and 50.5% had metastatic disease. Delays of planned treatment (>14 days) occurred in 80.3% for surgery, 48.8% for radiation therapy, and 32.9% for systemic therapy. Significant reduction in the delays of all three treatment modalities occurred after June 1, 2020. All-cause mortality rates at 30 and 90 days were 17.1% and 23.4%, respectively. All-cause mortality rates at 30 days did not change significantly after June 1, 2020; however, 90-day mortality increased from 33.4% to 42.9% before and after that date (p = 0.015). Multivariable regression analysis showed the following predictors of higher 30- and 90-day mortality: age older than 70 years, having metastatic disease, disease progression, and being off chemotherapy. Conclusion: Patients with cancer in the MENA region experienced similar risks and outcome of COVID-19 as reported in other populations. Although there were fewer treatment delays after June 1, 2020, 90-day mortality increased, which may be attributed to other risk factors such as disease progression or new patients who presented with more advanced disease.

Entropy generation (EG) in bioconvective nanofluid flows is a phenomenon that occurs due to the presence of nanoparticles and microorganisms within fluid flows. EG leads to an increase in thermodynamic irreversibility within the system. Understanding and quantifying EG in nanofluid flows is essential to optimize heat transfer processes and design efficient systems. Current investigation aims to analyze the irreversibility of the magnetized bioconvective flow of non-Newtonian micropolar type nanofluid. Flow features are modeled considering flow by a Darcy Forchheimer permeable surface of a stretched sheet. Phenomenon of bioconvection in a micropolar fluid is considered to regulate the solid tiny particles. Magnetic field and permeability effects are accounted in momentum relation. Energy relation is constructed in the presence of Joule heating, internal fluid friction, and thermal radiation. Mass concentration equation is formulated by accounting Arrhenius kinetics and binary chemical reaction. Furthermore, the buoyancy effects of solid tiny particles are considered in thermal and concentration relations. Total entropy of the considered flow is modeled using the second thermodynamics law. System of dimensional equations representing the flow is obtained with the implementation of boundary layer approximations. The acquired system is altered into an ordinary one through transformations and then tackled by numerical scheme Runge-Kutta-Fehlberg method (RKF-45) in the Mathematica package. Behavior of velocity, thermal field, entropy production, mass concentration, motile density, and Bejan number versus sundry variables is investigated through plots. Skin friction, local heat, mass, and density rates are numerically examined. Thermal field upsurges and velocity field decays versus higher Hartmann number. Entropy generation improved versus higher Brinkman number, microorganisms diffusion variable, and temperature difference ratio variable.

Abstract Carbon nanotubes (CNTs) have gained significant attention in the field of fluid dynamics and fluid flows due to their unique properties and the potential to enhance various aspects of fluid transport features. This article explores the behavior of Darcy–Forchheimer flow of Propylene glycol $$\left( {C_{3} H_{8} O_{2} } \right)$$ C 3 H 8 O 2 submerged single wall carbon nanotubes (SWCNT) and multi-wall carbon nanotubes (MWCNT). The flow features are examined over stretched preamble surface of sheet. Energy relation is acquired in manifestation of thermal radiation and Joule heating. Aspects of Arrhenius kinetics and chemical reaction are assimilated in mass transport relation. Furthermore, effects of intermolecular fluid friction is accounted. Flow prevailing mathematical model is acquired by implementing boundary layer assumptions. Transformations procedure is adapted to alter the dimensional model into non-dimensional one and then tackled through Runge–Kutta–Fehlberg method (RKF-45) in Mathematica. Effective consequences of influential flow controlling parameters on fluid velocity, thermal transport and concentration are inspected by plotting. Numerical computations for interesting engineering quantities like skin friction coefficient, mass and heat transfer rates are tabulated and investigated. It is noticed that thermal field boosts versus curvature variable, Eckert and Hartmann numbers. Retardation in mass concentration is noticed via Schmidt number and activation energy variable. Velocity field shows decreasing trend through porosity parameter, Hartmann number and Darcy–Forchheimer variable. Furthermore, it is noticed that magnitude of skin friction coefficient is higher for SWCNT as compared to MWCNT.

The main objective of this research paper is to scrutinize the entropy generation(EG) in the bioconvective flow of micropolar fluid by porous surface of a stretched sheet. Mixed convection effects are considered. Thermal radiation, internal fluid friction, Joule heating, and heat generation aspects are accounted for in the energy relation. Mass concentration relation is developed because of the binary chemical reactions associated with Arrhenius kinetics. The governing equations for the micropolar fluid are developed based on Buongiorno's model with the help of boundary layer restrictions. By invoking the transformation procedure, dimensional model is made dimensionless. The well-known shooting technique RKF-45 in Mathematica software is implemented to solve the transformed equations. Performance of micropolar fluid velocities, thermal field, entropy generation, mass concentration, Bejan number, and motile density versus influential variables are highlighted. Furthermore, the intensity of skin friction, Nusselt quantity, motile density, and Sherwood number is computed and analyzed. It is observed that the rate of EG improves while the Bejan number diminishes versus higher Brinkman number, and diffusion parameter due to gyrotactic microorganisms. Magnitude of heat transfer rate boosts through higher Γ, Qs, Ec, Ha and Rd at an average percentage of 44, 82, 189, 156 respectively.

Abstract Entropy generation is a concept that is primarily associated with thermodynamics and engineering, and it plays a crucial role in understanding and optimizing various processes and systems. Applications of entropy generation can be seen in turbo machinery, reactors, chillers, desert coolers, vehicle engines, air conditioners, heat transfer devices and combustion. Due to industrial applications entropy generation has gained attention of researchers. Owing such applications, current communication aims to model and analyzed the irreversibility in Sutterby nanoliquid flow by stretched cylinder. Momentum equation is reported by considering porosity, Darcy Forchheimer and magnetic field. While in energy equation radiation and Joule heating effects are accounted. Activation energy impact is accounted in the modeling of concentration equation. Thermodynamics second law is utilized for physical description of irreversibility analysis. Through similarity transformations dimensional equations representing flow are transformed to dimensionless ones. Numerical solution for ordinary system is obtained via Runge–Kutta-Fehlberg scheme in Mathematica platform through NDsolve code. Influence of prominent variables on velocity, entropy, temperature, Bejan number and concentration are graphically analyzed. Coefficient of skin friction, gradient of temperature and Sherwood number are numerically analyzed. The obtained results show that velocity field decreases through higher porosity and Forchheimer variables. Velocity and temperature curves shows an opposite trend versus magnetic parameter. A decay in concentration distribution is noticed through larger Schmidt number. Entropy generation amplifies against magnetic parameter and Brinkman number.

This paper proposes a novel approach to effectively manage distributed energy resources (DERs) by facilitating peer-to-peer (P2P) energy trading among smart homes. This system incentivizes peers to trade surplus energy, thereby maximizing social welfare. The proposed day-ahead demand-side management (DSM) technique considers load prioritization based on individual user needs. It employs an optimal energy allocation policy that encourages peers to purchase energy at reduced rates while selling it at advantageous prices. This strategy optimizes the allocation of traded energy to higher-priority loads, followed by lower-priority ones, all while considering user preferences. The proposed method results in an average reduction in energy costs from 14.80 % to 2.70 % for all consumers and an average increase in revenues from 17.35 % to 9.07 % for all prosumers compared to the grid and previously proposed studies. Moreover, the proposed approach significantly enhances the consumer satisfaction Index (SI), with a 46.8 % increase over the previous research study. Simulation results demonstrate substantial economic benefits for all market participants, improved SI, enhanced community self-sufficiency, and reduced dependence on the main grid. The proposed methodology offers a practical and fair solution for managing DERs through P2P energy trading by implementing load prioritization within smart homes. It highlights the potential to establish a sustainable and efficient energy ecosystem, thereby enhancing the effective utilization of surplus energy from DERs.

This paper is about the effectiveness of the Cobb-Douglas (C-D) production function in industrial optimization, estimating the number of factors used in the production process of the water industry, for instance, capital and human labor. Moreover, we have modeled a nonlinear optimization problem for a local water industry using two and three factors of production. For this purpose, we have taken into account the Cobb-Douglas production function with different production factors using the Lagrange multiplier method with the ordinary least squares method. In the course of the solution, a linear function is used to calculate the cost function, and the C-D production function is used to calculate the production function. The Lagrange multiplier method with the ordinary least squares method is then used to solve the constrained optimization problem for the product of production. Furthermore, we compared the outcomes from both examples of two- and three-factor C-D production functions in order to validate the Lagrange multiplier method for the C-D production function. Moreover, the three-factor C-D production function is solved by the Lagrange multiplier method with the ordinary least squares method, which provides optimal results as compared to previous studies in literature. The validity of the proposed methodology is explained by using the products of a local production industry in Pakistan.

The purpose of this technical note is to investigate the applicability of the Principle of Maximum Entropy in hydrodynamics, particularly in velocity distribution in open channels, by comparing the predictions of two 2-D models with measured data. Both models use a curvilinear coordinate system, but whereas the first uses logarithmic law for velocity distribution, the second is derived by the principle of maximum entropy. The computed values are then compared to 1,832 point velocity measurements by means of laser-Doppler velocimetry in five experiments with rectangular smooth-wall flumes. The results show that the entropy model performs better than the logarithmic model not only in general terms (mean absolute error 12.8% versus 19.1%), but in practically all flow regions, especially in those near the channel bed (absolute error 23.8% versus 55.3%). Because the models disregard secondary flow, velocity predictions in the upper region of near-side walls are relatively poor.

A fundamental aspect of network analysis involves pinpointing nodes that hold significant positions within the network. Graph theory has emerged as a powerful mathematical tool for this purpose, and there exist numerous graph-theoretic parameters for analyzing the stability of the system. Within this framework, various graph-theoretic parameters contribute to network analysis. One such parameter used in network analysis is the so-called closeness, which serves as a structural measure to assess the efficiency of a node’s ability to interact with other nodes in the network. Mathematically, it measures the reciprocal of the sum of the shortest distances from a node to all other nodes in the network. A bipartite network is a particular type of network in which the nodes can be divided into two disjoint sets such that no two nodes within the same set are adjacent. This paper mainly studies the problem of determining the network that maximize the closeness within bipartite networks. To be more specific, we identify those networks that maximize the closeness over bipartite networks with a fixed number of nodes and one of the fixed parameters: connectivity, dissociation number, cut edges, and diameter.

Abstract The most frequent complication of diabetes is peripheral neuropathy. The estimated prevalence of peripheral neuropathy in people with diabetes varies substantially between published studies in Pakistan. We conducted this meta-analysis to summarize the prevalence of peripheral neuropathy in people with diabetes. Different electronic databases were systematically searched using keywords and MeSH terms. Random-effects meta-analysis was conducted to pool the prevalence of peripheral neuropathy in people with diabetes in Pakistan. Heterogeneity was investigated by random-effects meta-regression and stratification. Two independent authors reviewed studies, extracted data, and conducted the risk of bias analysis. Nineteen studies with a total of 8487 diabetic patients were included. The overall pooled prevalence of diabetic peripheral neuropathy was 43.16% (95% CI 32.93–53.69%), with significant heterogeneity between estimates. The prevalence of peripheral neuropathy among those newly diagnosed with diabetes was 26.52% (95% CI 14.97–39.96%, n = 5). According to the subgroup meta-analysis, the pooled prevalence of diabetic peripheral neuropathy was highest in Khyber Pakhtunkhwa (55.29%; 95% CI 23.91–84.50%), followed by Sindh (40.04%; 95% CI 24.00–57.25%), and the lowest was found in Punjab (34.90%; 95% CI 15.05–57.95%). A significant association was found between the pooled prevalence estimate and the duration of diabetes. The results of this meta-analysis indicate a relatively high prevalence of peripheral neuropathy in people with diabetes in Pakistan. The study protocol has been registered in the PROSPERO, with the registration number CRD42022371617.

Allen Cahn (AC) equation is highly nonlinear due to the presence of cubic term and also very stiff; therefore, it is not easy to find its exact analytical solution in the closed form. In the present work, an approximate analytical solution of the AC equation has been investigated. Here, we used the variational iteration method (VIM) to find approximate analytical solution for AC equation. The obtained results are compared with the hyperbolic function solution and traveling wave solution. Results are also compared with the numerical solution obtained by using the finite difference method (FDM). Absolute error analysis tables are used to validate the series solution. A convergent series solution obtained by VIM is found to be in a good agreement with the analytical and numerical solutions.

In nonstructural infill panels, common materials like expanded polystyrene panels face fire susceptibility, autoclaved aerated concrete (AAC) incurs high production costs, and traditional bricks come with a significant carbon footprint and weight. So, there is a requirement for infill panels that are not just resilient and lightweight but sustainable as well. This study seeks to address these issues by introducing sustainable and lightweight expanded clay aggregate (ECA) in concrete. Firstly, eight ECA mix designs were prepared by integrating fly ash and kerosene with clay, and ECA with a bulk density of 0.59 g/cm³ and compressive strength of up to 1.73 MPa were prepared. The lightest ECA mix was then chosen to explore their use in lightweight aggregate concrete (LWAC) along with fly ash as a secondary cementitious material. The resulting LWAC had a minimum density of 1,050 kg/m³ and a compressive strength of 6.8 MPa, fulfilling the standard requirements of a minimum of 3.5 MPa for nonstructural concrete.

ABSTRACTHyperspectral unmixing (HU) is considered one of the most important ways to improve hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral signatures, often commonly referred to as endmembers, and determine the fractional abundance of those endmembers. Deep learning (DL) approaches have recently received great attention regarding HU. In particular, convolutional neural networks (CNNs)-based methods have performed exceptionally well in such tasks. However, the ability of CNNs to learn deep semantic features is limited, and computing cost increase dramatically with the number of layers. The appearance of the transformer addresses these issues by effectively representing high-level semantic features well. In this article, we present a novel approach for HU that utilizes a deep convolutional transformer network. Firstly, the CNN-based autoencoder (AE) is used to extract low-level features from the input image. Secondly, the concept of tokenizer is applied for feature transformation. Thirdly, the transformer module is used to capture the deep semantic features derived from the tokenizer. Finally, a convolutional decoder is utilized to reconstruct the input image. The experimental results on synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method compared with other unmixing methods.

Spatial distribution of water discharge can be obtained through a manifold consisting of main pipes and perforated laterals. This study formulates the problem of discharge distribution among lateral pipes and from laterals through orifices uniformly spaced along them. A nonlinear difference equation for flow in the pipes is solved numerically to determine the driving head as a function of the number of ports and a geometrical ratio. A dimensionless solution for simple manifolds is developed to obtain a direct method of evaluating discharges through all the orifices. The approach presented in this paper makes it possible to formulate the problem of restricting the maximum outlet-discharge variation through a dividing-flow manifold. A graphical solution to this problem is obtained that permits one to indicate the lateral-to-manifold diameter ratio for limiting this variation to a prescribed value. The results of this paper offer a means for comparing the uniformity of distribution expected from various combinations of the number of orifices and laterals and their sizes for a given manifold.

Background: Prostate cancer is a significant public health issue, ranking as the second most common cancer and the fifth leading cause of cancer-related deaths in men. In Pakistan, the prevalence of prostate cancer varies significantly across published articles. This study aimed to determine the pooled prevalence of prostate cancer and its associated risk factors in Pakistan. Methods: MEDLINE (via PubMed), Web of Science, Google Scholar, and local databases were searched from inception until March 2023, using key search terms related to the prevalence of prostate cancer. We considered a random-effects meta-analysis to derive the pooled prevalence and relative risks with 95% CIs. Two investigators independently screened articles and performed data extraction and risk of bias analysis. We also conducted meta-regression analysis and stratification to investigate heterogeneity. This study protocol was registered at PROSPERO, number CRD42022376061. Results: Our meta-analysis incorporated 11 articles with a total sample size of 184,384. The overall pooled prevalence of prostate cancer was 5.20% (95% CI: 3.72–6.90%), with substantial heterogeneity among estimates (I2 = 98.5%). The 95% prediction interval of prostate cancer was ranged from 1.74%–10.35%. Subgroup meta-analysis revealed that the highest pooled prevalence of prostate cancer was in Khyber Pakhtunkhwa (8.29%; 95% CI: 6.13–10.74%, n = 1), followed by Punjab (8.09%; 95% CI:7.36–8.86%, n = 3), while the lowest was found in Sindh (3.30%; 95% CI: 2.37–4.38%, n = 5). From 2000 to 2010 to 2011–2023, the prevalence of prostate cancer increased significantly from 3.88% (95% CI: 2.72–5.23%) to 5.80% (95% CI: 3.76–8.24%). Conclusions: Our meta-analysis provides essential insights into the prevalence of prostate cancer in Pakistan, highlighting the need for continued research and interventions to address this pressing health issue.

In this paper, we provide a solution to the Cahn–Hilliard equation using the q-homotopy analysis method (q-HAM). The q-HAM is a more general, simple and widely used method for solving stiff nonlinear partial differential equations. The Cahn–Hilliard equation is a classical model in material sciences that describe spinodal decomposition and phase separation in two-phase flows. Using the q-HAM, the effect of various parameters of physical interest such as diffusive parameter, thickness parameter, advection and reaction terms on concentration is studied. The comparison of the computed solution with the exact solution is presented for some fixed parameter values to validate the solution obtained using the q-HAM.

In this communication irreversibility minimization in bio convective Walter's-B nanofluid flow by stretching sheet is studied. Suspended nanoparticles in Walter's-B fluid are stabilized by utilizing microorganisms. Total irreversibility is obtained via thermodynamics second law. The influences of applied magnetic field, radiation, Joule heating and activation energy are accounted in momentum, temperature and concentration equations. Furthermore thermophoresis and Brownian movement impacts are also accounted in concentration and temperature expressions. The flow governing dimensional equations are altered into dimensionless ones adopting transformation procedure. Homotopy Analysis Method (HAM) code in Mathematica is implemented to get the convergent series solution. The influences of important flow variables on temperature, velocity, motile density, irreversibility, mass concentration, Bejan number and physical quantities are analyzed graphically. The obtained results revel that the velocity profile decreases for escalating magnetic parameter and Forchheimer number. Entropy generation is increased for higher Brinkman variable while Bejan number declines versus Brinkman variable. The important observations are given at the end.

ABSTRACT Phazolicin (PHZ) is a peptide antibiotic exhibiting narrow-spectrum activity against rhizobia closely related to its producer, Rhizobium sp. strain Pop5. Here, we show that the frequency of spontaneous PHZ-resistant mutants in Sinorhizobium meliloti is below the detection limit. We find that PHZ can enter S. meliloti cells through two distinct promiscuous peptide transporters, BacA and YejABEF, which belong to the SLiPT (SbmA-like peptide transporter) and ABC (ATP-binding cassette) transporter families, respectively. The dual-uptake mode explains the lack of observed resistance acquisition because the simultaneous inactivation of both transporters is necessary for resistance to PHZ. Since both BacA and YejABEF are essential for the development of functional symbiosis of S. meliloti with leguminous plants, the unlikely acquisition of PHZ resistance via the inactivation of these transporters is further disfavored. A whole-genome transposon sequencing screen did not reveal additional genes that can provide strong PHZ resistance when inactivated. However, it was found that the capsular polysaccharide KPS, the novel putative envelope polysaccharide PPP (PHZ-protecting polysaccharide), as well as the peptidoglycan layer jointly contribute to the sensitivity of S. meliloti to PHZ, most likely serving as barriers that reduce the amount of PHZ transported inside the cell. IMPORTANCE Many bacteria produce antimicrobial peptides to eliminate competitors and create an exclusive niche. These peptides act either by membrane disruption or by inhibiting essential intracellular processes. The Achilles’ heel of the latter type of antimicrobials is their dependence on transporters to enter susceptible cells. Transporter inactivation results in resistance. Here, we show that a rhizobial ribosome-targeting peptide, phazolicin (PHZ), uses two different transporters, BacA and YejABEF, to enter the cells of a symbiotic bacterium, Sinorhizobium meliloti. This dual-entry mode dramatically reduces the probability of the appearance of PHZ-resistant mutants. Since these transporters are also crucial for S. meliloti symbiotic associations with host plants, their inactivation in natural settings is strongly disfavored, making PHZ an attractive lead for the development of biocontrol agents for agriculture.

Malaria is one of the major public health issues globally. Malaria infection spreads through mosquito bites from infected female Anopheles mosquitoes. This study aims to conduct a systematic review and meta-analysis on malaria prevalence in Pakistan from 2006 to 2021. We searched PubMed, Science Direct, EMBASE, EMCare, and Google Scholar to acquire data on the prevalence of malaria infections. We performed a meta-analysis with a random-effects model to obtain the pooled prevalence of malaria, Plasmodium vivax, and Plasmodium falciparum. Meta-analysis was computed using R 4.1.2 Version statistical software. I2 and time series analysis were performed to identify a possible source of heterogeneity across studies. A funnel plot and the Freeman-Tukey Double Arcsine Transformed Proportion were used to evaluate the presence of publication bias. Out of the 315 studies collected, only 45 full-text articles were screened and included in the final measurable meta-analysis. Pooled malaria prevalence in Pakistan was 23.3%, with Plasmodium vivax, Plasmodium falciparum, and mixed infection rates of 79.13%, 16.29%, and 3.98%, respectively. Similarly, the analysis revealed that the maximum malaria prevalence was 99.79% in Karachi and the minimum was 1.68% in the Larkana district. Amazingly, this systematic review and meta-analysis detected a wide variation in malaria prevalence in Pakistan. Pakistan's public health department and other competent authorities should pay close attention to the large decrease in mosquito populations to curb the infection rate.

Background: Diabetes is not uncommon in patients requiring cardiac surgery. These patients have a higher incidence of morbidity and mortality. Subsequently, diabetes represents a major medico-economic problem in both developed and developing countries. This study was designed to observe the association between glycemic control and outcome of patients after open heart surgery in adult population.Materials and Methods: Data was collected retrospectively in all patients who underwent open cardiac surgery (coronary artery bypass grafting, valve, or bypass grafting with valve surgery) and survived 72 hours postoperatively and had diabetes. The study was conducted from January 2015 to December 2016.Results: Of the 129 patients included in the study, male dominated 101 (78.3%). Most frequent surgery was coronary artery bypass grafting (CABG) 123 (95.3%), CABG plus aortic valve replacement 4 (3.1%), and CABG plus mitral valve replacement 2 (1.6%). Considering diabetes, only 3 (2.3%) were on diet control, 112 (86.8%) on oral hypoglycemic agents (OHA), whereas 9 (7%) had control on both insulin and OHA. Only 5 (3.9%) had type I diabetes. The mean fasting blood sugar (FBS) was 154.58 g/dl, and the mean duration of diabetic mellitus was observed 12.32 years. Microvascular and macrovascular complications were 26/129 (20.16%) and 17/129 (13.17%), respectively. Total 75 (58.1%) patients did not require insulin and 54 (41.9%) were treated with insulin intraoperatively to keep the blood glucose level less than 200 g/dl. Cardiac arrhythmias were frequent in the insulin group (P < 0.05), which was also associated with increased stay in the cardiac intensive care unit.Conclusion: Inadequate glycemic control during open cardiac surgery can possibly lead to increased perioperative morbidity and mortality and with decreased long-term survival and recurrent ischemic events. Therefore, aiming for blood glucose levels around 140 mg/dl appears reasonable. Further studies are required to define specific glucose ranges for a clearer definition of recommended blood glucose goals in postoperative cardiac patients for the best outcomes in patients with diabetes mellitus. [ABSTRACT FROM AUTHOR]

Breast milk is the combination of bioactive compounds and microflora that promote newborn’s proper growth, gut flora, and immunity. Thus, it is always considered the perfect food for newborns. Amongst their bioactives, probiotic communities—especially lactic acid bacteria (LAB)—are characterized from breast milk over the first month of parturition. In this study, seven LAB were characterized phenotypically and genotypically as Levilactobacillus brevis BDUMBT08 (MT673657), L. gastricus BDUMBT09 (MT774596), L. paracasei BDUMBT10 (MT775430), L. brevis BDUMBT11 (MW785062), L. casei BDUMBT12 (MW785063), L. casei BDUMBT13 (MW785178), and Brevibacillus brevis M2403 (MK371781) from human breast milk. Their tolerance to lysozyme, acid, bile, gastric juice, pancreatic juice, and NaCl and potential for mucoadhesion, auto-aggregation, and co-aggregation with pathogens are of great prominence in forecasting their gut colonizing ability. They proved their safety aspects as they were negative for virulence determinants such as hemolysis and biofilm production. Antibiogram of LAB showed their sensitivity to more than 90% of the antibiotics tested. Amongst seven LAB, three isolates (L. brevis BDUMBT08 and BDUMBT11, and L. gatricus BDUMBT09) proved their bacteriocin producing propensity. Although the seven LAB isolates differed in their behavior, their substantial probiotic properties with safety could be taken as promising probiotics for further studies to prove their in vivo effects, such as health benefits, in humans.

IntroductionBy conducting a systematic review and meta-analysis, we investigated the prevalence of neuropsychiatric (NP) symptoms among systemic lupus erythematosus (SLE) patients in Pakistan.MethodsIn this review work, three electronic databases (Web of Science, MEDLINE, and Google Scholar) and local databases were screened for 20 years from 1 January 2002 to 30 September 2022, to identify the articles evaluating the prevalence of NP symptoms in SLE patients in Pakistan. We performed a random-effects meta-analysis to estimate the prevalence of NPSLE. Statistical heterogeneity was measured by the I2 index, and subgroup meta-analyses were used to access the statistical heterogeneity. Furthermore, meta-regression models were used to examine the associations between prevalence estimates and study characteristics of interest. Three independent authors reviewed existing studies, extracted data, and rated the qualities of selected studies. This review was registered on PROSPERO (Registration no. CRD42022361798).ResultsThirteen studies met the inclusion criteria out of the 322 studies with a total of 2,003 SLE patients for this systematic review and meta-analysis. The prevalence of NP disorders in SLE patients was estimated to be 30.42% (95% CI:18.26–44.11%), with cognitive dysfunction being the most common (31.51%; 95% CI:1.28–76.27%), followed by headache (10.22%; 95% CI: 0.00–33.43%), seizures (5.96%; 95% CI: 3.80–8.53%), psychosis (3.64%; 95% CI: 2.38–5.13%), and neuropathy is the least common (0.86%; 95% CI: 0.00–2.74%). The heterogeneity between studies was significant (p < 0.01). The pooled prevalence of NP disorders among SLE patients was found highest in Punjab (41.21%) and lowest in Sindh (17.60%).ConclusionFindings from this study revealed that SLE patients have a high prevalence of NP disorders. The most common symptoms were cognitive dysfunctions, headaches, seizures, psychosis, and neuropathy. Clinicians can manage these potentially deadly and disabling diseases more effectively if they understand the incidence of each NP symptom in SLE patients. NP symptoms among SLE patients are at their peak in Pakistan; policymakers should devise preventive strategies to curb the disease.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record. php?RecordID=361798, identifier CRD42022361798.

The developing interest in thermal engineering and nano-technology presented the idea of nanoparticles with enhanced thermal mechanisms and attained convinced novel applications in heat transportation systems, solar energy, micromechanics, air conditioning systems, cooling and heating facilities, bio-medical applications like cancer treatment, artificial heart surgery etc. Beside this, the phenomenon of entropy generation is another fascinating research area which used to improve the assessment of heat transportation with optimized procedure. The entropy generation in bio-convective flow of Eyring-Powell nanofluid over permeable stretched surface of cylinder is scrutinized in this research. The effects of magnetic field, thermal radiation and viscous dissipation are considered. Concentration communication is obtained in view of activation energy associated with binary chemical reaction. Furthermore, properties of Brownian diffusion and thermophoresis of nanoparticles are accounted. Suspended nanoparticles are made stable through mutual effects of bioconvection and buoyancy forces. Total entropy generation rate is modeled through second thermodynamics law. The governing flow model is obtained with the help of boundary layer approximations. Dimensional model is transformed into non-dimensional one by transformations and then tackled by Newton built-in shooting procedure. Impact of different influential variables on entropy generation, velocity, temperature, concentration, Bejan number and motile density of microorganisms are analyzed via graphs. Numerical values of skin friction coefficient, density, Sherwood and Nusselt numbers are tabulated and examined. Major findings are listed at the end. Since this analysis is based on theoretical flow assumptions, therefore the flow parameters specify the constant range like 0.0⩽Γ⩽6.0, 1.0⩽γ⩽4.0, 0.1⩽Ha⩽1.2, 0.2⩽λ⩽0.6,0.2⩽Rd⩽1.7, 0.1⩽K⩽3.0, 0.1⩽Pr⩽4.5, 0.3⩽Nb⩽1.8, 0.2⩽Nt⩽1.7, 0.0⩽E1⩽1.0, 1.0⩽Ec⩽4.0, 0.2⩽σ⩽1.4, 0.4⩽Sc⩽1.7, 1.0⩽δ⩽2.5, 0.5⩽Pe⩽2.0, 0.5⩽Ω°⩽2.0, 0.1⩽Br⩽1.0,0.1⩽Lb⩽1.5. The results show that fluid velocity decreases with curvature parameter and porosity constant. A decreasing variation in nanofluid temperature due to curvature parameter is examined. The entropy generation and Bejan number decline Brinkman number, motile density diffusion parameter and mass concentration diffusion variable. Furthermore, it is observed that skin friction coefficient increases with curvature parameter and porosity constant.

The connective eccentricity index (CEI) of a graph $G$ is defined as $\xi^{ce}(G)=\sum_{v \in V(G)}\frac{d_G(v)}{\varepsilon_G(v)}$, where $d_G(v)$ is the degree of $v$ and $\varepsilon_G(v)$ is the eccentricity of $v$. In this paper, we characterize the unique trees with the maximum and minimum CEI among all $n$-vertex trees and $n$-vertex conjugated trees with fixed maximum degree, respectively.