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Abstract L-Histidine immobilized montmorillonite is prepared and used as an adsorbent for removal of As(III) from aqueous solutions. The L-Histidine immobilized montmorillonite is characterized by FTIR, XRD, TGA and PZC techniques that confirmed the successful insertion of Histidine into interlayer of montmorillonite structure. The various parameters such as pH, reaction time, temperature, concentration of As(III) and adsorbent dosage are optimized to obtain maximum As(III) adsorption. It is observed that usually the removal of As(III) is increased with increasing the amount of adsorbent, contact time, temperature and till pH 6. The results showed that all these adsorption parameters greatly influenced the adsorption process. The various adsorption isotherm models including Langmuir, Freundlich, Temkin and Dubinin-Radishkevich (DR) as well as kinetics models are applied to the adsorption data. The Langmuir isotherm and pseudo-second order kinetics model are well-fitted to the adsorption data. Further, the probability distribution function (PDF) is applied that verified the adsorption data well while the Akaike’s Information Criterion (AICcorrected) and Absolute Average Relative Deviation (AARD) statistical models supported the best fit adsorption isotherms. The PDF model have provided a good statistical identification of adsorption parameters, adsorption rates and maxima whereas AICcorrected and AARD models revealed the well-fitting of Langmuir adsorption isotherm model in correlating the equilibrium data. Thermodynamic parameters (ΔH, ΔS, ΔG) proved the endothermic and spontaneous nature of adsorption process along with entropical changes occurring at solid-solution interface. The results showed that L-Histidine immobilized montmorillonite is an excellent material for As(III) adsorption with maximum adsorption capacity of 87.7 mg g−1.

In this paper we report the maximum potential for cement substitution with secondary materials to reduce CO2 emissions globally (1.3 Gt CO2-eq. in 2018) and on a country-by-country basis.

Objective: to identify the comorbid conditions associated with an increased risk of severe Dengue in clinically diagnosed patients with Dengue viral infections. Study Design: Comparative cross sectional study. Place and Duration of Study: Dengue ward, Department of Medicine, Combined Military Hospital Lahore, from Oct to Nov 2021. Methodology: Three hundred patients with confirmed Dengue infection were enrolled in the study. These patients were divided into two groups: 150 patients with comorbids (diabetes mellitus, hypertension, ischemic heart disease) and 150 patients without comorbids. The impact of comorbid conditions on the severity of dengue fever was studied. Results: Seventy-three out of the 300 patients had Dengue Fever without warning signs, while 79 out of the 300 patients had Severe Dengue Fever. Almost half of the patients (148) 300 had Dengue Fever with warning signs. The mean age of participants is 45.62±14.06 years. Patients with diabetes mellitus were more likely to develop Dengue without warning signs. Patients with hypertension were more likely to develop severe Dengue. Conclusion: This study showed that comorbid conditions such as Diabetes and Hypertension are important risk factors for developing severe Dengue.

In an asymmetric information environment, investors diversify their investments to minimize risk and maximize their wealth. Such diversification ranges from one market to another market and from one country to another country. Investors prefer foreign portfolio investment over foreign direct investment because of the economy's turmoil, changes in macroeconomic indicators, and market liberation. This study analyzes the dynamic relationship among stock market volatility, foreign portfolio investment, and macroeconomic indicators (foreign exchange rate, interest rate, and Gross Domestic Product) using the dynamic long-run Auto-regressive Distributed Lag (ARDL) model concerning the Pakistan environment. This study also considers the impact of multiple structural breaks, representing variables' endogenous and exogenous shocks. The secondary data is used from Oct. 01, 2009, to Sept. 30, 2019, with monthly frequency. The results indicate a co-integration between SMV, FPI, FXR, IR, and GDP. In short-run analyses, the error correction term is statistically significant, while in the long run, the SMV, FPI, and FXR are not impacted. As no evidence of volatility has been found between SMV and FPI, unidirectional or bi-directional policies can be devised to further attract the new FPI for strengthening the foreign reserves, the balance of payments, and other macroeconomic variables. Additionally, investors should update their knowledge based on considering the endogenous and exogenous shocks on the SMV.

In this work, an experimental and numerical investigation are carried out to study the impact of adding Liquefied Petroleum Gas (LPG) on the characteristics of diesel engine. New injection control system (ICS) is designed to manage an LPG injector on intake manifold a single-cylinder diesel engine. The engine test rig is powered with traditional diesel fuel to generate the base line data for comparison. Different conditions of load (0%, 25%, 50%, 75%, and 100%) in terms of brake power with three compression ratios of 14.5, 15.5 and 16.5 at constant engine speed of 1500 rpm. LPG is tested in four rates (5 L/min, 10 L/min, 15 L/min, and 20 L/min). The numerical analysis is performed with the help of Diesel-RK simulation software. The multizone combustion model is adopted. Same operating conditions in the experimental work are followed in the numerical simulation. The obtained results revealed that brake thermal efficiency (BTE) and exhaust temperature are reduced while brake specific fuel consumption (BSFC) is increased as the rate of LPG increased. Inducting LPG with (5,10,15,20) L/min reduced carbon monoxide (CO) by 16.6%, 14.7%, 20.3%, and 18.8% respectively. The maximum reduction in hydrocarbon emission (HC) is 8% at the rate of 15 L/min compared to diesel. The volumetric efficiency and NOx emissions are decreased with the use of LPG. As the compression ratio increases, BTE increases and BSFC decreases because of increasing combustion temperature and pressure which decreases delay period, ignites fuel fast and produces more power in small time. The impact of increasing compression ratio reported significant reduction in, CO, HC while NOx are increased. The experimental findings are compared to the results of the Diesel-RK software, as well as with the results of other researchers and good harmony among them is noticed.

Objective: To assess the effect of cochlear implants on quality of life in the children. Study Design: Cross-sectional study. Place and Duration of Study: CMH, Rawalpindi Pakistan, from Jul 2021 to Feb 2022. Methodology: Forty-five children, aged 2-5 years, of either gender having cochlear implantation already and came for followup were included in the study. Children with Cochlear Implants: Parental Perspectives (CCIPP) questionnaire were used for data collection. Results: Among 45 cases, mean age was 4.29±0.73 years. We found that the improvement in communication with the known people was 51.1%, and before cochlear implantation, the effectiveness of hearing aids was slightly low (26.7%). Social relations received the highest ratings (mean=3.33, SD=1.36), followed by Well-being (mean=3.22, SD=1.36) and effects of implantation (mean=2.89, SD=0.95). Of 45 children, 60% of parents were satisfied with the improvement in communication, 66.7% for general functionality and autonomy, 71.1% for well-being and happiness, 77.8% for social relations, 73.3% for education and 71.1% for effects of implantation. A strong correlation was found between function in general and self-confidence of the child (r=0.761, p=0.001), self-confidence and education of the child (r=0.720, p=0.001) and function in general of the child and effect of implantation (r=0.725, p=0.001). Conclusion: The Cochlear implantation was effective for children in hearing, language understanding, self-reliance and educational conditions.

This study examines the problems caused by intermittent renewable energy sources, especially wind farms, and suggests a different solar energy penetration strategy to improve their loading capacity. The study uses real-time data from a wind farm in Jhampir, Pakistan, to analyse and assess various aspects of grid stations connected to wind farms. Electrical Transient Analyzer Program is used to validate the results by linking these with actual grid system. The article focuses on creating a model for a grid connected to a wind farm and the simulation of outcomes following capacity expansion, with the installation of an autotransformer. The original capacity of the wind farm was 750 MW, which was increased to 1,250 MW, i.e., 1.66 times the actual capability. Furthermore, this capacity was further enhanced to 1,540 MW, which becomes 1.23 times the previous capacity by the penetration of a photovoltaic power plant.

Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets—ISIC2018 and HAM10000—have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time.

Diabetes mellitus (DM) is characterized by an absolute decline in insulin secretion and peripheral resistance and is the most prevalent metabolic and endocrine disorder. However, the pathogenesis of DM also includes adipocyte insulin resistance, increased glucagon secretion, increased renal glomerular glucose absorption, and neurotransmitter dysfunction. Although there is a wide spectrum of therapeutics available for glycemic control, owing to the identification of various pathogenic determinants of DM, management of DM remains challenging and complex. Current therapeutic interventions against DM focus mostly on glycemic control without considering the other pathological determinants that eventually lead to treatment failure and the progression of DM. Furthermore, long-term use of these conventionally available anti-diabetic drugs leads to various side effects, henceforth development of novel drugs against DM remains an unending search strategy for researchers. Various studies conducted in various parts of the world have proposed that these novel therapeutic interventions target multiple and alternate pathogenic hotspots involved in DM. The current review article discusses novel therapeutic options that hold particular promise to support their safety and discuss the side effects resulting from their use so that these novel candidate drugs can be effectively fabricated into potential drugs for the treatment of DM.

The availability of water is crucial for the growth and sustainability of human development. The effective management of water resources is essential due to their renewable nature and their critical role in ensuring food security and water safety. In this study, the multi-step-ahead modeling approach of the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) was utilized to gain insights into and forecast the fluctuations in water resources within Saudi Arabia. This study was conducted using mascon solutions obtained from the University of Texas Center for Space Research (UT-CSR) over the period of 2007 to 2017. The data were used in the development of artificial intelligence models, namely, an Elman neural network (ENN), a backpropagation neural network (BPNN), and kernel support vector regression (k-SVR). These models were constructed using various input variables, such as t-12, t-24, t-36, t-48, and TWS, with the output variable being the focus. A simple and weighted average ensemble was introduced to improve the accuracy of marginal and weak predictive results. The performance of the models was assessed with the use of several evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (CC), and Nash–Sutcliffe efficiency (NSE). The results of the estimate indicate that k-SVR-M1 (NSE = 0.993, MAE = 0.0346) produced favorable outcomes, whereas ENN-M3 (NSE = 0.6586, MAE = 0.6895) emerged as the second most effective model. The combinations of all other models exhibited accuracies ranging from excellent to marginal, rendering them unreliable for decision-making purposes. Error ensemble methods improved the standalone model and proved merit. The results also serve as an important tool for monitoring changes in global water resources, aiding in drought management, and understanding the Earth’s water cycle.

Microbiologically influenced corrosion (MIC) is a serious threat to the integrity of crude oil pipelines. Sulphate-reducing bacteria (SRB) are the primary microorganisms responsible for MIC, and their aggressiveness is dependent on the energy source available to them. Acetate, a common energy source, has been shown to accelerate the corrosion of carbon steel in the presence of SRB. This study investigated the effect of acetate on the growth of SRB and the corrosion of carbon steel plates in simulated anaerobic conditions. The corrosion kinetics were studied using linear polarization resistance (LPR) and weight loss immersion tests for 42 days. The samples were characterized using Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX). The results show that the addition of acetate to cultured media significantly increased the corrosion rate of carbon steel plates in both formation water and Postgate Medium B (PMB). This was due to increased growth of SRB in the presence of acetate, which led to the production of more corrosive hydrogen sulphide (H2S). The findings based on experimental data obtained from this study confirm that acetate can accelerate the corrosion of carbon steel in the presence of SRB.

Ride-hailing services are gaining momentum to meet the urban travel demand in the absence of proper public transport in developing countries. To the best of our knowledge, research on passenger's behavior and attitude towards the service quality of ride-hailing services and certainly studies focusing on developing countries are quite rare. Ride-hailing services were launched to meet the travel demand of residents throughout the city of Lahore, Pakistan in 2015. A revealed preferences study of 865 respondents consisting their demographic, travel information and perceptions related to selected attributes of service quality was carried out to examine their attitude and behavior towards the use of ride-hailing services in Lahore, Pakistan.Most of the users of these services are young, well-educated and have relatively high income. The travelers who were using Rickshaw services before are found to be the frequent users of these services and ride-hailing services have started replacing respondents' previous modes of travel. Exploratory factor analysis were employed on collected data to identify the factors effecting the traveler's perceptions regarding the use of ride-hailing services and structural model of users' satisfaction were constructed based on these factors later.The results exploratory factor analysis (EFA) and structural equation modeling (SEM) revealed that significant determinants of passenger's satisfaction with ride-hailing services are coverage and accessibility attributes (CAA), instrumental attributes (IA), service attraction attributes (SAA) and safety attributes (SA). The number of trips, trip purpose, income, education, time since the use of these services, profession and household size are also significant parameters in the determination of traveler's behavior and attitude towards the use of ride-hailing services in Lahore city. The commuter's overall satisfaction with service quality of app-based services have positive association with travelers intentions to continue use of app-based services and their believe that these services have filled the gap of public transport in the city and replacing previous modes of travel. The further improvements in attributes will enhance the traveler's satisfaction with these services. Insight into the attitude and perceptions of travelers would be useful for the transport planners, ride-hailing companies and policy makers for making appropriate improvements in the services and giving them space in existing transport system.

Objective: To compare etiological frequencies in patients of acute pancreatitis presenting to our setup with international data. Study Design: Cross-sectional study Place and Duration of Study: Department of Gastroenterology, Pakistan Emirates Military Hospital & Combined Military Hospital, Rawalpindi Pakistan, from Aug 2020 to Jan 2022. Methodology: Patients over 12 years suffering from pancreatitis were recruited using a convenience sampling technique based upon predefined criteria for diagnosis of pancreatitis on a questionnaire. Relevant basic lab tests, including chemistries and imaging, including Ultrasound abdomen and CECT abdomen, were analyzed to establish aetiology. Data were continuously uploaded into an electronic data sheet. International Consensus Diagnostic Criteria (ICDC) algorithms were applied to diagnose autoimmune pancreatitis. Results: Out of 120 patients, 74(61.7%) were males, and 46(38.3%) were females. Biliary pancreatitis was the most common aetiology 50(41.7%), followed in descending order by idiopathic 36(30%), drug-induced pancreatitis (DIP) 9(7.5%), Post ERCP Pancreatitis (PEP) 8(6.7%), tumours 5(4%), Autoimmune pancreatitis (AIP), Hypertriglyceridemia and alcohol-induced pancreatitis each 2(1.7%). Conclusion: Biliary pancreatitis has the highest frequency, followed by idiopathic and drug-induced pancreatitis.

ABSTRACT: In this study, the anti-inflammatory and antiapoptotic effects of hydroxytyrosol (HT) in Mycoplasma gallisepticum (MG)-infected chicken were investigated, and the underlying molecular mechanisms were explored. The results revealed severe ultrastructural pathological changes after MG infection in the lung tissue of chicken, including inflammatory cell infiltration, thickening of the lung chamber wall, visible cell swelling, mitochondrial cristae rupture, and ribosome shedding. MG possibly activated the nuclear factor κB (NF-κB)/nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3)/interleukin (IL)-1β signaling pathway in the lung. However, HT treatment significantly ameliorated MG-induced pathological damage of the lung. HT reduced the magnitude of pulmonary injury after MG infection by reducing apoptosis and releasing the proinflammatory factors. Compared with the MG-infected group, the HT-treated group exhibited significant inhibition of the expression of NF-κB/NLRP3/IL-1β signaling-pathway-related genes; for example, the expressions of NF-κB, NLRP3, caspase-1, IL-1β, IL-2, IL-6, IL-18, and TNF-α significantly decreased (P < 0.01 or

The study of the inclined flow along with the heterogeneous/homogeneous reactions in the fluid has been widely used in many industrial and engineering applications, such as petrochemical, pharmaceutical, materials science, heat exchanger design, fluid flow through porous media, etc. The purpose of this study is to present an infinite shear rate viscosity model using the inclined Carreau fluid with nanoscale heat transport. The model considers the effect of inclined angle on the fluid’s viscosity and the transfer of heat at the nanoscale. The result shows that the viscosity of the fluid decreases by increasing the inclination angle and the coefficient of heat transfer also increases with the inclination. The model can be used to predict the viscosity and heat transfer fluid’s behavior in the inclined systems that is widely used in the industrial and engineering applications. The results provide a better understanding of the inclined flow behavior of fluids and the heat transfer at the nanoscale, which can be useful in heat exchanger design, fluid flow through porous media, etc. Greater Infinite shear rate viscosity parameter gives the higher magnitude of Carreau fluid velocity. Moreover, inclined magnetic field reduces the velocity due to Lorentz force. Two numerical schemes are used to solve the model, BVP4C and Shooting.

Objective: To associate CT severity score with inflammatory markers and to determine outcomes of COVID-19 patients admitted to CMH Lahore. Study Design: Comparative cross-sectional study. Place and Duration of Study: Combined Military Hospital, Lahore Pakistan, from Mar to Jun 2021. Methodology: Patients of COVID-19 age 18 and above, with a positive RT-PCR, were included in the study Clinical and radiological data of 200 patients was retrieved and analysed from the hospital registry. Results: In the present study, we studied the role of inflammatory markers in predicting the severity of COVID-19. We have compared the levels of LDH, CRP, IL-6 and serum Ferritin between the two groups. LDH (p=0.015), IL-6 (p=0.001) and Ferritin (p=0.001) were significantly different between the two groups, but CRP was not (p=0.811) significant. Conclusion: CT severity score associates well with the COVID-19 clinical severity. Our data suggest that the chest CT scoring system can predict the severity of COVID-19 disease and significantly associates with inflammatory markers.

The development of a hybrid with high oleic acid is an important breeding goal for sunflower. High oleic acid sunflower has better cooking quality due to low oxidation and rancidity. Hence, inbred lines differing for oleic acid content were selected, alongside the development of hybrids where one or both parents exhibited high oleic acid content in edible oil, and then evaluated at various sites (i.e. with comparatively low temperature during sunflower reproductive phase at Sargodha and Faisalabad; while high temperature and low humidity at Bhawalpur and Multan) in Pakistan during spring season. Moreover, autumn season was relatively cool and high humid for sites (Faisalabad and Sargodha, Pakistan). DNA profiling of hybrids differing for oleic acid content using N1-3F/N2-1R confirmed the presence of a high oleic acid allele in the hybrids. Oleic acid content and seed yield components were increased at high temperature and low humidity to a greater extent in spring than in autumn season. Among the hybrids, one (H5) had stable high oleic acid content during the spring season with higher seed yield and kernel to seed percentage than the check cultivars (Hysun-33 and FH-331). Analysis of the combining ability of two locations revealed a relationship between mean oleic acid contents and combining ability, thereby suggesting the effectiveness of selection in developing high oleic acid inbred lines. Newly developed inbred C.112.P was a positive combiner for oleic acid at all sites except Sargodha, while restorer populations such as RH.344, RH.345 and RH.347 were positive male combiners.

Drought and heat stress are potential problems that can reduce wheat yield, particularly during the terminal growth stages in arid and semiarid regions of the world. The current study intended to examine the impact of individual and combined drought and heat stress on the biochemical contents (antioxidant enzymes, proline, soluble proteins, and soluble sugars), physiological parameters (chlorophyll content, cell membrane stability, photosynthesis, stomatal conductance, and transpiration), plant–water relations (relative water content, water potential, osmotic potential, and pressure potential), agronomic traits (flag leaf area, plant height, number of tillers per plant, spike length, grains per spike, and thousand-grain weight), and gene expression (TaHSF1a, TaWRKY-33, TaNAC2L, and TaGASR1) in four different thermostable and drought-tolerant wheat genotypes (i.e., Gold-16, HS-240, Suntop, and Hemai-13) collected from different countries. The tri-replicate experiment was conducted using two factorial arrangements in a randomized complete block design (RCBD). All measured traits, except total soluble sugars, proline, and cell membrane stability index, showed significant reduction under both combined and individual treatments. Furthermore, correlation analysis revealed a significant association between biochemical and physiological characteristics and crop agronomic productivity. Furthermore, principal component analysis (PCA) and heatmap analysis demonstrated significant levels of variation in traits according to the type of stress and nature of wheat genotype. The spectrographs and micrographs generated by scanning electron microscopy for the selected high- and low- tolerance samples revealed clear differences in mineral distribution and starch granulation. All studied genes showed comparatively high levels of relative expression under combined treatments of drought and heat stress in all wheat genotypes, but this expression was the highest in ‘Gold-16’ followed by ‘HS-240’, ‘Suntop’, and ‘Hemai-13’. Overall, this study concluded that plants are proactive entities and they respond to stresses at all levels; however, the tolerant plants tend to retain the integrity of their biochemical, physiological, and molecular equilibrium.

The coronavirus disease 2019 (COVID-19) pandemic rapidly spread globally. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is a positive-sense single-stranded RNA virus with a reported fatality rate ranging from 1% to 7%, and people with immune-compromised conditions, children, and older adults are particularly vulnerable. Respiratory failure and cytokine storm-induced multiple organ failure are the major causes of death. This article highlights the innate and adaptive immune mechanisms of host cells activated in response to SARS-CoV-2 infection and possible therapeutic approaches against COVID-19. Some potential drugs proven to be effective for other viral diseases are under clinical trials now for use against COVID-19. Examples include inhibitors of RNA-dependent RNA polymerase (remdesivir, favipiravir, ribavirin), viral protein synthesis (ivermectin, lopinavir/ritonavir), and fusion of the viral membrane with host cells (chloroquine, hydroxychloroquine, nitazoxanide, and umifenovir). This article also presents the intellectual groundwork for the ongoing development of vaccines in preclinical and clinical trials, explaining potential candidates (live attenuated-whole virus vaccines, inactivated vaccines, subunit vaccines, DNA-based vaccines, protein-based vaccines, nanoparticle-based vaccines, virus-like particles and mRNA-based vaccines). Designing and developing an effective vaccine (both prophylactic and therapeutic) would be a long-term solution and the most effective way to eliminate the COVID-19 pandemic.

With a longitudinal (2020–2021) completion, a set of 300 urine samples of individuals suspected of UTI was characterized based on the biochemical analysis and Kirby Bauer's disc diffusion method. Throughout, a total of 167 samples revealed UTI positivity by obtaining bacterial growth. Reporting that E. coli (69.4 %) was the most dominant when compared with Klebsiella (14.3 %), Proteus (5.9 %), S. aureus (4.7 %), P. aeruginosa (3.5 %), and Enterobacter (1.7 %), respectively. Gender-wise discrimination showed that E. coli was highly distributed among female (50.2 %) patients. A prominent percentage of E. coli (41.9 %) was found in the age group between 21-40 years. Isolates of E. coli represented the highest degree of resistance to Amoxicillin and Ceftriaxone (85.3 %), while highly sensitive to Amikacin, and Tazobactam (94.8 %). S. aureus was found highly resistant to Amoxicillin, Levofloxacin, Sulbactam, and Doxycycline (75 %), while highly sensitive to Meropenem, and Cefotaxime (100 %). Proteus spp. was found highly resistant to Sulbactam, and Cefotaxime (100 %), while highly sensitive to Tazobactam (100 %). Enterobacter was found highly resistant to Gentamicin (100 %), while highly sensitive to Aztreonam (100 %). Klebsiella was found highly resistant to Levofloxacin, and Doxycycline (91.6 %), while highly sensitive to Amikacin, Tazobactam, Gentamicin, and Cefoxitin (75 %). P. aeruginosa was found highly resistant to Nalidixic acid (100 %), while highly sensitive to Meropenem, Ceftriaxone, Gentamicin, and Cefotaxime. In conclusion, the Amikacin and Tazobactam were seen to be effective for empirical therapy of UTI.