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The study investigates the impact of incorporating Ni and Cu into the lattice of ZnO nanoparticles (NPs) to enhance their anticancer and antioxidant properties. Characterization techniques including pXRD, FTIR, UV–visible absorption spectroscopy, FESEM, and EDAX confirm the successful synthesis and structural modifications of Ni/Cu-ZnO NPs. Anticancer activity against breast cancer (MDA) and normal skin (BHK-21) cells reveals dose-dependent cytotoxicity, with Ni/Cu-ZnO NPs exhibiting higher efficacy against MDA cells while being less harmful to BHK-21 cells. Morphological studies corroborate these findings. Additionally, antioxidant assays using TAC, FRAP, and DPPH assay demonstrate the superior antioxidant activity of Ni/Cu-ZnO NPs matched to pure ZnO. Overall, the synergistic effect of Ni and Cu incorporation leads to improved therapeutic potential, making Ni/Cu-ZnO NPs promising candidates for cancer therapy and antioxidant applications.Molecular docking recreations were performed using Auto Dock Vina software to gain more insights and validate the observed biological activities of un-doped ZnO and bi-metal doped ZnO NPs, we investigated the interaction and binding affinities of pure ZnO and bimetallic metal co-doped ZnO for their antioxidant and anticancer studies. Ni/Cu-ZnO have shown good antioxidants and exhibited remarkable anticancer activities.

Hepatitis B virus (HBV) is the causes of liver infection and associated with cirrhosis, carcinoma, and fibrosis of liver. In this research study, we conducted an observational study to evaluate the distribution of HBV infection and its genotypes in selected cities of Khyber Pakhtunkhwa, Pakistan. A total of 426 samples were collected from selected cities of Khyber Pakhtunkhwa. Mardan (n = 163), Dir (Lower) (n = 116), Malakand (n = 53), Miran Shah (n = 29), Dera Ismail Khan (n = 28), Swat (n = 22), and Hangu (n = 15). For molecular identification of hepatitis B virus infection, the ICT (Immunochromatographic techniques) kits were used for the Screening of Hepatitis B virus infection among general population. The positive samples of ICT test were further subjected to Real-time Polymerase chain reaction (PCR), of which 282 (66.2 %) were males, and 144 (33.8 %) were females. Out of 426 ICT kit positive samples, 218 (51.18 %) were found PCR positive. The maximum number of positive cases, 157 (36.86 %), was found between 15 and 34 years. Sixty samples were further processed for HBV genotyping. A mixed genotype was detected in 29 (48.3 %) individuals, followed by genotype D in 23 (38.3 %) and untypable in 8 (13.3 %). The positivity rate of HBV was recorded high among males compared to females. Most patients who reported positive for HBV were co-infected with two genotypes. This research will help to provide background information to government and health authorities to raise public awareness and improve the capacity of the healthcare delivery system for hepatitis B virus infection.

This study aims to investigate the Joule effect, thermal radiation, and Coriolis effects in unsteady magnetohydrodynamics (MHD) micropolar flow over a 3-D unstable sheet. A magnetic field is typically applied to the surface; additionally, it is assumed that the fluid is conducting electricity. Other micro-rotations are also considered. The physical issue is resolved with the help of the fundamental equations, and the issue's complexity is reduced with the use of similarity variables. In the present approach, the homotopy analysis method (HAM) is employed. The heat, skin's friction factor, temperature, micro movements, and velocity associated with the emerging parameters and transfer rates are considered. The boundary layer thickness is increased as the parameter of vorticity increases. When the magnitude of the magnetic field increases, the skin friction coefficient decreases. The state variables are approximated up to six decimal places numerically. The numerical values of −f″(0) and −θ′(0) are computed to higher decimal places and compared with the available literature to validate the results. The outcomes obtained are compared with the available literature; the results here are corroborated, and the performance of the HAM is demonstrated.

This work aims to investigate the Cuckoo search-active set algorithm (CS-ASA), which is based on the artificial neural network (ANN) approach to the nonlinear partial differential equations (PDEs). CS-ASA technique modifies the Cuckoo search best possible solution to the next stage. This best solution is considered from all the best-chosen sets of Cuckoo search. This technique is applied to 1D and higher-order nonlinear PDEs that work as benchmark problems (heat, forced heat, Burger's, and Allen-Cahn). The results are obtained in the form of graphs by applying the CS-ASA algorithm. The state variables are plotted for a single hidden layer to reduce the computational cost. For the overall performance of CS-ASA, the fitness function is plotted against the number of iterations. It is clear that the fitness function minimizes with higher iterations, and hence achieve an optimal solution. The error in each case is presented at the grid points. Also, the fitness function for the description of the objective function is plotted. For best possible output, CS-ASA has chosen weight functions and its range in each case study accordingly. The obtained results (L2 error) are presented in the form of a table and compared with PSO, PSO-BP, and PSO-BP-CD. It is clear that CS-ASA perform better with a minimum L2 error for each problem under consideration.

Stratified flows are commonly observed in numerous industrial processes. For example, a gas-condensate pipeline typically uses a stratified flow regime. However, this flow arrangement is stable only under a specific set of operating conditions that allows the formation of stratification. In this study, the authors analyzed the flow attributes of Prandtl Eyring liquid past an inclined sheet immersed in a stratified medium. The flow also characterizes the features of the magnetic field along with a first-order chemical reaction. Convective boundary constraints associated with the thermosolutal exchange at the extremity of the domain are also prescribed. The fundamental equations of the study are formulated in dimensional PDEs and converted into dimensionless ODEs via similar variables. The numerical solution of the modelled setup is acquired by executing computations using shooting and RK-4 methods. The intelligent computing paradigm working on the mechanism of the back-propagated Levenberg-Marquardt strategy is also capitalized to forecast the behavior of related physical quantities. Graphs and tables are drawn to elaborate the impression of pertinent factors on flow distributions. It is perceived that the momentum profile diminishes with the magnetic field effect, whereas the opposite behavior is observed for the skin friction coefficient. The thermal and concentration distributions were found to dominate in the absence of stratification. Consideration of convective heating and concentration tends to elevate thermal and mass distributions.

Advancement in thermodynamical characteristics of non-Newtonian fluid is achieved by induction of gyrotactic microorganisms in order to attain efficient thermal and mechanical engineering setups, such as micromixers, microfluidic instruments, biosensors and microdevices. So, the primer motive to conduct this research is to examine the effectiveness of gyrotactic microorganisms in managing heat transport in Carreau nanofluid flow over a linearly stretched surface under the effectiveness of opposing and assisting buoyancy forces. Physical aspect of magnetic field along with slip boundary constraint also entertained by employing in vertical direction. The governing equations are modelled in the form of dimensional PDE's and latterly transformed into dimensionless ODEs via similarity transformations. Solution of obtained set of differential system is solved by implementing Runge-Kutta of order 4 and shooting procedure. Visualizations about influence of flow concerning parameters on associated distributions are analyzed in comparative manner for opposing and assisting buoyancy parameter. In addition, metrics for quantities of engineering interest, such as heat and mass fluxes for nano and gyrotactic particles along with skin friction coefficient are calculated in a comprehensive manner.

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.

Chitin and chitosan are the most significant polysaccharides, having a wide range of uses in biopolymers, the biological sciences, and other domains. Its derivatives, hydrogels, are frequently employed in biomedical settings. Adsorbents for metals and dyes from industrial waste are among the other uses. Chitosan-based hydrogels are commonly used in pharmaceuticals for tablets and proteins. Most of these hydrogels consist of polysaccharides, such as chitosan and its derivatives. Their chemical structure is depicted as a chemical blueprint, and the arrangement of components and vertex data is analyzed. In this work, polynomials of chitosan-derived polysaccharides are determined for various topology coindices using a non-adjacent pairing vertices approach. Extensions of M-polynomials for neighboring vertices are called CoM-polynomials. Real numbers known as topology coindices are used to describe a chemical compound's molecular structure and can be used to investigate physicochemical properties relevant to QSPR/QSAR (Quantitative structure- activity relationship) research. CoM-polynomials are calculated for chitosan derivatives, including gamma, beta, and alpha chitin.

This study introduces an innovative approach involving the synthesis of lead-free halide double perovskite (LFHDP) solar cell Cs2AgBiBr6 and that with the doping of trans-polyacetylene (t-PA) via the sol–gel method. Through X-ray diffraction (XRD) analysis, we confirm the cubic structure and, in addition, an observable growth in grain size attributed to t-PA doping. A comprehensive exploration of optical parameters, including band gap, refractive index, electronegativity, extinction coefficient, and dielectric constant was conducted through UV–Vis spectrophotometry. The pristine Cs2AgBiBr6 exhibited a band gap energy (Eg) of 1.9 eV, while with t-PA doping the Eg decreased to 1.84 eV, indicating a notable enhancement in light absorption. The calculated optical electronegativity is 0.49 which suggests a shift towards a more covalent nature. The J-V curve shows parameters for pristine thin film as Jsc of 5.13 mA/cm2, Voc of 0.92 V, FF of 0.69, and the efficiency of 3.29 %. On the other hand, the t-PA doped thin film exhibits improvement of the parameters which are Jsc of 5.7 mA/cm2, Voc of 0.929 V, and FF of 0.75, leading to a significantly elevated efficiency of 3.98 %. This enhanced efficiency is 20.97 % higher than that of the pure Cs2AgBiBr6, clearly showing an uplifted performance. In addition to the experiment, computational method based on finite difference time domain technique was used to check the stability of the material. The study shows the mechanical and dynamical stability of the Cs2AgBiBr6.

In this study, energy production by two solar energy technologies, namely concentrated solar power (CSP) and photovoltaic (PV) power, is compared from a technical, economic and environmental perspective. Initially, a 50 MW CSP plant is modeled and simulated at four selected sites in Pakistan. Then, the most feasible location of the CSP plant is compared with the solar PV plant of the same capacity. The effect of the solar thermal storage size and cooling system of the CSP system is investigated, while the photovoltaic tracking system is investigated to evaluate the technical and economic performance of the power plants. Technical performance is evaluated based on energy generation and capacity factors metrics, while economic performance is evaluated with respect to levelized cost, payback period and net present value. In addition, environmental criteria such as reducing greenhouse gas emissions (GHG), saving fossil fuels, and life-cycle water consumption are evaluated. From the results, it was concluded that the CSP plant located in Quetta is technically and economically viable. The capacity factor of the CSP plant is 36.6% compared to 19.8% for the PV plant, while the solar-to-electrical efficiency of the CSP plant is 14.2% compared to 20.8% for the PV plant. The required land area is 2.77 acres/GWh for the CSP plant and 2.33 acres/GWh for the PV plant, while the net capital cost of the CSP plant is five times higher than that of the PV plant. Various design parameters are optimized to obtain the minimum levelized cost of energy (LCOE) for both CSP and PV plants. The results of CSP and PV plants indicate that the LCOE can be reduced to 11.57 cents/kWh and 4.69 cents/kWh, respectively. Thus, the CSP plant performs better from the technical point of view while the PV plant performs better from the economic perspective.

This work aims to analyze the transfer of heat through new fractional-order convective straight fins by using the Riemann-Liouville type fractional derivatives. The convection through the fins is considered in such a way that the thermal conductivity depends on the temperature. The transformed fractional-order problems are constituted through an optimization problem in such a way that the L2 norm remains minimal. The objective functions are further analyzed with the hybrid Cuckoo search (HCS) algorithm that use the artificial neural network (ANN) mechanism. The impacts of the fractional parameter β, the thermo-geometric parameter of fin ψ, and dimensionless thermal conductivity α are explained through figures and tables. The fin efficiency during the whole process is explained with larger values of ψ. It is found that the larger values of ψ decline the fin efficacy. The fractional parameter declines the thermal profile as we approach the integer order. The convergence of HCS algorithm is performed in each case study. The residual error touches E−14 for the integer order of α. The present results are validated through Table 6 by comparing with HPM, VIM and LHPM, while the error for HCS-ANN touches E−13. This proves that the proposed HCS is efficient.

The use of Supplementary Cementitious Materials (SCMs) or industrial wastes as a partial replacement for cement in the production of concrete is an urgent need in the construction industry due to cement's growing environmental challenges and rising cost. In respect of this, we conducted research work on proportioning binary concrete mixes. Fly ash (FA) replaced 10 %, 20 %, and 30 % of the cement, while silica fume (SF) replaced 5 %, 10 %, and 15 % of the cement. A control concrete mix was also developed with 100 % cement and no SCM. The results showed no increase in compressive strength for FA concrete compared to control at the early age of 3–28 days, but a maximum increase in compressive strength of 4 % was discovered at a later age of 56 days for concrete with 20 % FA. For 5 % SF concrete, a considerable strength increase of 15 % was seen at the early age of 3 days. Like with FA concrete, 2 % improvement in strength was recorded at the later age of 56 days for 10 % SF concrete. This study further focused on the concrete's temporal evolution of compressive strength by developing a strength evolution model (SEM) using nonlinear regression analysis at a 95 % confidence level. Pearson correlation coefficient was used to determine the correlation between the model values and the experimental results. For comparison, the fib Model Code 2010 was applied to the experimental data, and a good agreement was observed among the proposed model, the fib Model values, and the experimental results. The proposed model can be expanded to address further regression-related problems. Finally, environmental life cycle assessment revealed that utilizing 10 %, 20 %, and 30 % of FA lowered Global Warming Potential (GWP) by 9 %, 19 %, and 29 %, respectively. Likewise, using 5 %, 10 %, and 15 % of SF reduced the GWP by 5 %, 9 %, and 14 %.

Helicobacter pylori (H. pylori) is mainly considered for causing chronic gastritis, which can lead to several secondary complications like peptic ulcer and pre-malignant lesions for example atrophic gastritis, intestinal dysplasia and metaplasia, with the etiological factor of developing gastric cancer. Recent research demonstrates that H.pylori colonizes the stomach mucosa of more than fifty populations around the globe. This research focuses on unveiling hub genes, and diagnostic and drug targets against said organism by utilizing various types of networking biology and biophysical approaches. In data retrieval, the GSE19826 dataset was obtained from the gene expression omnibus database and microarray data set from array express. Geo2r analysis predicted a total number of 7 DEGs and 10 hub genes, next functional protein association network analysis (STRING) unveiled that among 10 Hub genes only 3 genes were found more interactive with other genes and involved in pathogenesis, The shortlisted three genes were further analyzed for survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA) and predicted the survival rate of targeted genes. Moreover, functional enchainment analysis was done using the ToppFun server, the server predicted that COL11A1 and COL10A1 were more involved in the pathogenesis of the H. pylori infection. Furthermore, the COL10A1 gene was subjected to protein structure prediction. In molecular docking analysis, the asinex antibacterial library was screened for potential inhibitors, and one compound was predicted as a strong inhibitor with the best binding at −10.23 kcal/mol. The docking results were further validated through molecular dynamic simulation analysis and the MD simulation analysis evaluated the dynamic movement of the docked complex in various nanoseconds, the MD simulation results predicted that the docked complexes are stable throughout the simulation and can be used as a potential inhibitor against the said pathogen, however experimental study is required to further validate the predicted results and design drug against targeted pathogen.

Research is continuously being pursued to treat cancer patients and prevent the disease by developing new medicines. However, experimental drug design and development is a costly, time-consuming, and challenging process. Alternatively, computational and mathematical techniques play an important role in optimally achieving this goal. Among these mathematical techniques, topological indices (TIs) have many applications in the drugs used for the treatment of breast cancer. TIs can be utilized to forecast the effectiveness of drugs by providing molecular structure information and related properties of the drugs. In addition, these can assist in the design and discovery of new drugs by providing insights into the structure-property/structure-activity relationships. In this article, a Quantitative Structure Property Relationship (QSPR) analysis is carried out using some novel degree-based molecular descriptors and regression models to predict various properties (such as boiling point, melting point, enthalpy, flashpoint, molar refraction, molar volume, and polarizability) of 14 drugs used for the breast cancer treatment. The molecular structures of these drugs are topologically modeled through vertex and edge partitioning techniques of graph theory, and then linear regression models are developed to correlate the computed values with the experimental properties of the drugs to investigate the performance of TIs in predicting these properties. The results confirmed the potential of the considered topological indices as a tool for drug discovery and design in the field of breast cancer treatment.

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

The use of fly ash in cementitious composites has gained popularity. However, assessing the depth of wear (DW) of concrete requires expensive and destructive laboratory tests utilizing specialized equipment like the rotating-cutter method. Therefore, there is a need for alternative methods to predict the depth of wear of such composites more efficiently and cost-effectively. Accordingly, the objective of this research is to utilize machine learning (ML) approaches, including one individual algorithm (Decision Tree) and two ensemble algorithms (AdaBoost Regressor and Bagging Regressor) to estimate the depth of wear of fly-ash-based concrete. A collection of 216 experimental records was obtained from the existing literature. The efficiency of the models was examined with multiple statistical indexes. The bagging regressor (BR) model provided superior estimation performance with a correlation coefficient (R) of 0.999 compared to AdaBoost regressor (R = 0.965) and decision tree (R = 0.962). The ensemble models, notably BR, provided more accurate predictions with an 87.8 % lower mean absolute error (MAE) and an 85 % lower root mean square error (RMSE) compared to the decision tree model. In addition, the BR model exhibited the lowest performance index (ρ) values of 0.016 for training and 0.012 for validation. The SHapley Additive exPlanation (SHAP) revealed that the time of testing and age are the most dominant controlling features that significantly contribute to the estimation of the depth of wear. In conclusion, utilizing ML techniques and SHAP interpretation to estimate the DW of fly ash concrete significantly reduces reliance on expensive lab tests, making durability assessment more practical and cost-effective.

Steel-fiber-reinforced concrete (SFRC) has emerged as a viable and efficient substitute for traditional concrete in the construction industry. By incorporating steel fibers into the concrete mixture, SFRC offers enhanced crack resistance, improved post-cracking performance, and effective stress transfer. To optimize cost and time in the construction sector, the application of machine learning (ML) methods is now prevalent for accurately estimating concrete characteristics. Accordingly, the present study focuses on utilizing novel ML techniques that include adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN), and gene expression programming (GEP) to predict the compressive strength (CS) of SFRC at elevated temperatures. For developing the ML-based models, 307 experimental records were acquired from the published studies conducted between 2000 and 2022. The models' accuracy was assessed using multiple statistical indices. The developed models provide excellent performance with a correlation coefficient (R) value of 0.962 for ANN, 0.998 for GEP, and 0.968 for ANFIS models. Overall, the GEP model provided higher accuracy and less error compared to ANN and ANFIS models. Moreover, the SHapley Additive exPlanation (SHAP) technique was used to interpret the outcomes of the ML-based model predictions. The combined SHAP value of cement content, temperature, and water-to-cement ratio accounts for 80.7% of the total SHAP value across all features. In addition, the SHAP analysis revealed that the actual temperature holds greater significance compared to the heating rate when considering its impact on compressive strength. The comparison of the developed model with the multivariable regression (MLR) method provided that the ML-based models have more prediction accuracy than traditional prediction methods. The proposed models provide designers and builders with an efficient and versatile tool for evaluating attributes, enabling accurate predictions of the CS of SFRC under high temperatures in construction applications.

Chronic wounds are among the major healthcare issues affecting millions of people worldwide with high rates of morbidity, losses of limbs and mortality. Microbial infection in wounds is a severe problem that can impede healing of chronic wounds. Accurate, timely and early detection of infections, and real time monitoring of various wound healing biomarkers related to infection can be significantly helpful in the treatment and care of chronic wounds. However, clinical methodologies of periodic assessment and care of wounds require physical visit to wound care clinics or hospitals and time-consuming frequent replacement of wound dressing patches, which also often adversely affect the healing process. Besides, frequent replacements of wound dressings are highly expensive, causing a huge amount of burden on the national health care systems. Smart bandages have emerged to provide in situ physiochemical surveillance in real time at the wound site. These bandages integrate smart sensors to detect the condition of wound infection based on various parameters, such as pH, temperature and oxygen level in the wound which reduces the frequency of changing the wound dressings and its associated complications. These devices can continually monitor the healing process, paving the way for tailored therapy and improved quality of patient's life. In this review, we present an overview of recent advances in biosensors for real time monitoring of pH, temperature, and oxygen in chronic wounds in order to assess infection status. We have elaborated the recent progress in quantitative monitoring of several biomarkers important for assessing wounds infection status and its detection using smart biosensors. The review shows that real-time monitoring of wound status by quantifying specific biomarkers, such as pH, temperature and tissue oxygenation to significantly aid the treatment and care of chronic infected wounds.

This work explains the first-principle computational inquiry of physical features of BWF3 (W = S and Si) halide-Perovskites compounds. With the aid of the WIEN2K code, our recent computation solely relies on Density Functional Theory (DFT). Using the equation of states made by Birch Murnaghan for optimization. The elastic factors are figure out and used to compute elastic characteristics, we found that both composites are structurally as well elastically stable. These substances exhibit strong resistance to plastic strain and are show ductility, scratch-resistant and anisotropic. BSF3 and BSiF3 are both metal-like substances, according to calculations made using the Tb-mBJ) potential approach. Density of states calculations are recycled to conclude how different energy states are involved in band structures. These compounds' band gap energies make it simple to study various optical characteristics. These chemicals exhibit strong absorption at high energy ranges. The compound BSF3 is transparent to incoming photons at low energies, while the complex BSiF3 is opaque. We have come to the conclusion that the compounds BSF3 and BSiF3 can be employed for high-frequency UV device applications as a result of our study into their optical properties. For the first time, our computational effort is calculated to investigate these substances that have not yet been experimentally proven to our satisfaction.

The Asiatic black bear (Ursus thibetanus) is considered one of the largest carnivores inhabiting different territories including coniferous and broad-leaved forests, extending from sea level to the elevation of 4300 m. The Kaghan and Siran Valleys in Pakistan's District Mansehra were the location of the current study. To evaluate the altitudinal distribution and seasonal movement of Asiatic black bears in the region, we used three approaches (sign distribution survey, questionnaire-based survey, and scat collecting). A total of 1858 bear signs were observed during the field survey, the highest number of signs was dig marks (1213) followed by plant uprooting. Dig marks (MER: 7.574) were observed repeatedly in each potential site of both valleys followed by plant uprooting (MER: 1.594). Similarly, the altitudinal distribution of black bears was determined through the frequency of signs concerning elevation (m). Elevation of the study area ranged from 1300 to 3500 m and the most abundant population was recorded at the elevation of 2501–3000 m followed by 2001–2500 m. About 52% of local communities encountered black bears during the summer season in the field followed by Spring (23%.8) and winter (15.3%). Manshi Reserver forest (24.5%) from Kaghan and Panjul Reserve forest (31.6) from Siran Valley are the potential summer migratory spots. Eleven scat samples were collected with a 0.103 encounter rate (ER). Most of the scats (about 70%) were observed within the maize crop field in the summer season at low elevation, while just 30% of the scat was observed in the winter season in the forest.

Rare variants in TTN are the most common monogenic cause of early-onset atrial fibrillation and dilated cardiomyopathy. Whereas cardiac sarcoidosis is very underdiagnosed, a common presentation can be ventricular arrhythmias. This report presents a patient with a likely pathogenic TTN variant and cardiac sarcoidosis. (Level of Difficulty: Intermediate.)

In this work a hybrid scheme is proposed for the numerical study of various evolutionary partial differential equations (EPDEs). In proposed strategy, temporal derivatives are estimated via first and second order finite differences while the solution and spatial derivatives are approximated by Lucas and Fibonacci polynomials. Further, the collocation approach is applied which convert the EPDEs, to the system of coupled linear equations which are simple to solve. For non-linear problems, Quasilinearization is used to tackle nonlinearity. The scheme is implemented to solve different EPDEs which include, one dimensional non-linear Boussinesq, Hunter–Saxton, wave-like and two dimensional linear and non-linear wave like equations. Accuracy of the scheme is portrayed by computing L∞,L2 error norms and the relative error. Moreover, the calculated outcomes are compared with previously existing results in literature. Simulation demonstrates that the scheme works well for the mentioned EPDEs.

Lysozyme is a naturally occurring enzyme that is ubiquitous and plays an important role in innate immunity through its potent bacteriolytic activity. The main objective of this study was to isolate, screen and identify lysozyme-producing Bacillus subtilis strains by 16S rDNA sequencing. Of the four Bacillus subtilis strains isolated (B18, B66, B70, and B124), only one (B66) was selected for further study because it exhibited the comparatively highest enzymatic activity. The 16S rDNA sequence of strain B66 was submitted to the Genbank database with accession number OM327451 and was assigned the name Bacillus subtilis BSN314. Bacillus subtilis BSN314 was selected for further study to maximize biomass production. To obtain the maximum biomass product of Bacillus subtilis BSN314, two statistical methods were applied, one related to the nutritional conditions affecting the growth medium (chemical components), called the single factor optimization method, and the other showing their effects on the cultivation conditions (orthogonal method). The results showed that the composition of the culture medium (carbon, nitrogen, inorganic salts and surfactants) and the culture conditions (temperature, pH, shaking speed and percentage of inoculum) have a specific influence on the biomass production of Bacillus subtilis BSN314. The culture medium containing the combination of A2B3C1D2 with 1.5% glucose, 2.5% soybean peptone, 0.5% K2HPO4, and 0.1% Tween-20 was determined to be the optimal medium for biomass production whereas, the order of culture media based on the effects of the selected conditions having lysozyme activity is as follows; glucose % > soyapeptone % > Tween-20 % > K2HPO4 %. The results showed that the A2B3C1D2 ratio had the highest enzyme activity of 55 U/mL.

ABSTRACT: Plasmodium falciparum, the causative agent of malaria, has been developing resistance to several drugs worldwide for more than five decades. Initially, resistance was against drugs such as chloroquine, pyrimethamine, sulfadoxine, mefloquine and quinine. Research studies are now reporting parasites with resistance to the most effective and novel drug used against malaria infection worldwide, namely artemisinin. For this reason, the first-line treatment strategy of artemisinin-based combination therapy is becoming unsuccessful in areas where drug resistance is highly prevalent. The increase in artemisinin-resistant P. falciparum strains has threatened international efforts to eliminate malarial infections and to reduce the disease burden. Detection of several phenotypes that display artemisinin resistance, specification of basic genetic factors, the discovery of molecular pathways, and evaluation of its clinical outcome are possible by the current series of research on genomics and transcriptomic levels in Asia and Africa. In artemisinin resistance, slow parasite clearance among malaria-infected patients and enhanced in vitro survival of parasites occurs at the early ring stage. This resistance is due to single nucleotide polymorphisms within the Kelch 13 gene of the parasite and is related to significantly upregulated resistance signalling pathways; thus, the pro-oxidant action of artemisinins can be antagonised. New strategies are required to halt the spread of artemisinin-resistant malarial parasites.

As the world faces a fourth COVID-19 spike, scientists are learning a lot more about the new SARS-CoV-2 strains that were previously unknown. Currently, the Delta versions of SARS-CoV-2 have become the prevalent strains in much of the world since it first appeared in India in late 2020. Researchers believe they have discovered why Delta has been so successful: those infected with it create significantly more virus than those infected with the original strain of SARS-CoV-2, making it extremely contagious. This has redirected the focus to how our immune system defends us from these various pathogens and initiates such varied responses. Hundreds of research papers have been published on the origins of long-lasting immune responses and disparities in the numbers of different immune cell types in COVID 19 survivors, but the primary architect of these discrepancies has yet to be discovered. In this essay, we will concentrate on the primary architect protein, activation induced cytidine deaminase (AID), which triggers molecular processes that allow our immune system to produce powerful antibodies and SARS-CoV-2 specific B cells, allowing us to outwit the virus. We believe that if we ever achieve permanent immunity to SARS-CoV-2 infection, AID will be the key to releasing it.

Al-based fluoroperovskites compounds AlMF3 (M = Cu, Mn) are researched computationally, and their various aspects are examined using TB-MBJ potential approximations for energy generation. This is due to their increasing power conversion efficiency, which has drawn a lot of attention. These compounds are cubic and stable in terms of structure, according to the Birch Murnaghan curve and tolerance factor. Computed are the optimised lattice parameters and the optimal volume Vo of the compounds matching the ideal energies. These compounds are mechanically stable, anisotropic, and ductile, according to the IRelast algorithm's predictions of elastic properties. At (X-M) symmetry points, AlCuF3 and AlMnF3 have a small indirect band gap, with band gaps of −0.54 eV for AlCuF3 and 0.45 eV for AlMnF3, respectively. The AlCuF3 compounds' narrow indirect band gap reveals their metallic character. AlMnF3, however, is a semiconductor. The offering of various elemental states to the conduction and valence bands is calculated using the partial and total density of states (TDOS & PDOS). These compounds exhibit low refractive index, high absorption coefficient, and conductivity at high energy ranges, according to an analysis of their optical properties. These compounds have applications in conducting industries because of the indirect band gap. Here, we used these compounds for the first time and used a computational method to study them, which provides a comprehensive perspective of all the various features.

The hydro-thermal features of 3D (3-dimensional) nonlinear Carreau fluid motion over a paraboloid surface of revolution are examined. The impacts of thermal radiation and mass transfer are considered during the fluid motion. The flow is modeled by a set of nonlinear coupled partial differential equations (PDEs) satisfying certain associated boundary conditions. Appropriate transformations are used to convert the coupled system of PDEs to ODEs. The resultant coupled ODEs system is solved analytically by using the well developed analytical procedure of Homotopy Analysis Technique (HAM). The influence of relevant parameters are investigated on the state functions of the Carreau fluid MHD motion by depicting 2D graphs. It has been found that the increasing thickness of paraboloid surface of revolution augments the fluid velocity and shear stress components, whereas drops the gradient in the velocity distribution. The enhancing Grashof number increases the fluid velocity components as well as its gradients. The increasing magnetic field strength retards the fluid migration, and therefore drops the velocity gradients. The increasing rotation parameter augments the horizontal component of velocity and reduces the velocity vertical component. The fluid temperature enhances with the increasing strength of radiation source, internal heat source, and reflection parameters. The enhancing chemical reaction parameter values drop the fluid concentration. The convective heat energy transport increases with the enhancing Brownian motion, while the convective mass transfer rises with the increasing radiation source strength. The results obtained are compared with a numerical technique by graphs and tables, where the accuracy and effectiveness of HAM is proved The agreement of the obtained results and the published research validates the exactness of the applied analytical technique.

The recent discovery of various forms of carbon nanostructures inspired the researchers due to their uses in various fields. Numerous production approaches for carbon nanostructures have been presented; doping, functionalization, chemical modification, and filling have been achieved, and manipulation, characterization and separation of carbon nanostructures are now possible. On the other hand, the flexibility and strength of carbon nanosheets make them potential candidates in controlling further nanoscale structures, which indicates that they have an important role in nanotechnology engineering. Because of their uniform thickness of only around 1 nanometer(nm), and their high mechanical, thermal and chemical stability, such materials are of high concern for a wide range of applications.A topological index or molecular descriptor is a mathematical formulation that can be applied to any graph which models some molecular structure. Based on the molecular descriptor, it is convenient to analyze mathematical values and advance investigation on some physicochemical properties of a molecule. Consequently, it is an effective method in replacing laborious, expensive and time-consuming laboratory experimentations. Molecular descriptors perform an important part in mathematical chemistry, especially during the investigation of Quantitative structure property relationships (QSPRs) and Quantitative structure-activity relationships (QSARs) that provide insight into animate effects based on chemical structures. In this paper, we have computed some novel neighborhood version molecular descriptors for the two carbon nanosheets VC5C7p,q and (HC5C7p,q) and derived formulas for them. Our computed results are surely helpful in understanding the topology of understudy nanosheets. These computed indices have best correlation with acentric factor and entropy, therefore, are effective in QSPRs and QSARs analysis with powerful accuracy.

In the design of cold-formed steel (CFS) buildings, sheathings are used to provide lateral resistance to seismic or wind load. Many researchers have thoroughly studied the basic behavior of different sheathing (including walls sheathed with plywood, oriented strand board, gypsum wallboard, gypsum sheathing board, steel sheet sheathing, and fiberboard) with different thicknesses. However, despite many studies, the examination of existing experimental studies demonstrates that the lateral bracing and stiffness provided by the sheathing are generally ignored, and sheathing is provided as a non-structural component. This study aims at that neglected aspect. In this study, cold-formed steel shear wall (CFSSW) response has been investigated with varied thickness of hardboard sheathing used as a structural element. The main aim was to determine the contribution of sheathing in resisting lateral forces. For this purpose, three full-scale specimens (i) frame without sheathing, (ii) frame sheathed with 4 mm thick hardboard, (iii) frame sheathed with 10 mm thick hardboard were tested using a uni-directional shake table. The sheathing was screw-fastened to the cold-formed studs and tracked for the development of shear stiffness and strength in the wall system. The test results revealed that in addition to increasing the lateral stiffness of the structure, the hardboard sheathing also acts as an efficient bracing system. However, some minor but recoverable damages were also noticed. In addition, stud local buckling failure mode was observed, and it could be caused by the low anchor stiffness of using cleats instead of hold-downs. It is also important to mention that the number of tests performed in the current study was limited, and further similar research needs to be carried out before generalizing the results.

Heavy chain isotypes of low level monoclonal immunoglobulins are sometimes obscured in serum immunofixation electrophoresis (SIFE) by a heavy background of polyclonal immunoglobulins. However, accurate determination of the heavy chain isotype is essential for a complete diagnosis, as isotype determination of autoantibodies may have relevance in determining therapeutic procedures. Immune subtraction (IS) was employed in a patient with neuropathy and GD1a autoantibody. IS allowed identification of the cognate heavy chain related to a lambda light chain restriction noted on initial SIFE as well as isotype determination of the autoantibody.Antisera specific to individual heavy and light chains were used for depletion of specific immunoglobulin types. Depletion of kappa light chain associated immunoglobulins allowed unequivocal determination of the isotype of lambda light chain-associated low level monoclonal band to be IgG Lambda. Selective depletion of kappa, lambda, gamma and mu heavy chain immunoglobulins was employed to determine IgG Kappa isotype of the auto-antibody.

Extracellular vesicles (EVs) have emerged as potential mediators of intercellular communication. EVs are nano-sized, lipid membrane–bound vesicles that contains biological information in the form of proteins, metabolites and/or nucleic acids. EVs are key regulators of tissue repair mechanisms, such as in the context of lung injuries. Recent studies suggest that EVs have the ability to repair COVID19-associated acute lung damage. EVs hold great promise for therapeutic treatments, particularly in treating a potentially fatal autoimmune response and attenuate inflammation. They are known to boost lung immunity and are involved in the pathogenesis of various lung diseases, including viral infection. EV-based immunization technology has been proven to elicit robust immune responses in many models of infectious disease, including COVID-19. The field of EV research has tremendous potential in advancing our understanding about viral infection pathogenesis, and can be translated into anti-viral therapeutic strategies.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that causes coronavirus-19 disease (COVID-19), a severe respiratory damaging syndrome with serious health complications worldwide. SARS-CoV-2 was unfamilar before the epidemic started in Wuhan, China, in December 2019. COVID-19 is currently a pandemic influencing several countries worldwide. One of the mysteries of the new coronavirus is that it is deadlier for men than women with the male mortality rate is twice as high as that of females.

Background: This research investigated the association between school role in improving parenting skills and the academic performance of secondary school students. And to examine the role of student gender, literacy status of the parents and parental importance to children education in the association between school role in improving parenting skills and students' academic performance. Methods: A multistage stratified random sampling technique was adopted to portray information from 448 students on a Likert scale. Chi-square, Kendall's Tau-c tests were used to determine the direction and strength of association among variables. Results: The results depicted that the association of children's academic performance was found significant (P = 0.000) and positive (Tau-c = 0.215) with school capacitating parents how to monitor and discuss schoolwork at home with children, assisting parents in setting academic goals for children (P = 0.000, Tau-c = 0.225). Moreover, the respondent's gender, parent literacy status and parental importance to children's education explained variation in the association between school role in improving parenting skills and children's academic performance. Conclusions: The school role in improving parenting skills significantly and positively contributed to children's academic performance. Moreover, boys, children of literate parents and children whose parents give more importance to their education with school active role in improving parenting skills were more liable to score higher grades.

In this paper, an approximate analytical solution of the bistable Allen–Cahn equation is given. The Allen–Cahn equation is a mathematical model to study the phase separation process in binary alloys and emerged as a convection-diffusion equation in fluid dynamics or reaction-diffusion equation in material sciences. A phase transition occurs at the interface when one material changes its composition or structure. The homotopy perturbation method and homotopy analysis method are used for finding the approximate solution. These methods don't need the use of any transformation, discretization, unrealistic restriction and assumption. The error estimates are computed by comparing with a numerical method, and a good agreement is observed.

In this study, we explore a mathematical model of human immunodeficiency virus type 1 (HIV-1) infection in CD4+ T-cells, considering fractional-order dynamics in the Caputo sense. Fractional models are of great significance due to their capacity to predict disease outbreaks while incorporating memory and non-local characteristics. To establish the existence and uniqueness of solutions for the fractional model, a fixed-point theorem and an iterative technique are employed. Furthermore, it is confirmed that the solutions of the model are both positive and bounded. The basic reproduction number, denoted as R0, is calculated using the next-generation matrix approach, and the equilibrium points of the model, including disease-free and endemic equilibrium points, are presented. A sensitivity analysis of R0 is performed by varying different parameters. To analyze the local stability of equilibrium points, the Routh-Hurwitz technique is employed. The global stability of equilibrium points is demonstrated using the Lyapunov function and LaSalle's principle, as well as through UlamHyers (UH) stability and the generalized UH stability conditions. The proposed numerical scheme Adams-Bashforth predictor-corrector is validated by comparing it with the fourth-order Runge-Kutta (RK4) technique. We examine the influence of the fractional order (ξ) by conducting numerical simulations for different values of ξ using the Adams-Bashforth predictor-corrector approach. Furthermore, graphical presentations illustrate how different crucial model parameters impact disease dynamics. The results obtained from this study demonstrate that the adopted strategies significantly improve prediction accuracy.

The ongoing pandemic COVID-19 caused by Severe Acute Respiratory Coronavirus-2 (SARS-CoV-2) has wreaked havoc globally by affecting millions of lives. Although different countries found the implementation of emergency measures useful to combat the viral pandemic, many countries are still experiencing the resurgence of COVID-19 cases with new variants even after following strict containment guidelines. Country-specific lessons learned from the ongoing COVID-19 pandemic can be utilized in commencing a successful battle against the potential future outbreaks. In this article, we analyzed the overall scenario of the COVID-19 pandemic in Bangladesh from Alpha to Omicron variant and discussed the demographic, political, economic, social, and environmental influences on the mitigation strategies employed by the country to combat the pandemic. We also tried to explore the preparedness and precautionary measures taken by the responsible authorities, the choice of strategies implemented, and the effectiveness of the response initiated by the government and relevant agencies. Finally, we discussed the possible strategies that might help Bangladesh to combat future COVID-19 waves and other possible pandemics based on the experiences gathered from the ongoing COVID-19 pandemic.

This study focuses on analyzing the behavior of a time-varying magnetohydrodynamic (MHD) Williamson nanofluid (WNF) flowing over a stretched plate. The main objective is to understand the heat and mass transport properties of the nanofluid in this scenario. The mathematical model involves a system of partial differential equations (PDEs) that are transformed into a set of ordinary differential equations (ODEs) using a similarity transformation. These ODEs are then solved using the well-established MATLAB BVP4C technique; a part of the finite difference method. To ensure the reliability and accuracy of the approach, the obtained results are compared with previously published literature, serving as a validation step. The numerical findings are presented graphically and in tabular form. The study focuses on examining the impact of various external factors on the friction factor, Nusselt number, and Sherwood number, providing a comprehensive evaluation of heat transfer and mass transport characteristics. Specifically, the study investigates the flow and heat transfer properties near a stretched plate with a permeable layer. Also, takes into account the transverse dispersion, and heat generation effects in MHD WNF moreover with magnetic field and slip boundary conditions. The results of the study indicate that increasing the Brownian motion parameter (Nb) and Thermophoresis parameter (Nt) leads to higher local Nusselt numbers, reflecting the increased rates of transferring the heat. Similarly, an increase in the Nt is associated with higher local Sherwood numbers, indicating enhanced mass transport. Conversely, higher values of the Brownian motion parameter (Nb) result in lower local Sherwood numbers. By analyzing the influence of various external factors, such as nanofluid properties and magnetic fields, a better understanding of heat transfer and mass transport characteristics can be obtained.

Cyclin E1 (CCNE1) is a protein-coding gene that belongs to the Cyclin family of genes which controls the G1/S phase transition of the cell cycle. Previously, its abnormal expression pattern has been examined and found to be correlated with ovarian and breast cancer progression. Herein, we exploited a bioinformatics and database mining strategy to unveil the therapeutic and prognostic significance of CCNE1 gene expression in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). CCNE1 gene was reported to be highly expressed in LUAD and LUSC tissues. Its promoter and coding sequences were reported to be aberrantly methylated in LUAD and LUSC tissues than in normal tissues. Moreover, around 12 somatic mutations (frequency: 0.7%) were recorded in the CCNE1 coding region from different studies involving LUAD and LUSC patients' whole genome sequences. The CCNE1 gene expression was also correlated with LUAD and LUSC patients’ overall and disease-specific survival. Immune infiltration analysis revealed the association between CCNE1 gene expression and the abundance of numerous immune cells (i.e., T cells and B Cells) infiltration in LUAD and LUSC patients. Two previously known genes involved in oncogenic processes i.e., CDC45 and PDCD5 were identified as the most highly co-expressed genes of CCNE1 in LUAD and LUSC tissues. Altogether, the CCNE1 gene and its transcriptional and translational products may serve as a prognostic or therapeutic target in the diagnosis and treatment of LUAD and LUSC patients. The scientific findings of this study should assist in translating CCNE1 into clinical practice for lung cancer diagnosis and treatment.

This study explored the diagnostic and therapeutic values of the LSM5 gene in two commonest types of gliomas, i.e., lower-grade glioma (LGG) and glioblastoma multiforme (GBM) by exploiting a bioinformatics-based database mining pipeline. LSM5 gene was reported to be upregulated at both mRNA and protein levels in both LGG and GBM tissues compared to adjacent normal tissues. Moreover, overexpression of LSM5 was later observed to be linked with glioma patients' demographic status and advancing cancer stages. Additionally, a differential and distinct methylation pattern of the LSM5 promoter and coding sequence in glioma tissues was reported. The gene of our interest was also discovered to undergo multiple amplification events (frequency of alteration: 1.1%) in both LGG and GBM patients. Furthermore, LSM5 overexpression was reported to be correlated with the poor overall and relapse-free survival of glioma patients. Subsequently, the immune phenotype analysis revealed that the expression of LSM5 correlates with the abundance levels of various immune cells i.e., B cell, T cell, dendritic cell, and immunomodulators, i.e., IL10RB, CD48, CD274 in glioma microenvironment. Finally, functional enrichment analysis of the neighbor genes of LSM5 showed that most of the genes are involved in different pivotal cellular processes like RNA splicing, DNA mismatch repair, and DNA replication. Overall, this study suggests that LSM5 and its downstream products are potential candidates for glioma diagnosis and treatment.

Currently available screening instruments for evaluation of individuals with intellectual disabilities do not capture all the complications associated with Down Syndrome (DS). Here, we examined age and gender-specific variability revolving around major challenges related to ophthalmologic and auditory health, social integration, daily life, and behavioral problems in 468 (age: 2–84 years) individuals with DS living in all eight divisions of Bangladesh. More than half of the children presented with significant difficulty in walking or other targeted movements compared with 37.9% of adolescents (p = 0.03). Nearly 70% of children exhibited communication difficulties, particularly revolving around the understanding of speech, comprehending or learning tasks or new materials, and in expressing thoughts in words or behaviors (p = 0.003–0.006). Uncontrolled urination was frequent and predominantly found among children (p = 0.04). No significant differences were present in females vs. males except for concern about physical appearance (females: 58.5% vs. males: 47.5%; p = 0.02). The severity of DS was associated with intellectual performance, communication difficulties, and self-sufficiency (i.e., uncontrolled micturition or bowel movements) but not with psychotic, ophthalmologic, auditory, or motor skills-related problems. Increased awareness of DS phenotypic profiles among professionals and caregivers can foster earlier detection and counselling and help formulate appropriate interventions to reduce long-term sequelae and enhance cognitive and behavioral developmental outcomes.

Hepatocellular carcinoma (HCC) is the most common malignant condition of the liver that occurs as a result of uncontrolled cellular proliferation after a series of disruptions at cell cycle regulatory checkpoints in the normal cell. Due to the lack of appropriate therapeutics or remedial treatment methods, new treatment strategies against HCC need to be developed. Cyclin dependent kinases (CDKs) are required to control the cell cycle and apoptosis, but their overexpressionis critical in the progression of cancer and is often expressed in HCC. Thus, CDKs are considered a promising class of target-defined therapy for HCC. Milciclib is a potential candidate for HCC which exhibits inhibitory activity against CDK2 leading to cell cycle arrest and apoptosis of tumor cells. Herein, we have halogenated the parent drug milciclib to improve its efficacy against CDK2. The primary structure of milciclib (D) was modified with F, Cl and,CF3 groups. The frontier molecular orbital features, binding affinity, non-bonded interaction and the pharmacokinetic parameters were analyzed for milciclib and its derivatives. We also performed molecular docking and extended molecular dynamics simulation studies to study the binding interactions and binding affinity more closely. Our computational investigation showed the derivatives D-F and D-CF3 have significant chemical reactivity, the best binding affinity, nonbonding interactions, and improved pharmacokinetic properties compared to the parent drug milciclib. Molecular dynamics analysis and MM-PBSA calculations indicated that D-Cl had a slightly more stable conformation and higher binding affinity compared to D-CF3.

Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein which is involved in cell signaling, proliferation, maturation, and movement, all of which are crucial for the proper development of cells and tissues. Cleavage of the EpCAM protein leads to the up-regulation of c-myc, e-fabp, and cyclins A and E which promote tumorigenesis. EpCAM can act as potential diagnostic and prognostic biomarker for different types of cancers as it is also found to be expressed in epithelia and epithelial-derived neoplasms. Hence, we aimed to analyze the EpCAM gene expression and any associated feedback in the patients of two major types of lung cancer (LC) i.e., lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), based on the publicly available online databases. In this study, server-based gene expression analysis represents the up-regulation of EpCAM in both LUAD and LUSC subtypes as compared to the corresponding normal tissues. Besides, the histological sections revealed the over-expression of EpCAM protein in cancerous tissues by depicting strong staining signals. Furthermore, mutation analysis suggested missense as the predominant type of mutation both in LUAD and LUSC in the EpCAM gene. A significant correlation (P-value

As the number of infections and deaths caused by the recent COVID-19 pandemic is increasing dramatically day-by-day, scientists are rushing towards developing possible countermeasures to fight the deadly virus, SARS-CoV-2. Although many efforts have already been put forward for developing potential vaccines; however, most of them are proved to possess negative consequences. Therefore, in this study, immunoinformatics methods were exploited to design a novel epitope-based subunit vaccine against the SARS-CoV-2, targeting four essential proteins of the virus i.e., spike glycoprotein, nucleocapsid phosphoprotein, membrane glycoprotein, and envelope protein. The highly antigenic, non-allergenic, non-toxic, non-human homolog, and 100% conserved (across other isolates from different regions of the world) epitopes were used for constructing the vaccine. In total, fourteen CTL epitopes and eighteen HTL epitopes were used to construct the vaccine. Thereafter, several in silico validations i.e., the molecular docking, molecular dynamics simulation (including the RMSF and RMSD studies), and immune simulation studies were also performed which predicted that the designed vaccine should be quite safe, effective, and stable within the biological environment. Finally, in silico cloning and codon adaptation studies were also conducted to design an effective mass production strategy of the vaccine. However, more in vitro and in vivo studies are required on the predicted vaccine to finally validate its safety and efficacy.

Pancreatic Ductal Adenocarcinoma (PDAC) is a form of pancreatic cancer with poor prognosis and rising incidence. Difficulties in the early detection and aggressive biological nature of this disease are responsible for most of the therapeutic failures. In this study, publicly available microarray expression data of full RNA from the peripheral blood of PDAC patients has been utilized via a network-based approach in order to identify potential non-invasive biomarkers and drug targets for early diagnosis and treatment of PDAC. Analysis of differentially expressed genes revealed their predominant involvement in the translational process, apoptotic process, protein phosphorylation, immune response, ATP binding, protein binding, and signal transduction. Moreover, CREBBP, MAPK14, MAPK1, SMAD3, UBC, MAGOH, HSP90AB1, RPL23A, ACTB and STAT3 were identified as the best proteome signatures, GATA2, FOXC1, PPARG, E2F1, HINFP, USF2, MEF2A, FOXL1, YY1 and NFIC were identified as the best transcriptional regulatory signatures, and hsa-miR-93, hsa-miR-16, hsa-miR-195, hsa-miR-424, hsa-miR-506, hsa-miR-124, hsa-miR-590-3p, hsa-miR-1, hsa-miR-497 and hsa-miR-9 were identified as the best post-transcriptional regulatory signatures in PDAC patients. Analysis of drug-gene interactions revealed Anisomycin, Azactidine, Arsenic trioxide, Bortezomib, Ulixertinib, and some other molecules as probable candidate molecules which may reverse the PDAC condition.

Currently, dengue fever and human cervical cancer are two of the most dangerous diseases around the world. Dengue fever is caused by four serotypes of dengue virus (DENV)- 1, 2, 3 and 4 and most of the human cervical cancer cases are found to be caused by human papillomavirus type-16 (HPV-16). In this study, potential epitope-based subunit vaccines were designed using numerous tools of reverse vaccinology, targeting the DENV-1 envelope protein E and HPV-16 major capsid protein L1. Reverse vaccinology is a genome-based approach of vaccine designing, identifying potential antigenic epitopes of a pathogen. After obtaining the target protein sequences, their antigenicity and physicochemical properties were determined. Thereafter, the possible T cell and B cell epitopes were predicted and after analyzing their antigenicity, allergenicity, toxicity, and conservancy, as well as docking results (docked with MHC class-I and class-II alleles), the best epitopes were selected for vaccine construction. Thereafter, two best vaccine constructs (one for each of the virus) were selected for further analysis based on analyzing the docking results (docked with various MHC alleles and TLR molecules) of the initially constructed six vaccines. The molecular dynamics simulation of the two selected docked complexes with the two best vaccines showed quite satisfactory results. Finally, after codon adaptation, the vaccine constructs were inserted into the pET-19b plasmid vector for E. coli strain K12 using an in silico cloning approach. However, more in vitro and in vivo studies might be required on the suggested vaccines for their proper validation.

The inherent toxicity, mutagenicity and carcinogenicity of dyes that are discharged into aquatic ecosystems, harming the health of humans and animals. ZIF-8 based composites are regarded as good adsorbents for the breakdown of dyes in order to remove or degrade them. In the course of this research, metal-organic framework materials known as ZIF-8 and its two stable composites, ZIF-8/BiCoO 3 (MZBC) and ZIF-8/BiYO 3 (MZBY), were produced via a hydrothermal process and solvothermal process, respectively, for the dangerous Congo red (CR) dye removal from the solution in water using adsorption method. According to the findings, the most significant amount of CR dye that could be adsorbed is onto MZBC, followed by MZBY and ZIF-8. The pseudo-second-order kinetic model was used effectively to match the data for adsorption behavior and was confirmed using the Langmuir isotherm equation. There is a possibility that the pH and amount of adsorbent might influence the adsorption behavior of the adsorbents. According to the experiment results, the technique featured an endothermic adsorption reaction that spontaneously occurred. The higher adsorption capability of MZBC is because of the large surface area. This results in strong interactions between the functional groups on the surface of MZBC and CR dye molecules. In addition to the electrostatic connection between functional group Zn-O-H on the surface of ZIF-8 in MZBC and the -NH 2 or SO 3 functional group areas in CR molecules, it also includes the strong π-π interaction of biphenyl rings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)