Development and characterization of letrozole-encapsulated polymeric beads for sustained release.

The study aimed to prepare and characterize biodegradable sustained-release beads of letrozole (LTZ) for treating cancerous disease. The ionotropic gelation method was used for the preparation and calcium chloride (CaCl2) was used as a gelating agent, while chitosan (CTS) and sodium alginate (NaAlg) as biodegradable polymeric matrices in the blend hydrogel beads. The beads were characterized for their size, surface morphology, drug entrapment efficiency, drug-polymer interaction and crystallinity using different analytic techniques, including optical microscopy, Scanning Electron Microscopy (SEM), UV-spectroscopy, Fourier-transform Infrared Spectroscopy (FTIR), Thermo gravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and X-ray Diffraction Analysis (XRD) respectively. In vitro swelling studies were also applied to observe the response of these polymeric networks against different pH (at 1.2, 6.8 and 7.4 pH). The results from TGA and DSC exhibited that the components in the formulation possess better thermal stability. The XRD of polymeric networks displays a minor crystalline and significant amorphous nature. The SEM micrographs revealed that polymeric networks have uneven surfaces and grooves. Better swelling and in vitro outcomes were achieved at a high pH (6.8,7.4), which endorsed the pH-responsive characteristics of the prepared beads. Hence, beads based on chitosan and sodium alginate were successfully synthesized and can be used for the controlled release of letrozole.

Control synthesis of mesoporous silica microparticles: Optimization and in vitro cytotoxicity studies.

The current paper explains how to make mesoporous silica microparticles (MSM) by mixing water and dichloromethane. Several dichloromethane-water ratios were used to adjust the reaction mixture for the first time to easily synthesize mesoporous silica micro particles with regulated particle size. By carefully modifying the concentrations of water and dichloromethane, a higher level of consistency was achieved in the production of micro particles, i.e. to a 2:1 v/v ratio. It was discovered that variations in the dichloromethane-to-water ratios significantly affect the surface roughness and morphologies of mesoporous silica particles along with size. This is most likely because the solvent affects how quickly tetraethyl orthosilicate (TEOS) and how quickly inorganic species polymerize. In all experiments, conditions were maintained the same at 25oC temperature and 1000 rpm. Scanner electron microscopy (SEM), Fourier transform infrared (FTIR) and X-ray powder diffraction (XRD) methods were used to identify the structure of MSM. The in vitro cytotoxicity assays showed that the produced particles, which had a diameter of 1.0 m, were safe for usage in the cellular system.

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete.

The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms' performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R2 value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R2 values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.

A Comprehensive Review of Incorporating Steel Fibers of Waste Tires in Cement Composites and Its Applications.

Accumulating vast amounts of pollutants drives modern civilization toward sustainable development. Construction waste is one of the prominent issues impeding progress toward net-zero. Pollutants must be utilized in constructing civil engineering structures for a green ecosystem. On the other hand, large-scale production of industrial steel fibers (ISFs) causes significant damage to the goal of a sustainable environment. Recycled steel fibers (RSFs) from waste tires have been suggested to replace ISFs. This research critically examines RSF's application in the mechanical properties' improvement of concrete and mortar. A statistical analysis of dimensional parameters of RSFs, their properties, and their use in manufacturing various cement-based composites are given. Furthermore, comparative assessments are carried out among the improvements in compressive, split tensile, and flexural strengths of plain and RSF-incorporated concrete and mortar. In addition, the optimum contents of RSF for each strength property are also discussed. The influence of RSFs parameters on various strength properties of concrete and mortars is discussed. The possible applications of RSF for various civil engineering structures are reviewed. The limitations and errors noticed in previous review papers are also outlined. It is found that the maximum enhancement in compressive strength (CS), split tensile strength (STS), and flexure strength (FS) are 78%, 149%, and 157%, respectively, with the addition of RSF into concrete. RSF increased cement mortars' CS, STS, and FS by 46%, 50.6%, and 69%, respectively. The current study encourages the building sector to use RSFs for sustainable concrete.

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber-reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R2), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R2 = 0.96, as opposed to SVR bagging and support vector regression, which had R2 values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

Rising trend of Nutraceuticals: Evaluation of lyophilized beetroot powder at different doses for its hypolipidemic effects.

A diet comprising of nutrients that would control hypertension as well as hyperlipidemia would be very beneficial over all. This study aimed to assess the effect of lyophilized beet root powder at different doses on lipid profile and hyperlipidemia model. Albino rabbits weighing 1500-2000gms were taken for both studies. Beetroot powder was administered to animals at 500mg/kg and 1000mg/kg doses and after two month dosing the blood samples were withdrawn and lipid profile was assessed. Next a model of hyperlipidemia was created comprising of albino rabbits that were divided into five groups each containing n=6. Group I was considered as control, Group II was marked as Negative control, Group III was taken as standard, whereas Group IV and V were considered as treated and given different doses of beetroot. Blood samples were drawn at baseline, 45th day and at day 60th of study. Highly significant decrease in lipid profile (Cholesterol, LDL and TGS) and significant increase in HDL was observed by both doses after one month. HDL was increased more at 1000mg/kg dose. The presence of flavonoids and saponins in beetroot is responsible for hypolipidemic effect. From our research we came to the conclusion that beetroot powder reduced the lipid profile and could be beneficial in treatment of cardiovascular disease due to atherosclerosis and obesity.

Comparative proteome-wide study for in-silico identification and characterization of indispensable hypothetical proteins of food borne-pathogen Campylobacter jejuni (CJJ) by subtractive genomics approach.

Campylobacter jejuni (CJJ) is a source of bacterial foodborne diarrhea globally. Mostly found prevalent in children in the developing countries that may lead to mortality. The upsurge in antimicrobial resistance is causing hindrance in the treatment, as highlighted by CDC and WHO. The study hypothesized the application of subtractive genomics approach coupled with metabolic pathway to reveal unidentified essential proteins that could serve as potential drug target (s). The approach was employed to model the druggable proteome of C. jejuni resistant strain 81-176. We obtained 728/1744 non-homologous essential proteins by performing sequence similarity search against host proteome and DEG server, respectively. The KAAS annotated metabolic pathway information; PSORTb predicted their sub cellular localization and SVMPro functional annotated 104 hypothetical proteins while the Drug Bank for the druggability analysis. We found 04/104 protein druggable viz. synaptic vesicular amine transporter, Uracil-DNA glycosylase, Laccase domain protein YfiH, and Phosphoenolpyruvate protein phosphor transferase. The study has revealed a formerly uncharacterized pool of C. jejuni proteins that can play a significant role in controlling CJJ infection and presented previously uncharacterized four proteins as potential drug targets. These potential drug targets can further be explored employing structure-based and other biochemical methods by the scientific community.

An Experimental and Empirical Study on the Use of Waste Marble Powder in Construction Material.

Marble is currently a commonly used material in the building industry, and environmental degradation is an inevitable consequence of its use. Marble waste occurs during the exploitation of deposits using shooting technologies. The obtained elements most mainly often have an irregular geometry and small dimensions, which excludes their use in the stone industry. There is no systematic way of disposing of these massive mounds of waste, which results in the occurrence of landfills and environmental pollution. To mitigate this problem, an effort was made to incorporate waste marble powder into clay bricks. Different percentage proportions of marble powder were considered as a partial substitute for clay, i.e., 5-30%. A total of 105 samples were prepared in order to assess the performance of the prepared marble clay bricks, i.e., their water absorption, bulk density, apparent porosity, salt resistance, and compressive strength. The obtained bricks were 1.3-19.9% lighter than conventional bricks. The bricks with the addition of 5-20% of marble powder had an adequate compressive strength with regards to the values required by international standards. Their compressive strength and bulk density decreased, while their water absorption capacity and porosity improved with an increased content of marble powder. The obtained empirical equations showed good agreement with the experimental results. The use of waste marble powder in the construction industry not only lowers project costs, but also reduces the likelihood of soil erosion and water contamination. This can be seen to be a crucial factor for economic growth in agricultural production.

Effect of Coconut Fiber Length and Content on Properties of High Strength Concrete.

Recently, the addition of natural fibers to high strength concrete (HSC) has been of great interest in the field of construction materials. Compared to artificial fibers, natural fibers are cheap and locally available. Among all natural fibers, coconut fibers have the greatest known toughness. In this work, the mechanical properties of coconut fiber reinforced high strength concrete (CFR-HSC) are explored. Silica fume (10% by mass) and super plasticizer (1% by mass) are also added to the CFR-HSC. The influence of 25 mm-, 50 mm-, and 75 mm-long coconut fibers and 0.5%, 1%, 1.5%, and 2% contents by mass is investigated. The microstructure of CFR-HSC is studied using scanning electron microscopy (SEM). The experimental results revealed that CFR-HSC has improved compressive, splitting-tensile, and flexural strengths, and energy absorption and toughness indices compared to HSC. The overall best results are obtained for the CFR-HSC having 50 mm long coconut fibers with 1.5% content by cement mass.

Management of house fly, Musca domestica L. (Muscidae: Diptera), through botanical baits

ABSTRACT The house fly, Musca domestica L. (Diptera: Muscidae), is a major pest of all aspects of life, like the domestic, medical and veterinary and causal agent of several pathogenic diseases. The present study was conducted to evaluate the potential of different insecticide-free baits against house fly by incorporating flower methanol extract of Helianthus annuus (sunflower) and Tegetes erecta (marigold) at 10%, 20% and 30% bait formulation of corn syrup, dried milk and water. However, imidacloprid and thiacloprid (each at 5% concentration) were also included in the study for comparison. Results showed that insecticide baits were superior in causing mortality of adult house fly but dependent upon syrup. Overall, 20% baits of both extracts caused more than half population death of house fly within 48h. On the other hand, the mortality rate by 30% baits (from sunflower and marigold) had a similar impact as observed in case of imidacloprid and thiacloprid baits. Therefore, biological baits could play a more active and safer role in the management of house fly as compared to synthetic insecticides.

Proteome-wide subtractive approach to prioritize a hypothetical protein of XDR-Mycobacterium tuberculosis as potential drug target.

BACKGROUND: Among the resistant isolates of MTB, multidrug resistant tuberculosis (MDR-TB) and extensively drug resistant tuberculosis (XDR-TB) have been the areas of growing concern. The genomic analysis showed that the respective genomic pool of the XDR-MTB proteome contains more than 30% of the hypothetical proteins for which no functions have been annotated yet. This class of proteins presumably have their own importance to complete genome and proteome information. The bioinformatics advancements have helped to annotate those hypothetical proteins by using various computational tools and have potential to classify them functionally. OBJECTIVE: The objective of this study was to propose a new and unique drug target against the deadly Mycobacterium tuberculosis using Bioinformatics approaches to characterize the hypothetical proteins. RESULTS: We stepwise reduced the hypothetical proteins (total number: 1256) out of the complete proteome to only 26 essential hypothetical proteins. Out of those 26 proteins, the protein WP_003401246.1 was computationally characterized as the druggable target. CONCLUSION: The study proposed a hypothetical protein from complete proteome of the XDR-MTB as a new drug target against which new drug candidates can be proposed. Hence, the study opens up the new avenues in the areas of drug discovery against deadly M. tuberculosis.

Core Proteomic Analysis of Unique Metabolic Pathways of Salmonella enterica for the Identification of Potential Drug Targets.

BACKGROUND: Infections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host. METHODS: We took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen. RESULTS: The identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic pathways of the pathogens and mining the proteomic data of all completely sequenced strains of the pathogen, thus improving the quality and application of the results. We believe that the sharing of the knowledge from this study would eventually lead to bring about novel and unique therapeutic regimens against the infections caused by the S. enterica.

Conformational analysis and geometry optimization of apomorphine as an Anti-parkinsonian agen.

Apomorphine, a dopamine D1/D2agonist, is an important drug of choice for the treatment of Parkinson's and related disorders. The present study was designed to perform the conformational analysis and geometry optimization of apomorphine. Resultant optimized structure corresponds to a substance as it is found in nature. This could be used for a variety of experimental and theoretical investigations especially in the field of pharmacokinetics. The results indicate that the best conformation of the molecule is present at minimum potential energy -88702.9595 kcal/mol. At this point molecule will be more active as histamine H1 receptor agonist.