Explore the vast range of titles available.

Carbon nanodots with opposite chirality possess the same major physicochemical properties such as optical features, hydrodynamic diameter, and colloidal stability. Here, a detailed analysis about the comparison of the concentration of both carbon nanodots is carried out, putting a threshold to when differences in biological behavior may be related to chirality and may exclude effects based merely on differences in exposure concentrations due to uncertainties in concentration determination. The present study approaches this comparative analysis evaluating two basic biological phenomena, the protein adsorption and cell internalization. We find how a meticulous concentration error estimation enables the evaluation of the differences in biological effects related to chirality. Chirality is known to impact the biological activity of materials but concentration differences can often lead to errors in analysis. Here, the authors report on detailed concertation analysis of different chiral carbon nanodots to accurately investigate chiral effects on the protein absorption and cell internalisation.

The development of biosensors for infectious diseases is crucial, especially in the post pandemic era. Point-of-care (POC) devices are highly needed in developing countries to provide quick and cost-effective results. Nanoparticles with enzymatic-like activity, also known as nanozymes, show great promise for diagnostic applications and have recently gained significant attention. In this study, bovine serum albumin (BSA)-protected copper nanoclusters (Cu NCs) with enzymatic-like characteristics were created and analysed for their intrinsic enzymatic-like activity, physicochemical properties, and colloidal stability. The Cu NCs were synthesized via the use of BSA as a capping and reducing agent to ensure their biocompatibility and colloidal stability. Various techniques, such as UV‒Vis spectroscopy, photoluminescence (PL), dynamic light scattering (DLS), elemental analysis, and transmission electron microscopy (TEM), have been used to characterize the prepared Cu NCs. The Cu NCs demonstrated oxidase- and peroxidase-like activity by catalyzing the oxidation of o-phenylenediamine (OPD) into 2,3-diaminophenazine (DAP), the oxOPD product. Showing higher oxidase-like activity by 1.6x compared to the peroxidase-like activity at the same substrate concentration. Their catalytic properties were utilized to detect two common bacteria, gram-negative Escherichia coli ( E. coli ) and gram-positive Staphylococcus aureus ( S. aureus ), demonstrating a differential label-free detection of different strains at concentrations higher than 10 5 CFU mL − 1 . This study introduces a promising and affordable application for a label-free biosensor used in detecting infectious diseases.

In recent years, much effort has been invested into developing multifunctional drug delivery systems to overcome the drawbacks of conventional carriers. Magnetic nanoparticles are not generally used as carriers but can be functionalised with several different biomolecules and their size can be tailored to present a hyperthermia response, allowing for the design of multifunctional systems which can be active in therapies. In this work, we have designed a drug carrier nanosystem based on Fe3O4 nanoparticles with large heating power and 4-amino-2-pentylselenoquinazoline as an attached drug that exhibits oxidative properties and high selectivity against a variety of cancer malignant cells. For this propose, two samples composed of homogeneous Fe3O4 nanoparticles (NPs) with different sizes, shapes, and magnetic properties have been synthesised and characterised. The surface modification of the prepared Fe3O4 nanoparticles has been developed using copolymers composed of poly(ethylene-alt-maleic anhydride), dodecylamine, polyethylene glycol and the drug 4-amino-2-pentylselenoquinazoline. The obtained nanosystems were properly characterised. Their in vitro efficacy in colon cancer cells and as magnetic hyperthermia inductors was analysed, thereby leaving the door open for their potential application as multimodal agents.

Encapsulation with polymers is a well-known strategy to stabilize and functionalize nanomaterials and tune their physicochemical properties. Amphiphilic copolymers are promising in this context, but their structural diversity and complexity also make understanding and predicting their behavior challenging. This is particularly the case in complex media which are relevant for intended applications in medicine and nanobiotechnology. Here, we studied the encapsulation of gold nanoparticles and quantum dots with amphiphilic copolymers differing in their charge and molecular structure. Protein adsorption to the nanoconjugates was studied with fluorescence correlation spectroscopy, and their surface activity was studied with dynamic interfacial tensiometry. Encapsulation of the nanoparticles without affecting their characteristic properties was possible with all tested polymers and provided good stabilization. However, the interaction with proteins and cells significantly depended on structural details. We identified statistical copolymers providing strongly reduced protein adsorption and low unspecific cellular uptake. Interestingly, different zwitterionic amphiphilic copolymers showed substantial differences in their resulting bio-repulsive properties. Among the polymers tested herein, statistical copolymers with sulfobetaine and phosphatidylcholine sidechains performed better than copolymers with carboxylic acid- and dimethylamino-terminated sidechains.

In recent years, much effort has been invested into developing multifunctional drug delivery systems to overcome the drawbacks of conventional carriers. Magnetic nanoparticles are not generally used as carriers but can be functionalised with several different biomolecules and their size can be tailored to present a hyperthermia response, allowing for the design of multifunctional systems which can be active in therapies. In this work, we have designed a drug carrier nanosystem based on Fe O nanoparticles with large heating power and 4-amino-2-pentylselenoquinazoline as an attached drug that exhibits oxidative properties and high selectivity against a variety of cancer malignant cells. For this propose, two samples composed of homogeneous Fe O nanoparticles (NPs) with different sizes, shapes, and magnetic properties have been synthesised and characterised. The surface modification of the prepared Fe O nanoparticles has been developed using copolymers composed of poly(ethylene-alt-maleic anhydride), dodecylamine, polyethylene glycol and the drug 4-amino-2-pentylselenoquinazoline. The obtained nanosystems were properly characterised. Their in vitro efficacy in colon cancer cells and as magnetic hyperthermia inductors was analysed, thereby leaving the door open for their potential application as multimodal agents.

In recent years, much effort has been invested into developing multifunctional drug delivery systems to overcome the drawbacks of conventional carriers. Magnetic nanoparticles are not generally used as carriers but can be functionalised with several different biomolecules and their size can be tailored to present a hyperthermia response, allowing for the design of multifunctional systems which can be active in therapies. In this work, we have designed a drug carrier nanosystem based on Fe[sub.3] O[sub.4] nanoparticles with large heating power and 4-amino-2-pentylselenoquinazoline as an attached drug that exhibits oxidative properties and high selectivity against a variety of cancer malignant cells. For this propose, two samples composed of homogeneous Fe[sub.3] O[sub.4] nanoparticles (NPs) with different sizes, shapes, and magnetic properties have been synthesised and characterised. The surface modification of the prepared Fe[sub.3] O[sub.4] nanoparticles has been developed using copolymers composed of poly(ethylene-alt-maleic anhydride), dodecylamine, polyethylene glycol and the drug 4-amino-2-pentylselenoquinazoline. The obtained nanosystems were properly characterised. Their in vitro efficacy in colon cancer cells and as magnetic hyperthermia inductors was analysed, thereby leaving the door open for their potential application as multimodal agents.

Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength.

Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.

Extensive studies support using steel tubes to enhance the structural integrity of rubber aggregate concrete (RBAC), namely RBAC-filled steel tubes (RCFST). However, current design codes for assessing the axial compressive behaviour of circular stub RCFST (CS-RCFST) columns are limited. Furthermore, there is a scarcity of studies focused on ensuring the structural safety of these columns. Based on an extensive experimental database comprising 145 columns, this study explores machine learning (ML) capabilities for predicting the axial strength of CS-RCFST columns, using six typical machine-learning models, i.e., symbolic regression (SR), XGBoost, CatBoost, random forest, LightGBM, and Gaussian process regression models. The hyperparameter tuning of the introduced ML models is performed using the Bayesian Optimization technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R 2  = 0.999 and 0.993 for the training and testing sets, respectively). In addition, a simple and practical design expression for CS-RCFST columns has been developed with acceptable accuracy based on the SR model (an average test-to-prediction ratio of 0.99 and CoV of 0.132). Meanwhile, the axial strength predicted by ML models was compared with two prominent practice codes (i.e., AISC360 and EC4). The comparison results indicated that the ML models could introduce a highly reliable and accurate approach over current design standards for strength prediction. Furthermore, a reliability analysis is conducted on two different ML models to evaluate the reliability of utilising ML models in practical design applications. This assessment involves identifying the statistical properties associated with the compressive strength of RBAC, as well as introducing the required resistance design factors aligned with the target reliability recommended by code standards.

The current study describes fabrication of molecularly imprinted (MI) electrospun nanofibers of average diameter 500 nm for controlled delivery of two templates; ferulic acid (FA) and khellin. Polycaprolactone (PCL) was used as supporting matrix in fiber preparation while polyallylamine (PAM) was added to provide molecular recognition sites for the template. The preparation of MI fibers was optimized using design of experiments (DoE). For FA fibers, a fractional factorial deign was used to screen different factors involved in the electrospinning process and it was found that FA and PAM concentration; in addition to PCL concentration were significant. These two variables were used to create a central composite design to optimize fiber diameter. Control fibers were prepared by directly loading ferulic acid into the fiber matrix without PAM. In vitro release studies revealed that MI fibers had more controlled release of FA compared to control fibers. DoE were also used to optimize the diameter of khellin MI fibers. In vitro release studies for khellin MI fibers did not show any significant difference when compared to control fibers. MTT assay was conducted to assess the cytotoxicity of MI fibers against mouse melanoma B16F10 cell lines. FA fibers were well-tolerated by the cells up to a concentration of 250 µg/ml, while khellin fibers showed no cytotoxicity up to a concentration of 125 µg/ml. On the other hand, the non-medicated fibers prepared with only PCL/PAM were safe up to 500 µg/ml, suggesting that the cytotoxicity observed at higher concentration of the MI fibers is due to the effect of FA or khellin. The MI fibers were then subjected to ex-vivo skin permeation studies using vertical Franz diffusion cell with Sprague Dawley male rats back skin. After 24 h, the percentage of permeated drug was 12.71 ± 0.05% for FA and 22.99 ± 0.04% for Khellin.