Poly(lactic-co-glycolic acid) (PLGA) based nanoparticles have gained increasing attention in delivery applications due to their capability for controlled drug release characteristics, biocompatibility, and tunable mechanical, as well as degradation, properties. However, thorough study is always required while evaluating potential toxicity of the particles from dose dumping, inconsistent release and drug-polymer interactions. In this research, we developed PLGA nanoparticles modified by chitosan (CS), a cationic and pH responsive polysaccharide that bears repetitive amine groups in its backbone. We used a model drug, diclofenac sodium (DS), a nonsteroidal anti-inflammatory drug (NSAID), to study the drug loading and release characteristics. PLGA nanoparticles were synthesized by double-emulsion solvent evaporation technique. The nanoparticles were evaluated based on their particle size, surface charge, entrapment efficacy, and effect of pH in drug release profile. About 390–420 nm of average diameters and uniform morphology of the particles were confirmed by scanning electron microscope (SEM) imaging and dynamic light scattering (DLS) measurement. Chitosan coating over PLGA surface was confirmed by FTIR and DLS. Drug entrapment efficacy was up to 52%. Chitosan coated PLGA showed a pH responsive drug release in in vitro. The release was about 45% more at pH 5.5 than at pH 7.4. The results of our study indicated the development of chitosan coating over PLGA nanoparticle for pH dependent controlled release DS drug for therapeutic applications.

Degradation rate is an important property to evaluate bioabsorbable metallic material; however, values vary depending on the method of measurement. In this study, three different methods of measuring corrosion rate are compared. The degradable samples to analyze corrosion rates include pure magnesium (Mg), lab produced Mg–Zn–Ca alloy (47-7-2), Mg–Zn–Zr–RE (rare earth) alloys (60-13, 60-14), Mg–Zn–Ca–RE alloy (59B), and pure zinc (Zn). A eudiometer was used to measure hydrogen evolution from the reaction of degradable alloys in Hank’s Balanced Salt Solution (HBSS). Corrosion rates based on volume loss of tested alloys in 30 days were calculated using Micro-computed tomography (micro-CT). Final mass change due to corrosion and corrosion removal was measured with a scale. We observed that the corrosion rates indicated by hydrogen evolution were high initially, and slowed down sharply in the following measurements. The corrosion rates of tested alloys calculated by volume loss and mass loss from high to low are: 60–13 ≈ 60–14 ≈ 47–7–2 > 59B > Mg > Zn (p < 0.05). The results provide instruction to experimental methodology to measure corrosion rates of degradable alloys.

Composite nanofibers of biopolymers and inorganic materials have been widely explored as tissue engineering scaffolds because of their superior structural, mechanical and biological properties. In this study, magnesium ferrite (Mg-ferrite) based composite nanofibers were synthesized using an electrospinning technique. Mg-ferrite nanoparticles were first synthesized using the reverse micelle method, and then blended in a mixture of polycaprolactone (PCL), a synthetic polymer, and Aloe vera, a natural polymer, to create magnetic nanofibers by electrospinning. The morphology, structural and magnetic properties, and cellular compatibility of the magnetic nanofibers were analyzed. Mg-ferrite/PCL/Aloe vera nanofibers showed good uniformity in fiber morphology, retained their structural integrity, and displayed magnetic strength. Experimental results, using cell viability assay and scanning electron microscopy imaging showed that magnetic nanofibers supported 3T3 cell viability. We believe that the new composite nanofibrous membranes developed in this study have the ability to mimic the physical structure and function of tissue extracellular matrix, as well as provide the magnetic and soluble metal ion attributes in the scaffolds with enhanced cell attachment, and thus improve tissue regeneration.

Biomaterials, especially those based on nanomaterials, have emerged as critical tools in biomedical applications. The applications encompass a wide range such as implantable devices, tissue regeneration, drug delivery, diagnostic systems, and molecular printing. The type of materials used also covers a wide range: metals (permanent and degradable), polymers (permanent and degradable), carbon nanotubes, and lipid nanoparticles. This paper explores the use of microfluidic platforms as a high‐throughput research tool for the evaluation of nanobiomaterials. Typical screening of such materials involves cell/tissue cultures to determine attributes such as cell adhesion, proliferation, differentiation, as well as biocompatibility. In addition to this, other areas such as drug delivery and toxicity can also be evaluated via microfluidics. Traditional approach for screening of such materials is very time‐consuming, and a lot of animals should be sacrificed since it involves one material and a single composition or concentration for a single test. The microfluidics approach has the advantage of using multiple types of drugs and their concentration gradients to simultaneously study the effect on the nanobiomaterial and its interaction with cell/tissue. In addition to this, microfluidics provides a unique environment to study the effect of cell‐to‐extracellular interaction and cell‐to‐cell communication in the presence of the nanobiomaterials.

The biocompatibility of Magnesium-based materials (MBMs) is critical to the safety of biodegradable medical devices. As a promising metallic biomaterial for medical devices, the issue of greatest concern is devices’ safety as degrading products are possibly interacting with local tissue during complete degradation. The aim of this review is to summarize the biological responses to MBMs at the cellular/molecular level, including cell adhesion, transportation signaling, immune response, and tissue growth during the complex degradation process. We review the influence of MBMs on gene/protein biosynthesis and expression at the site of implantation, as well as throughout the body. This paper provides a systematic review of the cellular/molecular behavior of local tissue on the response to Mg degradation, which may facilitate a better prediction of long-term degradation and the safe use of magnesium-based implants through metal innovation.

There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively.