Medical-Grade Poly(Lactic Acid)/Hydroxyapatite Composite Films: Thermal and In Vitro Degradation Properties

Development of bioactive short fiber-reinforced printable hydrogels with tunable mechanical and osteogenic properties for bone repair

Personalized bone-regenerative materials have attracted substantial interest in recent years. Modern clinical settings demand the use of engineered materials incorporating patient-derived cells, cytokines, antibodies, and biomarkers to enhance the process of regeneration. In this work, we formulated short microfiber-reinforced hydrogels with platelet-rich fibrin (PRF) to engineer implantable multi-material core-shell bone grafts. By employing 3D bioprinting technology, we fabricated a core-shell bone graft from a hybrid composite hydroxyapatite-coated poly(lactic acid) (PLA) fiber-reinforced methacryolyl gelatin (GelMA)/alginate hydrogel. The overall concept involves 3D bioprinting of long bone mimic microstructures that resemble a core-shell cancellous-cortical structure, with a stiffer shell and a softer core with our engineered biomaterial. We observed a significantly enhanced stiffness in the hydrogel scaffold incorporated with hydroxyapatite (HA)-coated PLA microfibers compared to the pristine hydrogel construct. Furthermore, HA non-coated PLA microfibers were mixed with PRF and GelMA/alginate hydrogel to introduce a slow release of growth factors which can further enhance cell maturation and differentiation. These patient-specific bone grafts deliver cytokines and growth factors with distinct spatiotemporal release profiles to enhance tissue regeneration. The biocompatible and bio-responsive bone mimetic core-shell multi-material structures enhance osteogenesis and can be customized to have materials at a specific location, geometry, and material combination.

The Structural, Thermal and Morphological Characterization of Polylactic Acid/Β-Tricalcium Phosphate (PLA/Β-TCP) Composites upon Immersion in SBF: A Comprehensive Analysis

Biocomposite films based on PLA reinforced with different β-TCP contents (10%, 20%, and 25%wt.) were fabricated via solvent casting and immersed in SBF for 7, 14, and 21 days. The bioactivity, morphological, and thermal behavior of composites with immersion were studied using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) microanalysis, weight loss (WL), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and gel permeation chromatography (GPC). This broad analysis leads to a deeper understanding of the evolution of the polymer-filler interaction with the degradation of the biocomposites. The results showed that β-TCP gradually evolved into carbonated hydroxyapatite as the immersion time increased. This evolution affected the interaction of β-TCP with PLA. PLA and β-TCP interactions differed from PLA and carbonated hydroxyapatite interactions. It was observed that β-TCP inhibited PLA hydrolysis but accelerated the thermal degradation of the polymer. β-TCP retarded the cold crystallization of PLA and hindered its crystallinity. However, after immersion in SBF, particles accelerated the cold crystallization of PLA. Therefore, considering the evolution of β-TCP with immersion in SBF is crucial for an accurate analysis of the biocomposites' degradation. These findings enhance the comprehension of the degradation mechanism in PLA/β-TCP, which is valuable for predicting the degradation performance of PLA/β-TCP in medical applications.

Comparative In Vitro Dissolution Assessment of Calcined and Uncalcined Hydroxyapatite Using Differences in Bioresorbability and Biomineralization

This study reports the effect of the not-calcining process on the bioresorption and biomineralization of hydroxyapatite through in vitro dissolution assessment. The prepared calcined hydroxyapatite (c-HAp) and uncalcined hydroxyapatite (unc-HAp) have a particle size of 2 μm and 13 μm, surface areas of 4.47 m2/g and 108.08 m2/g, and a Ca/P ratio of 1.66 and 1.52, respectively. In vitro dissolution assessments of c-HAp and unc-HAp were performed for 20 days at 37 °C in a citric acid buffer according to ISO 10993-14. During the dissolution, the c-HAp and unc-HAp confirmed an increase in weight, and the calcium and phosphorous ions were rapidly released. The calcium ions released from c-HAp formed rod-shaped particles with a longer and thinner morphology, while in unc-HAp, they appeared thicker and shorter. In the ICP-OES results, the concentrations of calcium elements were initially increased and then decreased by this formation. The rod-shaped particles identified as calcium citrate (Ca-citrate) through the XRD pattern. The calcium content of Ca-citrate particles from unc-HAp was higher than that from c-HAp. The unc-HAp demonstrated non-toxic properties in a cytotoxicity evaluation. Therefore, due to its higher bioresorption and biomineralization, unc-HAp exhibits enhanced biocompatibility compared to c-HAp.

Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially 'black-box' in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings.