Recently, conducting polymers (CPs) have become promising coating materials in different medical applications. The principal property of conducting polymers is their metallic-like conductivity due to the conjugated double bond in their backbone. Besides interesting electronic properties, CPs reveal many other attractive characteristics including ion-transport, high biocompatibility, electrode effects allied to polymeric physical properties, or reversible switch between insulating and conducting forms. Another significant advantage of CPs is their processability. They can be synthesized on the conducting surfaces by electrodeposition. This process allows for the precise and controllable deposition of the coating, for example, onto a stent, leads to the formation of the coating with high adhesion to the metallic surface, allows incorporation of drug molecules into the coating in a one-step process, while the degradation and drug release rate could be tailored by deposition parameters. Because of the significant advantages of CPs, they began to be used commercially as an efficient anti-corrosive coating, smart active layers in bioelectronics, scaffolds for drug reservoirs, and more. Currently, the scientific work in our laboratory is focused on the use of CP in three main applications: cardiovascular stent, biosensor, and neural electrode. The presentation will show a cross-section of the promising results and the possibilities of cooperation with the industry in these specific areas. The presentation will answer the following specific questions: is it possible to tailor the degradation rate of active metals by CP coatings? Is it possible to use CP as an efficient drug reservoir without any external electrical stimulation? Or whether measurement conditions have a significant influence on the properties of the electrical interface of the CP electrode for neural stimulation.

(The work was supported by National Science Centre (NCN), Poland: Sonata grant based on the decision 2021/43/D/ST7/01362. K. Cysewska acknowledges the Polish Ministry of Education and Science for stipend)

Advances in materials science and in additive manufacturing allow more versatile structures to be produced as implants. Especially lattice structures, with their macro-porosity allowing better tissue integration, offer a wide range of possibilities. Aim of this study was to investigate the mechanical and geometrical properties of 3d printed lattices using non-metallic materials: high performance ceramic (ZrO2) and photopolymers.

Seven different lattice structures with each 80% porosity and a cross section dimensions of 4×4 mm were designed and 3d printed, in sets of each ten samples per mechanical tests. Optical profilometry and µ-computer tomography (µCT) was used to investigate the printed lattices. Four mechanical tests were performed: compressive, tensile, shear and 4-point bending, to measure the elasticity and breaking strength. Finite element simulation (FEM) was then utilized to predict the elastic properties of lattice structures.

The measured samples showed uniaxial Young’s moduli between 5% to 19% of the bulk materials Young’s modulus and breaking strength of 100MPa to 550MPa, depending on their individual structure. Lattices with continuous struts in direction of loading exert higher mechanical stability than those with diagonal or transversal structures. Struts dimensions with deviations of up to 30% in diameter were found, which can be related to manufacturing and post processing. FEM simulations showed satisfactory fit to the measurement results when the actual and not the nominal dimensions were used for simulations.

Lattice geometries fabricated in ceramic can not only mimic trabecular bone, but are also able to withstand physiological loads while providing good tissue integration, therefore making it a viable choice for implants. When considering small structures such as lattices, geometry deviations from the original design during the fabrication process must be taken into account. When planning and designing lattices, from photopolymer prototypes only inaccurate extrapolations can be anticipated, but with FEM simulations the lattice properties can be precisely adjusted.

Several molecules of natural origin (derived from plants or animals) are of great interest for surface functionalization (grafting of a molecular layer) or coating (nano or micrometric continuous films) of titanium alloys. They can be polypeptides, proteins, vitamins, oils, organic compounds, or natural polymers. Functionalization or coating of titanium must be coupled to a proper surface chemistry and topography according to the type of biomolecule to be grafted and rhe specific application or purposes. The modified titanium surfaces acquire biomimetic or antibacterial functionalities and/or ability to guide the tissue growth. Positive outcome on inflammatory or anticancer properties can be also induced. New characterization protocols (biological, chemical, physical, mechanical tests and analyses) are needed for characterizing the modified surfaces and the post-processing steps (packaging, sterilization) must be adapted.

An overview of the strategies and the benefits from grafting or coating titanium with nisin, vitamin E, tocopherol phosphate, mentha essential oil, chitin derivative, polyphenols, and keratin will be presented.

Surgical resection of chest wall tumours may lead to a loss of ribcage stability and requires reconstruction to allow for physical thorax functioning. When titanium implants are used especially for larger, lateral defects, they tend to break. Implant failures are mainly due to specific mechanical requirements for chest-wall reconstruction which must mimic the physiological properties and which are not yet met by available implants.

In this context, the implants must show some important characteristics: On the one hand, there are a variety of loads that they must be able to withstand permanently. On the other hand, they must have an appropriate stiffness to enable all daily movements and at the same time protect the vital inner organs. To meet these requirements, it is essential to understand the biomechanics of the thorax. For this purpose, a full thorax FEM model was developed.

This model was assembled stepwise. First a chest CT scan of a fresh, unembalmed cadaver in the supine position was made to reconstruct the bony and cartilaginous structures. Further CTs in different positions as well as stiffness measurements on several anatomical structures were used to define properties of the FE model and for step-by-step calibration and verification. Various activities such as ventilation, breathing, resuscitation, lying on the side or coughing were simulated on this verified FE model and the stresses and deformations were evaluated. The same simulations and evaluations were carried out with defects on ribs 5 to 9 and with corresponding implants according to the current state of the art. In this way, the critical activities that lead to damage of the implants could be identified. Based on these findings, an algorithm for determining the implant dimensions for different materials to achieve an appropriate stiffness of 3D-printed rib implants was developed.

Introduction: Layer-by-layer film coatings are an effective technique for surface modification, particularly in the biomedical industry. Despite the large number of papers on LbL assembly, prediction of LbL coating thickness, as a functional property, is a challenging and time consuming task from the aspect of experiment. Machine learning (ML) approaches that are already being developed have the potential to speed up and improve novel coating development thus reducing time and material consumption.

Materials and methods: The data used represented a combination of the literature and experimental data generated in-house using a Quartz Crystal Microbalance with dissipation monitoring (QCM-D). The whole dataset for coating thickness [nm] prediction included the 22 input features (Polycation, Polyanion, Polycation unit MW, Polyanion unit MW, Polycation MW, Polyanion MW, Number of the bilayer, Ending polymer etc.). In total, there were 98 instances from the literature and 33 from the in-house experiments. Proposed methodology included several preprocessing steps (such as outlier removal and missing data imputation) and machine learning techniques for coating thickness prediction (Linear regression, Logistic regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor). SMOGN was used to deal with skewed data.

Results and discussion: The results show that Extra Tree Regressor outperformed other algorithms when combined with optimal hyperparameters and missing data imputation. Relevant metrics achieved were R2 =0.980, MSE = 46933.204, RMSE = 216.64 and MAE = 111.414 on the test dataset. The 6 best predictors of coating thickness were identified, with top three being Polyanion, Number of the bilayer and Ending polymer, which can be used to predict coating thickness without the need for numerous parameter measurements.

Conclusions: Further research will focus on outputs associated with antibacterial, anti- inflammatory, and antiviral capabilities, allowing researchers to respond to real-world biomedical issues like as antibiotic resistance, implant rejection, and COVID-19 outbreaks.