Synergistic effect of crystalline phase on protein adsorption and cell behaviors on TiO2 nanotubes

Electrostatic Repulsive Features of Free-Standing Titanium Dioxide Nanotube-Based Membranes in Biofiltration Applications

This study presents the electrostatic repulsive features of electrochemically fabricated titanium dioxide nanotube (NT)-based membranes with different surface nanomorphologies in cross-flow biofiltration applications while maintaining a creatinine clearance above 90%. Although membranes exhibit antifouling behavior, their blood protein rejection can still be improved. Due to the electrostatically negative charge of the hexafluorotitanate moiety, the fabricated biocompatible, superhydrophilic, free-standing, and amorphous ceramic nanomembranes showed that about 20% of negatively charged 66 kDa blood albumin was rejected by the membrane with ∼100 nm pores. As the nanomorphology of the membrane was shifted from NTs to nanowires by varying fabrication parameters, pure water flux and bovine serum albumin (BSA) rejection performance were reduced, and the membrane did not lose its antifouling behavior. Herein, nanomembranes with different surface nanomorphologies were fabricated by a multi-step anodic oxidation process and characterized by scanning electron microscopy, atomic force microscopy, water contact angle analysis, X-ray diffraction, and energy-dispersive X-ray spectroscopy. The membrane performance of samples was measured in 3D printed polyethylene terephthalate glycol flow cells replicating implantable artificial kidney models to determine their blood toxin removal and protein loss features. In collected urine mimicking samples, creatinine clearances and BSA rejections were measured by the spectrophotometric Jaffe method and high-performance liquid chromatography.

Deciphering controversial results of cell proliferation on TiO2 nanotubes using machine learning

With the rapid development of biomedical sciences, contradictory results on the relationships between biological responses and material properties emerge continuously, adding to the challenge of interpreting the incomprehensible interfacial process. In the present paper, we use cell proliferation on titanium dioxide nanotubes (TNTs) as a case study and apply machine learning methodologies to decipher contradictory results in the literature. The gradient boosting decision tree model demonstrates that cell density has a higher impact on cell proliferation than other obtainable experimental features in most publications. Together with the variation of other essential features, the controversy of cell proliferation trends on various TNTs is understandable. By traversing all combinational experimental features and the corresponding forecast using an exhausted grid search strategy, we find that adjusting cell density and sterilization methods can simultaneously induce opposite cell proliferation trends on various TNTs diameter, which is further validated by experiments. This case study reveals that machine learning is a burgeoning tool in deciphering controversial results in biomedical researches, opening up an avenue to explore the structure-property relationships of biomaterials.

Titanium and Protein Adsorption: An Overview of Mechanisms and Effects of Surface Features

Titanium and its alloys, specially Ti6Al4V, are among the most employed materials in orthopedic and dental implants. Cells response and osseointegration of implant devices are strongly dependent on the body-biomaterial interface zone. This interface is mainly defined by proteins: They adsorb immediately after implantation from blood and biological fluids, forming a layer on implant surfaces. Therefore, it is of utmost importance to understand which features of biomaterials surfaces influence formation of the protein layer and how to guide it. In this paper, relevant literature of the last 15 years about protein adsorption on titanium-based materials is reviewed. How the surface characteristics affect protein adsorption is investigated, aiming to provide an as comprehensive a picture as possible of adsorption mechanisms and type of chemical bonding with the surface, as well as of the characterization techniques effectively applied to model and real implant surfaces. Surface free energy, charge, microroughness, and hydroxylation degree have been found to be the main surface parameters to affect the amount of adsorbed proteins. On the other hand, the conformation of adsorbed proteins is mainly dictated by the protein structure, surface topography at the nano-scale, and exposed functional groups. Protein adsorption on titanium surfaces still needs further clarification, in particular concerning adsorption from complex protein solutions. In addition, characterization techniques to investigate and compare the different aspects of protein adsorption on different surfaces (in terms of roughness and chemistry) shall be developed.