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Abstract: The current work presents a novel flexible multifunctional platform for biological interface applications. The use of titania nanotube arrays (TNAs) as a multifunctional material is explored for soft-tissue interface applications. In vitro biocompatibility of TNAs to brain-derived cells was first examined by culturing microglia cells-the resident immune cells of the central nervous system on the surface of TNAs. The release profile of an anti-inflammatory drug, dexamethasone from TNAs-on-polyimide substrates, was then evaluated under different bending modes. Flexible TNAs-on-polyimide sustained a linear release of anti-inflammatory dexamethasone up to ~11 days under different bending conditions. Finally, microfabrication processes for patterning and transferring TNA microsegments were developed to facilitate structural stability during device flexing and to expand the set of compatible polymer substrates. The techniques developed in this study can be applied to integrate TNAs or other similar nanoporous inorganic films onto various polymer substrates. Impact statement: Titania nanotube arrays (TNAs) are highly tunable and biocompatible structures that lend themselves to multifunctional implementation in implanted devices. A particularly important aspect of titania nanotubes is their ability to serve as nano-reservoirs for drugs or other therapeutic agents that slowly release after implantation. To date, TNAs have been used to promote integration with rigid, dense tissues for dental and orthopedic applications. This work aims to expand the implant applications that can benefit from TNAs by integrating them onto soft polymer substrates, thereby promoting compatibility with soft tissues. The successful direct growth and integration of TNAs on polymer substrates mark a critical step toward developing mechanically compliant implantable systems with drug delivery from nanostructured inorganic functional materials. Diffusion-driven release kinetics and the high drug-loading efficiency of TNAs offer tremendous potential for sustained drug delivery for scientific investigations, to treat injury and disease, and to promote device integration with biological tissues. This work opens new opportunities for developing novel and more effective implanted devices that can significantly improve patient outcomes and quality of life. Supplementary information: The online version contains supplementary material available at 10.1557/s43577-023-00628-y.
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BACKGROUND: Adaptive learning platforms (ALPs) can revolutionize medical education by making learning more efficient, but their potential has not been realized because students do not use them persistently. METHODS: We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( www.osmosis.org ) from 1 August 2014 to 31 July 2015. Multivariate logistic regressions modeled persistence on Osmosis as the dependent variable and Osmosis-collected variables as predictors. RESULTS: The 6787 students included in our analysis responded to a total of 887,193 items, with 2138 (31.5%) using Osmosis persistently. Number of items per student, mobile device use, subscription payment, and group membership were independently associated with persisting (p < 0.001 in all models). Persistent users rated quality more favorably (p < 0.01) but were not more confident in answer selections (p = 0.80). While persisters were more accurate than non-persisters (55% (SD 18%) vs 52% (SD 22%), p < 0.001), after adjusting for number of items, lower accuracy was associated with persistent use (OR 0.93 [95% CI 0.90-0.97], p < 0.01). CONCLUSIONS: Our study of a large sample of U.S. medical students illustrates big data medical education research and provides guidance for improving implementation of ALPs and further investigation.
