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Browsing by Subject "personalized medicine"
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Item 3D Printing of Human Ossicle Models for the Biofabrication of Personalized Middle Ear Prostheses(MDPI, 2022-10-31) Dairaghi, Jacob; Rogozea, Dan; Cadle, Rachel; Bustamante, Joseph; Moldovan, Leni; Petrache, Horia I.; Moldovan, Nicanor I.; Physics, School of ScienceThe middle ear bones (‘ossicles’) may become severely damaged due to accidents or to diseases. In these situations, the most common current treatments include replacing them with cadaver-derived ossicles, using a metal (usually titanium) prosthesis, or introducing bridges made of biocompatible ceramics. Neither of these solutions is ideal, due to the difficulty in finding or producing shape-matching replacements. However, the advent of additive manufacturing applications to biomedical problems has created the possibility of 3D-printing anatomically correct, shape- and size-personalized ossicle prostheses. To demonstrate this concept, we generated and printed several models of ossicles, as solid, porous, or soft material structures. These models were first printed with a plottable calcium phosphate/hydroxyapatite paste by extrusion on a solid support or embedded in a Carbopol hydrogel bath, followed by temperature-induced hardening. We then also printed an ossicle model with this ceramic in a porous format, followed by loading and crosslinking an alginate hydrogel within the pores, which was validated by microCT imaging. Finally, ossicle models were printed using alginate as well as a cell-containing nanocellulose-based bioink, within the supporting hydrogel bath. In selected cases, the devised workflow and the printouts were tested for repeatability. In conclusion, we demonstrate that moving beyond simplistic geometric bridges to anatomically realistic constructs is possible by 3D printing with various biocompatible materials and hydrogels, thus opening the way towards the in vitro generation of personalized middle ear prostheses for implantation.Item A Bayesian adaptive phase II clinical trial design accounting for spatial variation(Sage, 2018) Guo, Beibei; Zang, Yong; Biostatistics, School of Public HealthConventional phase II clinical trials evaluate the treatment effects under the assumption of patient homogeneity. However, due to inter-patient heterogeneity, the effect of a treatment may differ remarkably among subgroups of patients. Besides patient’s individual characteristics such as age, gender, and biomarker status, a substantial amount of this heterogeneity could be due to the spatial variation across geographic regions because of unmeasured or unknown spatially varying environmental and social exposures. In this article, we propose a hierarchical Bayesian adaptive design for two-arm randomized phase II clinical trials that accounts for the spatial variation as well as patient’s individual characteristics. We treat the treatment efficacy as an ordinal outcome and quantify the desirability of each possible category of the ordinal efficacy using a utility function. A cumulative probit mixed model is used to relate efficacy to patient-specific covariates and geographic region spatial effects. Spatial dependence between regions is induced through the conditional autoregressive priors on the spatial effects. A two-stage design is proposed to adaptively assign patients to desirable treatments according to each patient’s spatial information and individual covariates and make treatment recommendations at the end of the trial based on the overall treatment effect. Simulation studies show that our proposed design has good operating characteristics and significantly outperforms an alternative phase II trial design that ignores the spatial variation.Item Indiana Biobank (IB)(Office of the Vice Chancellor for Research, 2011-04-08) Nguyen, Anne; Shekhar, Anantha; Flockhart, DavidThe Indiana Biobank (IB) was established in July 2010 as a conduit in the new era of personalized medicine to serve the needs of the Indiana research communities and to make an impact on Hoosier health. The overall objective of the Indiana Biobank is to create a collection of high quality biospecimens that are well annotated and linked to the electronic health record, genomic and proteomic data, to provide to the research community to carry out translational research. The ability to successfully do research and translate to the clinical setting is greatly facilitated by the availability of an extensive biorepository of biological samples, with accompanying clinical and genomic data, procured from patients at IU Health, Wishard and other clinical venues throughout Indiana.Item The Pathologic and Molecular Genetic Landscape of the Hereditary Renal Cancer Predisposition Syndromes(Wiley, 2022) Al-Obaidy, Khaleel I.; Alruwaii, Zainab I.; Williamson, Sean R.; Cheng, Liang; Pathology and Laboratory Medicine, School of MedicineIt is estimated that 5-8% of renal tumors are hereditary in nature with many inherited as autosomal dominant. These tumors carry a unique spectrum of pathologic and molecular alterations, the knowledge of which is expanding in the recent years. Indebted to this knowledge, many advances in treatment of these tumors have been achieved. In this review, we summarize the current understanding of the genetic renal neoplasia syndromes, the clinical and pathologic presentations, their molecular pathogenesis, the advances in therapeutic implications and targeted therapy.Item Subgroup selection in adaptive signature designs of confirmatory clinical trials(Wiley, 2017-02) Zhang, Zhiwei; Li, Meijuan; Lin, Min; Soon, Guoxing; Greene, Tom; Shen, Changyu; Department of Medicine, IU School of MedicineThe increasing awareness of treatment effect heterogeneity has motivated flexible designs of confirmatory clinical trials that prospectively allow investigators to test for treatment efficacy for a subpopulation of patients in addition to the entire population. If a target subpopulation is not well characterized in the design stage, it can be developed at the end of a broad eligibility trial under an adaptive signature design. The paper proposes new procedures for subgroup selection and treatment effect estimation (for the selected subgroup) under an adaptive signature design. We first provide a simple and general characterization of the optimal subgroup that maximizes the power for demonstrating treatment efficacy or the expected gain based on a specified utility function. This characterization motivates a procedure for subgroup selection that involves prediction modelling, augmented inverse probability weighting and low dimensional maximization. A cross-validation procedure can be used to remove or reduce any resubstitution bias that may result from subgroup selection, and a bootstrap procedure can be used to make inference about the treatment effect in the subgroup selected. The approach proposed is evaluated in simulation studies and illustrated with real examples.Item Targeting fibroblast growth factor receptor (FGFR) pathway in renal cell carcinoma(Taylor and Francis, 2015) Massari, Francesco; Ciccarese, Chiara; Santoni, Matteo; Lopez-Beltran, Antonio; Scarpelli, Marina; Montironi, Rodolfo; Cheng, Liang; Department of Pathology and Laboratory Medicine, IU School of MedicineFibroblast growth factor receptor (FGFR) pathway is involved in driving vascular endothelial growth factor (VEGF)-independent tumor angiogenesis, as a compensatory mechanism to escape VEGF-targeted therapies. Therefore, targeting FGF/FGFR axis seems to be a promising strategy in order to inhibit tumor angiogenesis and reduce resistance to VEGF receptor-tyrosine kinase inhibitors. This editorial is focused on the role of FGF/FGFR pathway in renal cell carcinoma and on the ongoing trials of emerging agents targeting this axis.Item Towards Predictive Oral and Maxillofacial Medicine: Perspective on Zavras et al(2012) Edwards, Paul C.