The development of therapeutics for orthopedic clinical indications exploiting minimally invasive surgical techniques has substantial benefits, especially for treatment of fragility fractures in the distal radius of osteoporotics and vertebral compression fractures. We have designed six formulations of injectable polyurethane foams to address these clinical indications. The polyurethanes were prepared by mixing two liquid components and injecting the reactive liquid mixture into a mold where it hardens in situ. Porous polyurethane foams were synthesized from lysine methyl ester diisocyanate, a poly(epsilon-caprolactone-co-glycolide) triol, a tertiary amine catalyst, anionic and non-ionic stabilizers, and a fatty acid pore opener. The rise time of the foams varied from 8-20 min. The porosity was approximately 95% and the pores varied in size from 100-1000 microm. The polyurethane foams supported attachment of viable (>95%) MG-63 cells under dynamic seeding conditions. We anticipate compelling opportunities will be available as a consequence of the favorable biological and physical properties of the injectable polyurethane foams.

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BackgroundAccurate detection and estimation of true exposure-outcome associations is important in aetiological analysis; when there are multiple potential exposure variables of interest, methods for detecting the subset of variables most likely to have true associations with the outcome of interest are required. Case-cohort studies often collect data on a large number of variables which have not been measured in the entire cohort (e.g. panels of biomarkers). There is a lack of guidance on methods for variable selection in case-cohort studies.MethodsWe describe and explore the application of three variable selection methods to data from a case-cohort study. These are: (i) selecting variables based on their level of significance in univariable (i.e. one-at-a-time) Prentice-weighted Cox regression models; (ii) stepwise selection applied to Prentice-weighted Cox regression; and (iii) a two-step method which applies a Bayesian variable selection algorithm to obtain posterior probabilities of selection for each variable using multivariable logistic regression followed by effect estimation using Prentice-weighted Cox regression.ResultsAcross nine different simulation scenarios, the two-step method demonstrated higher sensitivity and lower false discovery rate than the one-at-a-time and stepwise methods. In an application of the methods to data from the EPIC-InterAct case-cohort study, the two-step method identified an additional two fatty acids as being associated with incident type 2 diabetes, compared with the one-at-a-time and stepwise methods.ConclusionsThe two-step method enables more powerful and accurate detection of exposure-outcome associations in case-cohort studies. An R package is available to enable researchers to apply this method.