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Proteomics has emerged as the next great scientific challenge in the post-genome era. But even the most basic form of proteomics, proteome profiling, i.e., identifying all of the proteins expressed in a given sample, has proven to be a demanding task. The proteome presents unique analytical challenges, including significant molecular diversity, an extremely wide concentration range, and a tendency to adsorb to solid surfaces. Microfluidics has been touted as being a useful tool for developing new methods to solve complex analytical challenges, and, as such, seems a natural fit for application to proteome profiling. In this review, we summarize the recent progress in the field of microfluidics in four key areas related to this application: chemical processing, sample preconcentration and cleanup, chemical separations, and interfaces with mass spectrometry. We identify the bright spots and challenges for the marriage of microfluidics and proteomics, and speculate on the outlook for progress.

Validation must be carried out before a model can be used confidently as a tool of managerial decision making in health care. The authors describe a bootstrap approach to validating models for predicting the utilization of four technologies used in neonatal care: measurement of blood gases (gasometry), the oxygen hood, continuous positive airway pressure (CPAP), and mechanical ventilation. These models were fitted by stepwise multiple linear regression from 20 prognostic covariates of 193 neonates. One hundred bootstrap samples were generated to validate the choices of covariates in the models based on their frequencies of selection. This approach validated the models for the oxygen hood and CPAP. The regression coefficients and standard deviations for the CPAP and oxygen hood models were estimated using 200 additional bootstrap samples. A close agreement between stepwise and bootstrap estimates was observed for both models. These results suggest that bootstrap can be useful for validating models for predicting the utilization of health technologies Key words: ne onatal intensive care, forecasting; health facility planning; health technology; bootstrap; model validation; regression analysis; linear models. (Med Decis Making 1998;18: 311-319)