Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data.

Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants.

Examining the feasibility of a "top-down" approach to enhancing the keratinocyte-implant adhesion.

The adhesion of human epidermal keratinocytes to the implant surface is one of the most critical steps during the patient's recovery from implantation of transcutaneous prosthesis. To improve the success rate of transcutaneous prosthetic implants, we explored a new "top-down" approach to promoting this dynamic adhering process through modulation of upstream cell signaling pathways. To examine the feasibility of this novel approach, we first established an in vitro platform that is capable of providing a non-invasive, real-time, quantitative characterization of the keratinocyte-implant interaction.