Integrated Metabolomics and Proteomics Analyses in the Local Milieu of Islet Allografts in Rejection versus Tolerance.

An understanding of the immune mechanisms that lead to rejection versus tolerance of allogeneic pancreatic islet grafts is of paramount importance, as it facilitates the development of innovative methods to improve the transplant outcome. Here, we used our established intraocular islet transplant model to gain novel insight into changes in the local metabolome and proteome within the islet allograft's immediate microenvironment in association with immune-mediated rejection or tolerance. We performed integrated metabolomics and proteomics analyses in aqueous humor samples representative of the graft's microenvironment under each transplant outcome.

A machine learning approach to predict pancreatic islet grafts rejection versus tolerance.

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls.

Correlation between plasma levels of arginine and citrulline in preterm and full-term neonates: Therapeutical implications.

Background: Preterm neonates exhibit several deficiencies that endanger their lives. Understanding those disturbances will provide tools for the management of preterm neonates. The present work focuses on arginine and citrulline which has been flagged among the biochemical landmarks of prematurity.

Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domain.

A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization.