Analytical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

PURPOSE: To characterize and analytically validate the MSDA Test, a multi-protein, serum-based biomarker assay developed using Olink® PEA methodology. EXPERIMENTAL DESIGN: Two lots of the MSDA Test panel were manufactured and subjected to a comprehensive analytical characterization and validation protocol to detect biomarkers present in the serum of patients with multiple sclerosis (MS). Biomarker concentrations were incorporated into a final algorithm used for calculating four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score. RESULTS: Analytical characterization demonstrated that the multi-protein panel satisfied the criteria necessary for a fit-for-purpose validation considering the assay's intended clinical use. This panel met acceptability criteria for 18 biomarkers included in the final algorithm out of 21 biomarkers evaluated. VCAN was omitted based on factors outside of analytical validation; COL4A1 and GH were excluded based on imprecision and diurnal variability, respectively. Performance of the four Disease Pathway and overall Disease Activity scores met the established acceptability criteria. CONCLUSIONS AND CLINICAL RELEVANCE: Analytical validation of this multi-protein, serum-based assay is the first step in establishing its potential utility as a quantitative, minimally invasive, and scalable biomarker panel to enhance the standard of care for patients with MS.

The genetics underlying metabolic signatures in a brown rice diversity panel and their vital role in human nutrition.

Brown rice (Oryza sativa) possesses various nutritionally dense bioactive phytochemicals exhibiting a wide range of antioxidant, anti-cancer, and anti-diabetic properties known to promote various human health benefits. However, despite the wide claims made about the importance of brown rice for human nutrition the underlying metabolic diversity has not been systematically explored. Non-targeted metabolite profiling of developing and mature seeds of a diverse genetic panel of 320 rice cultivars allowed quantification of 117 metabolites. The metabolite genome-wide association study (mGWAS) detected genetic variants influencing diverse metabolic targets in developing and mature seeds. We further interlinked genetic variants on chromosome 7 (6.06-6.43 Mb region) with complex epistatic genetic interactions impacting multi-dimensional nutritional targets, including complex carbohydrate starch quality, the glycemic index, antioxidant catechin, and rice grain color. Through this nutrigenomics approach rare gene bank accessions possessing genetic variants in bHLH and IPT5 genes were identified through haplotype enrichment. These variants were associated with a low glycemic index, higher catechin levels, elevated total flavonoid contents, and heightened antioxidant activity in the whole grain with elevated anti-cancer properties being confirmed in cancer cell lines. This multi-disciplinary nutrigenomics approach thus allowed us to discover the genetic basis of human health-conferring diversity in the metabolome of brown rice.

New developments in high-throughput resequencing and variation detection using high density microarrays.

We developed a high-throughput method for resequencing for single nucleotide polymorphism (SNP) discovery using high-density microarrays. Over the two-year course of this study a number of improvements in sample preparation methods, hybridization assay, array handling, and analysis method were developed and implemented. DNA from 40 unrelated individuals of three different ethnic origins was amplified, labeled, and hybridized to arrays designed with probes representing genomic, coding, and regulatory regions. Protocol improvements including the use of long PCR and semi-automation reduced labeling and fragmentation costs by 33%. Automation improvements include the development of a scanner autoloader for arrays, a faster array wash station, and a linked laboratory tracking and data management system. Validation of a smaller feature size, 20 x 24 microns, allowed the simultaneous screening of 30-kb sense and 30-kb antisense DNA on each microarray, increasing throughput to 1.4 Mb per day per two laboratory personnel. More than 15,000 SNPs were identified in 8.3 Mb of the human genome using high-density resequencing and variation detection arrays (microarrays).

Automated breast cancer diagnosis based on fine needle aspiration.

OBJECTIVE: To design and analyze an automated diagnostic system for breast carcinoma based on fine needle aspiration (FNA). STUDY DESIGN: FNA is a noninvasive alternative to surgical biopsy for the diagnosis of breast carcinoma. Widespread clinical use of FNA is limited by the relatively poor interobserver reproducibility of the visual interpretation of FNA images. To overcome the reproducibility problem, past research has focused on the development of automated diagnosis systems that yield accurate, reproducible results. While automated diagnosis is, by definition, reproducible, it has yet to achieve diagnostic accuracy comparable to that of surgical biopsy. In this article we describe a sophisticated new diagnostic system in which the mean sensitivity (of FNA diagnosis) approaches that of surgical biopsy. The diagnostic system that we devised analyzes the digital FNA data extracted from FNA images. To achieve high sensitivity, the system needs to solve large, equality-constrained, integer nonlinear optimization problems repeatedly. Powerful techniques from the theory of Lie groups and a novel optimization technique are built into the system to solve the underlying optimization problems effectively. The system is trained using digital data from FNA samples with confirmed diagnosis. To analyze the diagnostic accuracy of the system > 8,000 computational experiments were performed using digital FNA data from the Wisconsin Breast Cancer Database. RESULTS: The system has a mean sensitivity of 99.62% and mean specificity of 93.31%. Statistical analysis shows that at the 95% confidence level, the system can be trusted to correctly diagnose new malignant FNA samples with an accuracy of 99.44-99.8% and new benign FNA samples with an accuracy of 92.43-93.93%. CONCLUSION: The diagnostic system is robust and has higher sensitivity than do all the other systems reported in the literature. The specificity of the system needs to be improved.