Objective. To determine the value of cell-bound complement activation products in combination with antinuclear antibody (ANA), anti-double-stranded DNA antibody (anti-dsDNA), and anti-mutated citrullinated vimentin antibody (anti-MCV) for the diagnosis of systemic lupus erythematosus (SLE).Methods. This was a multicenter cross-sectional study in which 593 subjects were enrolled (210 SLE patients, 178 patients with other rheumatic diseases, and 205 healthy subjects). Complement receptor 1 levels on erythrocytes (ECR1) together with complement C4d levels on erythrocytes (EC4d), platelets (PC4d), and B cells (BC4d) were determined using fluorescenceactivated cell sorting. Serologic markers were measured by enzyme-linked immunosorbent assay. Statistical analyses were performed using area under the curve (AUC), logistic regression, and calculations of diagnostic sensitivity and specificity.Results. Anti-dsDNA was an insensitive (30%) but specific (>95%) marker for SLE. Levels of EC4d, BC4d, and PC4d were several times higher, and levels of ECR1 lower, in SLE patients compared to patients with other rheumatic diseases and healthy subjects. Among 523 anti-dsDNA-negative subjects, multivariate logistic regression analysis revealed that SLE was associated with ANA positivity (>20 units), anti-MCV negativity (<70 units), and elevated levels of both EC4d and BC4d (AUC 0.918, P < 0.001). A positive index score corresponding to the weighted sum of these 4 markers correctly categorized 72% of SLE patients. Specificity in relation to patients with other rheumatic diseases and healthy controls was >90%. The combination of anti-dsDNA and index score positivity yielded 80% sensitivity for SLE and 87% specificity against other rheumatic diseases.Conclusion. An assay panel combining antidsDNA, ANA, anti-MCV, EC4d, and BC4d is sensitive and specific for the diagnosis of SLE.Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that results in autoantibodymediated tissue damage and potentially life-threatening multiorgan failure (1). This heterogeneous inflammatory disorder affects between 161,000 and 322,000 adults in the US, with the prevalence in women being 9 times Supported by Exagen Diagnostics.

No abstract

Currently used clinical and histopathological parameters imprecisely define the risk of distant recurrence in breast cancer, underscoring the need for more informative prognostic markers. In the present fluorescence in situ hybridization study of archived surgical specimens, we derived an algorithm for computing a prognostic index (PI) from DNA copy numbers of three genomic regions (CYP24, PDCD6IP, and BIRC5) for estrogen/ progesterone receptor-positive (ER/PR ؉ ) cancers and a distinct PI (based on NR1D1, SMARCE1, and BIRC5) for estrogen/progesterone receptor-negative (ER/PR ؊ ) cancers. Among independent test cases stratified by PI, recurrence rates were significantly higher among high-risk patients than low-risk patients for both ER/ PR ؉ (odds ratio ‫؍‬ 9.52, 95% confidence interval >2.12, P ‫؍‬ 0.0024) and ER/PR ؊ (odds ratio ‫؍‬ 12.3, 95% confidence interval >1.45, P ‫؍‬ 0.0188) cancers. Among the entire population, recurrences were significantly more prevalent for cases with PI above the medians for both ER/PR ؉ (Fisher's exact, P ‫؍‬ 1.19 ؋ 10 ؊5 ) and ER/PR ؊ (P ‫؍‬ 0.0025) patients and for the node-negative subsets (ER/PR ؉ node-negative, P ‫؍‬ 0.042 and ER/PR ؊ node-negative, P ‫؍‬ 0.039). In conclusion, these markers perform well in comparison with other criteria for recurrence risk assessment and can be used with routinely formalin-fixed, paraffin-embedded surgical specimens. (J Mol Diagn

The growing body of DNA microarray data has the potential to advance our understanding of the molecular basis of disease. However annotating microarray datasets with clinically useful information is not always possible, as this often requires access to detailed patient records. In this study we introduce GLAD, a new Semi-Supervised Learning (SSL) method for combining independent annotated datasets and unannotated datasets with the aim of identifying more robust sample classifiers.In our method, independent models are developed using subsets of genes for the annotated and unannotated datasets. These models are evaluated according to a scoring function that incorporates terms for classification accuracy on annotated data, and relative cluster separation in unannotated data. Improved models are iteratively generated using a genetic algorithm feature selection technique.Our results show that the addition of unannotated data into training, significantly improves classifier robustness.

No abstract