The response of macrophages to agents such as lipopolysaccharide (LPS) and interferon (IFN) includes the transcriptional activation of numerous genes. We have used the method of differential screening of a RAW 264.7 macrophage cell line cDNA library to isolate and characterize LPS-induced messages. One such message, LRG-47, is induced by LPS, IFN-gamma, and IFN-alpha/beta, but not by a panel of other cytokines or pharmacological activating agents. LRG-47 is homologous to two other IFN-gamma-induced genes, IRG-47 and Mg21. The LRG-47 sequence is approximately 33% identical and 52% similar to both these putative protein products. All three putative proteins, particularly Mg21, bear homology to a T cell product, Tgtp, induced by T cell receptor cross-linking. The three macrophage-derived proteins share areas of homology with GTP-binding proteins, are approximately 415 amino acids in length, and have similar kinetics of induction by IFN-gamma. This suggests that these genes may be members of a new family of IFN-inducible proteins.
BackgroundThe early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research.ResultsThe mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10-6 or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity.ConclusionThe ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods.
Pathology and radiology form the core of cancer diagnosis, yet the workflows of both specialties remain ad hoc and occur in separate "silos," with no direct linkage between their case accessioning and/or reporting systems, even when both departments belong to the same host institution. Because both radiologists' and pathologists' data are essential to making correct diagnoses and appropriate patient management and treatment decisions, this isolation of radiology and pathology workflows can be detrimental to the quality and outcomes of patient care. These detrimental effects underscore the need for pathology and radiology workflow integration and for systems that facilitate the synthesis of all data produced by both specialties. With the enormous technological advances currently occurring in both fields, the opportunity has emerged to develop an integrated diagnostic reporting system that supports both specialties and, therefore, improves the overall quality of patient care.
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