Search PubMed⌕ Search

Biomedical subjects

Joseph S Verducci

Publications and source records attributed to Joseph S Verducci.

4 recordsLinked to original sources

50-year appraisal of gastrinoma: recommendations for staging and treatment.

BACKGROUND: Gastrinoma is a rare neuroendocrine tumor associated with ulcerogenic syndrome. The purpose of this study was to provide information on current controversies related to treatment, including staging, patient selection, and outcomes for surgical resection. STUDY DESIGN: A retrospective review of 106 patients with gastrinoma. Patients were classified as sporadic gastrinoma (SG) or MEN. End points of analysis included disease-free and disease-specific survival. Kaplan-Meier survival analysis was performed and significance (p < 0.05) was determined by Mantel-Haenszel log-rank test. RESULTS: Gastrinoma can be staged by TNM criteria into four groups (stage 0, I, II, and III), which had notably different survival curves, dependent on tumor size and distant metastases (p < 0.0001), but independent of lymph node metastases (p = 0.324). Surgical resection was possible in 72 patients (SG, n = 50; MEN, n = 22). Durable cure rate for SG was 26%, compared with 4% for MEN-1. Surgical resection achieving gross removal of all tumor resulted in improved survival in both SG and MEN patients (p < 0.0001). Improved survival was independent of a normal postoperative serum gastrin. Stage III was highly predictive of incomplete resection and the associated failure to improve survival (p = 0.0001). CONCLUSIONS: Staging provides a reliable method for the clinician to select patients for operation and to provide a prognosis, and should permit better comparisons of treatment between institutions. In the management of gastrinoma, it is recommended that SG and MEN patients with clinical stage I and II disease have surgical exploration, patients with stage III disease not have mandatory surgical treatment, and some stage 0 patients might not need routine surgical exploration.

Disease-Free Survival↗

Microarray analysis of gene expression: considerations in data mining and statistical treatment.

DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

Cluster Analysis↗

Systematic analysis of large screening sets in drug discovery.

Each year large pharmaceutical companies produce massive amounts of primary screening data for lead discovery. To make better use of the vast amount of information in pharmaceutical databases, companies have begun to scrutinize the lead generation stage to ensure that more and better qualified lead series enter the downstream optimization and development stages. This article describes computational techniques for end to end analysis of large drug discovery screening sets. The analysis proceeds in three stages: In stage 1 the initial screening set is filtered to remove compounds that are unsuitable as lead compounds. In stage 2 local structural neighborhoods around active compound classes are identified, including similar but inactive compounds. In stage 3 the structure-activity relationships within local structural neighborhoods are analyzed. These processes are illustrated by analyzing two large, publicly available databases.

Algorithms↗

Finding discriminating structural features by reassembling common building blocks.

We present a new method for constructing discriminating substructures by reassembling common medicinal chemistry building blocks. The algorithm can be parametrized to meet differing objectives: (1) to build features that discriminate for biological activity in a local structural neighborhood, (2) to build scaffolds for R-group analysis, (3) to construct cluster signatures that discriminate for membership in the cluster and provide a graphical representation for its members, and (4) to identify substructures that characterize major classes in a heterogeneous compound set. We illustrated the results of the algorithm on a literature dataset is of 118 compounds with in vitro inhibition data against recombinant human protein tyrosine phosphatase 1B (PTP-1B).

Algorithms↗