An Explainable-AI approach for Diagnosis of COVID-19 using MALDI-ToF Mass Spectrometry

The severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) caused a global pandemic and imposed immense effects on the global economy. Accurate, cost-effective, and quick tests have proven substantial in identifying infected people and mitigating the spread. Recently, multiple alternative platforms for testing coronavirus disease 2019 (COVID-19) have been published that show high agreement with current gold standard real-time polymerase chain reaction (RT-PCR) results. These new methods do away with nasopharyngeal (NP) swabs, eliminate the need for complicated reagents, and reduce the burden on RT-PCR test reagent supply. In the present work, we have designed an artificial intelligence-based (AI) testing method to provide confidence in the results. Current AI applications to COVID-19 studies often lack a biological foundation in the decision-making process, and our AI approach is one of the earliest to leverage explainable-AI (X-AI) algorithms for COVID-19 diagnosis using mass spectrometry. Here, we have employed X-AI to explain the decision-making process on a local (per-sample) and global (all samples) basis underscored by biologically relevant features. We evaluated our technique with data extracted from human gargle samples and achieved a testing accuracy of 94.44%. Such techniques would strengthen the relationship between AI and clinical diagnostics by providing biomedical researchers and healthcare workers with trustworthy and, most importantly, explainable test results

p53 andc-erbB-2 expression in human prostatic adenocarcinoma cell lines and tumor tissues.

p53 andc-erbB-2 expression in human prostatic adenocarcinoma cell lines and tumor tissues

Cyst Fluid Biosignature to Predict Intraductal Papillary Mucinous Neoplasms of the Pancreas with High Malignant Potential

BACKGROUND: Current standard-of-care technologies, such as imaging and cyst fluid analysis, are unable to consistently distinguish intraductal papillary mucinous neoplasms (IPMNs) of the pancreas at high risk of pancreatic cancer from low-risk IPMNs. The objective was to create a single-platform assay to identify IPMNs that are at high risk for malignant progression.STUDY DESIGN: Building on the Verona International Consensus Conference branch duct IPMN biomarker review, additional protein, cytokine, mucin, DNA, and microRNA cyst fluid targets were identified for creation of a quantitative polymerase chain reaction-based assay. This included messenger RNA markers: ERBB2, GNAS, interleukin 1 beta, KRAS, MUCs1, 2, 4, 5AC, 7, prostaglandin E2R, PTGER2, prostaglandin E synthase 2, prostaglandin E synthase 1, TP63; microRNA targets: miRs 101, 106b, 10a, 142, 155, 17, 18a, 21, 217, 24, 30a, 342, 532, 92a, and 99b; and GNAS and KRAS mutational analysis. A multi-institutional international collaborative contributed IPMN cyst fluid samples to validate this platform. Cyst fluid gene expression levels were normalized, z-transformed, and used in classification and regression analysis by a support vector machine training algorithm.RESULTS: From cyst fluids of 59 IPMN patients, principal component analysis confirmed no institutional bias/clustering. Lasso (least absolute shrinkage and selection operator)-penalized logistic regression with binary classification and 5-fold cross-validation used area under the curve as the evaluation criterion to create the optimal signature to discriminate IPMNs as low risk (low/moderate dysplasia) or high risk (high-grade dysplasia/invasive cancer). The most predictive signature was achieved with interleukin 1 beta, MUC4, and prostaglandin E synthase 2 to accurately discriminate high-risk cysts from low-risk cysts with an area under the curve of up to 0.86 (p = 0.002).CONCLUSIONS: We have identified a single-platform polymerase chain reaction-based assay of cyst fluid to accurately predict IPMNs with high malignant potential for additional studies. (C) 2019 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved

Genetic, Immunohistochemical, and Clinical Features of Medullary Carcinoma of the Pancreas : A Newly Described and Characterized Entity

Medullary carcinomas of the pancreas are a recently described, histologically distinct subset of poorly differentiated adenocarcinomas that may have a unique pathogenesis and clinical course. To further evaluate these neoplasms, we studied genetic, pathological, and clinical features of 13 newly identified medullary carcinomas of the pancreas. Nine (69%) of these had wild-type K-ras genes, and one had microsatellite instability (MSI). This MSI medullary carcinoma, along with three previously reported MSI medullary carcinomas, were examined immunohistochemically for Mlh1 and Msh2 expression, and all four expressed Msh2 but did not express Mlh1. In contrast, all of the medullary carcinomas without MSI expressed both Msh2 and Mlh1. Remarkably, the MSI medullary carcinoma of the pancreas in the present series arose in a patient with a synchronous but histologically distinct cecal carcinoma that also had MSI and did not express Mlh1. The synchronous occurrence of two MSI carcinomas suggests an inherited basis for the development of these carcinomas. Indeed, the medullary phenotype, irrespective of MSI, was highly associated with a family history of cancer in first-degree relatives (P < 0.001). Finally, one medullary carcinoma with lymphoepithelioma-like features contained Epstein-Barr virus-encoded RNA-1 by in situ hybridization. Therefore, because of medullary carcinoma’s special genetic, immunohistochemical, and clinical features, recognition of the medullary variant of pancreatic adenocarcinoma is important. Only by classifying medullary carcinoma as special subset of adenocarcinoma can we hope to further elucidate its unique pathogenesis