BackgroundDiabetes mellitus (DM), a metabolic disease, is characterized by impaired fasting glucose levels. Type 2 DM is adult onset diabetes. Long non-coding RNAs (lncRNAs) regulate gene expression and multiple studies have linked lncRNAs to human diseases.MethodsSerum samples obtained from 96 participating veterans at JAH VA were deposited in the Research Biospecimen Repository. We used a two-stage strategy to identify an lncRNA whose levels correlated with T2DM. Initially we screened five serum samples from diabetic and non-diabetic individuals using lncRNA arrays. Next, GAS5 lncRNA levels were analyzed in 96 serum samples using quantitative PCR. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff GAS5 for diagnosis of DM.ResultsOur results demonstrate that decreased GAS5 levels in serum were associated with diabetes in a cohort of US military veterans. The ROC analysis revealed an optimal cutoff GAS5 value of less than or equal to 10. qPCR results indicated that individuals with absolute GAS5 < 10 ng/μl have almost twelve times higher odds of having diabetes (Exact Odds Ratio [OR] = 11.79 (95% CI: 3.97, 37.26), p < 0.001). Analysis indicated area under curve (AUC) of ROC of 0.81 with 85.1% sensitivity and 67.3% specificity in distinguishing non-diabetic from diabetic subjects. The positive predictive value is 71.4%.ConclusionlncRNA GAS5 levels are correlated to prevalence of T2DM.General SignificanceAssessment of GAS5 in serum along with other parameters offers greater accuracy in identifying individuals at-risk for diabetes.

Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. It has had a dramatic impact on society and world economies in only a few months. COVID-19 presents numerous challenges to all aspects of healthcare, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications to healthcare. Machine learning is a subset of AI that employs deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than humans. In this manuscript, we explore the potential for a simple and widely available test as a chest x-ray (CXR) to be utilized with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. Finally, we developed and described a publicly available website to demonstrate how this technology can be made readily available in the future.

This project aims to use our robust women's health patient data to analyze the correlation between cytology and high-risk human papillomavirus (Hr-HPV) testing, study the performance of Hr-HPV testing for detecting cytology lesions, and examine epidemiologic measures of human papillomavirus (HPV) infections in the women's veteran population. MethodsWe collected patient data from 2014 to 2020 from our computerized patient record system. We performed HPV assays using the cobas® 4800 system (

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The role of artificial intelligence (AI) in health care delivery is growing rapidly. Due to its visual nature, the specialty of anatomic pathology has great promise for applications in AI. We examine the potential of six different AI models for differentiating and diagnosing the three most common primary liver tumors: hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and combined HCC and CCA (cHCC/CCA). Our results demonstrated that for all three diagnoses, the sensitivity, specificity, positive predictive value, and negative predictive value was greater than or equat to 94% in the best model tested, with results greater than or equat to 92% in all categories in three of the models. These values are comparable to interpretation by general pathologists alone and demonstrate AI's potential in interpreting patient specimens for primary liver carcinoma. Applications such as these have multiple implications for delivering quality patient care, including assisting with intraoperative consultations and providing a rapid "second opinion" for confirmation and increased accuracy of final diagnoses. These applications may be particularly useful in underserved areas with shortages of subspecialized pathologists or after hours in larger medical centers. In addition, AI models such as these can decrease turnaround times and the inter- and intra-observer variability well documented in pathologic diagnoses. AI offers great potential in assisting pathologists in their day-to-day practice.