Effect of liver transplantation on inflammatory bowel disease in patients with primary sclerosing cholangitis

This report investigates the influence of liver transplantation and concomitant immunosuppression on the course of progression of inflammatory bowel disease (IBD) and discusses statistical methodology appropriate for such settings. The data on 303 patients who underwent liver transplantation for primary sclerosing cholangitis (PSC) were analyzed using person-time analysis and Cox regression, with the duration of IBD as the time variable and transplantation as a segmented time-dependent covariate, to take into account both posttransplant and pretransplant history of IBD. The need for colectomy and appearance of colorectal cancer were taken as outcome measures. The only significant risk factor in the multivariate model for colectomy was transplantation itself, which increased the risk of colectomy due to intractable disease (Wald statistic; P =. 001). None of the variables available for analysis were found to influence the risk of colon cancer significantly. Graphs showing the dependence of the instantaneous risk of cancer on the time from onset of IBD and its independence from the latter in the case of colectomy are presented. The use of a unique statistical methodology described for the first time in this setting led us to the somewhat surprising conclusion that transplantation and concomitant use of immunosuppression accelerate the progression of IBD. At the same time, transplantation does not affect the incidence of colorectal cancer. These results confirm the findings of some recent studies and can potentially shed new light on the disease pathogenesis

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

PURPOSEChest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.METHODSA retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student’s t-test or Mann-Whitney U test. Cohen’s kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.RESULTSFifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.CONCLUSIONChest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting

A Multidisciplinary Lens

Digital Microscopic Photography and Numismatic Research: The View from Corinth Dr. Michael Ierardi In the 2012/3 funding cycle CARS purchased a Dynolite digital handheld microscopic camera to facilitate my research work in the coin room in the museum at Ancient Corinth. This paper illustrates the applications of the camera to a series of problems, ranging from the fairly specific to the general: identification of a barely legible medieval German coin; a typological study of the long-lived late Classical Pegasus/Trident series; coin iconography as evidence for cultural history and civic identity in the high Roman Empire; and using the mint control marks on coins struck as provision allowance for the Macedonian occupying garrison to reconstruct of a lost episode in the political economy of early Hellenistic Greece. International Financial Reporting Standards Without an International Financial Reporting Language: Evidence of Information Asymmetry in the European Union Dr. Mark Crowley Since 2005, companies in the European Union (EU) are required by Regulation 1606/2002 to prepare financial statements in accordance with IFRS; however, these listed companies are not required to prepare financial statements and disclosures in an International Financial Reporting Language (IFRL) or lingua franca. We question whether firms that file IFRS financial statements only in the local language but not in English, the IFRL or lingua franca, limit the benefits of IFRS; whereas, companies that also file in English reduce information asymmetry and increase the effectiveness of IFRS as a global informational tool. This paper examines annual filings from 6,000 listed companies in the European Union (EU) for the years 2009 and 2012. We found that approximately 60 percent of companies from non-English speaking countries file in their local language and voluntary disclose in English; while 40 percent report only in their native languages. Our research suggests that until international companies adopt English as the International Financial Reporting Language (IFRL) the full benefit of complying with IFRS will not be recognized. Exemplar or Empire: McKinley, Bryan, and the Philippines in the 1900 Presidential Election Dr. Jason Edwards This particular project focused on how William Jennings Bryan and President William McKinley articulated what America’s role in the world should be. Specifically, I examined Bryan’s 1900 Acceptance Address of the Democratic Nomination, while exploring several addresses made by President McKinley late in 1899 and early 1900. I argue that William Jennings Bryan’s rhetoric represents the classical position within American foreign policy that the United States best influences the affairs of the world by focusing on perfecting its domestic institutions at home, while not directly involving itself with other nations. President McKinley’s rhetoric represents a new and updated position that the United States best exemplifies its greatness by direct involvement with the world and other nations. This particular project is part of a larger book I am developing tracing the historical debates over America’s development from an exemplar nation to one that emphasizes empire and direct involvement with the world

Anton: A Special-Purpose Machine for Molecular Dynamics Simulation

The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macromolecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond—about three orders of magnitude beyond the duration of the longest current MD simulations. In this paper, we describe a massively parallel machine called Anton, which should be capable of executing millisecondscale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communicatio

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls