Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

Increasing the Potency and Breadth of an HIV Antibody by Using Structure-Based Rational Design

Antibodies against the CD4 binding site (CD4bs) on the HIV-1 spike protein gp120 can show exceptional potency and breadth. We determined structures of NIH45-46, a more potent clonal variant of VRC01, alone and bound to gp120. Comparisons with VRC01-gp120 revealed that a four-residue insertion in heavy chain complementarity–determining region 3 (CDRH3) contributed to increased interaction between NIH45-46 and the gp120 inner domain, which correlated with enhanced neutralization. We used structure-based design to create NIH45-46^(G54W), a single substitution in CDRH2 that increases contact with the gp120 bridging sheet and improves breadth and potency, critical properties for potential clinical use, by an order of magnitude. Together with the NIH45-46–gp120 structure, these results indicate that gp120 inner domain and bridging sheet residues should be included in immunogens to elicit CD4bs antibodies

Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

Endoscopic ultrasound in chronic liver disease.

Endoscopic ultrasound (EUS) is a minimally invasive diagnostic and therapeutic modality with a number of established as well as evolving uses in patients with chronic liver disease. Compared to other diagnostic tools such as cross-sectional imaging or conventional endoscopy, EUS has been shown to increase diagnostic sensitivity and therapeutic success for many clinical scenarios and applications with a low rate of adverse events. In this review, we discuss and focus on the current and growing role of EUS in the evaluation and/or treatment of hepatobiliary masses, hepatic parenchymal disease, portal hypertension, esophageal and other varices, and indeterminate biliary strictures

Econometric Forecasting

Several principles are useful for econometric forecasters: keep the model simple, use all the data you can get, and use theory (not the data) as a guide to selecting causal variables. But theory gives little guidance on dynamics, that is, on which lagged values of the selected variables to use. Early econometric models failed in comparison with extrapolative methods because they paid too little attention to dynamic structure. In a fairly simple way, the vector autoregression (VAR) approach that first appeared in the 1980s resolved the problem by shifting emphasis towards dynamics and away from collecting many causal variables. The VAR approach also resolves the question of how to make long-term forecasts where the causal variables themselves must be forecast. When the analyst does not need to forecast causal variables or can use other sources, he or she can use a single equation with the same dynamic structure. Ordinary least squares is a perfectly adequate estimation method. Evidence supports estimating the initial equation in levels, whether the variables are stationary or not. We recommend a general-to-specific model-building strategy: start with a large number of lags in the initial estimation, although simplifying by reducing the number of lags pays off. Evidence on the value of further simplification is mixed. If cointegration among variables, then error-correction models (ECMs) will do worse than equations in levels. But ECMs are only sometimes an improvement eve