Wernicke's encephalopathy (WE) is a severe and life-threatening illness resulting from vitamin B1 (thiamine) deficiency. The prevalence of WE has been estimated from 0.4 to 2.8%. If not treated properly, severe neurologic disorders such as Korsakoff psychosis and even death may occur. The classical triad of clinical symptoms (abnormal mental state, ataxia, and ophthalmoplegia) is found in only 16-33% of patients on initial examination. The originally described underlying condition of WE is alcoholism, but it accounts for about 50% of causes of WE. Nonalcoholic patients are also affected by WE and likely to present symptoms and radiological imaging findings different from patients with alcoholism, which further complicates the diagnosis of WE. Being familiar with predisposing causes, symptoms and radiological imaging findings of WE is important for radiologists and clinicians when making the diagnosis to start immediate treatment. This review discusses pathophysiologies, underlying causes, clinical symptoms, imaging findings and their mimics.

• In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. • The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis. • Hepatic fibrosis masks the characteristic texture features of NASH.

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Underbalanced drilling (UBD) holds several important advantages over conventional drilling technology. These include minimization of formation damage, faster penetration rate, and ability for evaluation of reservoir productivity during the drilling process. As UBD technology matures, it has also been used more and more in different applications. However, many aspects of UBD technology remain poorly understood. The model presented in this paper seeks to understand the mechanisms involved in the transport of cuttings in UBD.The model simulates the transport of drill cuttings in an annulus of arbitrary eccentricity and includes a wide range of transport phenomena, including cuttings deposition and resuspension, formation, and movement of cuttings bed. The model consists of conservation equations for the fluid and cuttings components in the suspension and the cuttings deposit bed. Interaction between the suspension and the cuttings deposit bed, and between the fluid and cuttings components in the suspension, are incorporated. Solution of the model determines the distribution of fluid and cuttings concentration, velocity, fluid pressure, and velocity profile of cuttings deposit bed at different times.The model is used to determine the critical transport velocity for different hydrodynamic conditions. Results from the model agree quite closely, qualitatively, with experimental data obtained from a cuttings transport flow loop at the Technology Research Center of the Japan Natl. Oil Corp. (TRC/JNOC)'s Kashiwazaki Test Field in Japan. These results show the importance of slippage in the formation of the cuttings deposit bed. The model is useful in evaluating the minimum flow rate for effective cuttings removal in UBD.

Objectives: This study investigated the performance and robustness of radiomics in predicting COVID-19 severity in a large public cohort. Methods: A public dataset of 1110 COVID-19 patients (1 CT/patient) was used. Using CTs and clinical data, each patient was classified into mild, moderate, and severe by two observers: (1) dataset provider and (2) a board-certified radiologist. For each CT, 107 radiomic features were extracted. The dataset was randomly divided into a training (60%) and holdout validation (40%) set. During training, features were selected and combined into a logistic regression model for predicting severe cases from mild and moderate cases. The models were trained and validated on the classifications by both observers. AUC quantified the predictive power of models. To determine model robustness, the trained models was cross-validated on the inter-observer classifications. Results: A single feature alone was sufficient to predict mild from severe COVID-19 with 〖AUC〗_valid^provider=0.85 and 〖AUC〗_valid^radiologist=0.74 (p<<0.01). The most predictive features were the distribution of small size-zones (GLSZM-SmallAreaEmphasis) for provider classification and linear dependency of neighboring voxels (GLCM-Correlation) for radiologist classification. Cross-validation showed that both 〖AUC〗_valid^ ≈0.80 (p<<0.01). In predicting moderate from severe COVID-19, first-order-Median alone had sufficient predictive power of 〖AUC〗_valid^provider=0.65 (p=0.01). For radiologist classification, the predictive power of the model increased to 〖AUC〗_valid^radiologist=0.66 (p<<0.01) as the number of features grew from 1 to 5. Cross-validation yielded 〖AUC〗_valid^radiologist=0.63 (p=0.002) and 〖AUC〗_valid^provider=0.60 (p=0.09). Conclusions: Radiomics significantly predicted different levels of COVID-19 severity. The prediction was moderately sensitive to inter-observer classifications, and thus need to be used with caution.