The common truncation variant in pancreatic lipase related protein 2 (PNLIPRP2) is expressed poorly and does not alter risk for chronic pancreatitis

A nonsense variant (p.W358X) of human pancreatic lipase related protein 2 (PNLIPRP2) is present in different ethnic populations with a high allele frequency. In cell culture experiments, the truncated protein mainly accumulates inside the cells and causes endoplasmic reticulum stress. Here, we tested the hypothesis that variant p.W358X might increase risk for chronic pancreatitis through acinar cell stress. We sequenced exon 11 of PNLIPRP2 in a cohort of 256 subjects with chronic pancreatitis (152 alcoholic and 104 non-alcoholic) and 200 controls of Hungarian origin. We observed no significant difference in the distribution of the truncation variant between patients and controls. We analyzed mRNA expression in human pancreatic cDNA samples and found the variant allele markedly reduced. We conclude that the p.W358X truncation variant of PNLIPRP2 is expressed poorly and has no significant effect on the risk of chronic pancreatitis

Challenges and Rewards of the Electrosynthesis of Macroscopic Aligned Carbon Nanotube Array/Conducting Polymer Hybrid Assemblies

Hybrid assemblies based on conducting polymers and carbon nanomaterials with organized nanoscale structure are excellent candidates for various application schemes ranging from thermal management to electrochemical energy conversion and storage. In the case of macroscopic samples, however, precise control of the nanoscale structure has remained a major challenge to be solved for the scientific community. In this study we demonstrate possible routes to homogeneously infiltrate poly(3-hexylthiophene), poly(3,4-ethylenedioxythiophene), and polyaniline into macroscopic arrays of vertically aligned multiwalled carbon nanotubes (MWCNTAs). Electron microscopic images and Raman spectroscopic analysis (performed along the longitudinal dimension of the hybrid samples) both confirmed that optimization of the electropolymerization circumstances allowed fine tuning of the hybrid structure towards the targeted application. In this vein, three different application avenues were tested. The remarkable anisotropy in both the electrical and thermal conductivity of the nanocomposites makes them eminently attractive candidates to be deployed in thermal management. Thermoelectric studies, aimed to understand the effect of organized nanoscale morphology on the important parameters (Seebeck coefficient, electrical-, and thermal conductivity) compared to their non-organized hybrid counterparts. Finally, extraordinary high charge storage capacity values were registered for the MWCNTA/PANI hybrids (500 F g(-1) and 1-3 F cm(-2)). (C) 2015 Wiley Periodicals, Inc

Hematological, Micro-Rheological, and Metabolic Changes Modulated by Local Ischemic Pre- and Post-Conditioning in Rat Limb Ischemia-Reperfusion

In trauma and orthopedic surgery, limb ischemia-reperfusion (I/R) remains a great challenge. The effect of preventive protocols, including surgical conditioning approaches, is still controversial. We aimed to examine the effects of local ischemic pre-conditioning (PreC) and post-conditioning (PostC) on limb I/R. Anesthetized rats were randomized into sham-operated (control), I/R (120-min limb ischemia with tourniquet), PreC, or PostC groups (3 × 10-min tourniquet ischemia, 10-min reperfusion intervals). Blood samples were taken before and just after the ischemia, and on the first postoperative week for testing hematological, micro-rheological (erythrocyte deformability and aggregation), and metabolic parameters. Histological samples were also taken. Erythrocyte count, hemoglobin, and hematocrit values decreased, while after a temporary decrease, platelet count increased in I/R groups. Erythrocyte deformability impairment and aggregation enhancement were seen after ischemia, more obviously in the PreC group, and less in PostC. Blood pH decreased in all I/R groups. The elevation of creatinine and lactate concentration was the largest in PostC group. Histology did not reveal important differences. In conclusion, limb I/R caused micro-rheological impairment with hematological and metabolic changes. Ischemic pre- and post-conditioning had additive changes in various manners. Post-conditioning showed better micro-rheological effects. However, by these parameters it cannot be decided which protocol is better

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

BACKGROUND: Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. METHODS: The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). RESULTS: The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/). CONCLUSIONS: The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model