Effect of a Mycotoxin Binder (MMDA) on the Growth Performance, Blood and Carcass Characteristics of Broilers Fed Ochratoxin A and T-2 Mycotoxin Contaminated Diets

The contamination of feed with mycotoxins is a global concern, resulting in adverse effects on productivity and animal health and, therefore, a great economic loss. Ochratoxin A and T-2 mycotoxins are among the mycotoxins that contaminate animal feed. These mycotoxins could adversely affect the health of broilers, and the most effective method to mitigate the toxic effects of mycotoxins is the use of detoxifying agents. In the present experiment, broiler chickens were allotted into five groups. Group 1 received a non-contaminated diet; group 2 received a non-contaminated diet + 3 g/kg of a mycotoxin binder (MMDA); group 3 received a non-contaminated diet + 0.5 mg/kg OTA + 1 mg/kg T-2 toxin; group 4 received a non-contaminated diet + 0.5 mg/kg OTA + 1 mg/kg T-2 toxin + 1 g/kg MMDA; and group 5 received a non-contaminated diet + 0.5 mg/kg OTA + 1 mg/kg T-2 toxin + 3 g/kg MMDA for 35 days. The results revealed that OTA and T-2 toxin negatively affected the productive parameters and some blood and carcass characteristics of broiler chickens. The addition of the detoxifying agent (MMDA at 1 or 3 g/kg feed) to contaminated diets alleviated the adverse effects observed on productivity and the broilers heath related parameters.This research was funded by Patent Co., Vlade Ćetkovića 1A, 24 211 Mišićevo, Serbia

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

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.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).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/).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

Hypertriglyceridemia-induced acute pancreatitis: A prospective, multicenter, international cohort analysis of 716 acute pancreatitis cases

Background Hypertriglyceridemia is the third most common cause of acute pancreatitis (AP). It has been shown that hypertriglyceridemia aggravates the severity and related complications of AP; however, detailed analyses of large cohorts are inadequate and contradictory. Our aim was to investigate the dose-dependent effect of hypertriglyceridemia on AP. Methods AP patients over 18 years old who underwent triglyceride measurement within the initial three days were included into our cohort analysis from a prospective international, multicenter AP registry operated by the Hungarian Pancreatic Study Group. Data on 716 AP cases were analyzed. Six groups were created based on the highest triglyceride level (Peer reviewe

Multiple Hits in Acute Pancreatitis: Components of Metabolic Syndrome Synergize Each Other's Deteriorating Effects

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A Multicenter, International Cohort Analysis of 1435 Cases to Support Clinical Trial Design in Acute Pancreatitis

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Virtual material characterization process for composite materials: an industrial solution

Detailed material modeling covering low continuum scales and scale-transition methods offers material and design engineers a virtual tool for the virtual material assessment and optimization, relaxing on the experimental testing efforts. This contribution demonstrates the different steps of a Virtual Material Characterization (VMC) process applied for the prediction of elastic properties of a real meso-scale composite geometry. The presented R&D results have been achieved as part of a running R&D program (M3 MacroModelMat), in which the meso-modeling framework will be extended with a key and unique aspect: non-linear and damage composite behavior to be used for attributes such as static strength, dynamic strength and NVH

Preliminary Assessment of Risk Factors for Tooth Wear

Background: Epidemiological studies have reported an increasing prevalence of tooth wear, and general dental practitioners see a greater number of patients seeking treatment with worn dentition. Improper oral hygiene, unhealthy habits, occupational hazards, and potentially erosive processed foods seem to play a major role in the non-carious loss of dental hard tissue. The aim of this study is to assess the factors and cofactors that may play a major role in the genesis of dental attrition, dental abrasion, and, especially, dental erosion