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Liver transplantation is the last life-saving solution for patients with end stage liver disease. The discrepancy between waiting list and available organs has led to the appearance of extended donation criteria and the development of several scores (Child-Pugh score, MELD score, DRI score, SOFT score), in order to find the most suitable donor-recipient match. But none of these scores can predict survival after transplantation. Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver and comes in this aid with its various methods (random forest, artificial neural networks, decision tree, Bayesian networks, and support vector machine). Materials and Methods. By reviewing the literature (mostly retrospective multicenter studies), we aimed to establish if the AI is a proper or even a more accurate method of predicting posttransplant survival, in comparison with the existing linear statistical models. Results. Machine learning showed better results than several current scoring systems that use either isolated donor/recipient scores or combined donor/recipient factors. The advantages of this model are its capacity for analyzing both linear and nonlinear relationships between features and outcomes, its robustness of overfitting by design, and built-in insights into feature importance aiding model explainability. Nevertheless, machine learning has its limitations because it requires large amounts of data, which can be difficult to obtain, it also requires high levels of technical skill, can be difficult to engineer and it’s expensive. Conclusion. AI may have significant potential in aiding clinical decision-making during liver transplantation, including donor-recipient matching.

Background. The relationship between chronic pancreatitis and diabetes is well established. This form of diabetes is secondary to exocrine pancreatic disorder and is known as diabetes mellitus type 3c (T3cDM). Materials and Methods. In this retrospective study we included 261 patients, 59 patients being diagnosed with chronic pancreatitis and secondary diabetes mellitus, and admitted in the Fundeni Clinical Institute, 2nd Department of Gastroenterology or N.C. Paulescu Institute/ Carol Davila University of Medicine and Pharmacy. Results and Discussions. Patients were 22.2% women and 77.8% men, with an average age of 56.8 years and 53.4 years respectively. 63% came from urban areas. The mean duration of chronic pancreatitis was six years. Non-diabetic patients were compared with patients who were previously diagnosed with T3cDM and who had been analyzed for body mass index (BMI). Imaging investigations were also performed to confirm pseudotumors or pancreatic tumours. Patients already considered non-diabetic had basal blood glucose values and were mostly overweight and obese. In this context, insulin resistance cannot be excluded for this group of patients. Conclusions. T3cDM is a new pathological entity that needs to be explored more deeply, and that should benefit from both a diagnostic stratification and treatment.

Pyrrolizidine alkaloids (PAs) are a class of toxic compounds found in the composition of more than 6000 plants. People can be exposed to PAs by consuming phytotherapeutic products, food from crops contaminated with seeds of some species with high content of PAs, and/ or contaminated animal products like bee products. For this reason we developed and validated a method for quantitative determination of PAs, from the most frequently contaminated food sources, honey and flour. Colorimetric Ehrlich reagent method was used with standard addition (1mg/kg senecionine). The extraction solvent was methanol 50% acidified with citric acid to pH 2-3, as this solvent can be used for alkaloids and N-oxides. We found that, in extracting the alkaloid only once from the dietary sources, the percent of recovery is low (52.5% for honey, and 45.75% for flour). Using successive extractions, three times with the same solvent, the senecionine retrieval percentage increased to 86.0% for honey and 76.0% for flour. The method was validated using the following parameters: selectivity, linearity (0,25- 20 mg/ mL senecionine), accuracy (average recovery 93.5 - 107.93%) and precision (RSD 3,26-4.55%.). The calculated limit of quantification (0.174 mg/ mL) makes this method applicable for determining Pas occurring at toxic levels for consumers.