Acute pancreatitis (AP) is one of the most common diseases of the gastrointestinal tract, which in 20% of cases can turn into a severe form, with mortality reaching up to 30%. One of the cornerstones of AP treatment is early nutritional treatment. Feeding intolerance (FI) occurs in up to 25% of patients with AP and is associated with a more severe disease course and poorer clinical outcome. Feeding intolerance can have a multifaceted clinical presentation. The early identification of FI risk factors and appropriately conducted nutritional treatment are critical to the course of the disease. In this review, we summarize the current knowledge of feeding intolerance in AP, its pathomechanisms and risk factors, and its impact on disease progression. We also present suggestions for the management of feeding intolerance.

Pancreatic ductal adenocarcinoma is one of the deadliest human neoplasms. Despite the development of new surgical and adjuvant therapies, the prognosis remains very poor, with the overall survival rate not exceeding 9%. There is now increasing evidence that the human microbiome, which is involved in many physiological functions, including the regulation of metabolic processes and the modulation of the immune system, is possibly linked to pancreatic oncogenesis. However, the exact mechanisms of action are poorly understood. Our review summarizes the current understanding of how the microbiome affects pancreatic cancer development and progression. We discuss potential pathways of microbe translocation to the pancreas, as well as the mechanism of their action. We describe the role of the microbiome as a potential marker of pancreatic cancer diagnosis, progression, and survival. Finally, we discuss the possibilities of modifying the microbiome to improve treatment effectiveness for this deadly disease.

Purpose: Due to the lack of systematic data on antibiotic sensitivity, the treatment of the highly prevalent and pathogenic Helicobacter pylori (H. pylori) infection still poses a significant problem. Therefore, the aim of our study was to compare the efficacy of the three most commonly used anti-H. pylori therapies in northeastern Poland. Patients and Methods: This was a retrospective, single-center study performed on 289 outpatients with an H. pylori infection. Patients received one of the following three treatment regimens: (1) bismuth quadruple therapy (BQT) for 10 days, (2) metronidazole-based triple therapy (M-TT) for 10 or 14 days, and (3) levofloxacin-based triple therapy (L-TT) for 10 or 14 days. Results: BQT, M-TT, and L-TT accounted for 93.2% of prescribed anti-H. pylori therapies. The overall success rate for all treatment regimens was 84.1% (243/289). The effectiveness of first- and second-line therapy was similar and reached 83.8% and 86.2%, respectively. The efficacy of the individual treatment regimens was as follows: (1) BQT—89.4% (84/94), (2) M-TT—80.6% (112/139) and 78.8% (26/33) for 10 and 14 days, respectively, and (3) L-TT—84.6% (11/13) and 100% (10/10) for 10 and 14 days, respectively. The overall duration of treatment and type and dose of proton pump inhibitor (PPI) had no effect on the treatment efficacy. Conclusions: In the northeastern part of Poland, 10-day BQT and 10- or 14-day L-TT are effective treatment regimens for H. pylori eradication and have appear to be superior to M-TT. Practitioners in our clinic followed mostly local anti-H. pylori therapy guidelines.

Background: Gastroesophageal reflux disease (GERD) may present as nonerosive reflux disease (NERD), erosive esophagitis (EE), or be complicated by Barrett’s esophagus (BE). The explanation as to what determines the phenotype of GERD is awaited. Therefore, we assessed the correlation between the growth factors expression and endoscopic as histologic findings in GERD patients. Methods: The squamous esophageal epithelium of 50 patients (20-NERD, 7-EE, 15-BE, 8 controls) was examined by: (1) magnification endoscopy with evaluation of minimal GERD changes such as: microerosions, white spots, palisade blood vessels visibility, and intrapapillary capillary loops (IPCLs) appearance, (2) histology, (3) immunohistochemistry with evaluation of the expression of vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), and their receptors (VEGFR and EGFR). Results: The expression of VEGF, but not VEGFR, EGF, and EGFR, was significantly increased in EE patients compared to NERD patients and controls. VEGF levels correlated significantly with the presence of white spots, but not with other minimal endoscopic and histologic features. The EGFR expression correlated positively with basal cell hyperplasia and enlarged IPCLs. Conclusions: Our findings suggest a correlation between growth factors expression and findings in conventional endoscopy, formation of endoscopic minimal changes, and histologic lesions.

Acute pancreatitis (AP) is one of the most common gastrointestinal diseases and is characterized by a highly variable clinical course ranging from mild self-limiting inflammation to severe disease associated with persistent organ failure and high mortality. Early risk stratification is essential for timely therapeutic decision-making and improved patient outcomes. Although conventional prognostic scoring systems remain widely used, their predictive performance is limited by moderate accuracy, delayed applicability and the requirement for numerous clinical or imaging parameters. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of predictive models capable of integrating demographic, clinical, laboratory and imaging data to improve early outcome prediction. This review summarizes current evidence on the application of AI and ML in AP, focusing on the prediction of disease severity, organ failure, intensive care unit admission and mortality. We also discuss explainable AI approaches and the methodological strengths and limitations of existing models, including validation, calibration, overfitting and reproducibility. Finally, we highlight future perspectives, including multimodal AI, prospective clinical validation and regulatory considerations that should facilitate the safe integration of AI-based decision-support tools into routine clinical practice.