Background: Anastomotic leakage (AL) still remains a common complication after colorectal anastomosis that leads to increased morbidity and mortality. The gut microbiota has been hypothesized as one of the risk factors associated with anastomotic leakage. The aim of the present study was to summarize all existing clinical and experimental studies that evaluate the impact of intestinal microbiota on anastomotic leakage after colorectal resection. Methods: The present scoping review was designed according to PRISMA recommendations and a systematic search in Medline, Scopus, EMBASE, Clinicaltrials.gov, Google Scholar, and CENTRAL was conducted until September 2024. Results: Overall, 7 clinical and 5 experimental studies were included. A diminished α-diversity of the gut microbiota in patients suffering from AL was demonstrated. Specific microbe genera, such as Lachnospiraceae, Bacteroidaceae, Bifidobacterium, Acinetobacter, Fusobacterium, Dielma, Elusimicronium, Prevotella, and Faecalibacterium, seem to be associated with AL. However, specific genera, like Prevotella, Streptococcus, Eubacterium, Enterobacteriaceae, Klebsiella, Actinobacteria, Gordonibacter, Phocaeicola, and Ruminococcus2, seem to be protective against AL. Experimental studies highlighted that the Western diet seems to affect microbiota diversity and increases the AL rate, whereas anastomotic healing seems to be impaired by high metalloproteinase production and increased collagenase activity. Conclusions: The intestinal microbiota seems to play an important role in anastomotic leakage after colorectal resection. Specific interventions targeting the microbiota’s composition and the pathophysiological mechanisms by which it impairs anastomotic healing could diminish the risk for anastomotic leakage and improve clinical outcomes. However, future studies should be based on prospective design and eliminate heterogeneity.

Background/Objectives: Esophageal cancer (EC) represents a major global contributor to cancer-related mortality. The advent of artificial intelligence (AI), including machine learning, deep learning, and radiomics, holds promise for enhancing treatment decisions and predicting outcomes. The aim of this review is to present an overview of the current landscape and future perspectives of AI in the management of EC. Methods: A literature search was performed on MEDLINE using the following keywords: “Artificial Intelligence”, “Esophageal cancer”, “Barrett’s esophagus”, “Esophageal Adenocarcinoma”, and “Esophageal Squamous cell carcinoma”. All titles and abstracts were screened; the results included 41 studies. Results: Over the past five years, the number of studies focusing on the application of AI to the treatment and prognosis of EC has surged, leveraging increasingly larger datasets with external validation. The simultaneous incorporation in AI models of clinical factors and features from several imaging modalities displays improved predictive performance, which may enhance patient outcomes, based on direct personalized therapeutic options. However, clinicians and researchers must address existing limitations, conduct randomized controlled trials, and consider the ethical and legal aspects that arise to establish AI as a standard decision-support tool. Conclusions: AI applications may result in substantial advances in EC management, heralding a new era. Considering the complexity of EC as a clinical entity, the evolving potential of AI is anticipated to ameliorate patients’ quality of life and survival rates.

Background: The management of complex hepato-pancreato-biliary (HPB) pathologies demands exceptional surgical precision. Traditional two-dimensional imaging has limitations in depicting intricate anatomical relationships, potentially complicating preoperative planning. This review explores the synergistic application of three-dimensional (3D) reconstruction and artificial intelligence (AI) to support surgical decision-making in complex HPB cases. Methods: This narrative review synthesized the existing literature on the applications, benefits, limitations, and implementation challenges of 3D reconstruction and AI technologies in HPB surgery. Results: The literature suggests that 3D reconstruction provides patient-specific, interactive models that significantly improve surgeons’ understanding of tumor resectability and vascular anatomy, contributing to reduced operative time and blood loss. Building upon this, AI algorithms can automate image segmentation for 3D modeling, enhance diagnostic accuracy, and offer predictive analytics for postoperative complications, such as liver failure. By analyzing large datasets, AI can identify subtle risk factors to guide clinical decision-making. Conclusions: The convergence of 3D visualization and AI-driven analytics is contributing to an emerging paradigm shift in HPB surgery. This combination may foster a more personalized, precise, and data-informed surgical approach, particularly in anatomically complex or high-risk cases. However, current evidence is heterogeneous and largely observational, underscoring the need for prospective multicenter validation before routine implementation.

Laparoscopic total gastrectomy is on the rise. One of the most technically demanding steps of the approach is the construction of esophago-jejunal anastomosis. Several laparoscopic anastomotic techniques have been described, like linear stapler side-to-side or circular stapler end-to-side anastomosis; limited data exist regarding hand-sewn esophago-jejunal anastomosis. The study took place between January 2018 and June 2021. Patients enrolled in this study were adults with proximal gastric or esophago-gastric junction Siewert type III tumors that underwent 3D-assisted laparoscopic total gastrectomy. A hand-sewn esophago-jejunal anastomosis was performed in all cases laparoscopically. Forty consecutive cases were performed during the study period. Median anastomotic suturing time was 55 min, with intra-operative methylene blue leak test being negative in all cases. Median operating time was 240 min, and there were no conversions to open. The anastomotic leak rate and postoperative stricture rate were zero. The 30- and 90-day mortality rates were zero. Laparoscopic manual esophago-jejunal anastomosis utilizing a 3D platform in total gastrectomy for cancer can be performed with excellent outcomes regarding anastomotic leak and stricture rate. This anastomotic approach, although technically challenging, is safe and reproducible, with prominent results that can be disseminated in the surgical community.

Bacterial translocation is defined as the invasion of gut bacteria or bacterial products to the systemic circulation via permeation through the gastrointestinal mucosal wall. In this article, we present the case of a patient with postoperative fever of unknown origin which was attributed to bacterial translocation after revisional surgery due to malabsorptive complications after an initial duodenal switch for super-morbid obesity.

Background: The management of complex hepato-pancreato-biliary (HPB) pathologies demands exceptional surgical precision. Traditional two-dimensional imaging has limitations in depicting intricate anatomical relationships, potentially complicating preoperative planning. This review explores the synergistic application of three-dimensional (3D) reconstruction and artificial intelligence (AI) to enhance surgical management in complex HPB cases. Methods: This narrative review comprehensively synthesized the existing literature on the applications, benefits, and limitations of 3D reconstruction and AI technologies in the context of HPB surgery. Results: The literature demonstrates that 3D reconstruction provides patient-specific, interactive models that significantly improve surgeons' understanding of tumor resectability and vascular anatomy, contributing to reduced operative time and blood loss. Building upon this, AI algorithms automate image segmentation for 3D modeling, enhance diagnostic accuracy, and offer predictive analytics for postoperative complications, such as liver failure. By analyzing large datasets, AI can identify subtle risk factors to guide clinical decision-making. Conclusion: The convergence of 3D visualization and AI-driven analytics is creating a paradigm shift in HPB surgery. This powerful combination is fostering a move toward a more personalized, precise, and data-informed surgical approach, with the potential to optimize outcomes for the most challenging patient cohorts.