Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems—costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a 5-year overall survival below 12% and high recurrence rates even after R0 resection. Traditionally managed with a “surgery-first” approach, two consistent observations—the near-universal presence of micrometastatic disease at diagnosis and the frequent inability to complete adjuvant therapy—have driven the integration of neoadjuvant therapy (NAT) into clinical practice. NAT offers several theoretical and practical advantages: early systemic control of occult disease, improved delivery and completion of multimodal treatment, biological selection of surgical candidates, and increased R0 resection rates. While in borderline resectable PDAC, randomized trials have consistently demonstrated improved margin-negative resection rates and early survival benefits compared with upfront surgery, in resectable PDAC, evidence is more heterogeneous. Real-world studies corroborate trial findings, reporting higher R0 rates and reduced lymph node positivity without increased perioperative risk, but also highlight substantial heterogeneity in regimens, duration, and radiotherapy use. Limitations to universal NAT adoption include reliance on anatomy-based resectability criteria, absence of validated predictive biomarkers, challenges in response assessment, and concerns over disease progression during preoperative treatment. Future developments will focus on integrating molecular profiling, circulating tumor DNA dynamics, and advanced imaging into patient selection and treatment adaptation, supported by biomarker-enriched and adaptive trial designs. NAT is thus evolving from a selective strategy for borderline disease to an innovative framework to optimize multimodal treatment delivery and refine patient selection in PDAC, with the potential to improve surgical outcomes and inform systemic therapy decisions in both resectable and borderline resectable settings

Background. Postoperative pancreatic fistula (POPF) remains one of the most relevant complications following pancreaticoduodenectomy (PD), significantly impacting short-term outcomes and delaying adjuvant therapies. Current predictive models offer limited accuracy, often failing to incorporate early postoperative data. This retrospective study aimed to develop and validate machine learning (ML) models to predict the absence and severity of POPF using clinical, surgical, and early postoperative variables. Methods. Data from 216 patients undergoing PD were analyzed. A total of twenty-four machine learning (ML) algorithms were systematically evaluated using the Matthews Correlation Coefficient (MCC) and AUC-ROC metrics. Among these, the GradientBoostingClassifier consistently outperformed all other models, demonstrating the best predictive performance, particularly in identifying patients at low risk of postoperative pancreatic fistula (POPF) during the early postoperative period. To enhance transparency and interpretability, a SHAP (SHapley Additive exPlanations) analysis was applied, highlighting the key role of early postoperative biomarkers in the model predictions. Results. The performance of the GradientBoostingClassifier was also directly compared to that of a traditional logistic regression model, confirming the superior predictive performance over conventional approaches. This study demonstrates that ML can effectively stratify POPF risk, potentially supporting early drain removal and optimizing postoperative management. Conclusions. While the model showed promising performance in a single-center cohort, external validation across different surgical settings will be essential to confirm its generalizability and clinical utility. The integration of ML into clinical workflows may represent a step forward in delivering personalized and dynamic care after pancreatic surgery.

Sepsis is a leading cause of morbidity and mortality worldwide, and its outcomes depend on early recognition and timely intervention. Conventional clinical scores and biomarkers provide prognostic value but often lack accuracy for individualized prediction. Machine learning (ML) offers the ability to integrate multidimensional data to improve risk stratification. We analyzed 477 patients admitted to our hospital, including 251 with sepsis, 100 with septic shock, and 126 controls. Demographic, clinical, and laboratory data were collected. Univariate correlation analyses explored associations with sepsis severity and mortality (in-hospital, 30-day, and 90-day). Several ML models were tested, with performance assessed by area under the receiver operating characteristic curve (AUC-ROC) and Matthews’s correlation coefficient (MCC). Model interpretability was evaluated using SHAP (SHapley Additive exPlanations). Sepsis severity and mortality correlated with biomarkers (procalcitonin, mid-regional pro-adrenomedullin, lactate) and clinical scores (SOFA, qSOFA). In-hospital mortality was associated with ADM, catecholamine use, and SOFA, while 90-day mortality involved smoking and Gram-negative or polymicrobial infections. Different machine learning models were evaluated, and the model achieving the highest performance on the validation set was selected. The selected model either outperformed or demonstrated comparable performance to logistic regression, depending on the specific prediction task (AUC 0.99 for sepsis, 0.96 for septic shock, 0.70 for ICU admission; 0.90, 0.72, and 0.87 for in-hospital, 30-day, and 90-day mortality). SHAP confirmed the clinical relevance of these predictors. ML models integrating clinical and biochemical data outperform conventional methods in predicting sepsis progression and mortality, while maintaining interpretability. These findings support the use of ML-based tools for early diagnosis and personalized risk stratification in sepsis, though external validation is required before clinical application.

This editorial reviews the early experiences and outcomes associated with the application of the Hugo™ Robotic-Assisted Surgery (RAS) system in general surgical procedures, highlighting its technical advantages and clinical implications. A retrospective analysis of three robotic cholecystectomies and three robotic rectal resections performed with the Hugo™ RAS system was conducted, with a focus on operative times, docking efficiency, and patient outcomes. Key system features and procedural strategies were evaluated. Robotic cholecystectomies demonstrated a significant reduction in docking times and excellent patient outcomes, with no complications and short hospital stays. Robotic rectal resections showcased the system's adaptability for intricate pelvic dissections, achieving clear oncological margins and favorable postoperative recovery. The Hugo™ RAS system proves to be a versatile and cost-effective platform for general surgery, with potential to democratize access to robotic-assisted procedures. Future research is warranted to optimize workflows and validate its benefits across broader surgical.

Background/Objectives: Colorectal cancer (CRC) is one of the most commonly diagnosed malignancies worldwide and a leading cause of cancer-related mortality. Surgical resection remains the cornerstone of curative treatment. Over the past two decades, robotic-assisted surgery has emerged as an evolution of minimally invasive surgery, aiming to overcome several limitations of conventional laparoscopy. This narrative review summarizes the current state of the art of robotic surgery in CRC. Methods: A narrative review of the literature was conducted using PubMed/MEDLINE and Scopus databases, focusing on publications from 2015 to 2026. The review provides an overview of robotic platforms and summarizes the available clinical evidence. Priority was given to randomized controlled trials, meta-analyses, large observational studies, and clinical practice guidelines. The review focuses on major commercially available robotic systems, including the da Vinci^®, Hugo™ RAS, and Versius^® platforms, as well as emerging robotic technologies. Results: Robotic colorectal surgery showed potentially favorable perioperative and oncological outcomes compared with laparoscopy. In rectal cancer, robotic approaches were associated with improved total mesorectal excision quality, lower conversion rates, and improved postoperative functional outcomes. Emerging evidence also suggested potential improvements in disease-free survival and local disease control following robotic rectal surgery. In colon cancer, robotic colectomy were associated with lower conversion rates, reduced blood loss, and faster postoperative recovery, with comparable long-term oncological outcomes. However, robotic procedures showed longer operative times and higher procedural costs. Conclusions: Robotic colorectal surgery appears to be a safe and effective minimally invasive approach, particularly in rectal cancer surgery. The development of new robotic platforms and increasing market competition may improve cost sustainability and expand its future role in colorectal cancer management.