Carol Davila University of Medicine and Pharmacy (Romanian: Universitatea de Medicină și Farmacie „Carol Davila”) or University of Medicine and Pharmacy Bucharest, commonly known by the abbreviation UMFCD, is a public health sciences university in Bucharest, Romania. It is one of the largest and oldest institutions of its kind in Romania. The university uses the facilities of over 20 clinical hospitals all over Bucharest.The Carol Davila University is classified as an "advanced research and education university" by the Ministry of Education. Created as part of the University of Bucharest in 1869, the institution is considered one of the most prestigious of its kind in Romania and in Eastern Europe.
In 2024, a comprehensive framework for the screening, diagnosis, and management of metabolic dysfunction–associated steatotic liver disease (MASLD) was incorporated in the EASL-EASD-EASO clinical practice guidelines. However, physicians often face barriers applying these recommendations in routine clinical care, especially in the Southeastern Europe, Middle East, and Africa (SEEMEA) region. As a multidisciplinary group of physicians involved in MASLD and metabolic dysfunction-associated steatohepatitis (MASH) management, our objective is to provide a practice-oriented roadmap including practical and educational considerations beyond the hepatology field that could improve patient care and support implementation of clinical guidance within the SEEMEA region. This work is informed by a narrative review and expert input obtained through structured discussions, to examine the status quo and identify key gaps in the MASLD/MASH management, unravelling the patient journey from screening and diagnosis to treatment and follow-up. Furthermore, we advise on priorities on screening triggers and, considering the limited availability of vibration-controlled transient elastography (VCTE), discuss alternative approaches to achieve accurate and timely diagnosis. Finally, following the approval of resmetirom and semaglutide 2.4 mg for MASH treatment, we review the evolving pharmacotherapy landscape and propose a “blueprint” for a specialised MASLD clinic, suggesting mandatory and optional facilities for optimised care.
Chemoresistance remains a major barrier to durable disease control in gastric cancer, limiting the effectiveness of perioperative and palliative systemic therapies. Increasing evidence indicates that resistance is driven by a complex interplay between tumor-intrinsic adaptations and tumor microenvironment-mediated survival pathways. Although chemoresistance can be innate or acquired, a variety of mechanisms could coexist within the tumor, with various processes acting synergistically. MicroRNAs, as key post-transcriptional regulators, have emerged as central modulators of these resistance networks. This review summarizes the principal mechanisms of chemoresistance in gastric cancer and synthesizes current evidence on how microRNAs-derived from tumor cells and the tumor microenvironment-promote or reverse resistance to commonly used chemotherapeutic agents and targeted therapy/immunotherapies, highlighting therapeutic opportunities. Evidence was organized by mechanism (drug transport and metabolism, DNA damage response, apoptosis and survival signaling, autophagy, epithelial-to-mesenchymal transition/cancer stem cell plasticity, and immune escape) and by drug class.
The present Research Topic consists of twenty articles: 3 systematic reviews with meta-analysis, 3 reviews, and 14 original research articles. These articles provide a diverse range of innovative viewpoints on the significance of comprehending the genetic, molecular, and cellular mechanisms associated with gastrointestinal complications and diseases. Such understanding has the potential to guide the advancement of therapies in areas where clinical problems remain unsolved.Regarding cellular and molecular mechanisms, Sui C et al. examined the relationship between various immune cells and postoperative ileus, aiming to provide potential therapeutic solutions for this unresolved clinical issue. Jiang N et al. analyzed the potential antifibrotic effects of salvianolic acid B in autophagy in liver fibrosis. Their findings revealed that inducing TGF-β1 resulted in a significant increase in autophagosome formation and autophagic flux in different molecular cascades. Another original research studied by Jia Q et al. demonstrated the in vivo efficacy of hesperidin in promoting gastric motility in rats with functional dyspepsia, suggesting a potential treatment approach for this condition.Screening and early detection are among the primary priorities of the current European anticancer plan and should be implemented at the national level (Pana et al., 2023)
Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search-Appraisal-Synthesis-Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.
We introduce MedQARo, the first large-scale medical QA benchmark in Romanian, alongside a comprehensive evaluation of state-of-the-art large language models (LLMs). We construct a high-quality and large-scale dataset comprising 105,880 QA pairs about cancer patients from two medical centers. The questions regard medical case summaries of 1,242 patients, requiring both keyword extraction and reasoning. Our benchmark contains both in-domain and cross-domain (cross-center and cross-cancer) test collections, enabling a precise assessment of generalization capabilities. We experiment with four open-source LLMs from distinct families of models on MedQARo. Each model is employed in two scenarios: zero-shot prompting and supervised fine-tuning. We also evaluate two state-of-the-art LLMs exposed only through APIs, namely GPT-5.2 and Gemini 3 Flash. Our results show that fine-tuned models significantly outperform zero-shot models, indicating that pretrained models fail to generalize on MedQARo. Our findings demonstrate the importance of both domain-specific and language-specific fine-tuning for reliable clinical QA in Romanian.
