Background: Tumour mutational burden (TMB) is an emerging biomarker for predicting the efficacy of immune checkpoint inhibitors (ICIs) in cancer therapy. While its role is well established in lung cancer and melanoma, its predictive value for breast and prostate cancers remains unclear. Objective: This systematic review aimed to assess the predictive value of TMB for ICI therapy across four major cancer types—lung, melanoma, breast, and prostate—and to explore factors contributing to the variability in its effectiveness as a biomarker. Methods: A systematic search and a review of the literature were conducted in accordance with PRISMA guidelines. Studies examining the relationship between TMB levels and clinical outcomes following ICI therapy in the specified cancers were analyzed. The data were synthesized to evaluate TMB’s predictive value and identify gaps in the current research. Results: High TMB consistently correlated with improved outcomes in lung cancer and melanoma, confirming its predictive utility in these cancers. Conversely, the findings for breast and prostate cancers were inconclusive. The variability in TMB’s predictive value for these cancers suggests the need for complementary biomarkers or refined criteria to enhance its reliability. Methodological inconsistencies in TMB evaluation were also noted as a significant limitation. Conclusions: TMB serves as a robust biomarker for predicting ICI response in lung cancer and melanoma, but demonstrates limited predictive utility in breast and prostate cancers. Future research should prioritize standardizing TMB assessment protocols and investigating additional biomarkers to improve treatment personalization for these cancer types.

Nutritional support in critically ill patients has been acknowledged as a pillar of ICU care, playing a pivotal role in preserving muscle mass, supporting immune function, and promoting recovery during and after critical illness. Providing effective nutritional support requires adapting it to the patient’s diagnosis, unique characteristics, and metabolic state to minimize the risks of overfeeding or underfeeding while mitigating muscle loss. This level of care requires a comprehensive nutritional assessment and the establishment of a nutrition-focused protocol. Regular, consistent and detailed nutritional evaluation can influence both therapeutic decisions and clinical interventions, thus ensuring that the specific needs of critically ill patients are met from the acute phase through their entire recovery process. Bioelectrical impedance analysis (BIA) is increasingly recognized as a valuable tool for enhancing nutritional care in critically ill patients. By delivering precise, real-time insights into key aspects of body composition, BIA is thought to provide clinicians with a more comprehensive understanding of the complex physiological changes that occur during critical illness. This narrative review highlights the potential of BIA in offering these precise assessments, facilitating the development of more accurate and personalized nutritional strategies for critically ill patients. If BIA can reliably assess dynamic shifts in hydration and tissue integrity, it holds the promise of further advancing individualized care and optimizing clinical outcomes in this vulnerable population.

Background and Objectives: Perforated gastro-duodenal ulcers (PGDUs) are life-threatening surgical emergencies with high morbidity and mortality. This study aimed to evaluate surgical strategies, outcomes, and prognostic factors in patients treated for PGDUs in a tertiary Eastern European center. Materials and Methods: We conducted a retrospective cross-sectional analysis of 156 patients admitted with PGDUs between 2020 and 2024. Data on demographics, risk factors, ulcer location, type of surgical approach, operative details, hospital stay, and mortality were collected. Statistical analysis included chi-square, Mann–Whitney U, and multivariate logistic regression. Results: The mean age was 57.6 ± 15.9 years (range 18–91), with men accounting for 64.7% of cases. Alcohol use was significantly associated with male sex (p = 0.012), while NSAID use was equally distributed. Open surgery was the mainstay of treatment (85.9%), with laparoscopy performed in 12.8% and conversion in 1.9%. Median hospital stay was shorter after laparoscopic repair (7.5 vs. 9 days, p = 0.039. On multivariate analysis, both age and comorbidity burden were independent predictors of mortality (p < 0.01). Conclusions: PGDU management in Eastern Europe remains dominated by open surgery. Laparoscopy, though underutilized, is associated with shorter recovery. Age is the strongest determinant of mortality, highlighting the need for early risk stratification, wider adoption of minimally invasive techniques, and preventive measures targeting modifiable risk factors.

One of the main issues in oncology is tumor recurrence following resection, which is responsible for a large number of patient deaths and treatment failures in a variety of malignancies. Despite advancements in adjuvant chemotherapy and radiation, residual disease that remains inside or close to the resection cavity and is difficult for systemic therapies to eradicate is the cause of further local recurrence. Hydrogel-nanoparticle (HNP) composites are a new class of therapeutic platforms that emerged from the recent convergence of biomaterial science and nanomedicine. Their specific goals are to fill the surgical gap, provide long-term localized drug release, and energetically remodel the post-surgical tumor microenvironment (TME). This paper includes the biological foundations of localized post-surgical therapy, important physicochemical considerations of the hydrogel matrix and nanoparticle carrier design, and a compilation of mechanistic and preclinical evidence for HNP hybrid platforms. Immunomodulatory strategies, stimuli-responsive release mechanism engineering, and novel techniques, including combination immunotherapy and 3D-printed customized scaffolds, are all given special attention. Examples of translational challenges are also addressed, such as manufacturing repeatability, biocompatibility, and regulatory classification. When considered collectively, the data demonstrate that HNP platforms are a convincing, practically feasible approach to reducing post-surgical recurrence rates and enhancing patient outcomes.

Hyaluronic acid (HA) is a glycosaminoglycan that is found within the body and has both structural and signaling functions in the extracellular matrix. HA is biocompatible and biodegradable; it has a high water content and binds directly to certain cell-surface proteins. Due to these characteristics, it is considered a promising component for the design of biomaterials for regenerative wound healing. This review covers the most recent findings on the use of HA-based biomaterials in soft tissue repair, while also incorporating earlier, foundational studies relevant to the field, focusing on HA’s characteristics, cellular interactions, design, and preclinical and clinical results. The physicochemical characteristics of HA and their influence on cellular responses and tissue regeneration are discussed to show how material properties can be adjusted for specific therapeutic purposes. There have been great advances in chemically modified composite scaffolds and HA matrices, which offer better mechanical stability and controlled degradation. At the same time, new delivery systems have been built using HA, from nanoparticles to gene delivery platforms and growth factors, and these have given the material an active role as a therapeutic agent rather than just a passive one. This narrative review covers the clinical evidence for the effectiveness of commercial products for acute and diabetic wounds, as well as burns and chronic wounds, and discusses where their use is indicated. In the end, the current limitations of the research and future applications and directions are discussed.

Background/Objectives: Acute gastrointestinal (GI) emergencies require timely and accurate prediction of surgical necessity to avoid delayed intervention and improve patient outcomes. Traditional scoring systems offer limited accuracy and fail to integrate multimodal data. This study aimed to develop and validate an explainable machine learning model for early prediction of surgical necessity in patients presenting with GI emergencies. Methods: A retrospective cohort of 1032 consecutive adult patients admitted with acute GI emergencies at a tertiary referral center (2019–2024) was analyzed. Three predictive models were developed: logistic regression, Random Forest, and XGBoost. Features included clinical, laboratory, and contrast-enhanced CT imaging variables available within the first 24 h. Model performance was evaluated using AUC, sensitivity, specificity, PPV, NPV, and F1-score. Shapley Additive Explanations (SHAP) were applied for global and individual-level interpretability. The study followed STROBE and TRIPOD+AI reporting guidelines. Results: Surgical intervention was required in 312 patients (30.2%). The XGBoost model achieved the highest AUC (0.89; 95% CI: 0.86–0.92), outperforming Random Forest (AUC 0.86) and logistic regression (AUC 0.79), with sensitivity 0.84, specificity 0.81, and NPV 0.90. The most influential predictors were serum lactate, CT findings (free intraperitoneal air, bowel ischemia), IL-6, and shock index. Decision curve analysis confirmed net clinical benefit across threshold probabilities of 10–70%. Subgroup performance remained robust across diagnostic categories (AUC 0.87–0.91). Conclusions: An explainable XGBoost model integrating early clinical, laboratory, and imaging data accurately predicts surgical necessity in GI emergencies and outperforms traditional scoring systems. SHAP-based explainability supports clinical adoption and transparency. Prospective multicenter validation is warranted. The positive predictive value of 0.74 indicates that approximately one in four patients flagged as requiring surgery may not need operative intervention. The model should be regarded as a decision-support adjunct, rather than a standalone surgical decision tool, that is most relevant in settings where immediate experienced surgical judgment is limited.