Background: Nutritional deterioration is a common consequence of gastrectomy for gastric cancer and may persist despite standardized perioperative care. However, prospective longitudinal evidence describing the early course of postoperative nutritional recovery remains limited. This study aimed to characterize nutritional recovery trajectories follo-wing curative gastrectomy and to evaluate the influence of the extent of gastric resection on postoperative recovery. Methods: We conducted a prospective longitudinal cohort stu-dy including 217 consecutive patients who underwent curative-intent subtotal or total gastrectomy for gastric adenocarcinoma between January 2022 and December 2025. Nutri-tional status was assessed preoperatively (T0), at hospital discharge (T1), and three mon-ths after surgery (T3) using serum albumin, total cholesterol, absolute lymphocyte count, and the Controlling Nutritional Status (CONUT) score. Longitudinal changes were evalu-ated using linear mixed-effects models with patient-specific random intercepts. Results: All nutritional parameters deteriorated significantly after surgery, reaching their lowest values at hospital discharge (all p < 0.001), followed by partial recovery at three months. Nevertheless, none returned to preoperative baseline values. Recovery between discharge and three months represented 65.6% of the initial decline for serum albumin, 50.5% for absolute lymphocyte count, 51.7% for total cholesterol, and 70.1% for the CONUT score. Longitudinal mixed-effects analyses demonstrated significantly less favorable recovery trajectories after total compared with subtotal gastrectomy for serum albumin (p = 0.025), lymphocyte count (p < 0.001), and CONUT score (p < 0.001), whereas cholesterol recovery did not differ significantly between procedures (p = 0.267). Conclusions: Nutritional reco-very following gastrectomy is a dynamic and prolonged process characterized by marked early deterioration and incomplete restoration during the first three postoperative months. Patients undergoing total gastrectomy experience slower recovery trajectories, supporting the implementation of risk-adapted postoperative nutritional surveillance and individua-lized nutritional interventions extending beyond hospital discharge.

Background and Objectives: Preoperative prediction of difficult laparoscopic cholecystectomy (LC) has expanded from point-based scores to regression, nomogram, machine-learning, and quantitative imaging approaches. We examined whether this diversification was accompanied by stronger validation and clinical translation. Materials and Methods: Following a registered Open Science Framework protocol, we conducted a scoping review using Joanna Briggs Institute methodology and PRISMA-ScR. PubMed/MEDLINE, Scopus, and Web of Science were searched on 8 August 2026 for English-language publications from 1 January 2010 through 31 July 2026. Eligible studies evaluated structured multivariable tools using exclusively preoperative inputs. Screening and data charting were performed independently and in duplicate. Results: Searches identified 893 records; removal of 55 records outside the publication window and 372 duplicates left 466 for screening. Sixty reports were assessed as full text, and 59 were included; 47 (79.7%) were published from 2020 onward. Point-based scores predominated (45/59, 76.3%), followed by nomograms (5/59, 8.5%), regression-based probability models (4/59, 6.8%), machine-learning models (4/59, 6.8%), and one quantitative imaging tool (1/59, 1.7%). Four model-development publications (6.8%) included a geographic external validation component, including one with mixed temporal and geographic validation. Calibration was reported in nine publications (15.3%), decision-curve analysis in seven (11.9%), and SHAP-based explainability in two (3.4%). Two publications evaluated scores during clinical use; no controlled clinical-impact study was identified. Conclusions: Evidence of transportability and clinical impact remains limited. Future studies should prioritize standardized prediction targets, complete model specification, calibration, independent validation, and controlled evaluation of model-guided care.

Background: Gastrectomy for gastric cancer is associated with substantial metabolic, nutritional, and immunological disturbances that may significantly influence postoperative recovery. Increasing evidence suggests that perioperative immunonutritional status, particularly as assessed by the Controlling Nutritional Status (CONUT) score, represents an important predictor of surgical outcomes. However, prospective data evaluating sex-related differences in postoperative nutritional recovery after gastrectomy remain limited. Methods: This prospective observational cohort study included 150 consecutive patients undergoing curative-intent gastrectomy for gastric adenocarcinoma at a tertiary referral center between 2021 and 2024. Nutritional and immune status were longitudinally assessed using the CONUT score at predefined perioperative timepoints: preoperatively (T0), early postoperatively (T1), and at 3-month follow-up (T3). Functional recovery outcomes, postoperative complications, and mid-term functional parameters were compared between male and female patients. Multivariable logistic regression analysis was performed to identify independent predictors of delayed postoperative recovery. Results: The study population included 91 male patients (60.7%) and 59 female patients (39.3%). Significant postoperative deterioration of albumin level, lymphocyte count, total cholesterol, and CONUT score was observed in the entire cohort (p-time < 0.001 for all comparisons), followed by partial recovery during follow-up. No significant sex-related differences were identified regarding longitudinal immunonutritional evolution, postoperative complications, gastrointestinal recovery, or functional outcomes (p > 0.05). Overall postoperative complications occurred in 31.3% of patients, while 90-day mortality was 2.7%. Elevated baseline CONUT score ≥5 (OR 2.74, 95% CI 1.48–5.09, p = 0.001), postoperative CONUT score T1 ≥5 (OR 3.36, 95% CI 1.82–6.19, p < 0.001), ASA class III (OR 2.08, 95% CI 1.19–3.63, p = 0.010), and anastomotic leakage (OR 4.91, 95% CI 1.74–13.88, p = 0.003) independently predicted delayed functional recovery. Male sex was not independently associated with adverse postoperative recovery (OR 1.18, 95% CI 0.74–1.89, p = 0.44). Conclusions: Gastrectomy induces significant postoperative immunonutritional deterioration irrespective of sex. Although biological sex did not independently influence postoperative recovery trajectories, impaired perioperative immunonutritional status—particularly elevated postoperative CONUT score—was strongly associated with delayed functional recovery. Serial perioperative CONUT assessment may represent a valuable tool for individualized postoperative risk stratification and nutritional management in gastric cancer patients undergoing gastrectomy.

Background/Objectives: Emergency abdominal surgery is associated with substantial morbidity and mortality due to disease severity, physiological instability, and limited opportunities for preoperative optimization. Accurate risk stratification is therefore essential for guiding clinical decision-making and resource allocation. This review aims to provide a contemporary overview of predictive models and artificial intelligence (AI) applications for risk stratification in emergency abdominal surgery. Methods: A narrative review of the literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science databases. Studies addressing conventional risk assessment tools, predictive modeling, machine learning, deep learning, radiomics, explainable AI, and clinical implementation in emergency abdominal surgery were evaluated. Relevant publications concerning acute appendicitis, acute cholecystitis, intestinal obstruction, emergency laparotomy, abdominal sepsis, and acute mesenteric ischemia were included. Results: Conventional risk assessment systems, including ASA, APACHE II, SOFA, POSSUM, SORT, and NELA, remain widely used but are limited by static risk calculations and restricted adaptability. Recent advances in AI have enabled the development of machine learning and deep learning models capable of integrating complex clinical, laboratory, and imaging data to improve the prediction of disease severity, postoperative complications, and mortality. Disease-specific applications have demonstrated promising results in acute appendicitis, acute cholecystitis, intestinal obstruction, abdominal sepsis, and emergency laparotomy. Emerging technologies such as radiomics, computer vision, and explainable AI further enhance predictive performance and model interpretability. However, challenges related to external validation, algorithmic bias, transparency, regulatory compliance, and clinical integration remain significant barriers to widespread implementation. Conclusions: Artificial intelligence has the potential to significantly enhance risk stratification in emergency abdominal surgery by enabling more precise and individualized prediction of adverse outcomes. Although current evidence is encouraging, robust prospective validation and responsible clinical implementation are required before AI-driven predictive models can be routinely incorporated into emergency surgical practice.

Background and Objectives: BRAF p.V600E status has diagnostic, prognostic, and therapeutic relevance in colorectal carcinoma. Molecular assays remain the reference methods, whereas mutation-specific BRAF (VE1 clone) immunohistochemistry offers a rapid tissue-based alternative. We evaluated VE1–molecular concordance and factors affecting diagnostic performance. Materials and Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched in August 2025 and updated on 27 July 2026 for English-language original studies published from 2014 through the update date. Eligible reports applied VE1 and a recognized molecular reference method to the same colorectal carcinoma specimens or a defined overlapping cohort. Study selection and data extraction were performed by one reviewer and independently verified by a second reviewer. Risk of bias and applicability were assessed at the estimate level with QUADAS-3. Because of substantial clinical and methodological heterogeneity, no meta-analysis was performed, and the findings were synthesized descriptively. Results: Of 704 database records, 358 unique records were screened; 38 full-text reports were assessed and 28 were included. Twenty-three reports or defined subcohorts provided complete or clearly reconstructible 2 × 2 comparisons. Sensitivity ranged from 74.6% to 100% and specificity from 66.7% to 100%. Most optimized protocols exceeded 90% for both measures, whereas selected sampling, weak or focal staining, post hoc thresholds, protocol effects, and selective molecular verification reduced reliability. Conclusions: VE1 immunohistochemistry may provide a rapid, locally validated triage method for detecting BRAF p.V600E in colorectal carcinoma. However, substantial heterogeneity and recurrent risk-of-bias concerns limit the generalizability of the available estimates and do not support universal substitution for molecular testing. Equivocal, technically inadequate, or clinically discordant results require molecular clarification.

Background and Objectives: The leuko-glycemic index (LGI) is an emerging immunometabolic biomarker that integrates systemic inflammation and stress hyperglycemia and has shown prognostic value in various acute conditions. Its utility in patients with acute surgical abdomen remains unknown. We hypothesized that elevated admission LGI values would be independently associated with higher in-hospital mortality in patients with acute surgical abdomen and would show discriminative performance at least comparable to established inflammation-based indices (NLR, MLR, PLR, SII). Materials and Methods: We conducted a single-center retrospective cohort study including 1927 adults admitted with acute surgical abdomen who underwent emergency surgery between March 2019 and March 2021. Patients were stratified according to in-hospital outcome into survivors (Group 1, n = 1625) and non-survivors (Group 2, n = 302). Demographic, hematological, and biochemical parameters, including inflammation-based ratios (NLR, MLR, PLR, SII) and the LGI, were collected upon admission. Non-parametric tests were used for between-group comparisons, and receiver operating characteristic (ROC) curve analysis assessed the discriminative performance of each index. Multivariable logistic regression was used to assess the independent association of LGI with in-hospital mortality. Results: Non-survivors were older and exhibited more pronounced hematologic and biochemical derangements, including higher leukocyte, neutrophil, glucose, urea, creatinine, and AST levels, along with lower hemoglobin, hematocrit, red blood cell, and lymphocyte counts. All inflammation-based indices were significantly elevated in Group 2 (all p < 0.0001). LGI was markedly higher in non-survivors (median 1986 [IQR 1263–3113] vs. 1568 [980–2083]; p < 0.0001) and showed the highest AUC among the indices tested (0.646, 95% CI 0.607–0.685), significantly higher than MLR, PLR and SII (DeLong p ≤ 0.0002) and nominally higher than NLR (p = 0.042). An LGI cut-off of 1665 yielded a sensitivity of 60.17% and a specificity of 57.73% for in-hospital mortality. In multivariable analysis, log-transformed LGI remained independently associated with in-hospital mortality (OR 1.44 per 1 SD, 95% CI 1.20–1.73; p = 0.0001). Conclusions: Admission LGI was independently associated with in-hospital mortality and outperformed the inflammation-based ratios tested, but its standalone discrimination was modest and its threshold unstable; these findings do not support its use for triage or risk stratification, and external prospective validation would be required before any clinical role could be considered.