Search Results (2,711)

Background/Objectives: Lipoprotein(a) [Lp(a)] is a genetically determined, largely stable risk factor for atherosclerotic cardiovascular disease, and guidelines recommend at least one measurement during adulthood. Whether intra-individual change in Lp(a) between repeated measurements carries prognostic information beyond static risk categories in routine practice is uncertain. Methods: In a multicenter retrospective cohort from three South Korean tertiary hospitals, we studied adults with two Lp(a) measurements at least 90 days apart (2019–2024; n = 13,914). High variability was defined as an absolute change > 10 mg/dL combined with a relative change > 25%. The primary outcome was major adverse cardiovascular events (MACE), analyzed with Cox proportional hazards models adjusted for clinical covariates including baseline and follow-up Lp(a) risk categories. Results: Despite a strong rank correlation between measurements (Spearman’s ρ = 0.92), 2156 patients (15.5%) showed high variability. MACE occurred more frequently in the high-variability group (6.8% vs. 4.2%, p < 0.001), and high variability remained independently associated with MACE (adjusted hazard ratio 1.46, 95% CI 1.18–1.80). The association was consistent across multiple sensitivity analyses, persisted after excluding heart-failure hospitalization from MACE (HR 1.41), and was present for both increases and decreases in Lp(a). Incremental discrimination over a base model was modest, though statistically significant (ΔC-statistic 0.006, 95% CI 0.001–0.012; continuous net reclassification index 0.090, p < 0.001). Conclusions: Intra-individual change in Lp(a) between two measurements is independently associated with cardiovascular outcomes but provides only modest incremental discrimination. These hypothesis-generating findings require prospective validation before they can inform repeat-testing strategies. Full article

Background: Prognostic assessment after neoadjuvant chemotherapy and curative gastrectomy remains challenging in patients with gastric adenocarcinoma because postoperative outcomes are influenced by both pretreatment disease burden and pathological response. We aimed to develop and internally validate a clinicopathological prognostic model and a simple postoperative risk classification for predicting disease-free survival (DFS). Methods: This single-center retrospective cohort study included patients with gastric adenocarcinoma who underwent neoadjuvant chemotherapy followed by curative gastrectomy. Pretreatment clinicopathological variables, Becker tumor regression grade (TRG), and serum tumor markers were evaluated. Logistic regression was used to identify predictors of favorable pathological response, whereas Cox proportional hazards regression was performed to identify independent prognostic factors for disease-free survival (DFS). Sequential prognostic models were developed and internally validated using 1000 bootstrap resamples. A simplified postoperative clinicopathological risk classification based on pretreatment clinical N stage and Becker tumor regression grade was additionally developed to facilitate clinical interpretation and postoperative risk stratification. Results: A total of 109 patients were included. Favorable pathological response (Becker TRG1–2) was achieved in 68 patients (62.4%), whereas 41 patients (37.6%) had minimal or no pathological response (TRG3). In multivariable logistic regression analysis, pretreatment clinical T stage (cT4 vs. cT1–3) and clinical N stage (cN2–3 vs. cN0–1) were independently associated with a lower likelihood of achieving a favorable pathological response. For disease-free survival, pretreatment clinical N stage, Becker tumor regression grade, and log10-transformed CA19-9 remained independent prognostic factors in the multivariable Cox model. Sequential model development demonstrated progressive improvement in model discrimination, with the optimism-corrected Harrell’s C-index increasing from 0.697 for the clinical N stage model to 0.770 for the final model incorporating clinical N stage, Becker tumor regression grade, and CA19-9. Bootstrap internal validation demonstrated minimal optimism, and calibration analysis showed good agreement between predicted and observed disease-free survival. A simple postoperative clinicopathological risk classification successfully stratified patients into distinct prognostic groups. Conclusions: A clinicopathological prognostic model integrating pretreatment clinical N stage, Becker tumor regression grade, and serum CA19-9 demonstrated improved prognostic discrimination for disease-free survival compared with clinical N stage alone. The derived postoperative risk classification may provide a simple framework for postoperative risk stratification and could assist in individualizing postoperative surveillance. External validation is warranted before routine clinical implementation. Full article

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, whether their internal dynamics are consistent with irreversible degradation mechanisms remains largely unexplored. This study revisits GRU-based prognostics from a physics-consistency perspective and analyzes the potential mismatch between gating mechanisms and degradation evolution. A full-life benchmarking framework is developed based on the XJTU-SY bearing run-to-failure dataset. A training-based health indicator (HI) is constructed through multi-domain vibration feature extraction and principal component analysis, where the degradation-state representation and RUL prediction objective are explicitly distinguished to avoid physically inconsistent supervision. Several representative approaches, including statistical models and deep learning architectures (LSTM, GRU, TCN, and Transformer), are evaluated using both prediction accuracy metrics (RMSE, MAE, and R²) and physical consistency criteria (monotonicity index, monotonicity violation index, and degradation trend consistency). Experimental results demonstrate that superior prediction accuracy does not necessarily guarantee physically consistent degradation modeling. Although GRU provides competitive RUL prediction performance, its hidden-state evolution and gating responses exhibit noticeable non-monotonic behaviors during degradation progression. These findings reveal a potential discrepancy between prediction-oriented recurrent learning mechanisms and irreversible degradation dynamics, highlighting the importance of incorporating physics-consistency evaluation into reliable data-driven prognostic models. Full article

(1) Background: Drug-eluting bead transarterial chemoembolization (DEB-TACE) is increasingly used for unresectable colorectal liver metastases (CRLM), yet individualized prognostic tools are lacking. We developed and internally validated nomograms predicting overall survival (OS) and hepatic progression-free survival (hPFS). (2) Methods: In this single-center retrospective cohort, reported per the TRIPOD guideline, OS and hPFS were estimated by Kaplan–Meier methods, and independent predictors from multivariable Cox regression were assembled into nomograms. Internal validation combined 1000-sample bootstrap optimism-corrected concordance indices (C-index), a uniform shrinkage factor, a bootstrap calibration slope, and a LASSO–Cox sensitivity analysis. (3) Results: Among 63 patients (44 deaths; 42 intrahepatic-progression events), median OS was 10.9 months and median hPFS was 5.8 months. Independent OS predictors were baseline CEA, high liver tumor burden (≥10 lesions), CEA decline (protective), and second-line-or-beyond interventional therapy (corrected C-index: 0.796). Independent hPFS predictors were high liver tumor burden, CEA decline, and age (corrected C-index: 0.718). Nomogram-defined high-risk groups had markedly shorter OS (5.3 vs. 22.8 months) and hPFS (3.3 vs. 8.6 months; both p < 0.001). Grade ≥3 toxicity occurred in 6%. (4) Conclusions: In real-world DEB-TACE-treated CRLM, liver tumor burden and CEA dynamics dominated prognosis; the internally validated nomograms provide individualized estimates and risk stratification, pending external validation. Full article

Preoperative immunonutritional indices are widely reported to predict survival after colorectal cancer (CRC) resection, yet their independence varies across cohorts and no data exist from the Arab Gulf. In this retrospective cohort study we analyzed 316 patients undergoing curative resection for stage I–III colorectal adenocarcinoma at a Saudi tertiary center between 2013 and 2022, of whom 48 (15.2%) presented as emergencies. The prognostic nutritional index (PNI) and a composite albumin–neutrophil-to-lymphocyte ratio (albumin–NLR) score were assessed against overall survival (OS) and disease-free survival (DFS) using Cox models adjusted for age, sex, emergency presentation, tumor site, neoadjuvant therapy, adjuvant chemotherapy and lymphovascular invasion and stratified by stage and American Society of Anesthesiologists class. Over a median follow-up of 58.1 months there were 88 deaths and 124 DFS events. The PNI independently predicted OS (adjusted hazard ratio 0.958, 95% CI 0.929–0.987) and DFS (0.964, 0.940–0.988); the albumin–NLR score did not. Albumin alone carried the signal (OS 0.938), with lymphocytes, neutrophils and the NLR all null. The PNI effect was confined to the first 36 months (0.944 versus 1.003 thereafter), and published cut-offs classified 70.8% of the cohort as high-risk. Immunonutritional prognostication in CRC is albumin-driven, time-limited and sensitive to cut-off provenance. Full article

Background/Objectives: The inflammatory burden index (IBI), calculated as C-reactive protein (CRP) multiplied by the neutrophil-to-lymphocyte ratio (NLR), has demonstrated prognostic value across several solid tumors. Its role in breast cancer, however, has not been investigated. This study evaluated whether pretreatment or post-treatment IBI could predict pathological complete response (pCR) in patients with HER2-positive or triple-negative breast cancer (TNBC) receiving neoadjuvant chemotherapy (NAC). Methods: This single-center retrospective study included 61 patients who completed NAC followed by surgery between 2019 and 2025. IBI was calculated before and after NAC, and the treatment-related change (ΔIBI) was assessed. Conventional inflammatory indices, including NLR, platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), and absolute lymphocyte count (ALC), were evaluated for comparison. Analyses included Mann–Whitney U tests, paired Wilcoxon signed-rank tests, receiver operating characteristic (ROC) curve analysis, and multivariable logistic regression. Results: Twenty-nine patients (47.5%) achieved pCR. No pretreatment or post-treatment inflammatory index was significantly associated with pCR. In paired within-patient analysis, IBI increased significantly during treatment only in patients achieving pCR (p = 0.036), while remaining unchanged in the non-pCR group (p = 0.627). CAR showed an identical pattern, increasing exclusively in the pCR group (p = 0.013). This selective rise was not observed for any index lacking a CRP component and was independent of molecular subtype, anti-HER2 therapy, and chemotherapy regimen. On ROC analysis, ΔCAR yielded the highest area under the curve (AUC) among all inflammatory indices (0.637; p = 0.067), followed by ΔIBI (0.606; p = 0.157); neither reached statistical significance. Ki-67 was the only independent predictor of pCR (AUC 0.724; p = 0.003; optimal cutoff ≥25%). Conclusions: This is the first study to evaluate IBI in HER2-positive and TNBC receiving NAC. Static IBI values did not predict pCR. The selective rise in IBI and CAR during treatment in patients achieving pCR—two independently formulated CRP-based indices showing an identical pattern—suggests that the CRP component carries the biologically relevant signal. This hypothesis-generating observation warrants prospective validation in larger cohorts. Full article

Background/Objectives: Atrial fibrillation (AF) and heart failure (HF) frequently coexist, but later-pandemic data from Eastern Europe are limited. We evaluated their documented coexistence in patients hospitalized with COVID-19; the study was not designed to determine whether SARS-CoV-2 modifies the established AF–HF relationship. Methods: We retrospectively analysed 395 adults admitted with RT-PCR-confirmed SARS-CoV-2 infection between 1 September 2022 and 31 December 2024. AF was identified from the admission record, while HF was determined by an audited, rule-based review of cardiovascular free-text entries. A modified Poisson model with robust variance was the primary analysis and estimated adjusted prevalence ratios (aPRs) after adjustment for age, sex, body mass index, hypertension, ischaemic heart disease, pre-existing type 2 diabetes, chronic kidney disease, chronic obstructive pulmonary disease, smoking, and prior ischaemic stroke. Logistic regression and a restrictive NYHA-coded HF definition were sensitivity analyses. Results: AF was documented in 68 patients (17.2%), HF in 106 (26.8%), and both conditions in 26 (6.6%). HF prevalence was 38.2% among patients with AF and 24.5% among those without AF. AF was associated with documented HF in the primary model (aPR 1.51, 95% confidence interval 1.06–2.16; p = 0.022); age was also associated with HF (aPR 1.02 per year, 95% confidence interval 1.01–1.04; p = 0.010). Results were similar with the restrictive HF definition (aPR 1.54, 95% confidence interval 1.07–2.23) and logistic regression (adjusted odds ratio 1.89, 95% confidence interval 1.06–3.37). Exploratory mortality estimates were imprecise and were not used for prognostic inference. Conclusions: AF identified a subgroup with a higher prevalence of documented HF within this hospitalized COVID-19 cohort. The findings are best interpreted as evidence of cardiovascular and multimorbidity complexity, not as proof of a COVID-specific, temporal, or causal AF–HF effect. Full article

Background/Objectives: This study assessed the impact of variations in autophagy-related genes (ATG) on baseline characteristics of cutaneous melanoma, laboratory parameters (including inflammatory biomarkers), response to therapy, and survival in patients treated with immune checkpoint inhibitors (ICIs) as first-line therapy. Methods: DNA was extracted from blood samples of 144 melanoma patients. Genotyping of ATG5 (rs2245214 and rs510432), ATG10 (rs1864183 and rs1864182), and ATG16L1 (rs2241880) was performed using an allelic discrimination method on the StepOnePlus^TM Real-Time PCR System. Correlations with laboratory parameters, response to therapy, and survival were assessed only in the subgroup of patients who received ICIs in first-line treatment (n = 74). Statistical significance was calculated, and p values were adjusted for multiple testing using the Benjamini–Hochberg False Discovery Rate (FDR). Results: Considering baseline characteristics of 144 patients, ATG5 rs2245214 showed a trend with regression (p = 0.042) and lymphovascular invasion (p = 0.05), while ATG16L1 rs2241880 was associated with lymphovascular invasion (p = 0.056), with corrected FDR q value for all histopathological characteristics of 0.076. In patients who received ICIs in first-line, ATG5 rs2245214 genotypes were associated with LDH (p = 0.001, q = 0.004) and the number of metastatic sites (p < 0.001, q = 0.004). Also, ATG5 rs2245214 was associated with neutrophil-to-lymphocyte ratio (NLR) (p = 0.036, q = 0.045) systemic immune-inflammation (SII) index (p = 0.038, q = 0.048) and pan-immune-inflammation value (PIV) (p = 0.031,q = 0.041), while ATG10 rs1864183 was associated with PIV (p = 0.047, q = 0.047). The association of ATG genotypes with disease control rate (DCR) was demonstrated for ATG5 rs2245214 (p = 0.013, q = 0.029) and ATG16L1 rs2241880 (p = 0.032,q = 0.032). Progression-free survival (PFS) was significantly associated with ATG10 rs1864183 (p = 0.023, q = 0.046). The significance of the ATG10 rs1864183 C/T genotype as a prognostic marker for progression was confirmed in both univariate and multivariate Cox proportional hazards regression analyses (p = 0.025, q = 0.028 and p = 0.022, respectively). Conclusions: This study shows that ATG5 rs2245214, ATG10 rs1864183, and ATG16L1 rs2241880 correlate with systemic inflammation, response to ICIs, and had a trend toward melanoma characteristics. However, these findings should be confirmed in larger patient cohorts. Full article

Atopic dermatitis (AD) and food allergy frequently coexist during early childhood and are increasingly linked through interactions within the gut–skin axis. We evaluated the clinical outcomes of an integrated nutritional and microbiome-oriented management strategy in children with food allergy-associated AD under real-world conditions. This prospective, two-center, real-world observational cohort study included 85 children (median age, 11 months) with AD diagnosed according to the Hanifin–Rajka criteria and confirmed IgE- and/or non-IgE-mediated food allergy. Participants received individualized nutritional and microbiome-oriented management comprising elimination diets, conventional topical therapy, microbiome-directed interventions, and nutritional supplementation tailored to their clinical, allergological, and microbiological profile. Disease severity was assessed using the Scoring Atopic Dermatitis (SCORAD) index at baseline and after approximately 1 and 3 months of follow-up. Cow’s milk, egg, and wheat were the predominant food allergens, and 69.4% of children were polyallergic. SCORAD decreased significantly during follow-up, with a mean relative reduction of 51.6% and 63.5% of participants achieving a SCORAD50 response. Food allergy burden independently predicted baseline disease severity, whereas documented gut dysbiosis was associated with polyallergy and greater absolute clinical improvement, although this association lost statistical significance after adjustment for baseline disease severity and should therefore not be interpreted as a favorable prognostic effect of dysbiosis. Baseline SCORAD remained the strongest independent predictor of clinical improvement. Integrated nutritional and microbiome-oriented management was associated with substantial clinical improvement and may represent a valuable adjunct to standard care in children with food allergy-associated AD. Because of the observational, uncontrolled design, these findings describe associations rather than causal treatment effects. These findings support further evaluation of personalized nutritional strategies in randomized controlled trials. Full article

Machine learning and statistical classification methods are widely applied to microbiome data for diagnostic, prognostic, and phenotypic insights. However, the complex, multivariate nature of microbiome communities makes it difficult to assess the relative performance of these methods. Most comparisons rely on a small number of published datasets, without considering their underlying ecological properties or how these properties may, in turn, influence classification performance. We introduced a coenocline-based simulation framework to generate synthetic microbiome datasets that incorporate realistic ecological variation arising from species’ responses to host-associated gradients such as disease severity. To evaluate the ecological fidelity of these simulations, we compared synthetic datasets to five widely used real-world microbiome datasets: Cirrhosis, Colorectal Cancer (CRC), Type 2 Diabetes (Chinese and Women cohorts), and the Human Microbiome Project (HMP). Comparisons across α-diversity (species richness), β-diversity (species composition and turnover), and abundance distributions demonstrated that coenocline simulations closely recapitulate the key ecological structures of empirical data. Synthetic datasets exhibited similar richness and abundance patterns to disease-associated microbiomes, with realistic distributions of few dominant and many rare taxa. Moreover, community composition analyses (Bray–Curtis index) revealed that the simulated datasets captured natural levels of compositional dissimilarity among samples, spanning the same variability range observed in real data. When compared against 100 independently simulated datasets, the coenocline model consistently reproduced empirical ranges of species diversity, relative abundance, and between-group compositional differences (ANOSIM-R values), confirming the model’s robustness and reproducibility. This coenocline-based simulation framework provides a novel, flexible, and ecologically grounded approach for generating synthetic microbiome data with controlled complexity. By reproducing realistic ecological gradients and community structures, the framework supplies the controlled test beds needed for systematic future benchmarking of machine learning and statistical classification methods across diverse and biologically meaningful scenarios. In doing so, it will help bridge the gap between ecological realism and computational modeling, thereby supporting more reliable and generalizable inference from microbiome data. Full article

Background/Objectives: This study aimed to evaluate the prognostic role of the extracellular volume (ECV) assessed by cardiac magnetic resonance imaging (MRI) in relation to systolic dysfunction and unfavorable remodeling of the left ventricular (LV) at 24 weeks after myocardial infarction (MI) and revascularization. Methods: The study included 154 patients aged 56 ± 8 years who had been diagnosed with their first MI. Cardiac MRI was performed at 7–10 days and after 24 weeks, including assessment of indexed volumes, LV ejection fraction (LVEF), ECV, and patterns of ischemic and reperfusion injury. The study is registered in the international clinical trials registry with the number NCT04347434 (ClinicalTrials.gov). Results: Patients were divided into two groups after 24 weeks: group 1 (n = 24) with LVEF < 50% and group 2 (n = 130) with LVEF ≥ 50%. At 7–10 days, the scar mass in group 1 was 58.5 (39.5; 69.8) g vs. 17.1 (9.2; 29.7) g in group 2 (p < 0.001); microvascular obstruction was present in 23 cases (95.8%) vs. 55 (42.3%) (p < 0.001). After 24 weeks, inter-group differences increased (p < 0.05). Global ECV and remote myocardial ECV were significantly higher in patients with LVEF < 50% both at 7–10 days and after 24 weeks compared to those with LVEF ≥ 50% (p < 0.05). Infarct zone ECV did not differ between groups. Systolic dysfunction after 24 weeks was predicted by global ECV > 38.3% (p < 0.001) and remote myocardial ECV > 33.6% (p = 0.008). Predictors of an increase in end-diastolic volume index > 12% after 24 weeks in the subgroup of patients with initial systolic dysfunction were global ECV > 42.8% (p = 0.017) and remote myocardial ECV > 35.6% (p = 0.012). Conclusions: Global ECV and ECV of the remote myocardium, which exceed a certain level established by a cardiac MRI in the acute stage of MI, are among the predictors of LVEF < 50% and unfavorable LV remodeling in the medium term. Full article

Background: The Falls Efficacy Scale-International (FES-I) is an assessment tool designed to measure the level of fear of falling in older adults. This scale was developed to evaluate how much an individual fears falling during various daily activities. The aim of the study was to assess the discriminatory power of FES-I to predict falls within 2 years. The secondary objective was to evaluate the relationship between FES-I and vascular status. Material and Methods: In this prospective study, 130 patients (mean age, 64.7 ± 14.6 years) with chronic kidney disease (CKD) were analyzed. Of these, 90 patients had nondialysis CKD: 15 had stage G3 (G3a, n = 5; G3b, n = 10), 38 had stage G4, and 37 had stage G5. The remaining 40 patients (30.7%) were prevalent patients receiving maintenance hemodialysis. The most frequent cause of CKD was hypertension and diabetes in 57 (43.8%) of patients. The Falls Efficacy Scale-International (FES-I) was used to assess fear of falling. The Charlson Comorbidity Index (CCI) was used to measure comorbidity, and the 10-year cardiovascular risk was assessed using a web-based calculator QRESEARCH Cardiovascular Risk Algorithm, version 3 (QRISK^®3), and hemodynamic parameters were measured using a Mobil-O-Graph monitor. Participants were followed for 2 years to assess the occurrence of falls. Results: During the two-year follow-up, 49 of 130 participants (37.7%) experienced at least one fall. FES-I demonstrated good discrimination for falls (AUC 0.836; bootstrap 95% CI, 0.760–0.903). In multivariable logistic regression adjusted for age, sex, dialysis status, comorbidity, and functional status, FES-I remained independently associated with falls (adjusted OR per 1-point increase, 1.145; 95% CI, 1.060–1.237; p < 0.001). Addition of FES-I to the basic clinical model increased the AUC from 0.826 to 0.874, although the difference was of borderline statistical significance in paired receiver operating characteristics (ROC) comparison (p = 0.053). The data-derived Youden-optimal threshold was 25 points; however, bootstrap analysis indicated threshold variability, and this cut-off should be considered exploratory. Conclusions: Higher FES-I scores were independently associated with falls during two-year follow-up and may provide additional prognostic information beyond selected clinical risk factors. FES-I may represent a candidate screening instrument for fall-risk assessment in CKD; however, the proposed cut-off (25 points) requires prospective external validation. Full article

Background: Systemic inflammation significantly impacts graft survival and clinical outcomes following living donor liver transplantation (LDLT). The systemic immune-inflammation index (SII), which integrates peripheral neutrophil, platelet, and lymphocyte counts, has shown prognostic value in various clinical settings, but its role in LDLT has not been thoroughly investigated. Methods: This retrospective cohort study included 378 consecutive adult patients with end-stage liver disease (ESLD) who underwent primary LDLT between March 2016 and February 2025. The SII was calculated as (neutrophil × platelet)/lymphocyte. Spearman correlation analysis was used to assess the relationships between the preoperative SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) and clinical outcomes, including the preoperative Model for End-Stage Liver Disease (MELD) score, duration of mechanical ventilation, and lengths of stay in the intensive care unit (ICU) and hospital. Receiver operating characteristic (ROC) curve analysis determined the optimal cut-off values for predicting 30-day mortality, and the areas under the curve (AUROCs) were compared using the DeLong test. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Results: Among 378 analyzable patients, the 30-day mortality rate was 7.9% (30/378). Both the SII and the NLR were significantly higher in non-survivors than in survivors (SII: median 368.8 vs. 169.1, p < 0.001; NLR: 6.0 vs. 2.4, p < 0.001), whereas the PLR did not differ significantly. On ROC analysis for 30-day mortality, the NLR and the SII showed comparable discrimination (NLR AUROC = 0.729, 95% CI: 0.63–0.82; SII AUROC = 0.705, 95% CI: 0.60–0.80; DeLong p = 0.43), both exceeding the PLR (AUROC = 0.541). The optimal SII cut-off was 275 × 10⁹ cells/L (sensitivity 70.0%; specificity 69.5%). Patients with an SII ≥ 275 × 10⁹ cells/L had significantly lower 30-day survival than those below the cut-off (83.5% vs. 96.4%; log-rank p < 0.001). On multivariable logistic regression adjusting for age and MELD score as continuous variables, an SII ≥ 275 × 10⁹ cells/L remained an independent predictor of 30-day mortality (aOR = 3.78; 95% CI: 1.60–8.93; p = 0.002); the MELD score was also independently predictive (aOR = 1.04 per point; 95% CI: 1.01–1.08; p = 0.018). The association persisted when the SII was modelled continuously (aOR = 1.60 per unit log SII; 95% CI: 1.08–2.38; p = 0.020). At the 275 cut-off, sensitivity for 30-day death was 70.0% and the positive predictive value 16.5%. Conclusions: The preoperative SII and NLR are simple, inexpensive, CBC-derived inflammatory indices significantly associated with 30-day mortality after LDLT for ESLD. An elevated preoperative SII independently predicts early post-transplant mortality and may aid perioperative risk stratification, although it does not outperform the NLR. Full article

Background: The prognostic nutritional index (PNI) is an inexpensive marker derived from serum albumin and peripheral lymphocyte count. Although low PNI has been associated with adverse surgical outcomes, its incremental value beyond basic clinical variables and the stability of commonly used thresholds remain uncertain in heterogeneous colorectal surgical populations. We evaluated the association between preoperative PNI and postoperative complications and examined whether adding PNI improved a basic clinical model. Methods: This retrospective single-center cohort included 205 consecutive adults undergoing colorectal surgery between January 2024 and March 2026. PNI was calculated as albumin (g/L) + 5 × lymphocyte count (10⁹/L). PNI was modeled primarily as a continuous predictor; PNI < 45 was examined secondarily. ROC analysis, multivariable logistic regression, nested-model comparison, calibration assessment, bootstrap internal validation, complete-case sensitivity analysis, and exploratory subgroup analyses were performed. Results: Fifty-six patients (27.3%) had PNI < 45. Postoperative complications occurred in 67 patients (32.7%) overall and were more frequent with PNI < 45 (51.8% vs. 25.5%; RR 2.03, 95% CI 1.40–2.95; OR 3.14, 95% CI 1.65–5.95; Fisher p < 0.001). PNI alone showed modest discrimination (AUC 0.659); the Youden cutoff was 45.45 (sensitivity 50.7%, specificity 78.3%). In the complete-case multivariable model (n = 191), each 5-point decrease in PNI was associated with higher odds of complications (OR 1.52, 95% CI 1.22–1.90; p < 0.001). Adding continuous PNI to age, BMI, and operative approach increased AUC from 0.663 to 0.711 and improved model fit (likelihood-ratio χ² = 15.89, p < 0.001). Bootstrap resampling showed substantial cutoff variability (95% percentile interval 32.05–52.00). Conclusions: Lower preoperative PNI was associated with postoperative complications and added discriminatory information to a basic clinical model. However, its stand-alone discrimination was modest, the data-derived cutoff was unstable on bootstrap resampling, and residual confounding and clinical heterogeneity limit causal or treatment-directed interpretation. PNI should therefore be considered a risk marker rather than a stand-alone decision rule. Prospective interventional studies in well-defined patient groups are needed to determine whether PNI-guided nutritional optimization improves outcomes. Full article

Background: Accurate prognosis in diffuse large B-cell lymphoma (DLBCL) is limited by biological heterogeneity and the absence of formal per-patient uncertainty quantification for treatment-response prediction. This study introduces a multi-layer evidence fusion framework combining gene expression profiling and clinical features with distribution-free uncertainty quantification. Methods: The proposed framework integrates four evidence layers—WGCNA co-expression eigengenes, ssGSEA pathway scores, bootstrap-stable prognostic genes, and the International Prognostic Index—through AUC-proportional reliability discounting and sequential Dempster–Shafer fusion. The primary endpoint was three-year overall survival (OS₃yr) as a surrogate for R-CHOP treatment response. Inductive conformal prediction (ICP, ε = 0.10) was applied to provide per-patient uncertainty sets with a distribution-free coverage guarantee. Training used GSE10846 (n = 223, Affymetrix); external validation used GSE181063 (n = 479, Illumina). Results: The proposed framework achieved internal AUC = 0.808 (95% CI [0.750, 0.863]), significantly outperforming logistic stacking (AUC = 0.786, p = 0.0009) and unweighted DS fusion (AUC = 0.767, p = 0.037). External AUC = 0.791 was statistically comparable to logistic stacking (DeLong p = 0.21). AUC-proportional discounting reduced inter-source conflict K^- by 75% (0.093→0.023). ICP achieved 90.1% internal and 94.6% external coverage; 43.5% of training patients received uncertain predictions ({S,R}). Conclusions: The proposed framework provides an uncertainty-aware approach for multi-layer genomic–clinical evidence fusion in DLBCL, with cross-platform discrimination validated on an independent Illumina cohort. Full article