Search Results (457)

Balancing structural safety and economic efficiency in super-tall building design remains a formidable challenge. To address this issue, this study proposes a genetic-algorithm-based multi-variable, multi-objective optimization method. The design variables include the member sizes and vertical layout positions of outrigger and belt trusses, as well as the cross-sectional dimensions of mega-columns. Total structural weight and maximum inter-story drift ratio are adopted as objective functions, while code-specified constraints, such as shear-weight ratio, stiffness-weight ratio, and axial compression ratio, are incorporated to formulate the fitness evaluation for optimization. Taking a 300 m baseline structure designed for 6-degree seismic intensity and equipped with two outrigger trusses and three belt trusses as an example, single-variable sensitivity analyses are first performed. The results show that optimizing any single parameter can yield certain local improvements, yet it cannot overcome the weight–deformation trade-off induced by strong variable coupling. By selecting representative feasible solutions from the multi-variable solution set that match the “optimal” values identified by single-variable optimization as benchmarks, the multi-variable optimum reduces the total structural weight by approximately 6.5–18.4% relative to these representative designs. Moreover, optimal layout strategies of outrigger and belt trusses are investigated for two typical building heights (200 m and 300 m) and two seismic intensity levels associated with design ground motions having a 10% exceedance probability in 50 years, namely 6-degree (0.05 g) and 8-degree (0.20 g). Finally, the proposed method is validated through a case study of a super-tall financial center in Chongqing, where the total structural weight is reduced by 12.3% after optimization while the inter-story drift ratio still satisfies relevant code requirements. The results demonstrate that the proposed framework can generate competitive feasible solutions and provide a systematic means to achieve a balanced trade-off between structural safety and economic efficiency for outrigger–belt-truss super-tall buildings. Full article

Background/Objectives: Sarcopenia (SP) and osteoporosis (OP) are common yet underrecognized complications of liver cirrhosis, contributing to increased morbidity and mortality. Their coexistence, termed osteosarcopenia (OS), represents a compounded musculoskeletal impairment. Insulin-like growth factor 1 (IGF-1), synthesized in the liver, has been implicated in muscle and bone metabolism. This study aimed to assess the prevalence and association of laboratory and clinical parameters with SP, OP, and OS in cirrhotic patients, with a focus on IGF-1 deficiency and their impact on mortality. Methods: This cross-sectional study included 100 cirrhotic patients at a tertiary center. Sarcopenia was diagnosed using CT-derived L3 skeletal muscle index and osteoporosis via the DEXA scan. IGF-1 levels and metabolic parameters were measured. Multivariate logistic regression identified laboratory and clinical factors associated with musculoskeletal complications. However, due to the cross-sectional design, causal relationships could not be inferred. Results: SP, OP, and OS were present in 41%, 22%, and 11% of patients, respectively. IGF-1 levels were significantly lower in patients with SP, OP, and OS (p < 0.05) and were independently associated with increased risk of SP (OR = 1.797, p = 0.006), OP (OR = 1.873, p = 0.045), and OS (OR = 2.326, p = 0.003). Mortality rates were significantly higher among patients with OS (72.7%), OP (77.3%), and SP (56.1%). OS conferred the highest adjusted mortality risk (OR = 2.739, p = 0.009), followed by SP (OR = 2.278, p = 0.015) and OP (OR = 1.958, p = 0.036). Conclusions: Musculoskeletal complications are highly prevalent and predictive of mortality in cirrhosis. IGF-1 deficiency is a strong independent biomarker for SP, OP, and OS. Routine screening and early intervention targeting IGF-1 pathways and nutrition may improve outcomes in this population. Full article

Objective: To evaluate the utility of synovial iron quantification using Magnetic resonance imaging (MRI) in assessing structural joint damage in the knee of patients with rheumatoid arthritis (RA). Methods: This cross-sectional study employed a two-stage design. In the initial comparative stage, 6 patients with RA and 5 patients with osteoarthritis (OA) were recruited to compare synovial R2* values, a metric derived from iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) MRI sequences representing synovial iron content. Following this, the RA cohort was expanded to a total of 51 patients to investigate the association between R2* values and clinical parameters, including disease activity and bone erosion. Synovial fluid iron levels were measured with an Iron Assay Kit and synovial iron deposits were semi-quantified via Prussian blue staining. Associations between R2* and clinical and laboratory parameters, including inflammatory factors and joint damage indices, were analyzed using Spearman’s rank correlation. Univariate and multivariate ordered logistic regression models were employed to identify factors associated with bone erosion severity. An R2*-based nomogram was developed and validated using receiver operating characteristic (ROC) analysis and calibration curves. Results: Synovial R2* values were significantly higher in RA patients than those with osteoarthritis (53.66 S⁻¹ vs. 31.38 S⁻¹, p < 0.05), consistent with Prussian blue staining results. While synovial R2* values showed no significant correlation with systemic iron metabolic markers, inflammatory indicators, or the Disease Activity Score 28, they were positively correlated with bone erosion severity (ρ = 0.500, p < 0.001) and negatively associated with the joint space width (ρ = −0.307, p < 0.05). Multivariate analysis identified R2* as an independent indicator linked to bone erosion extent (OR = 2358.336, p < 0.001). The R2*-based nomogram demonstrated good discriminative performance. (AUC = 0.83). Conclusions: The R2* value derived from IDEAL-IQ MRI is a reliable tool for quantifying synovial iron and may represent a promising non-invasive imaging biomarker reflecting bone erosion in RA patients. Full article

Background: The impact of thoracolumbosacral orthosis (TLSO) bracing on sagittal spinopelvic alignment and psychosocial outcomes in adolescent idiopathic scoliosis (AIS) remains debated. Methods: This retrospective comparative study included 120 girls (10–18 years) with AIS (baseline Cobb angle 20–40°) and skeletal immaturity (Risser 0–1). Patients were managed with a thoracolumbosacral orthosis (TLSO) brace (n = 60) or observation alone (n = 60). Standing posteroanterior and lateral full-spine radiographs were obtained at baseline and at 24 ± 6 months; follow-up radiographs were acquired out of brace after a standardized 48 h brace-free interval. They were used to measure coronal and sagittal spinopelvic parameters. Patient-reported outcomes included the Scoliosis Research Society-22r (SRS-22r), Pediatric Quality of Life Inventory (PedsQL), Spinal Appearance Questionnaire (SAQ), Trunk Appearance Perception Scale (TAPS), and Beck Depression Inventory (BDI). The primary endpoint was curve progression (≥5° increase or exceeding 40°) at 24 months. Multivariable regression was used to adjust for baseline Cobb angle and maturity. Mean follow-up was 24 ± 6 months. Results: Mean Cobb change was +1.2° in the brace group vs. +7.3° in the observation group (group × time interaction p < 0.001). Progression (≥5°) occurred in 15% vs. 45%, respectively, and 18% of observed patients exceeded 40° (risk ratio 0.33, 95% CI 0.17–0.65; number needed to treat 4, 95% CI 3–7). Sagittal spinopelvic parameters showed no significant group-by-time interaction. No significant between-group differences were observed in SRS-22r, PedsQL, SAQ, TAPS, or BDI at baseline or follow-up. Patients with curve progression exhibited worse appearance-related scores. In multivariable analysis, depressive symptoms were the strongest determinant of PedsQL (β = −0.55, p < 0.001). Conclusions: Brace treatment was associated with reduced curve progression in girls with moderate AIS. Over approximately two years of follow-up, we did not observe clinically relevant between-group differences in sagittal spinopelvic alignment or psychosocial patient-reported outcomes. Given the retrospective, non-randomized design and self-reported adherence, psychosocial findings should be interpreted cautiously and require confirmation in prospective, objectively monitored, psychologically informed bracing studies. Full article

Kidney transplantation (KTx) corrects many uremia-related metabolic disturbances; however, dyslipidemia remains common in kidney transplant recipients and contributes to persistent cardiovascular risk. Lipoprotein(a) [Lp(a)] is a largely genetically determined proatherogenic lipoprotein that increases in advanced chronic kidney disease (CKD) and may decrease after restoration of renal function. Autotaxin (ATX), an enzyme involved in proinflammatory lipid signaling through the ATX–lysophosphatidic acid axis, has also been implicated in cardiovascular pathology, but its early post-transplant dynamics remain poorly characterized. In addition to quantitative lipid abnormalities, CKD is associated with high-density lipoprotein (HDL) dysfunction and reduced paraoxonase-1 (PON-1) activity; however, data on early post-transplant changes in PON-1 activity are limited. In this prospective observational study, lipid profile parameters, Lp(a) concentration, ATX activity, and PON-1 activity were assessed in 55 Caucasian patients with CKD stage 5, most of whom were dialysis-dependent, before and 2–3 weeks after KTx. All recipients received tacrolimus-based maintenance immunosuppression with corticosteroids and mycophenolate mofetil. After KTx, Lp(a) levels decreased by a median of 21% and ATX activity by 28% (both p < 0.001). Lp(a) and ATX showed no cross-sectional or longitudinal association either before or after transplantation, and their percentage changes were not correlated. In contrast, conventional lipid fractions increased significantly, including total cholesterol (+22%), LDL cholesterol (+27%), HDL cholesterol (+24%), and triglycerides (+55%) (all p < 0.001). PON-1 activity increased by approximately 13% after KTx (p < 0.001), and its percentage change correlated positively with the increase in HDL cholesterol. In exploratory analyses, the magnitude of Lp(a) reduction was associated with early graft function: patients with eGFR <45 mL/min/1.73 m² exhibited a significantly smaller decline in Lp(a) than those with better graft function (−4.8% vs. −26.7%, p = 0.009). Multivariable analysis showed that demographic characteristics, body mass index, tacrolimus exposure, and post-transplant eGFR did not independently predict the magnitude of Lp(a) reduction. Tacrolimus trough concentrations and cumulative corticosteroid exposure were not associated with lipid parameters or their changes, except for a single subgroup difference in PON-1 activity of uncertain clinical significance. In summary, in the early period after KTx under tacrolimus-based immunosuppression, Lp(a) concentration and ATX activity decrease, whereas conventional lipid fractions increase and PON-1 activity improves. These changes were not associated with tacrolimus exposure or cumulative corticosteroid dose. The reduction in Lp(a) was associated with early graft function in exploratory analyses, suggesting that recovery of renal function may contribute to early post-transplant Lp(a) dynamics; however, no independent causal relationship was established, and the findings should be interpreted cautiously given the limited sample size and exploratory design. The clinical significance of these changes for long-term cardiovascular and graft outcomes requires further investigation. Full article

Light availability drives Stevia rebaudiana productivity, yet how incident radiation interacts with genotype and site under tropical field conditions remains unclear. We evaluated four genotypes (L020, L102, L082, and ‘Morita II’) across three Caribbean locations in Colombia under two contrasting light levels (600 vs. 1800 μmol photons m⁻² s⁻¹) using a split-plot randomised complete block design with four replicates. Incident photosynthetic photon flux density (PPFD) was logged and, at 85 days after transplanting (DAT), net CO₂ assimilation, stomatal conductance, transpiration, and intercellular CO₂ concentration were measured alongside light-adapted chlorophyll fluorescence parameters, including the effective quantum yield of photosystem II (ΦPSII), the maximum efficiency of PSII in the light (Fv′/Fm′), photochemical quenching (qP), and electron transport rate (ETR); biomass and leaf yield were quantified at harvest. Data were analysed using factorial analysis of variance (ANOVA) and complementary multivariate approaches, including Pearson correlation analysis and principal component analysis (PCA). Radiation responses were strongly site-dependent: under 1800 μmol photons m⁻² s⁻¹, net CO₂ assimilation increased by 90.2% at El Carmen de Bolívar and 21.5% at Polonuevo but decreased by 36.4% at Montería. Leaf yield was highest in El Carmen de Bolívar (1951.46 ± 182.03 kg ha⁻¹), followed by Montería (1510.94 ± 173.75 kg ha⁻¹) and Polonuevo (576.31 ± 42.36 kg ha⁻¹). Genotype rankings shifted with environment and radiation, with L102 reaching 3256.25 ± 126.39 kg ha⁻¹ under direct radiation in El Carmen de Bolívar and ‘Morita II’ showing strong responsiveness in Montería. These results demonstrate that photosynthetic plasticity and leaf yield in S. rebaudiana depend on genotype × radiation × environment interactions, supporting location-tailored radiation management combined with targeted genotype deployment. Full article

This study evaluated the technological and sensory effects of incorporating oyster mushroom (Pleurotus ostreatus) powder and sylvinite as strategies to reduce salt content in beef patties while maintaining product quality. A 4 × 4 full factorial design was implemented to develop sixteen distinct formulations, evaluating the interaction between four levels of mushroom powder (0, 3, 5, and 10% w/w) as a partial meat replacer and four levels of sylvinite (0, 0.5, 1, and 2% w/w) as a NaCl substitute. To establish a baseline for comparison, control samples were prepared without sylvinite, with a fixed concentration of 1% NaCl. Patties were produced with low-fat content (6%), formed into 100 g portions, and evaluated in raw and cooked states. Physicochemical analyses included color (CIE L*, a*, b*), cooking yield, shrinkage, and texture profile analysis, while sensory quality was assessed by an expert panel and complemented with consumer discriminative tests, specifically a triangle test. Multivariate analysis revealed that mushroom powder significantly influenced color parameters, increasing redness and yellowness, whereas sylvinite tended to reduce color intensity; however, their interaction mitigated these effects at intermediate inclusion levels. Mushroom incorporation improved cooking yield and reduced hardness, particularly at 3–5% inclusion, enhancing elasticity and cohesiveness. Sensory results indicated that formulations containing 3–5% mushroom powder and up to 2% sylvinite achieved high overall acceptability. Consumer tests confirmed that these formulations effectively modulated saltiness and texture perception. Overall, the combined use of oyster mushroom powder and sylvinite represents a viable approach for developing reduced-sodium beef patties with acceptable technological and sensory properties. Full article

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

This paper presents the design and industrial implementation of an adaptive discrete control system for a rotary dryer operating in potash fertilizer production. The drying process is characterized by high inertia, multivariable interactions, transport delay, and non-stationary behavior resulting from variations in raw material properties and external disturbances, which significantly reduce the effectiveness of conventional fixed-parameter controllers. A discrete-time mathematical model of the rotary drying process was developed using industrial experimental data collected from a full-scale production plant. The process was modeled as a coupled 2 × 2 multivariable system with pronounced time-delay effects in the main control channels. System identification was carried out using statistical and frequency-domain methods to capture the dominant dynamic characteristics required for controller synthesis. Based on the identified model, an adaptive discrete controller with online parameter adjustment was developed to regulate outlet moisture content and exhaust gas temperature. Simulation and industrial results confirmed stable operation under varying conditions, improved regulation accuracy, enhanced process stability, and an average production efficiency increase of approximately 1.8%, accompanied by reduced fuel consumption. Full article

Under global climate change, flood processes exhibit significant non-stationarity due to multiple driving factors, rendering traditional frequency analysis methods based on stationarity assumptions inadequate for accurate risk assessment. This study, focusing on the Kuitun River Basin and utilizing observed data from the Jiangjunmiao Hydrological Station (1959–2014), develops a joint design approach that addresses both non-stationarity and multivariate dependence. The approach integrates the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) with copula functions and employs a parametric bootstrap to quantify the impacts of marginal parameter estimation and sample size uncertainty on design floods. The results indicate that flooding in the Kuitun River is influenced by precipitation, temperature, and snowmelt, with summer precipitation having the greatest impact. Marginal parameter uncertainty is significantly amplified at high return periods, and the confidence intervals of design values expand as the return period increases. In the joint framework, the OR criterion is more sensitive to parameter perturbations, with the 100-year flood peak and flood volume design values approximately 24.2% and 19.8% higher than those of the AND criterion, respectively. Increasing the sample size significantly reduces uncertainty; when the sample size increases from 56 to 500, the HDR area and confidence interval width decrease by approximately 60–70%, and the stability of joint flood design estimates improves significantly. The research findings can provide a scientific basis and technical support for flood analysis and risk management in the Kuitun River Basin under changing environmental conditions. Full article

Accurate carbon price forecasting is vital for risk management but is hindered by high volatility and sensitivity to external shocks. Existing multivariate models typically overlook unstructured news sentiment, failing to capture irrational fluctuations driven by market public opinion. To address this, this paper proposes VBN-Net, a hybrid model integrating carbon-specific news sentiment with Variational Mode Decomposition (VMD). Two core innovations are presented: First, a multi-modal input mechanism combines structured financial data with unstructured carbon news sentiment to effectively capture policy-driven shocks. Second, a Sequential Beluga Whale Optimization strategy is designed to adaptively optimize feature engineering in steps. Unlike conventional approaches, the VBN-Net first employs VMD for denoising and frequency decomposition, and then optimizes the fusion weights of news sentiment across different frequency components derived from multi-source news. This strategy effectively overcomes the subjectivity of manual parameter selection, providing high-quality features for a fixed CNN-BiLSTM backbone. By integrating VMD-based denoising with optimized multi-source news fusion, the model achieves consistent performance improvements across multiple evaluation metrics. The empirical findings validate the effectiveness of the proposed model in enhancing forecasting performance, thereby providing a reliable analytical tool for participants in the carbon market. Full article

This study demonstrates that compressive strength in cement-stabilized rammed earth is governed by conditional, threshold-controlled interactions rather than by intrinsic mineralogical effects. A B + K (beidellite + kaolinite) content exceeding 15% defines a low-strength regime (median ≈ 44.6 kN), whereas B + K ≤ 5% allows medians above 90 kN under 7% forming moisture. Quartz-rich fractions show a global correlation of r = 0.71. The Kruskal–Wallis test confirms strong clay grouping influence (H = 72.78, p < 0.001). Analysis of the experimental dataset shows that most strength distributions deviate from normality, invalidating pooled parametric inference and justifying the use of distribution-free methods. At the global level, bulk density and quartz-rich fractions are the dominant positive contributors to strength. Meanwhile, forming moisture and high combined beidellite–kaolinite content (>15%) exerts a negative influence under elevated forming moisture (8%), whereas the effect of 1:1 and 2:1 clay minerals differs depending on their hydro-affinity and moisture regime. However, subgroup analyses reveal frequent reversals in both magnitude and sign of correlations, proving that mineral effects depend critically on cement dosage and moisture regime, revealing discrete strength regimes defined by hierarchical interactions between moisture, cement content, and mineralogical thresholds. The combined beidellite–kaolinite content was classified into ≤5%, 5–15%, and >15% groups. Specimens with B + K > 15% consistently formed a low-strength regime, with a median destructive load of approximately 44.6 kN (≈1.1–1.3 MPa depending on cross-sectional area). In contrast, mixtures with B + K ≤ 5% achieved median loads above 90 kN (≈2.5–3.0 MPa). Quartz-rich fractions showed a strong global positive correlation with strength (r = 0.71), while the grouped clay fraction exhibited a highly significant effect (Kruskal–Wallis H = 72.78, p < 0.001). A regime shift was observed between 7% and 8% forming moisture, where quartz correlation changed from strongly positive (r ≈ 0.70) to negative (r ≈ −0.69). Increasing cement content from 6% to 9% significantly improved strength (H = 12.30, p = 0.0005), although this effect diminished when B + K exceeded 15% or forming moisture reached 8%. Association rules further confirm that high or low strength emerges only from specific multivariate combinations. The results show that mineralogy influences CSRE strength primarily through interaction with technological parameters, providing a robust basis for regime-based interpretation and rational mixture design. Full article

Extrusion-based three-dimensional (3D) bioprinting has enabled the fabrication of complex, cell-laden constructs; however, process parameter selection remains largely empirical and system-specific. As biofabrication workflows scale in complexity and translational ambition, trial-and-error optimization increasingly limits reproducibility, transferability, and informed decision-making. In this work, a formal, optimization-oriented design framework is proposed to structure extrusion-based bioprinting as a constrained, multivariable design problem. Rather than introducing a system-specific predictive model, the framework organizes process parameters, material descriptors, scaffold architecture, and biological feasibility into a unified formulation based on objective functions and admissible constraints. Symbolic coupling relationships are employed to make parameter dependencies, trade-offs, and constraint interactions explicit without imposing restrictive assumptions on material behavior or biological response. A demonstrative computational case study is presented to illustrate how qualitative predictive reasoning emerges through constraint-driven design space analysis and multi-objective considerations. The framework reveals how feasible operating regions are shaped by competing biological, mechanical, and manufacturing limitations, emphasizing robustness-aware parameter selection over isolated optimization. The proposed approach is intended as a transferable methodological foundation that supports structured reasoning, experimental planning, and future integration with numerical models, data-driven tools, and closed-loop biofabrication systems. Full article

This paper proposes the Fuzzy Difference Equation Matrix Model (FDEMM), a novel predictive control algorithm designed for nonlinear multivariable systems. Standard Dynamic Matrix Control (DMC) often struggles with computational load and nonlinearities. FDEMM addresses this by integrating the Difference Equation Matrix Model (DEMM) with a generalized Takagi-Sugeno (T-S) fuzzy framework, utilizing a parameter-weighting scheme to handle overlapping membership functions. The method is validated on two distinct nonlinear systems: a binary distillation column and a delayed thermal mixing tank. Results demonstrate FDEMM’s ability to control complex systems achieving the desired output even in the presence of disturbances and noise. The proposed strategy offers a computationally efficient alternative for real-time control of complex nonlinear processes. Full article

Background: Gemcitabine–cisplatin (GC) combined with a programmed death-ligand 1 (PD-L1) inhibitor has become an important first-line regimen for advanced intrahepatic cholangiocarcinoma (ICC). However, overall efficacy remains modest, and inter-patient heterogeneity in outcomes is substantial, highlighting the need for simple biomarkers for pretreatment risk stratification. The systemic immune-inflammation index (SII), derived from peripheral neutrophil, lymphocyte, and platelet counts, has been associated with prognosis in various malignancies, but its clinical relevance in advanced ICC treated with first-line GC plus PD-L1 inhibitor remains unclear. Aims: To evaluate the association of baseline SII with objective response and survival outcomes in patients with advanced ICC receiving first-line GC plus PD-L1 inhibitor. Methods: We retrospectively analyzed 193 consecutive patients with advanced ICC who received first-line GC plus a PD-L1 inhibitor at our center. Baseline clinicopathologic characteristics and laboratory parameters were collected, and SII was calculated as platelet count (×10⁹/L) × neutrophil count (×10⁹/L)/lymphocyte count (×10⁹/L). Receiver operating characteristic (ROC) analysis was performed to assess the discriminative ability of baseline SII for objective response and to determine an internally derived cut-off value. Patients were categorized into low- and high-SII groups accordingly. Logistic regression was used to identify factors associated with objective response rate (ORR). Progression-free survival (PFS) and overall survival (OS) were estimated by the Kaplan–Meier method and compared using the log-rank test. Multivariable Cox proportional hazards models were constructed to evaluate the independent prognostic significance of SII for PFS and OS. Results: Among the 193 patients included, 55 achieved complete or partial response and 138 had stable or progressive disease, yielding an ORR of 28.5%. Baseline SII showed good discrimination for objective response (AUC = 0.91), and the optimal cut-off value was 495.75. Patients in the low-SII group had a significantly higher ORR than those in the high-SII group (p < 0.001). Kaplan–Meier analysis demonstrated that both PFS and OS were longer in the low-SII group than in the high-SII group (median OS: 13.0 vs. 8.0 months, log-rank p < 0.001; median PFS: 8.5 vs. 6.0 months, p = 0.025). In multivariable Cox models adjusting for differentiation, CA19-9, tumor multiplicity, and distant metastasis, SII grouping remained independently associated with PFS and OS, and distant metastasis was consistently associated with increased risks of progression and death. Conclusions: Baseline SII is a readily available prognostic biomarker associated with objective response and survival in patients with advanced ICC treated with first-line GC plus PD-L1 inhibitor. Given the retrospective single-center design, the absence of a non-immunotherapy comparator cohort, and internal cut-off derivation, these findings should be interpreted as hypothesis-generating and warrant external validation. Full article