Search Results (2,091)

Background: Multiple meta-analyses have compared liver resection (LR) with liver transplantation (LT) for hepatocellular carcinoma (HCC), but overlapping primary studies and heterogeneous outcome definitions have complicated interpretation. Methods: A PRISMA/PRIOR-compliant umbrella review (PROSPERO CRD420251069248) was conducted. PubMed, Embase, and CENTRAL were searched for meta-analyses published between 1 January 2000 and 30 September 2025. Quantitative meta-analyses comparing LT and LR were included, while one systematic review of meta-analyses was synthesised narratively. Effect directions were standardised; hazard ratio (HR)-based summaries (LR:LT; values > 1 favour LT) were pooled using random-effects models, whereas odds ratio (OR)-based summaries were described qualitatively because of heterogeneity in endpoint definitions. Results: Four quantitative meta-analyses and one systematic review of meta-analyses met the inclusion criteria. Pooled HRs confirmed LT superiority: overall survival (OS) HR 1.35 (95% CI 1.17–1.55) and disease-free survival (DFS) HR 2.58 (95% CI 2.25–2.96). OR-based summaries from recent meta-analyses were directionally consistent but were not pooled. Conclusions: This umbrella synthesis demonstrates that LT provides superior long-term OS and DFS compared with LR for HCC, with consistent robustness across both Milan and extended selection criteria. Methodological safeguards against study overlap and subgroup insights—including intention-to-treat analyses, viral etiology (hepatitis B virus/hepatitis C virus), era, and geographic region—reinforce LT as the preferred strategy for eligible patients, while LR remains a critical option where graft availability is limited. Full article

Achieving the multi-beam directional manipulation of electromagnetic waves and energy management of each beam has important application value in fields such as satellite communications. Existing methods for realizing multi-beam formation and energy distribution using metasurfaces have problems such as slow optimization speeds and low design accuracies. To break through this design bottleneck, a new design paradigm is proposed that introduces a 2D image matching algorithm into the design process of metasurfaces for multi-beam energy management. By using 2D grayscale images to characterize the far-field patterns generated by the metasurface array and the expected multi-beams, a wavefront characterization and matching optimization model driven by 2D grayscale images is established, ultimately achieving the customized design of beam position, intensity, and sidelobe constraints. The fully customized beam metasurface constructed using this method generates multiple y-polarized reflected directional beams under an x-polarized wave incidence, and the beam energy distribution ratio can be customized as expected. Both the simulation and test results verify the effectiveness of the proposed method. This method provides a new design idea for metasurface devices for multi-beam energy management. The meta-device has potential applications in satellite communications, holography, biomolecular detection, and radar systems. This device can also enhance the capacity of wireless communication. Full article

Background/Objectives: The diagnosis of vestibular migraine (VM) and Meniere’s disease (MD) is based mainly on clinical criteria. The aim of this study is to systematically review and investigate the potential role of the video Head Impulse Test (vHIT) in the differential diagnosis between VM and MD. Methods: A systematic review of the English-language literature was conducted, including studies from database inception to November 2023, in accordance with PRISMA guidelines. Medline (via PubMed), Cochrane Database and Scopus were reviewed. The review included studies involving adult patients diagnosed with VM, MD, or healthy control individuals who underwent vHIT and reported data on vHIT abnormalities, gain, and refixation saccades. The AXIS tool was applied for risk of bias assessment in all cross-sectional studies. A random-effects meta-analysis was performed to compare vHIT gains between individuals with VM and those with MD. Results: Eleven cross-sectional observational studies with a case–control comparison design were included, comprising a total of 362 patients with VM, 307 patients with MD, and 135 healthy control subjects. All studies applied the same diagnostic criteria for VM; however, varying criteria were used for the diagnosis of MD. Four studies evaluated the duration of vestibular symptoms, two assessed migraine duration, and six provided a rationale for excluding individuals with overlapping VM and MD diagnoses. Criteria for defining an abnormal vHIT result were specified in six studies. Seven studies reported vHIT gain values for the lateral semicircular canal, while eight presented data on saccade incidence and characteristics. Additionally, four studies were included in the meta-analysis, which yielded a mean difference in the vHIT gain of −0.0203 (95% CI: −0.0789 to 0.0383; p = 0.4968), indicating no statistically significant difference between patients with VM and those with MD. Conclusions: In this review, vHIT gain did not differ significantly between VM and MD groups, suggesting that vHIT gain alone has limited utility in their differential diagnosis. Combined saccade patterns may still prove clinically useful as more robust and consistent data become available. Full article

To elucidate the long-term spatiotemporal patterns and key drivers factors, this study employed a meta-analysis of data from soil containing Potentially Toxic Elements (Cd, As, Cr, Hg, and Pb) in Chinese farmland soils from 2003 to 2025. The geoaccumulation index, the potential ecological risk index, and standard deviation ellipses were used to assess the spatiotemporal evolution of heavy metal accumulation and ecological risk, while the Random forest–SHapley Additive exPlanations (RF-SHAP) method was employed to identify driving mechanisms. At the national scale, Cd and Hg are significantly enriched relative to the background values, whereas As, Cr, and Pb remained at relatively low levels, with enrichment ranked as Cd > Hg > Pb > Cr > As. Cd and Hg indicated mild pollution, but the Sichuan Basin emerged as a hotspot, where Cd reached moderate pollution and showed strong ecological risk, and Hg also exhibited high ecological risk. Over the past two decades, the contamination center shifted from coastal to southwestern inland regions, with an expanded and more dispersed distribution. Since 2017, Cd and Hg pollution levels have stabilized, suggesting that the aggravating trend has been preliminarily curbed. Industrial waste and wastewater discharge, irrigation and fertilization were identified as the primary anthropogenic factors of soil heavy metal accumulation, while climatic factors (temperature, precipitation, and solar radiation) and soil physicochemical properties (pH, clay content, and organic matter) played fundamental roles in spatial distribution and accumulation. Our findings call for targeted predictive research and policies to manage heavy metal risks and preserve farmland sustainability in a changing climate. Full article

CO₂-enhanced oil recovery (CO₂-EOR) with water-alternating-gas (WAG) injection offers the dual benefit of boosted oil production and CO₂ storage, addressing both energy needs and climate goals. However, designing CO₂-WAG schemes is challenging; maximizing oil recovery, CO₂ storage, and economic returns (net present value, NPV) simultaneously under a limited simulation budget leads to conflicting trade-offs. We propose a novel closed-loop multi-objective framework that integrates high-fidelity reservoir simulation with stacking surrogate modeling and active learning for multi-objective CO₂-WAG optimization. A high-diversity stacking ensemble surrogate is constructed to approximate the reservoir simulator. It fuses six heterogeneous models (gradient boosting, Gaussian process regression, polynomial ridge regression, k-nearest neighbors, generalized additive model, and radial basis SVR) via a ridge-regression meta-learner, with original control variables included to improve robustness. This ensemble surrogate significantly reduces per-evaluation cost while maintaining accuracy across the parameter space. During optimization, an NSGA-II genetic algorithm searches for Pareto-optimal CO₂-WAG designs by varying key control parameters (water and CO₂ injection rates, slug length, and project duration). Crucially, a decision-space diversity-controlled active learning scheme (DCAF) iteratively refines the surrogate: it filters candidate designs by distance to existing samples and selects the most informative points for high-fidelity simulation. This closed-loop cycle of “surrogate prediction → high-fidelity correction → model update” improves surrogate fidelity and drives convergence toward the true Pareto front. We validate the framework of the SPE5 benchmark reservoir under CO₂-WAG conditions. Results show that the integrated “stacking + NSGA-II + DCAF” approach closely recovers the true tri-objective Pareto front (oil recovery, CO₂ storage, NPV) while greatly reducing the number of expensive simulator runs. The method’s novelty lies in combining diverse stacking ensembles, NSGA-II, and active learning into a unified CO₂-EOR optimization workflow. It provides practical guidance for economically aware, low-carbon reservoir management, demonstrating a data-efficient paradigm for coordinated production, storage, and value optimization in CO₂-WAG EOR. Full article

Background/Objectives: Cardiovascular diseases continue to be the foremost global cause of morbidity and mortality, representing about 40% of all causes of death. Atherosclerotic cardiovascular disease is the most common and clinically important type of these, occurring when cholesterol accumulates over time in the artery intima, which induces an inflammatory process that leads to the production of atherosclerotic plaques. Nowadays, lipid profile alterations and high/very high cardiovascular risk can be observed in more and more patients. Combination therapy, which includes high-intensity statins, ezetimibe, bempedoic acid, and PCSK9-targeted medicines, can lower LDL-C by more than 80%, which is far more than the 50% that statin monotherapy usually achieves. Thus, novel lipid-lowering therapies are needed, as current agents—though effective in reducing cardiovascular events—leave considerable residual risk in many patients. Methods: The aim of our study was to evaluate the cost-effectiveness of Inclisiran and its association with standard of care for the prevention of cardiovascular events across multiple international settings, in articles that reported quality-adjusted life years gained and cost-effectiveness metrics. Results: Our findings suggest that the cost-effectiveness of Inclisiran is highly context-dependent, shaped by local pricing, population risk, and system-level capacity. While Inclisiran demonstrates potential economic value in high-income settings or among high-risk patients, its widespread adoption for primary prevention appears unjustified under current conditions. Conclusions: Policymakers should consider risk-based targeting, price renegotiation, and performance-based reimbursement models to improve the value proposition of such interventions. Full article

Alkaline delignification is a keystone pretreatment that governs carbohydrate accessibility, energy use, and yields across pulp and biorefinery value chains, yet its kinetic understanding remains fragmented and largely confined to bench-scale studies. This review provides an integrated assessment of the evolution and current state of kinetic approaches applied to alkaline delignification of lignocellulosic biomass, aiming to bridge academic research and industrial application. A systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) guidelines identified 74 peer-reviewed articles and 359 patents published between 1995 and 2025. Kinetic models were classified into conventional (nth-order and pseudo-first-order) and emerging categories (Avrami/Š–B, diffusion-based, mechanistic multistep, isoconversional, and ML/statistical). The results show that pseudo-first-order kinetics and batch-scale studies dominate the literature, while pilot-scale validation and hybrid mechanistic data-driven frameworks remain limited. Patent analysis revealed technological convergence within D21C and C08B IPC domains, reflecting growing industrial interest in alkaline pulping and cellulose valorization. Unlike previous reviews, this work uniquely integrates conventional and emerging kinetic models with a patent-based technological perspective, providing a unified view of academic and industrial progress. The insights presented here provide a foundation for advancing future research, particularly by encouraging the development of standardized experimental protocols and the validation of kinetic models across multiple scales. Moreover, this review provides a consolidated reference for both academic researchers and industrial practitioners seeking to enhance delignification efficiency, reduce reagent consumption, and improve the sustainability of biorefinery processes. Full article

Background: Triple-negative breast cancer (TNBC) is associated with poor prognosis and a high risk of early relapse. The incorporation of platinum-based agents into neoadjuvant chemotherapy (NACT) regimens has been linked to improved pathological complete response (pCR) rates. However, the clinical benefit of carboplatin (CBDCA) remains debated due to variable long-term survival outcomes and concerns over cumulative toxicity. This meta-analysis evaluates the efficacy of adding CBDCA to NACT in early-stage TNBC (eTNBC). Methods: A systematic review and meta-analysis were conducted by searching MEDLINE, PubMed, and major oncology conference proceedings (2014–2024), with no language restrictions. Randomized phase II–III trials assessing the addition of CBDCA to standard NACT in eTNBC and reporting pCR and survival outcomes were included. The systematic review followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The protocol has not been registered. The primary endpoint was pCR; the secondary endpoint was disease-free survival (DFS). For pCR, a random-effects model was used, and odds ratios (OR) were log-transformed. For DFS, a mixed-effects model was applied, extracting hazard ratios (HR) and converting them into logHR values. Heterogeneity was assessed using I² statistics, and publication bias was evaluated through the Fail-Safe N method and Egger’s regression test. Statistical analyses were performed using Jamovi v2.4.11. Results: Of 30 studies identified, 9 randomized clinical trials were eligible; 6 (BrighTNess, GeparSixto, GS5-01, BR-15-1 PEARLY, NACATRINE, CALGB 40603) met all inclusion criteria, totaling 3402 patients. The addition of CBDCA to NACT significantly improved pCR (OR 1.63; 95% CI: 1.38–1.92; p < 0.001), with low heterogeneity (I² = 0.81%) and no publication bias. DFS was also significantly improved (SHR 0.81; 95% CI: 0.63–0.91; p = 0.003), with moderate heterogeneity (I² = 27.95%) and no bias detected. Conclusions: Adding carboplatin to NACT significantly improves pCR and DFS in patients with early-stage TNBC. Full article

Background and Objectives: Artificial intelligence (AI) has shown promising performance in skin-lesion classification; however, its fairness, external validity, and real-world reliability remain uncertain. This systematic review and meta-analysis evaluated the diagnostic accuracy, equity, and generalizability of AI-based dermatology systems across diverse imaging modalities and clinical settings. Materials and Methods: A comprehensive search of PubMed, Embase, Web of Science, and ClinicalTrials.gov (inception–31 October 2025) identified diagnostic accuracy studies using clinical, dermoscopic, or smartphone images. Eighteen studies (11 melanoma-focused; 7 mixed benign–malignant) met inclusion criteria. Six studies provided complete 2 × 2 contingency data for bivariate Reitsma HSROC modeling, while seven reported AUROC values with extractable variance. Risk of bias was assessed using QUADAS-2, and evidence certainty was graded using GRADE. Results: Across more than 70,000 test images, pooled sensitivity and specificity were 0.91 (95% CI 0.74–0.97) and 0.64 (95% CI 0.47–0.78), respectively, corresponding to an HSROC AUROC of 0.88 (95% CI 0.84–0.92). The AUROC-only meta-analysis yielded a similar pooled AUROC of 0.88 (95% CI 0.87–0.90). Diagnostic performance was highest in specialist settings (AUROC 0.90), followed by community care (0.85) and smartphone environments (0.81). Notably, performance was lower in darker skin tones (Fitzpatrick IV–VI: AUROC 0.82) compared with lighter skin tones (I–III: 0.89), indicating persistent fairness gaps. Conclusions: AI-based dermatology systems achieve high diagnostic accuracy but demonstrate reduced performance in darker skin tones and non-specialist environments. These findings emphasize the need for diverse training datasets, skin-tone–stratified reporting, and rigorous external validation before broad clinical deployment. Full article

Farmers’ markets in Latin America continue to face structural challenges such as informality limited value addition, and weak institutional support. This article applies a hybrid multi-criteria model that integrates DEMATEL, ANP, and SBWM to prioritize agri-food transformation strategies in the department of Meta, Colombia. The methodology combined stakeholder mapping, Delphi-based validation of criteria, and the evaluation of strategic robustness under three prospective scenarios. The results show that the creation of working groups with public and private institutions (E.3) consistently emerged as the most robust strategy (weight = 0.0669 in SBWM), followed by infrastructure development (E.2. 0.0649) and participatory workshops on food sustainability (E.8. 0.0602). Sensitivity analysis confirmed the stability of the prioritization structure, with strategy E.3 remaining in the first position across all scenarios and only moderate variations in the positions of E.2 and E.8. The study contributes methodologically by demonstrating the integration of DEMATEL–ANP with SBWM to address interdependencies and uncertainty in participatory decision contexts. It also provides practical guidance for local governments and cooperatives to strengthen farmers’ markets through institutional coordination, capacity building, and infrastructure investment, thereby supporting progress toward several Sustainable Development Goals (SDGs). Full article

Background: Breast cancer (BC) is the most frequently diagnosed malignancy and a dominant cause of cancer mortality among women worldwide. Alongside established risk factors, recent studies highlight oncoviruses like Epstein–Barr virus (EBV) and human papillomavirus (HPV) as potential contributors. However, their role and association with BC development is still debatable. Study design and Methods: This systematic review and meta-analysis involved two distinct approaches: one assessing the worldwide prevalence of EBV and HPV coinfection in BC patients and another investigating the association between such coinfection and BC risk. A systematic search across PubMed, Scopus, Web of Science, and Embase was conducted up to 5 May 2025. Studies using PCR to detect both viruses in breast tissue samples were included. Random-effects models were used to estimate pooled prevalence and odds ratios with 95% confidence intervals. Results: Out of 307 non-duplicate records, 16 studies were found to be eligible for quantitative analysis. The pooled prevalence of EBV/HPV coinfection among BC patients was 14% (95% CI: 12–16%; I² = 91.0%). Prevalence varied by region, ranging from 6% in South America to 22% in the Middle East. In addition, a general trend towards increasing EBV/HPV coinfection prevalence among women with BC over time was detected. Moreover, analyzing case–control studies to investigate the relationship between EBV/HPV coinfection and the risk of BC, the pooled odds ratio was 5.87 (95% CI: 2.31–14.93; I² = 0%, p = 0.91). Conclusion: Our analysis shows that EBV and HPV coinfection prevalence varies by region and appears to be rising over time among women with breast cancer. Additionally, the strong statistical association between coinfection and breast cancer risk suggests a potential role for these oncoviruses in disease development, highlighting the possible preventive value of EBV and HPV vaccination. Full article

With the introduction of emerging digital technologies, sensory and consumer science has evolved beyond traditional laboratory-based and self-response-centered sensory evaluations toward more objective assessments that reflect real-world consumption contexts. This review examines recent trends and potential applications in sensory evaluation research focusing on key enabling technologies—artificial intelligence (AI) and machine learning (ML), extended reality (XR), biometrics, and digital sensors. Furthermore, it explores strategies for establishing personalized, multimodal, and intelligent–adaptive sensory evaluation systems through the integration of these technologies, as well as the applicability of sensory evaluation software. Recent studies report that AI/ML models used for sensory or preference prediction commonly achieve RMSE values of approximately 0.04–24.698, with prediction accuracy ranging from 79 to 100% (R² = 0.643–0.999). In XR environment, presence measured by the IPQ (7-point scale) is generally considered adequate when scores exceed 3. Finally, the review discusses ethical considerations arising throughout data collection, interpretation, and utilization processes and proposes future directions for the advancement of sensory and consumer science research. This systematic literature review aims to identify emerging technologies rather than provide a quantitative meta-analysis and therefore does not cover domain-specific analytical areas such as chemometrics beyond ML approaches or detailed flavor and aroma chemistry. Full article

Background: Systemic inflammation plays a pivotal role in heart failure (HF) progression, yet no meta-analysis has synthesized prospective cohort data on interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), and neutrophil-to-lymphocyte ratio (NLR) as prognostic biomarkers. Objectives: To quantify the independent prognostic value of IL-6, hs-CRP, and NLR for mortality and HF-related outcomes across HF phenotypes. Methods: Following PRISMA and MOOSE guidelines, we searched PubMed, Embase, Scopus, Web of Science, and CENTRAL from January 2014 to October 2025 for prospective cohorts reporting adjusted hazard ratios (HRs). Random-effects meta-analysis pooled HRs; heterogeneity was assessed via I² statistic, with subgroup and sensitivity analyses for robustness. Quality was evaluated using Newcastle–Ottawa Scale (NOS) and GRADE. Results: Thirteen cohorts (n ≈ 19,000) were included. Elevated IL-6 (five studies) was associated with increased all-cause mortality and composite outcomes (low-moderate heterogeneity, I² < 35%). hs-CRP (five studies) showed similar prognostic strength, with trajectories amplifying risk. NLR (three studies) independently predicted adverse events with negligible heterogeneity. Associations persisted across HFrEF and HFpEF, acute/chronic settings, and geographic regions, independent of natriuretic peptides and comorbidities (NOS median 8/9; GRADE moderate-to-high). Conclusions: IL-6, hs-CRP, and NLR are robust, independent prognostic biomarkers in HF, supporting their integration into clinical risk stratification and inflammation-targeted therapies. PROSPERO: CRD420251207035. Full article

Accurate prediction of photosynthetic parameters is pivotal for precision viticulture, as it enables non-invasive monitoring of plant physiological status and informed management decisions. In this study, spectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (${\Phi }_{\mathrm{PSII}}$), and electron transport rate (ETR), as well as stem and leaf water potential (${\Psi }_{\mathrm{stem}}$ and ${\Psi }_{\mathrm{leaf}}$), in Vitis vinifera (cv. Müller-Thurgau) grown in an experimental vineyard in Lower Franconia (Germany). Measurements were obtained on 25 July, 7 August, and 12 August 2024 using a LI-COR LI-6800 system and a PSR+ hyperspectral spectroradiometer. Various machine learning models (SVR, Lasso, ElasticNet, Ridge, PLSR, a simple ANN, and Random Forest) were evaluated, both as standalone predictors and as base learners in a stacking ensemble regressor with a Random Forest meta-learner. First derivative reflectance (FDR) preprocessing enhanced predictive performance, particularly for ${\Phi }_{\mathrm{PSII}}$ and ETR, with the ensemble approach achieving R² values up to 0.92 for ${\Phi }_{\mathrm{PSII}}$ and 0.85 for A at 1 nm resolution. At coarser spectral resolutions, predictive accuracy declined, though FDR preprocessing provided some mitigation of the performance loss. Diurnal patterns revealed that morning to mid-morning measurements, particularly between 9:00 and 11:00, captured peak photosynthetic activity, making them optimal for assessing vine vigor, while midday water potential declines indicated favorable timing for irrigation scheduling. These findings demonstrate the potential of integrating hyperspectral data with ensemble machine learning and FDR preprocessing for accurate, scalable, and high-throughput monitoring of grapevine physiology, supporting real-time vineyard management and the use of cost-effective sensors under diverse environmental conditions. Full article

This study reviews 33 meta-analyses and systematic reviews on Computational Thinking (CT), focusing on research quality, intervention effectiveness, and content. Quality assessment of included studies was conducted using the AMSTAR 2 tool. The meta-analysis achieved an average score of 10.9 (a total of 16 points), while systematic reviews scored an average of 6.1 (a total of 11 points). The 15 meta-analyses showed diverse intervention strategies. Project-based learning, text-based programming, and game-based learning demonstrate more pronounced effects in terms of effect size and practical outcomes. Curricular integration, robotics programming, and unplugged strategies offered additional value in certain contexts. Gender and disciplinary background were stable moderators, while grade level and educational stage had more conditional effects. Intervention duration, sample size, instructional tools, and assessment methods were also significant moderators in several studies. The 18 systematic reviews used a five-layer framework based on ecological systems theory, covering educational context (microsystem), tools and strategies (mesosystem), social support (exosystem), macro-level characteristics (macrosystem), and CT development (chronosystem). Future research should focus on standardizing meta-analyses, unifying effect size indicators, and strengthening longitudinal studies with cognitive network analysis. Additionally, systematic reviews should improve evidence credibility by integrating textual synthesis and data-driven reasoning to reduce redundancy and homogeneity. Full article