Search Results (25,490)

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

Background/Objectives: Anesthesia for intracranial neurosurgery presents unique challenges because of the sensitivity of the brain to perioperative physiological disturbances, yet neuroanesthetic practice remains highly variable and supported by a limited high-level evidence base. We conducted a scoping review to map and characterize multicenter randomized controlled trials (RCTs) evaluating perioperative management strategies in adults undergoing intracranial neurosurgery. Methods: This scoping review was reported in accordance with the PRISMA extension for Scoping Reviews. MEDLINE, PubMed, EMBASE, Cochrane Central, and Web of Science were searched from inception to 25 June 2025. Multicenter RCTs enrolling adults undergoing intracranial neurosurgery and evaluating anesthetic, hemodynamic, ventilatory, or perioperative interventions were included. We prioritized mapping multicenter designs for their greater external validity and implementation potential. Data were extracted in duplicate and summarized descriptively. Results: Of 417 records identified, 13 multicenter trials (≥2 recruiting sites) involving 2765 participants across nine countries from 1997–2025 were included. Most trials evaluated anesthetic maintenance or opioid regimens (7/13), followed by post-craniotomy pain control (3/13), ventilation/brain relaxation strategies (1/13), antiemetic prophylaxis (1/13), and temperature management (1/13). Outcomes were predominantly short-term and process-based (hemodynamics 7/13, opioid use 7/13, emergence metrics 5/13). Patient-centered outcomes were rarely measured (mortality 1/13, functional neurological outcome 1/13, cognitive outcome 1/13; quality of life 0/13). Only one trial assessed outcomes at ≥72 h postoperatively. Over half of the included trials were judged at high risk of bias. Conclusions: Multicenter RCT activity in neuroanesthesia remains sparse and narrowly focused, highlighting the need for larger, methodologically robust trials targeting patient-centered and long-term outcomes. Full article

This study aimed to develop and validate a machine learning-based model for predicting 30-day mortality in cardiac surgery patients and to implement a functional, clinician-oriented web application that enables the real-time use of the model. A retrospective cohort of 325 cardiac surgery patients was analysed using supervised machine learning. After preprocessing and clinical feature selection, several models were trained and evaluated through cross-validation. XGBoost achieved the best results, with an AUC-ROC of 0.968, recall of 0.800, and Brier score of 0.058. To facilitate clinical usability, a web-based application was developed using StreamLit, enabling clinicians to input patient data and predict mortality in real time. The application includes SHAP-based explainability for each prediction, thereby ensuring model transparency. Preliminary feedback from clinicians indicated that the tool was intuitive and informative and showed potential for preoperative risk assessment. The integration of a robust ML (machine learning) model with a functional clinical application offers a practical tool for supporting decision-making in cardiac surgery. This combined approach enhances both accuracy and accessibility, which are key to real-world impacts. Future work will involve multicentre validation and user-centred refinement. Full article

Prostate cancer frequently progresses to lethal, drug-resistant castration-resistant prostate cancer (CRPC), where conventional therapies often fail due to intrinsic and acquired resistance mechanisms. This resistance creates a critical therapeutic impasse, leaving patients with limited options and poor prognoses. Immunotherapy has emerged as a promising strategy to harness the immune system against these treatment-refractory tumors, offering a potential avenue to overcome the immunosuppressive barriers that underlie CRPC drug resistance. This review synthesizes findings from a structured search of PubMed, Web of Science, and Embase (2020–2025), revealing significant clinical progress: 4 vaccine trials, 5 immune checkpoint inhibitor trials, 18 combination therapy trials (≥2 agents), and 6 targeted drug trials have been conducted. Preliminary efficacy was observed in novel approaches like bispecific antibodies (e.g., Xaluritamig achieving 59% PSA50 response), PSMA-CAR-T (P-PSMA-101), and oncolytic viruses (Ad5 PSA/MUC-1/brachyury). Basic research identified four targeted resistance mechanisms (e.g., AR-LLT1, Pygo2, and HnRNP L) and one nanoparticle-mediated triple-combination therapy (CM-AMS@AD NPs integrating photothermal, chemotherapy, and immunotherapy), which enhanced cytotoxic T-cell infiltration and suppressed CRPC growth preclinically. These collective findings suggest the potential of immunotherapy for CRPC in overcoming resistance barriers and improving patient outcomes, with bispecific T cell engagers (Xaluritamig, 59% PSA50) and PSMA-directed CAR-T therapy (P-PSMA-101, >50% PSA reduction) emerging as the most promising near-term candidates and biomarker-stratified combinations (nivolumab plus rucaparib, 84.6% PSA50, in HRR-deficient patients) illustrating the transformative power of precision patient selection; however, these findings require validation in larger, biomarker-stratified trials before definitive conclusions can be drawn. Translating this potential into clinical reality requires optimized patient selection through predictive biomarkers and rigorously validated Phase III trials to confirm durable clinical responses and long-term survival benefits. Full article

In this paper, the forked-web joint configuration was introduced first, in order to transfer the shear and moment forces better and avoid the local buckling problem that usually happens in narrow steel beams and concrete-filled steel tubular (CFST) column joints. Experiments including three specimens of that joint were then conducted, considering different axial compression ratios of the column. The test results indicated that no failure phenomenon happened to the proposed joint when the equivalent rotational angle was no more than 1/50. However, the final failure mode of each specimen was still local buckling and tearing failure of beam flanges due to the excessively large stress. Finally, based on the tests and FEA results, a corresponding improvement, including a single-web configuration with U-shape and triangular stiffeners, was thus brought forward and numerically verified in terms of rotational stiffness, failure mode, and the hysteretic curve. The FEA results revealed that the rotational stiffness of the proposed single-web joint with triangular stiffeners for beams and U-shape stiffeners for CFST columns efficiently increased from 0.87 to 3.83, and it was almost twice that of the narrow beam-column joint with internal horizontal diaphragms. Moreover, the previous undesirable tearing failure mode was finally avoided by adopting high-strength steel Q550 for the joint beam part. Full article

Long-read sequencing (LRS) has transformed life science research by introducing third-generation sequencing (TGS) platforms applicable across various research fields, including environmental sciences. In the past decade, LRS platforms have been utilized to extensively study algal systems by improving genomic approaches such as metabarcoding, chromosome-level genome and pangenome assemblies, as well as providing new insights into algae-associated microbiomes and host–symbiont interactions. This review aims to discuss recent advancements in LRS in algal research. To achieve this aim, a systematic review was conducted according to the PRISMA 2020 guidelines and across three electronic databases (Web of Science, Scopus, and Google Scholar), with additional citation searching for relevant studies in four key algal research areas: metabarcoding, genomics, pangenomics, and host–symbionts interactions. Following the inclusion and exclusion criteria, only 51 studies were selected for this review. Throughout the review, we summarize the challenges of short-read sequencing (SRS) and discuss how LRS platforms address these challenges in algal studies. Furthermore, we discuss the future of LRS and explore how artificial intelligence (AI) can advance research on algal biology and ecology. Full article

Hyperspectral (HSI) remote sensing has emerged as a transformative technology for Earth Observation, enabling detailed assessments across different domains. The current PRISMA-aligned systematic review aims to compare classical physics-based algorithms with emerging machine learning (ML), deep learning (DL) and hybrid approaches across two relevant application domains (vegetation and raw materials), analyzing over 350 peer-reviewed studies (194 after the screening) sourced from Scopus and Web of Science and accessed in July 2025. Specific domain-related studies have been considered, excluding duplicates and studies not strictly related to HSI. Risk of bias was assessed qualitatively based on different criteria. The efficiency of the techniques was analyzed by comparing the accuracy metrics reported in the studies. The heterogeneity of the evaluation metrics used across the different categories of the studies and the underrepresentation of some application domains is the final baseline of the work. The results were synthesized, grouping by application domains and algorithm category: ML and DL models dominate vegetation applications, and physics-based methods remain prevalent in raw materials. Hybrid models achieve the highest performances across all domains. This review highlights the importance of the hyperspectral operational requirements identified for upcoming missions (CHIME, SBG and IRIDE) and points out the opportunity for algorithm development. Full article

Mucuna pruriens (L.) DC. (Fabaceae), commonly known as velvet bean, is an annual tropical legume widely distributed in India, Africa, and the Americas. It has a long history of use in traditional medicine for managing various health conditions. It is renowned for its anti-inflammatory, antimicrobial, aphrodisiac, and anti-Parkinson effects. The entire plant is considered health-promoting, particularly the seeds. They have been used for their neuroprotective, fertility-enhancing, and antioxidant effects. This review aims to compile all available information regarding the chemical composition of all parts of this medicinal plant. For this purpose, the complete databases of Google Scholar, Scopus, PubMed, and Web of Science available to date were utilized. All studies reported the presence of a diverse range of secondary metabolites, including phenolic compounds, such as phenolic acids, flavonoids, and tannins, as well as saponins and alkaloids. Most studies concentrated on the chemical characterization of the seeds, whereas the leaves, roots, and pods have received comparatively limited scientific attention. The seeds of M. pruriens are renowned for their high concentration of L-3,4-dihydroxyphenylalanine (L-DOPA), a metabolic precursor of dopamine. A large proportion of these studies originated from countries where M. pruriens naturally occurs. Few studies have been conducted on the chemical composition of velvet bean outside these regions. Despite the existing information on the chemical composition of M. pruriens. (seeds, leaves, and roots), further research beyond its natural habitat is required to gain a broader understanding of its chemical profile and pharmacological effects. Full article

Background/Objectives: This study systematically synthesized the evidence on the effectiveness of aquatic exercise (AE)-based interventions for improving sleep quality in patients with chronic diseases and identified key moderating factors. Methods: A meta-analysis of 11 randomized controlled trials sourced from Google Scholar, PubMed, Web of Science, Embase, Cochrane Library, and Scopus (published between 2016 and 2025) was conducted. Sleep quality was assessed using subjective tools (e.g., PSQI). Results: While AE-based interventions showed potential for enhancing nighttime sleep quality (standard mean difference = 0.825, p < 0.001), high statistical heterogeneity (I² = 93.41%) was observed. Given this variance, the analysis prioritized the clinical outcomes of specific patient populations over the pooled effect size. Preliminary evidence suggests significant improvements were confirmed in populations with post-COVID syndrome (p < 0.001), Parkinson’s disease (p = 0.002), and chronic back pain (p = 0.008). Conversely, no significant benefits were observed in fibromyalgia (p = 0.191), ankylosing spondylitis (p = 0.737), or type 2 diabetes (p = 0.836). Moderator analysis further indicated that the mode of AE might influence outcomes, with recreational aquatic therapy and deep-water running suggesting superior efficacy compared to resistance training. Conclusions: AE-based interventions were suggested as an effective intervention for improving sleep quality. The observed benefits likely stem from the synergistic effects of physical exercise and the unique physiological properties of the aquatic environment, such as buoyancy and hydrostatic pressure. However, the field relies heavily on subjective questionnaires and lacks physiological mechanism studies. These findings provide a preliminary evidence-based framework for clinicians to develop targeted AE-based interventions for chronic disease patients. Full article

Background/Objectives: Accurate diagnosis and staging of periodontitis rely on clinical measurements and radiographic assessment of alveolar bone loss. Methods: Studies published between 1 January 2020 and 31 October 2025 were searched in the Web of Science and PubMed databases in accordance with the PRISMA guidelines. Original research articles that evaluated periodontal pathology on radiographic images using fractal analysis and/or artificial intelligence approaches, with clearly defined methodologies, were included. Due to methodological heterogeneity, a quantitative meta-analysis was not performed, and the findings were summarized using a narrative synthesis approach. Results: Of 346 records, 80 studies (9 fractal, 71 AI) met the inclusion criteria. Fractal analysis studies predominantly calculated the fractal dimension on panoramic or periapical radiographs using the box-counting method. In artificial intelligence studies, the task types mainly comprised classification, segmentation, detection, and hybrid approaches (multi-stage models or models combining multiple tasks). Panoramic and intraoral radiographs were the predominant imaging modalities. Performance metrics were reported across wide ranges (sensitivity 0.23–1.00; accuracy 0.506–1.00; specificity 0.41–0.99; F1 score 0.15–0.99; AUC 0.75–0.99), and in some studies, these metrics were only partially reported. Conclusions: Fractal analysis and artificial intelligence approaches offer objective and reproducible assessment of periodontal bone loss; however, methodological and reporting heterogeneity limit comparability and generalizability. Standardization of ROI definitions, datasets, study designs, and performance reporting is needed to improve clinical applicability. Future research should also explore hybrid models that combine the quantitative microstructural insights of fractal analysis with the automated detection capabilities of artificial intelligence to enhance diagnostic precision. Full article

Schistosomiasis elimination is increasingly constrained less by the technical efficacy of single interventions than by systemic dynamics in coupled human–animal–environment settings, including nonlinear feedback, spatial heterogeneity, and cross-sectoral govern frictions. We conducted a systematic methodological review (search date: 1 January 2026) across PubMed, Web of Science, Scopus, EconLit, and CNKI to identify studies that (i) addressed schistosomiasis control, (ii) used explicit system-based, causal, or network-oriented analytical structures, and (iii) incorporated economic evaluation with multi-domain outcomes. We synthesized modeling architectures, economic methods, and approaches to trade-offs and uncertainty, and applied an evidence-informed systemic causality framework to assess decision-analytic adequacy. The literature grouped into three related strands: transmission and system dynamics models that capture feedback processes and rebound risks; economic evaluations dominated by cost-effectiveness analyses; and cross-sectoral or surveillance-oriented decision models optimizing implementation under resource constraints. Across strands, elimination-stage investments such as surveillance, environmental management, and coordination exhibit strong externalities and quasi-public-good properties that are systematically undervalued in single-sector, single-metric frameworks. We argue that decision-relevant evaluation should be reframed as a multi-objective resource allocation problem that integrates systemic modeling with economic valuation, explicitly addresses uncertainty, and applies multi-criteria decision analysis to support long-horizon, cross-sectoral decision-making. Full article

Background and Purpose: The effects of B-vitamin combinations on the prevention of cardiovascular diseases, such as myocardial infarction (MI) and stroke, remain controversial. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) over three decades to evaluate the association between B-vitamin combinations and mortality and arterial thrombotic outcomes. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched for RCTs with minimal duration over 24 months published between January 1996 and November 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2.0 tool. Random-effects models were used in this meta-analysis to calculate pooled risk ratios (RRs) and 95% confidence intervals (CIs). Results: Thirteen randomized trials enrolling 68,363 participants across both primary and secondary prevention populations were included. B-vitamin combinations were associated with a nonsignificant reduction in stroke and 3-point major adverse cardiovascular events (MACE) (stroke: RR 0.91, 95% CI 0.81–1.04; MACE: RR 0.93, 95% CI 0.86–1.01). No significant effects were observed for all-cause mortality (RR 1.01, 95% CI 0.96–1.06), cardiovascular mortality (RR 0.97, 95% CI 0.88–1.07), or MI (RR 0.97, 95% CI 0.91–1.03). In primary prevention populations, B-vitamin combinations were associated with significant reductions in stroke (RR 0.79, 95% CI 0.68–0.93) and MACE (RR 0.80, 95% CI 0.69–0.92). A modest reduction in MACE was also observed in secondary prevention populations (RR 0.91, 95% CI 0.83–0.99). Between-study heterogeneity was minimal to low for ischemic outcomes, supporting the robustness of these estimates, whereas substantial heterogeneity was observed for mortality outcomes in secondary prevention populations. Conclusions: The evidence is limited by heterogeneity in trial populations, vitamin formulations and doses, and outcome definitions, with substantial between-study inconsistency for mortality outcomes and imprecision in subgroup estimates derived from a small number of contributing trials. Overall, B-vitamin combinations do not confer consistent benefit for major cardiovascular outcomes but may reduce stroke and MACE in selected primary prevention populations, suggesting that baseline cardiovascular risk and regional folic acid fortification modify treatment effects and should guide future trial design and clinical use. Full article

Green FinTech involves facilitating sustainable payments, banking, and investment; nevertheless, it is subject to consumer trust and perceptions of ‘green’ value. The literature on this topic is fragmented, with information systems literature typically considering trust as a broad acceptance construct, while sustainable literature considers it as a risk of ‘greenwashing’ without integrating credibility into adoption models. This systematic review aggregates 15 empirical studies and addresses five research questions. RQ1 examines the theoretical models applied to examine trust in green/sustainable FinTech adoption. RQ2 examines the conceptualization and measurement of trust across different contexts, distinguishing institutional/provider trust, platform/tech trust, and sustainability claim credibility trust. RQ3 examines the function of trust within behavioral models (predictor, mediator, moderator). RQ4 examines methodological characteristics and quality indicators (research design, sampling frame, reliability, and bias). RQ5 examines the direct relationship between trust and adoption intention using meta-analysis. The systematic review follows a set of PRISMA guidelines, where we searched Scopus and Web of Science (2015–2026) and applied an RQ-based coding scheme to peer-reviewed articles. Measures of trust varied significantly (unidimensional, integrity–competence–benevolence, and technology-specific scales), limiting cross-study comparability. Using random effects, we found a significant positive relationship between trust and intention (pooled standardized direct path coefficient β = 0.27, 95% CI [0.14, 0.41]) with considerable heterogeneity (I² = 88%) and a wide prediction interval including near-zero effects. Literature essentially endorses trust as a significant yet context-dependent construct, emphasizing the necessity for measurement standardization, a more distinct differentiation between sustainability trust and general platform trust, regular reporting of reliability and bias assessments, and focused evaluations of boundary conditions (e.g., environmental skepticism, regulatory framework, and FinTech type). Full article

This study examines how research in the United States has addressed infrastructure resilience across different disaster contexts, situating the topic within broader discussions on climate-related risks and adaptation. Infrastructure resilience has gained increasing importance as communities face more frequent and severe natural hazards and as infrastructure systems become more complex and interconnected. A database of more than 7000 studies published over the past century by universities, research centers, and government agencies was compiled and organized, including supplemental works from regions such as Europe, Australia, Japan, Africa, and South America. The dataset provides a long-term perspective on the evolution of resilience-related research and reflects the scope of accessible literature indexed in major research repositories. Using systematic classification, each study was categorized by disaster type (i.e., floods, hurricanes, wildfires, heatwaves, and snowstorms) and by infrastructure system (i.e., transportation, water, energy, telecommunications, and buildings). A keyword-based relevance scoring method was applied to distinguish studies in which resilience is a central analytical focus from those in which it appears as a secondary or contextual concept. The results are presented through an interactive web-based platform that enables users to explore resilience research by state, year, disaster type, infrastructure category, and level of relevance. The analysis reveals a substantial increase in resilience-related publications in recent decades, with notable geographic and thematic concentrations. Transportation and water infrastructure dominates the literature, while energy systems, telecommunications, and digital infrastructure remain underrepresented. These findings highlight both progress and persistent gaps in infrastructure resilience research and support more integrated, system-oriented, and future-focused resilience planning. Full article

Background. NOTCH receptors play a pivotal role in carcinogenesis. Upon ligand binding, a cascade of proteolytic cleavages mediated by ADAM proteases and the γ-secretase complex activates the receptor, ultimately releasing the NOTCH intracellular domain (NICD). NICD translocates to the nucleus, where it regulates gene expression. This review mainly aims to evaluate γ-secretase inhibitors (GSIs) as anticancer agents in preclinical and clinical settings, with a focus on their ability to block tumor progression, target cancer stem cells, and overcome resistance to standard therapies. Methods. A systematic search was conducted in the ISI Web of Science, PubMed, and Scopus databases, following PRISMA guidelines. The review included preclinical in vitro and in vivo studies, as well as clinical trials, investigating GSIs, either as monotherapy or in combination with other treatments, in TNBC, metastatic melanoma, PDAC, gastric cancer, and NSCLC. Exclusion criteria included duplicates, non-English articles, studies published before 2010, studies on non-cancer conditions, research unrelated to NOTCH signaling, and studies outside the selected cancer types. Overall, 69 articles were included and categorized into the five types of cancer analyzed (20 on NSCLC, 22 on TNBC, 11 on metastatic melanoma, 7 on GC, and 9 on PDAC). Of these, 60 studies corresponded to preclinical research in the types of cancer, and 9 studies corresponded to clinical trials in the types of cancer except for GC. Two independent authors screened and extracted relevant data, with disagreements resolved by the corresponding author. Findings were synthesized qualitatively across cancer types under study. Results. This review summarizes therapeutic advances involving GSIs in cancers driven by oncogenic NOTCH signaling, based on the 69 articles included. Preclinical studies show that GSIs synergize with chemotherapy and radiotherapy, particularly in NSCLC, melanoma, and TNBC, and block EMT, overcome therapeutic resistance, and improve prognosis. Commonly used GSIs include DAPT and RO4929097, which enhance the efficacy of agents, such as gemcitabine (PDAC), paclitaxel, osimertinib, erlotinib, and crizotinib (NSCLC), and 5-FU (gastric cancer, TNBC). Promising strategies include combining GSIs with SAHA, ATRA, CB-103, and other NOTCH signaling targeting molecules, either alone or with chemo- and radiotherapy. Clinical trials with GSIs, however, remain limited. RO4929097 is the most extensively tested GSI in clinical settings. PDAC trials combining GSIs with gemcitabine showed no benefit; melanoma trials yielded modest outcomes; and TNBC trials demonstrated partial responses to GSIs but overall low efficacy and significant adverse events. Discussion and Conclusions. Despite encouraging preclinical evidence, clinical trials with GSIs have underperformed, largely due to tumor heterogeneity, dosing limitations, and the non-selective nature of γ-secretase inhibition. Other NOTCH inhibitors, such as DLL4 antibodies, also resulted in partial responses and secondary effects. Future strategies should prioritize receptor-specific NOTCH inhibitors, patient stratification based on NOTCH pathway activation, and optimized combination regimens. Emerging approaches include integrating immunotherapy with advanced technologies such as CRISPR, CAR-T cells, and bispecific antibodies, as well as targeted delivery systems to enhance efficacy and reduce toxicity. Additional research directions include addressing the tumor microenvironment and EMT-driven resistance, elucidating the mechanisms of immune evasion, and inhibiting tumor angiogenesis. Finally, leveraging artificial intelligence and big-data-driven personalized medicine, including sex-specific considerations, will be essential for improving patient outcomes. Full article