Search Results (17,587)

Background/Objectives: This review and meta-analysis assessed whether combining transarterial chemoembolization (TACE) with iodine-125 brachytherapy (I-125 brachytherapy) offers greater efficacy and safety than TACE alone in treating hepatocellular carcinoma (HCC). Methods: PubMed, EMBASE, the Cochrane Library, Scopus, and Web of Science were searched for articles published between 1 January 2010 and 30 November 2023. Eligible studies compared TACE with and without I-125 brachytherapy from randomized controlled trials (RCTs) and non-randomized comparative studies published in English. The primary outcome was overall survival (OS) at 1, 2, and 3 years. The secondary outcomes included progression-free survival (PFS), overall response rate (ORR), disease control rate (DCR), and adverse events. ROB-2 and ROBINS-I tools were used to assess study quality. Results: Eighteen studies (n = 1872 patients) were included. All 18 studies originated from China, with the majority being retrospective cohorts (n = 16), one non-randomized prospective study, and one RCT. Compared with TACE alone, TACE + I-125 brachytherapy significantly improved OS at 1 year (OR = 3.64, 95% CI: 2.92–4.55), 2 years (OR = 3.93, 95% CI: 2.29–6.77), and 3 years (OR = 4.12, 95% CI: 2.24–7.56). The tumor response rates, including the ORR and DCR, were also significantly higher in the combination group. Subgroup analysis revealed that the survival benefit was maintained in studies without systemic chemotherapy (OR = 3.68, 95% CI: 2.89–4.70) and in studies with systemic chemotherapy (OR = 4.13, 95% CI: 1.69–10.09). Although larger effect estimates were observed with low-dose I-125 brachytherapy (<80 Gy; OR = 8.55, 95% CI: 4.32–16.92) compared to high-dose (≥100 Gy; OR = 2.87, 95% CI: 2.05–4.00), this finding is hypothesis-generating rather than conclusive and should be interpreted cautiously as it is based on only three studies. Adverse event rates were comparable between groups. GRADE assessment indicated low to very low certainty for all outcomes, primarily due to the retrospective nature of most included studies. Conclusions: TACE combined with I-125 brachytherapy was associated with improved survival and tumor response without a statistically significant increase in adverse events. High-quality, multicenter RCTs are warranted to confirm these results. Full article

In the context of advancing manufacturing, production systems are shifting toward human-centric and personalized production. However, accurately quantifying subjective user needs into precise product specifications remains a challenge. Taking child companion robots as an example, this paper proposed a novel product innovation design framework based on Extenics and Kansei engineering to optimize the texture design of smart products. By systematically integrating synergistic relationships among colour, material, and surface processing technology, the framework aimed to enhance the sustainable value and social sustainability of products by more precisely meeting users’ perceptual and emotional needs. The research methodology employed the semantic differential method to quantify user perception and utilized the K-means clustering algorithm to construct a chromatic colour sample library for smart products. Subsequently, by combining the multi-criteria decision-making tool grey relational analysis with statistical verification, the optimal design scheme was selected from the generated alternatives. Experimental results demonstrated that this method significantly reduced design subjectivity and ambiguity. By bridging the gap between user expectations and engineering solutions, the framework provides a systematic solution for mass customization and process optimization that promotes resource efficient and sustainable production, while also reducing the resource waste associated with traditional trial and error design processes. Full article

Background: Preventing diabetic foot ulcer (DFU) recurrence after healing is a major challenge in the remission phase. In this context, remission is not synonymous with healed; it refers to a confirmed post-healing state in which the ulcer is closed, but the individual remains at high risk of recurrence and requires ongoing preventive care. Armstrong, Boulton, and Bus suggested that DFU recurrence is about 40% at 1 year, 60% at 3 years, and 65% at 5 years and argued that limb preservation should follow a long-term “survivorship” model similar to cancer care. However, these estimates combine heterogeneous follow-up intervals and definitions, and there is limited work focusing specifically on the first 12 months after confirmed remission. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Searches of PubMed/MEDLINE, Scopus, ScienceDirect, and the Cochrane Library were performed on 16 December 2025. Eligible studies enrolled adults with diabetes in confirmed remission after a healed DFU and reported an exact 12-month recurrence outcome (n/N or Kaplan–Meier estimate). Risk of bias was assessed using the Critical Appraisal Skills Program and Joanna Briggs Institute tools, and certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Twelve-month recurrence proportions were pooled using a random-effects model on the logit scale, and results were interpreted cautiously due to the limited number of eligible cohorts. Results: Across three cohorts with confirmed remission at baseline (total n = 469) and an exact 12-month outcome, the pooled 12-month recurrence proportion was 29.3% (random-effects; 95% CI 24.9–34.1), i.e., about one in three. Although this estimate is lower than the widely cited ~40% 1-year recurrence benchmark, it reflects a strictly defined remission population and a fixed 12-month timepoint, rather than mixed follow-up intervals or less precise definitions. Conclusions: Approximately one in three adults in remission after a healed DFU develop a recurrent ulcer within 12 months. Because this estimate is based on a small number of cohorts and on strictly confirmed remission, it should be interpreted cautiously and should not be generalized to all individuals with a healed DFU. These findings support prevention-focused surveillance and ongoing risk management during remission. Larger, preregistered multicenter cohorts with standardized remission and recurrence definitions are needed to refine short-term recurrence estimates and inform survivorship-style models of care. Full article

Predictive Digital soil mapping (PDSM) in Eastern Amazonia faces challenges due to its environmental complexity, difficult access, and scarce legacy data. While legacy soil maps contain valuable tacit knowledge, updating them requires methods that can handle uncertainty. This study evaluates the integration of old soil maps with machine learning to update soil information in Tracuateua, Pará, with a specific focus on the performance of ensemble learning and the explicit incorporation of uncertainty metrics in soil mapping units under hydromorphic influence, which, in addition to being difficult to access, are influenced by complex pedogenetic processes. We combined 270 sampling points, equivalent to the total pixels that captured the variability of soil mapping units, with environmental covariates and historical data. Several algorithms were tested, including an ensemble approach, to predict mapping units and quantify uncertainty through entropy and confusion indices. The ensemble model demonstrated improved stability and reduced classification uncertainty compared to single models, particularly in challenging hydromorphic environments. Although accuracy gains were modest, the models captured soil–environment relationships, with climate (Tmean_22k), topographic (CNBL and altitude) and organism variables (LST) emerging as the main predictors. Spatialized uncertainty estimates, expressed through entropy and the confusion index, provide a practical decision-support tool for guiding field surveys and identifying areas of low mapping reliability. By explicitly transferring the pedologist’s mental model—encoded as tacit knowledge in legacy soil maps—into ensemble learning, this study presents a robust and transferable framework for updating soil maps in data-scarce tropical regions, balancing predictive performance, spatial consistency, and uncertainty-aware interpretation. Full article

Quantitative polymerase chain reaction (qPCR) is a widespread method to quantify RNA or DNA. The results are reported as cycle of quantification (Cq), scaled to absolute numbers of copies or relative to reference genes. The reported Cq values of the same reaction vary between different machines and cannot be compared between different laboratories. This study shows that the third derivative zero (TD0) method is machine independent and more reproducible than the classic Cq calculations. Together with the mean PCR efficiency it allows the calculation of the number of copies initially present (Ncopy), a parameter easy to interpret. A large dataset was created for the evaluation of this method including amplicons with different length, primer concentrations, reaction mixes, and fluorescence reporter systems. Furthermore, the calculated Ncopy values can be corrected at the same time using known concentrations of a standard and for the expression of reference genes and combining absolute and relative quantification. The algorithms were implemented in the open-source program RDML-Tools, which can perform all steps of a qPCR analysis using the raw fluorescence amplification data and is available on the internet. We conclude that qPCR analysis today should widen its focus and include the three essential parameters, TD0, mean PCR efficiency and Ncopy. Full article

Almost all publications in biomedical literature have employed statistical tests, with p-values being considered of particular importance in the assessment of the presence of a link between two variables. However, these tests and p-values have been the subject of considerable criticism. It may appear paradoxical that tools utilised by the scientific community for nearly a century could possess all the flaws attributed to them. This paradox can partially be explained by the counterintuitive nature of p-values and the fact that the test that generates them is the result of a combination of two tests that were developed to answer statistical questions of a very different nature. The respective characteristics of these two tests are essentially unknown to the majority of users of p-values. The aforementioned paradox can be partially explained by the paucity of publications that seek to elucidate these concepts for users of p-values, the majority of whom are not statisticians. The recently introduced Bayesian methods have properties that enable us to understand the limitations of traditional methods. In Bayesian methods, the use of a specific interpretation of probability allows for better exploitation of clinical research data. The aim of this article is to highlight the limits of non-Bayesian methods and explain the principles and functioning of Bayesian methods to a non-statistical audience. Full article

Horses presenting with temporomandibular joint (TMJ) dysfunctions are often clinically evaluated for TMJ osteoarthritis (OA). Due to the unique characteristic of TMJ-related pain, the clinical diagnosis of equine TMJ OA is challenging; however, it may be supported by computer-aided tools incorporating biomarker data. This study aims to evaluate a machine learning-based approach to address a binary classification distinguishing healthy TMJs from TMJ OA. Among 50 equine cadaver heads, 82 TMJs were included and annotated as healthy or OA based on histological and computed tomography (CT) findings. For each TMJ, nine CT findings were assessed, and synovial fluid was collected for the evaluation of twelve biomarkers. Using a biomarker dataset, correlations among biomarkers were calculated and supported with a mixed-effects logistic regression model. Using a combined dataset, twelve machine learning models, incorporating two feature selection methods and six classification algorithms, were evaluated. Specific biomarker levels showed predominately positive correlations with TMJ OA, age, and with each other; however, only age had a significant effect on OA assignment in the mixed model. The best-performing machine learning model achieved an accuracy of 0.82 and an area under the curve (AUC) of 0.85 for binary TMJ classification. The proposed classification model outperforms conventional diagnostic methods and may therefore be considered beneficial in aiding the diagnosis of equine TMJ OA. Full article

Although greenhouse vegetable production is rapidly shifting toward innovative soilless systems, soil-based conventional cultivation still dominates globally. This production system faces growing pressure to transition to sustainable practices. However, introducing biofertilisers into intensive systems often yields inconsistent results. Specifically, their effects on different lettuce traits vary due to complex relationships between genotype, biofertiliser, environmental conditions, and market demands. Single-parameter evaluations fail to balance conflicting criteria, necessitating multi-criteria decision-making (MCDM) methods for selecting optimal choices. This study aims to overcome these inconsistencies through an integrated fuzzy MCDM-based optimisation model. Three lettuce cultivars (‘Carmesi’, ‘Aquino’, and ‘Gaugin’) were grown in an unheated Surčin (Serbia) greenhouse during a 58-day autumn experiment using a complete block design. Four treatments were applied: a control (without fertilisation), effective microorganisms, a Trichoderma-based fertiliser, and their combination. Biofertilisers were applied before transplanting and four times foliarly during the vegetation period via battery sprayer. This defined 12 production models (cultivar–fertiliser pairs), evaluated across 10 criteria: agronomic (core ratio, number of leaves), quality (nitrate content, total antioxidant capacity, total soluble solids, and chlorogenic acid), sensory (overall taste, overall quality), and economic (total variable costs, total income). Four decision-making experts from the Faculty of Agriculture and the ready-to-eat salad industry assessed weighting coefficients using the fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used to rank the alternatives. To confirm the stability of the obtained ranking with the fuzzy MARCOS method, we performed sensitivity analysis through 20 different scenarios. Applied fuzzy methods identified alternative A11—‘Aquino’ cultivar with combined biofertilisers—as the best-ranked option, followed by A6 and A7. This study validates fuzzy PIPRECIA and fuzzy MARCOS as effective tools for optimising lettuce production models. They support farmers in selecting the most favourable solution based on multiple criteria, aiding the shift from mineral fertilisers to sustainable biofertiliser-based systems in intensive production—especially helpful for producers making this transition. Full article

Sustainability literacy is increasingly invoked in construction and planning research, yet it is most often framed as an educational construct concerned with awareness, knowledge, and attitudes. This framing provides limited explanatory power for understanding how sustainability values are translated into in real-world planning decisions, particularly under conditions of uncertainty and value conflict. In parallel, artificial intelligence (AI) has been introduced into planning practice largely as an optimization-driven analytical tool, reinforcing instrumental conceptions of rationality. This study reconceptualizes sustainability literacy as a decision capability and develops an AI-enabled theoretical framework that positions AI as a cognitive partner in sustainability-oriented construction planning. Methodologically, the study adopts a conceptual research design grounded in a systematic interdisciplinary literature synthesis spanning planning theory, circular economy, social sustainability, and AI-enabled decision support, combined with theory-building and framework development procedures. The proposed framework clarifies how human judgment can be cognitively augmented through AI-supported interpretation, trade-off exploration, and value-informed deliberation, thereby reframing sustainability as an internal driver of planning judgment rather than an external performance criterion. By conceptualizing human–AI collaboration as an iterative, reflective process, the study establishes a coherent theoretical basis for context-sensitive sustainability planning in the built environment, with implications for decision-support system design, planning practice, and professional education. Full article

Systems thinking is a core competence in logistics management, as decisions across transportation, warehousing, and delivery functions are highly interconnected and often generate delayed, trade-off, or system-wide consequences. Despite the increasing integration of generative artificial intelligence (GenAI) tools into logistics education, limited research has examined how to enhance systems thinking in such contexts. Drawing on triadic reciprocal determinism, this study conceptualizes systems thinking enhancement as an emergent outcome of interactions among behavioral regulation, cognitive conditions, and environmental scaffolding. Using survey data from 236 logistics management students in Chinese universities, we integrate Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine both net effects and configurational mechanisms. Results show that self-regulated learning exhibits the strongest positive association with systems thinking, while germane cognitive load is positively associated and extraneous cognitive load is negatively associated with systems thinking. Teacher GenAI scaffolding is linked to more favorable cognitive load allocation. fsQCA findings further reveal that high-level systems thinking emerges from specific combinations where self-regulated learning and germane cognitive load are fundamental conditions, whereas the absence of self-regulated learning consistently leads to low-level systems thinking. These findings provide guidance for the design of GenAI-supported curricula and scaffolding strategies. Full article

Introduction: Apert syndrome is a rare genetic disorder characterized by craniofacial anomalies and limb malformations, often accompanied by neurodevelopmental abnormalities that can considerably affect motor development. Aim: The aim of this study was to document the progress in motor development of a girl with Apert syndrome, with an emphasis on assessing functional needs and evaluating the effects of a multidisciplinary rehabilitation approach. Materials and Methods: Motor functions were evaluated using the Gross Motor Function Measure (GMFM-88) at 16 and 24 months of age. Rehabilitation consisted of an intensive physiotherapy program, Dynamic Movement Intervention (DMI), delivered in monthly cycles over eight months. The therapeutic approach focused on developing postural control, transitional positions, and functional mobility while stimulating sensorimotor integration and neuroplasticity. Results: The initial GMFM score was 29.00%, and the final assessment score reached 68.68%, representing a relative improvement of 136.83%. The most considerable progress was observed in sitting, crawling, and kneeling, with initial improvements in standing. Despite the limitations of this study, the results suggest a positive effect of early, intensive, and individualized rehabilitation combined with active family involvement. Conclusions: The outcomes highlight the importance of early assessment, continuous monitoring of motor development, and a multidisciplinary rehabilitation approach in children with Apert syndrome, with the GMFM serving as a valuable tool for evaluating gross motor function. Full article

The Guanfang tungsten deposit in the Bozhushan ore district, southeastern Yunnan, is genetically linked to Late Yanshanian granitic intrusions. To elucidate the petrogenesis and mineralization potential of the causative granite, this study presents a detailed mineral chemical analysis of biotite from the Guanfang pluton using electron probe microanalysis (EPMA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The biotite crystals exhibit relatively high euhedrality, show no obvious alteration features, and are chemically characterized by reduced Na and Ca contents. These features, along with petrographic observations, confirm its origin as primary magmatic biotite. Crystallization conditions, calculated from biotite chemistry, indicate temperatures of 700–720 °C and pressures of 1.22–1.73 kbar, corresponding to a mesozonal emplacement depth of 4.6–6.5 km. Oxygen fugacity estimates, plotting near the Ni-NiO buffer, reveal an oxidized magmatic environment. Geochemical discrimination diagrams suggest the Guanfang granite exhibits transitional features between S-type and I-type affinities and is classified as a syn-melting (high-temperature) type. The biotite contains relatively low F (0.71–0.97 wt%), but elevated Cl (0.13–0.20 wt%) and Sn (43–56 µg/g) contents. This specific geochemical signature—combined with the medium- to high-temperature crystallization setting—is highly favorable for W-Sn mineralization. Furthermore, the high-Ti, syn-melting character of the granite implies additional potential for Cu-Pb-Zn-Au-Ag polymetallic mineralization. This study employs biotite chemistry to assess the petrogenesis and metallogenic potential of the Guanfang granite. The subsequent discovery of industrial ore bodies corresponding to some of the elements identified as having metallogenic potential confirms the feasibility of this approach. Accordingly, this method provides a new tool for future exploration in the Bozhushan district. Full article

The southern Junggar Basin in Xinjiang is rich in coalbed methane (CBM) resources. Large-scale development is underway in the Sigong River block (SGR block) of the Fukang West Block. Based on an integrated analysis of geological and hydrogeochemical characteristics, this study clarifies the key factors affecting CBM well productivity in the SGR block. Based on gas and water production performance, four distinct productivity types of CBM wells are identified, which are jointly controlled by burial depth, local structural and hydraulic disturbance, and also governed by synergistic interplay between gas content and permeability. The optimal geological combination—comprising the 700–1000 m burial depth, syncline core structure, stagnant hydrodynamic conditions, relatively high gas content, and favorable permeability—collectively contributes to the high-productivity Type I wells with low water production. In contrast, deep coal seams (>1400 m), characterized by reduced gas content and extremely low permeability, correspond to Type IV wells, which exhibit low gas and water production. Type II wells, located in the 1000–1400 m interval, exhibit moderate and variable productivity controlled by the interplay between high gas content and a wide range of permeability. Shallow margins (<700 m) affected by coal combustion and surface water influx produce high-water and low-gas wells (Type III). Geochemical signatures effectively differentiate between these types: closed, stagnant environments (Types I/II) are marked by a Na-Cl-HCO₃/Na-HCO₃-Cl water type, moderate total dissolved solids, and low sodium chloride coefficients, while open hydrodynamic conditions (Type III) are indicated by Na-SO₄-HCO₃ water with high sodium chloride coefficients. A δD-H₂O/δ¹⁸O-H₂O ratio of 7–9, combined with favorable TDS and water type, is identified as a key indicator of high productivity. Based on these relationships, a productivity response index model incorporating critical geological and geochemical parameters was developed. This model provides a practical tool for predicting CBM well performance and targeting sweet spots, offering significant value for exploring geologically and hydrologically complex basins. Full article

Vaccines worldwide reduce severe coronavirus disease 2019 (COVID-19) consequences; however, viral evolution escapes immunity, raising global concerns about vaccine protection and requiring monitoring. Bioinformatics is crucial for studying vaccine escape, speeding up variant detection, mapping antibody evasion epitopes and ensuring updated vaccines and public health responses. This study combines bibliometric analysis of the Scopus literature (n = 416) on SARS-CoV-2 immune evasion using bioinformatics tools with descriptive analysis of the top ten most highly cited original articles. Our results showed the United States (USA) as the dominant contributor, leading in publication output, citation impact and collaboration networks. The key themes identified were immune evasion, spike protein mutations, and viral evolution, highlighting the structural, functional and immune evasion mechanisms of spike mutations. Leading authors and journals reveal a globally connected research community that is making advances in our understanding of SARS-CoV-2 vaccine evasion, and supporting the development of future treatments and vaccines. The top ten articles showed molecular docking, dynamics simulations, and protein modeling as crucial to studying vaccine escape. In conclusion, global research led mainly by the USA and supported by active contributions has used bioinformatics to elucidate SARS-CoV-2 immune evasion, guiding variant future vaccine and treatment development, variant monitoring, and preparedness for emerging variants. Full article

To address the problems of insufficient light energy utilization and light leakage in existing concentrator lighting systems, this paper proposes a novel Stepped Integrated No-Leakage Concentrator Lighting System. This system adopts a design that combines a concentrator module array with a stepped light guide plate. By constructing a stepped integrated concentrator structure and a composite parabolic coupling configuration, the system enables efficient solar energy collection and delivery, significantly improving concentration efficiency and energy utilization. First, based on the principles of geometric optics, theoretical modeling of the concentrator modules and light guide plate was conducted. The relationships among the paraboloid coefficient, step height of the light guide plate, and the number of concentrator modules were analyzed to clarify their influence on the geometric concentration ratio and concentration efficiency of the system. Subsequently, optical performance simulations under varying structural parameters were performed using a joint simulation platform based on SolidWorks Premium 2024 SP5.0 and LightTools(64) 8.6.0 Copyright (c) 1994-2018 Synopsys, Inc. The results indicate that the proposed structure achieves excellent light-guiding performance and high optical efficiency, with a maximum concentration efficiency of 94% and a geometric concentration ratio of 50. On this basis, a physical prototype was fabricated, and experimental testing was carried out. The results validated the accuracy of the simulation, with the system reaching a concentration efficiency of 54.6% at noon, further confirming the feasibility and superior performance of the proposed design. This study demonstrates that the Stepped Integrated No-Leakage Concentrator Lighting System offers significant advantages in enhancing light energy utilization and reducing leakage losses, providing an efficient solution for natural daylighting and interior illumination in green buildings. Full article