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Search Results (18,514)

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80 pages, 5283 KB  
Review
Monoamine Oxidase Inhibitors in Drug Discovery Against Parkinson’s Disease: An Update
by Luana Vergueiro Ribeiro, Larissa Emika Massuda, Vanessa Silva Gontijo and Claudio Viegas Jr.
Pharmaceuticals 2025, 18(10), 1526; https://doi.org/10.3390/ph18101526 (registering DOI) - 10 Oct 2025
Abstract
Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder with substantial socioeconomic impact, characterized by the gradual loss of dopaminergic neurons, dopamine deficiency, and pathological processes such as neuroinflammation, oxidative stress, and α-synuclein aggregation. Monoamine oxidases (MAOs) are enzymes responsible for the degradation [...] Read more.
Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder with substantial socioeconomic impact, characterized by the gradual loss of dopaminergic neurons, dopamine deficiency, and pathological processes such as neuroinflammation, oxidative stress, and α-synuclein aggregation. Monoamine oxidases (MAOs) are enzymes responsible for the degradation of neuroactive amines, including dopamine, a neurotransmitter essential for motor, cognitive, and behavioral functions. Among these, MAO-B plays a central role in dopamine metabolism, producing reactive metabolites and oxidative species that contribute to the oxidative stress associated with PD pathophysiology. In this context, MAO-B inhibition has emerged as a promising therapeutic strategy. However, specific limitations, such as motor complications linked to prolonged levodopa use and the adverse effects of currently available MAO inhibitors, remain significant clinical challenges. Methods: A comprehensive literature search was conducted using PubMed and SciFinder databases. Keywords such as “MAO inhibitors”, “Parkinson’s pathology,” and “Parkinson’s disease” were combined with Boolean operators (AND, OR, NOT). The search covered publications from 2010 to 2025. Results: While previous reviews, particularly those by the groups of Guglielmi and Alborghetti, mainly emphasized the clinical use of MAO-B inhibitors and advances in patents, the present review identified approximately 300 compounds synthesized and evaluated as MAO inhibitors, encompassing diverse chemical classes. Among them, selective MAO-B inhibitors exhibited the greatest pharmacological potential, reinforcing the relevance of this isoform as a strategic target in PD therapy. Conclusion: These findings highlight the advances of Medicinal Chemistry in the development of novel MAO-B inhibitors, both as monotherapies for early-stage PD and as adjuvants to levodopa in advanced disease. Collectively, they emphasize the promise of MAO-B inhibitors as candidates for more effective therapeutic interventions in Parkinson’s disease. Full article
(This article belongs to the Special Issue Potential Pharmacotherapeutic Targets in Neurodegenerative Diseases)
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34 pages, 910 KB  
Review
The Role of the Built Environment in Achieving Sustainable Development: A Life Cycle Cost Perspective
by Ivona Gudac Hodanić, Hrvoje Krstić, Ivan Marović and Martina Gudac Cvelic
Sustainability 2025, 17(20), 8996; https://doi.org/10.3390/su17208996 - 10 Oct 2025
Abstract
Life cycle cost (LCC) analysis has become a key tool for evaluating the long-term economic and environmental performance of built assets, yet its application in marinas and marine infrastructure remains underdeveloped. This review provides the first structured attempt to apply LCC to marina [...] Read more.
Life cycle cost (LCC) analysis has become a key tool for evaluating the long-term economic and environmental performance of built assets, yet its application in marinas and marine infrastructure remains underdeveloped. This review provides the first structured attempt to apply LCC to marina infrastructure, addressing the lack of sector-specific models for pontoons, mooring systems, and marina operations. It also synthesizes research on LCC methodologies, challenges, and emerging trends relevant to coastal facilities, with a particular focus on pontoons, mooring systems, and marina management practices. Studies reveal persistent barriers to effective implementation, including fragmented data systems, inconsistent regulations, and limited sector-specific tools. Existing models, largely adapted from other construction contexts, often overlook the unique technical, environmental, and operational demands of marine assets. The review critically examines international standards, procurement frameworks, and methodological approaches, highlighting opportunities to integrate sustainability considerations and address gaps in cost forecasting. It also identifies the need for standardized data collection practices and risk-based maintenance strategies tailored to harsh marine environments. By mapping current knowledge and methodological limitations, this work provides a foundation for developing more accurate, sector-specific LCC models and guidance. This literature review contributes to the advancement of sustainable coastal infrastructure planning by consolidating scattered research, emphasizing knowledge gaps, and outlining priorities for future studies, supporting policymakers, practitioners, and researchers seeking to optimize investment decisions in marinas and related facilities. Full article
(This article belongs to the Special Issue Novel Technologies and Digital Design in Smart Construction)
18 pages, 960 KB  
Article
Quality Risk Identification and Fuzzy Comprehensive Assessment of Land Trusteeship Services in China
by Yunlong Sui and Lianghong Yu
Land 2025, 14(10), 2027; https://doi.org/10.3390/land14102027 - 10 Oct 2025
Abstract
The quality risks of land trusteeship services are increasingly prominent, leading to reduced crop yields for farmers and land degradation; however, relevant research remains insufficient. This paper aims to identify and evaluate the quality risk level of land trusteeship services. It comprehensively adopts [...] Read more.
The quality risks of land trusteeship services are increasingly prominent, leading to reduced crop yields for farmers and land degradation; however, relevant research remains insufficient. This paper aims to identify and evaluate the quality risk level of land trusteeship services. It comprehensively adopts a field survey, web crawler technology, and expert consultation methods to identify quality risk types, and then uses the fuzzy comprehensive evaluation method to assess the risk level based on survey data from Chinese farmers. The main conclusions are as follows: (1) Overall, the quality risk level of land trusteeship services is at a relatively high risk level. In terms of spatio-temporal patterns, the quality risk level shows an upward trend, and the quality risk level of mid-production services is increasing at the fastest rate. There are significant variations in service quality risk across prefecture-level cities in the Shandong Province of China. (2) In terms of risk heterogeneity, the quality risk level of small-scale pure farmers is higher than that of part-time farmers and large professional farmers, in that order. The quality risk level of the “farmer + service organization” model is higher than that of the “farmer + intermediary + service organization” model. According to the order of the quality risk level of different crops, the ranking (from highest to lowest) is cash crops, wheat, and corn. (3) The high quality risks of land trusteeship services will impact the multifunctionality of land systems. It exacerbates the land pollution and fertility degradation because of excessive application of chemical inputs like pesticides, fertilizers, and mulch by service organizations. It consequently destroys ecological systems, hinders sustainable agricultural development, and impacts farmers’ income and national food security by reducing yields. The research findings contribute to controlling the quality risks of land trusteeship services and protecting land. Full article
(This article belongs to the Section Land Systems and Global Change)
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27 pages, 3885 KB  
Article
Experimental and Machine Learning-Based Assessment of Fatigue Crack Growth in API X60 Steel Under Hydrogen–Natural Gas Blending Conditions
by Nayem Ahmed, Ramadan Ahmed, Samin Rhythm, Andres Felipe Baena Velasquez and Catalin Teodoriu
Metals 2025, 15(10), 1125; https://doi.org/10.3390/met15101125 - 10 Oct 2025
Abstract
Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior [...] Read more.
Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H2–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H2 and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article
(This article belongs to the Special Issue Failure Analysis and Evaluation of Metallic Materials)
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21 pages, 4298 KB  
Article
Growth and Photosynthetic Responses of Lactuca sativa L. to Different Zinc Fertilizer Sources and Applications
by Marina de-Francisco, Esther Hernández-Montes, Sarah DeSanto, Monica Montoya, Ana Obrador and Patricia Almendros
Horticulturae 2025, 11(10), 1221; https://doi.org/10.3390/horticulturae11101221 - 10 Oct 2025
Abstract
Zinc (Zn) is an essential micronutrient for plant growth, serving as a co-factor in enzymatic processes and pigment biosynthesis. In horticultural crops such as lettuce, Zn fertilization is increasingly relevant for optimizing yield and nutritional quality. In this study, a greenhouse pot experiment [...] Read more.
Zinc (Zn) is an essential micronutrient for plant growth, serving as a co-factor in enzymatic processes and pigment biosynthesis. In horticultural crops such as lettuce, Zn fertilization is increasingly relevant for optimizing yield and nutritional quality. In this study, a greenhouse pot experiment was conducted using Lactuca sativa L. cv. Romana Verano (Ramiro Arnedo) to evaluate the effects of four Zn sources with contrasting physio-chemical properties—ZnSO4, a synthetic chelate containing DTPA, EDTA, and HEDTA, a Zn–lignosulphonate complex, and ZnO nanoparticles—applied to soil at rates of 15, 30, 60, and 120 mg Zn·kg−1. Morphometric traits, photosynthetic pigmentation, and photosystem performance were assessed to determine differences in plant response. Results showed that low to moderate Zn supply (15–60 mg Zn·kg−1) maintained growth, leaf number, stem diameter, and biomass without significant changes compared to the control. In contrast, the highest dose (120 mg Zn·kg−1), particularly in chelated forms, led to reductions in growth and yield exceeding 80%, reflecting supra-optimal effects. Although lignosulphonate and nanoparticles sources lowered soil Zn availability, they did not affect lettuce growth or yield, indicating their potential as safer agricultural alternatives to conventional Zn fertilizers. Photosynthetic efficiency, measured through chlorophyll fluorescence and electron transport activity, was positively modulated by adequate Zn levels but declined at excessive concentrations. These findings highlight that Zn efficiency strongly depends on its chemical form and applied dose, providing practical insights for optimizing Zn fertilization strategies in lettuce and other horticultural crops. Full article
(This article belongs to the Special Issue 10th Anniversary of Horticulturae—Recent Outcomes and Perspectives)
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21 pages, 2777 KB  
Article
Protective Effects of Cuscuta australis Against CCl4-Induced Hepatic Injury in Rats: Antioxidant, Anti-Inflammatory, and In Silico Insights
by Hanen Baccari, Arij Bedoui, Anouar Feriani, Amal Bouallegue, Nihad Sahri, Sohaib Khatib, Mohamed Kharrat, Nizar Tlili, Mansour Sobeh, Moez Amri and Zouhaier Abbes
Pharmaceuticals 2025, 18(10), 1524; https://doi.org/10.3390/ph18101524 - 10 Oct 2025
Abstract
Background/Objectives: The search for new bioactive molecules increasingly extends beyond conventional medicinal plants, highlighting the importance of exploring alternative botanical sources. Parasitic plants represent a promising but underexploited reservoir of pharmacologically relevant compounds. Cuscuta australis (CA), a parasitic species with a history of [...] Read more.
Background/Objectives: The search for new bioactive molecules increasingly extends beyond conventional medicinal plants, highlighting the importance of exploring alternative botanical sources. Parasitic plants represent a promising but underexploited reservoir of pharmacologically relevant compounds. Cuscuta australis (CA), a parasitic species with a history of traditional use, remains poorly characterized. This study aimed to investigate its phytochemical composition and evaluate its antioxidant, anti-inflammatory, and hepatoprotective properties. Methods: The phytochemical profile of CA extract was characterized by LC-MS. Antioxidant capacity was assessed using DPPH and ABTS assays. In vivo hepatoprotection was evaluated in male rats subjected to CCl4-induced hepatotoxicity and treated orally with CA (30 or 60 mg/kg body weight). Biochemical, lipid, oxidative stress, and histological parameters were determined. Molecular docking was conducted to predict the binding of major identified compounds against selected protein targets. Results: CA significantly and dose-dependently improved biochemical and histological markers. At 60 mg/kg, ALT, AST, ALP, and bilirubin were reduced by 32%, 33%, 63%, and 51%, respectively. Lipid metabolism was improved by decreased TC, TG, and LDL-C with increased HDL-C. Antioxidant defense was enhanced through elevated CAT, SOD, and GPx activities, accompanied by reduced MDA levels. TNF-α and IL-6 decreased by 48% and 53%, respectively. Histopathology confirmed hepatoprotection and reduced fibrosis. Docking studies revealed strong binding affinities (−7.07 to −19.20 kcal/mol) for several metabolites, notably quercetin glucoside, diosmetin glucoside, caffeic acid glucoside, feruloylquinic acid, and isorhamnetin glucoside, against CYP450, IL-2, TNF-α, and IL-6. Conclusions: These findings demonstrate that C. australis is a promising source of bioactive compounds with hepatoprotective, antioxidant, antihyperlipidemic, and anti-inflammatory effects, supporting its potential as a natural therapeutic agent. Full article
(This article belongs to the Section Natural Products)
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34 pages, 5466 KB  
Article
Artificial Neural Network-Based Heat Transfer Analysis of Sutterby Magnetohydrodynamic Nanofluid with Microorganism Effects
by Fateh Ali, Mujahid Islam, Farooq Ahmad, Muhammad Usman and Sana Ullah Asif
Magnetochemistry 2025, 11(10), 88; https://doi.org/10.3390/magnetochemistry11100088 - 10 Oct 2025
Abstract
Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics [...] Read more.
Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article
18 pages, 1676 KB  
Article
Comparative Analysis of Different AI Approaches to Stroke Patients’ Gait Analysis
by Izabela Rojek, Emilia Mikołajewska, Olga Małolepsza, Mirosław Kozielski and Dariusz Mikołajewski
Appl. Sci. 2025, 15(20), 10896; https://doi.org/10.3390/app152010896 - 10 Oct 2025
Abstract
Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis [...] Read more.
Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis of different AI approaches used to analyze gait of stroke patients using a retrospective dataset of 120 individuals. The main objective is to evaluate the effectiveness, accuracy, and clinical relevance of machine learning (ML) and deep learning (DL) models in identifying gait abnormalities and predicting rehabilitation outcomes. Multiple AI techniques—including support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), and convolutional neural networks (CNN)—were trained and tested on time-series gait data with spatiotemporal parameters. Performance metrics such as accuracy, precision, recall, and area under the curve (AUC) were used to compare model results. Initial results indicate that DL models, particularly CNNs, outperform traditional ML methods in capturing complex gait patterns and providing reliable classification. However, simpler models showed advantages in interpretability and computational efficiency. This study highlights the potential and shortcomings of AI-based gait analysis tools in supporting clinical decision-making and planning personalized stroke rehabilitation. Full article
(This article belongs to the Special Issue Novel Approaches of Physical Therapy-Based Rehabilitation)
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30 pages, 712 KB  
Review
A Review on Scholarly Publication Recommender Systems: Features, Approaches, Evaluation, and Open Research Directions
by Anita Khadka and Saurav Sthapit
Informatics 2025, 12(4), 108; https://doi.org/10.3390/informatics12040108 - 10 Oct 2025
Abstract
The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and [...] Read more.
The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and lack mechanisms to incorporate full-text content or time-awareness. This review systematically examines the landscape of scholarly publication recommender systems, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology for a comprehensive and transparent selection of relevant studies. We highlight the limitations of current systems and explore the potential of integrating fine-grained citation knowledge—such as citation proximity, context, section, graph, and intention—extracted from full-text documents. These elements have shown promise in enhancing both the contextual relevance and recency of recommendations. Our findings highlight the importance of moving beyond accuracy-focused metrics toward user-centric evaluations that emphasise novelty, diversity, and serendipity. This paper advocates for the development of more holistic and adaptive recommender systems that better align with the evolving needs of researchers. Full article
14 pages, 1619 KB  
Article
Process-Oriented Dual-Layer Knowledge GraphRAG for Reservoir Engineering Decision Support
by Bin Jiang, Zhaonian Liu, Ning Wang, Zhuoyang Li, Yinliang Shi and Botao Lin
Processes 2025, 13(10), 3230; https://doi.org/10.3390/pr13103230 - 10 Oct 2025
Abstract
This study presents a dual-layer GraphRAG framework for petroleum engineering question answering, in which instance-level facts and domain-level concepts are explicitly separated and integrated into retrieval-augmented generation. To evaluate the framework, a benchmark of 40 expert-constructed Q&A pairs was developed, covering factual, definitional, [...] Read more.
This study presents a dual-layer GraphRAG framework for petroleum engineering question answering, in which instance-level facts and domain-level concepts are explicitly separated and integrated into retrieval-augmented generation. To evaluate the framework, a benchmark of 40 expert-constructed Q&A pairs was developed, covering factual, definitional, and explanatory queries derived from a real offshore oilfield dataset. Results show that the dual-layer graph consistently outperforms a single-layer baseline. Answer accuracy improves from 0.65 to 0.70, faithfulness from 0.54 to 0.61, and context relevance from 0.69 to 0.72, confirming that the system retrieves factual parameters more reliably and provides conceptually grounded explanations. Gains in evidence recall and coverage are more modest, highlighting areas for further optimization. A case study illustrates the framework’s ability to expand petroleum terminology (e.g., “sandstone → clastic rock”), producing responses that are not only quantitatively more reliable but also qualitatively more informative. The dual-layer design effectively addresses the semantic consistency gap in petroleum QA, offering practical value for reservoir evaluation, lithology interpretation, and technical decision support. These findings demonstrate the potential of GraphRAG to enhance knowledge management and intelligent services in petroleum engineering. Full article
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21 pages, 9205 KB  
Article
Effect of Different Printing Designs and Resin Types on the Accuracy of Orthodontic Model
by Sabahattin Bor and Fırat Oğuz
Polymers 2025, 17(20), 2724; https://doi.org/10.3390/polym17202724 - 10 Oct 2025
Abstract
This study aimed to evaluate the effect of resin type and printing design on the dimensional accuracy of three dimensional (3D) printed orthodontic models, considering their clinical relevance for applications such as in-house aligner fabrication. Since low-cost Liquid Crystal Display (LCD) printers have [...] Read more.
This study aimed to evaluate the effect of resin type and printing design on the dimensional accuracy of three dimensional (3D) printed orthodontic models, considering their clinical relevance for applications such as in-house aligner fabrication. Since low-cost Liquid Crystal Display (LCD) printers have been increasingly adopted in practice but data on their trueness and precision with different resins and print designs were limited, the study sought to provide evidence-based insights into their reliability. A mandibular model was designed using Blenderfordental (B4D, version 1.1.2024; Dubai, United Arab Emirates) software and fabricated with the Anycubic Photon Mono 7 Pro 14K (Anycubic, Shenzhen, China) LCD printer. The model was printed in vertical orientation using three different print designs at two layer thicknesses (50 µm and 100 µm). Four resins (Elegoo, Anycubic, eSUN, and Phrozen) were used, and each resin was printed with all three designs, yielding 126 models per resin and a total of 504 printed models. Dimensional deviations between the printed and reference models were assessed using root mean square (RMS) values and color-coded deviation maps. Significant differences in trueness were found among resins and print designs at both layer thicknesses (p < 0.001). At a layer thickness of 50 µm, eSUN and Anycubic showed superior trueness, whereas Phrozen exhibited the highest deviations. At a layer thickness of 100 µm, Anycubic, eSUN, and Phrozen generally performed better than Elegoo. Overall, printing at 100 µm yielded better performance than at 50 µm. Precision analysis revealed resin-dependent differences, with eSUN showing significantly higher precision than Elegoo at both layer thicknesses (p = 0.006 at 100 µm, p < 0.001 at 50 µm) and superior precision compared to Phrozen at 50 µm (p = 0.019). Both resin selection and print design significantly affect the dimensional accuracy of 3D-printed dental models. Full article
(This article belongs to the Section Polymer Applications)
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13 pages, 241 KB  
Article
Orofacial Pain and Temporomandibular Disorders Education at Umm Al-Qura University: Perceptions and Curriculum Improvement Recommendations
by Mohammad Hasan Al-Harthy
Dent. J. 2025, 13(10), 465; https://doi.org/10.3390/dj13100465 - 10 Oct 2025
Abstract
Objectives: To evaluate dental students’ and recent graduates’ perceptions of the integration, effectiveness, quality, and clinical relevance of orofacial pain (OFP) and temporomandibular disorders (TMDs) education in the Oral Medicine (OM) course at Umm Al-Qura University’s Faculty of Dental Medicine (UQUDENT), and to [...] Read more.
Objectives: To evaluate dental students’ and recent graduates’ perceptions of the integration, effectiveness, quality, and clinical relevance of orofacial pain (OFP) and temporomandibular disorders (TMDs) education in the Oral Medicine (OM) course at Umm Al-Qura University’s Faculty of Dental Medicine (UQUDENT), and to identify educational gaps and opportunities for curriculum improvement. Methods: This cross-sectional study was conducted using a self-administered online questionnaire distributed via Google Forms to 117 participants, including fourth- to sixth-year students, interns, and recent (2022–2024) graduates. Respondents provided demographic information and assessed the effectiveness (10 items), quality (4 items), and value/relevance (4 items) of the OM course using a 5-point Likert scale. Results: Respondents provided moderate ratings for course effectiveness (mean = 35.2/50) and quality (mean = 13.5/20), and rated OFP/TMD content as having high clinical value (mean = 16.1/20). They had limited confidence in OFP/TMD diagnosis (mean = 3.09/5) and management (mean = 3.19/5). More than 80% believed the curriculum should include more OFP/TMD content. No significant differences were observed by gender, sector, study/work area, clinical exposure (all p > 0.05). Conclusions: Students recognize the importance of OFP/TMD education, but the current curriculum may be insufficiently structured to build competence. Improvement of curricular depth, teaching methods, and clinical exposure is recommended. Full article
(This article belongs to the Section Dental Education)
24 pages, 815 KB  
Systematic Review
Driving Performance in Schizophrenia: The Role of Neurocognitive Correlates—A Systematic Review
by Georgia Karakitsiou, Spyridon Plakias, Aikaterini Arvaniti, Magdalini Katsikidou, Katerina Kedraka and Maria Samakouri
Brain Sci. 2025, 15(10), 1094; https://doi.org/10.3390/brainsci15101094 - 10 Oct 2025
Abstract
Background/Objectives: Schizophrenia is associated with cognitive deficits that may compromise everyday functioning, including driving. This review systematically examined recent original research (2015–2025) on driving performance in individuals with schizophrenia with a focus on neuropsychological factors, applying a narrative synthesis given the heterogeneity of [...] Read more.
Background/Objectives: Schizophrenia is associated with cognitive deficits that may compromise everyday functioning, including driving. This review systematically examined recent original research (2015–2025) on driving performance in individuals with schizophrenia with a focus on neuropsychological factors, applying a narrative synthesis given the heterogeneity of designs and outcomes, while no quantitative meta-analysis was feasible. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a structured search of PubMed and Scopus was conducted on 4 May 2025. The inclusion criteria were original studies involving individuals diagnosed with schizophrenia, published between 2015 and 2025. Studies on animals, other psychiatric or neurological conditions, and healthy populations were also excluded. Critical appraisal was performed using the Joanna Briggs Institute (JBI) tools. Extracted data included sample demographics, cognitive deficits, neuropsychological assessments, brain imaging, and the main findings. A narrative synthesis was then performed. Results: Six high-quality studies met the inclusion criteria. Findings were grouped into three categories: (1) driving behavior: fitness to drive varied widely across individuals, (2) cognitive deficits and brain activity: poorer driving-related performance was consistently associated with specific impairments in cognition and brain structure, and (3) medication effects: individuals taking certain atypical antipsychotics demonstrated better driving performance compared to those on other types of medication, while extrapyramidal symptoms negatively influenced driving fitness. Conclusions: Driving in schizophrenia is shaped by cognitive, clinical, and pharmacological factors. These findings highlight the clinical relevance of individualized evaluations, integration into personalized care and targeted rehabilitation to promote driving autonomy and community inclusion. This area remains under-researched, as only six studies met the inclusion criteria, which restricts the robustness and generalizability of the conclusions. Funding: This review received no funding from any external sources. Registration: The review protocol was submitted to PROSPERO (International Prospective Register of Systematic Reviews) under registration number CRD420251060580. Full article
20 pages, 5463 KB  
Article
From TNM 8 to TNM 9: Stage Migration and Histology-Specific Patterns in Lung Cancer
by Amalia Constantinescu, Radu-Nicolae Căprariu, Emil-Robert Stoicescu, Roxana Iacob, Marius Mânzatu, Janet Camelia Drimus, Alessia-Stephania Roșian, Alexandre Ionescu, Cristian Oancea and Diana Manolescu
Cancers 2025, 17(20), 3290; https://doi.org/10.3390/cancers17203290 (registering DOI) - 10 Oct 2025
Abstract
Introduction: The 9th edition of the TNM classification for lung cancer implemented significant revisions, notably the subdivision of the N2 and M1c categories, to enhance anatomical precision and prognostic accuracy. Nonetheless, the actual effects of these modifications on stage distribution, histology-specific patterns, and [...] Read more.
Introduction: The 9th edition of the TNM classification for lung cancer implemented significant revisions, notably the subdivision of the N2 and M1c categories, to enhance anatomical precision and prognostic accuracy. Nonetheless, the actual effects of these modifications on stage distribution, histology-specific patterns, and clinical interpretation remain to be fully evaluated. Objectives: To compare lung cancer staging distributions between the 8th and 9th TNM editions, analyze patterns of stage migration, and evaluate histology-specific reclassification trends. Although TNM 9 applies the same descriptors across all histological subtypes, the magnitude of stage migration varies. In our cohort and in international datasets, adenocarcinoma demonstrated a higher likelihood of reclassification into advanced stages compared to other subtypes. Methods: A retrospective analysis was performed on a cohort of lung cancer patients staged according to the 8th and 9th editions of the TNM classification. Stage distribution alterations were analyzed by chi-squared tests, whereas McNemar’s test examined the directional shifts in upstaging and downstaging. Further investigations evaluated the correlation between histological subtype and stage reclassification. Results: A statistically significant redistribution of stages was noted (χ2 = 1013.03, df = 64, p < 0.0001), with a notable prevalence of upstaging (p = 0.0019). The most significant proportional increase was observed in stage IIIA, mostly attributable to the N2 subdivision (N2a vs. N2b). Adenocarcinoma was the predominant histological subtype at all stages and showed a greater tendency for reclassification into advanced stages, specifically IIIA and IIIB. Squamous cell carcinoma was predominantly observed in stages IIB and IIIA, whereas small cell and large cell carcinomas were concentrated in advanced stages. These histology-specific patterns correspond with international findings, including research confirming the prognostic relevance of N2 subdivision. Conclusions: The 9th edition of the TNM classification results in significant stage migration, particularly in adenocarcinoma cases, indicating the improved sensitivity of the updated criteria in identifying advanced nodal disease. These modifications significantly impact prognostic evaluation and global comparability of clinical cohorts, supporting the implementation of TNM 9 as a more anatomically and biologically relevant staging system. Full article
(This article belongs to the Section Methods and Technologies Development)
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Article
The Impact of Product Environmental Innovation in Process Industries: Evidence from Innovation Efficiency and Performance
by Yeongjun Kim, Jiyun Seong and Changhee Kim
Processes 2025, 13(10), 3227; https://doi.org/10.3390/pr13103227 - 10 Oct 2025
Abstract
This study examines the heterogeneous effects of product environmental innovation on firm-level innovation efficiency and performance in process industries, with a focus on the chemical and electronics sectors. Following the Organisation for Economic Co-operation and Development (OECD)’s Oslo Manual, four types of product [...] Read more.
This study examines the heterogeneous effects of product environmental innovation on firm-level innovation efficiency and performance in process industries, with a focus on the chemical and electronics sectors. Following the Organisation for Economic Co-operation and Development (OECD)’s Oslo Manual, four types of product environmental innovation are considered: reducing energy use and emissions (RUE), reducing pollution (RP), promoting recycling (PR), and enhancing durability and extending product life (EDEL). Innovation efficiency is evaluated using the input-oriented Banker–Charnes–Cooper (BCC) Data Envelopment Analysis (DEA) model, and regression analyses are applied to test the effects of each innovation type on efficiency and sales outcomes. The results reveal that RUE and EDEL consistently enhance both efficiency and performance, whereas PR has a negative impact on performance, and RP shows no significant effect. These findings demonstrate that product environmental innovation is not a homogeneous construct but yields heterogeneous outcomes depending on type and industry context. The study contributes to the literature by jointly examining efficiency and performance outcomes and by overcoming the limitations of single-metric evaluations, and it provides practical implications by clarifying which innovation types deliver immediate value in business-to-consumer (B2C) markets and which are more relevant for business-to-business (B2B) settings. Full article
(This article belongs to the Special Issue Innovation and Optimization of Production Processes in Industry 4.0)
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