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Recent Articles  

24 pages, 1600 KB  
Review
AI-Enabled Reduction of Animal Use in Cardiovascular Translational Medicine: Regulatory and Technological Perspectives
by Rasit Dinc and Nurittin Ardic
Life 2025, 15(12), 1916; https://doi.org/10.3390/life15121916 (registering DOI) - 14 Dec 2025
Abstract
Background: Animal studies remain fundamental to cardiovascular drug and device development, yet their ability to predict human responses is increasingly being questioned. The US Food and Drug Administration (FDA)’s April 2025 roadmap supports alternative testing approaches that strategically reduce animal use while increasing [...] Read more.
Background: Animal studies remain fundamental to cardiovascular drug and device development, yet their ability to predict human responses is increasingly being questioned. The US Food and Drug Administration (FDA)’s April 2025 roadmap supports alternative testing approaches that strategically reduce animal use while increasing human relevance by combining laboratory methods, computer simulations, and artificial intelligence. This review examines AI-enabled alternative methodologies for cardiovascular safety assessment within established validation frameworks and regulatory acceptance programs. We describe machine learning approaches for predicting cardiac safety risks, automated analysis of human heart cells, and patient-specific computer simulations for evaluating medical devices. These tools can improve our understanding of biological mechanisms, focus limited animal studies on critical questions, and accelerate decision-making. Regulatory acceptance requires rigorous validation appropriate to each specific use and decision context. Conclusion: We outline practical steps for establishing credibility, including transparent data documentation, independent testing, and identifying where models can be reliably applied, and identify remaining challenges in data standardization and regulatory readiness. With ongoing alignment between regulators, standards bodies, and product developers, these alternative approaches could significantly reduce reliance on animal testing in cardiovascular research while maintaining or improving the quality of evidence. Full article
(This article belongs to the Topic Animal Models of Human Disease 3.0)
21 pages, 6537 KB  
Article
In Silico Lead Identification of Staphylococcus aureus LtaS Inhibitors: A High-Throughput Computational Pipeline Towards Prototype Development
by Abdulaziz H. Al Khzem, Tagyedeen H. Shoaib, Rua M. Mukhtar, Mansour S. Alturki, Mohamed S. Gomaa, Dania Hussein, Ahmed Mostafa, Layla A. Alrumaihi, Fatimah A. Alansari and Maisem Laabei
Int. J. Mol. Sci. 2025, 26(24), 12038; https://doi.org/10.3390/ijms262412038 (registering DOI) - 14 Dec 2025
Abstract
The emergence of multidrug-resistant Staphylococcus aureus underscores the urgent need for novel therapeutic agents targeting essential bacterial pathways. The lipoteichoic acid synthase (LtaS) is crucial for the synthesis of lipoteichoic acid in the cell wall of Gram-positive bacteria and represents a promising and [...] Read more.
The emergence of multidrug-resistant Staphylococcus aureus underscores the urgent need for novel therapeutic agents targeting essential bacterial pathways. The lipoteichoic acid synthase (LtaS) is crucial for the synthesis of lipoteichoic acid in the cell wall of Gram-positive bacteria and represents a promising and vulnerable target for antimicrobial drug development. This study employed a comprehensive computational pipeline to identify potent inhibitors of the LtaS enzyme. A library of natural compounds was retrieved from the COCONUT database and screened against the crystal structure of the extracellular domain of LtaS (eLtaS) (PDB ID: 2W5R, obtained from the Protein Data Bank) through a multi-stage molecular docking strategy. This process started with High-Throughput Virtual Screening (HTVS), followed by Standard Precision (SP) docking, and culminated in Extra Precision (XP) docking to refine the selection of hits. The top-ranking compounds from XP docking were subsequently subjected to MM-GBSA binding free energy calculations for further filtration. The stability and dynamic behavior of the resulting candidate complexes were then evaluated using 100 ns molecular dynamics (MD) simulations, which confirmed the structural integrity and binding stability of the ligands. Density Functional Theory calculations revealed that screened ligands exhibit improved electronic stabilization and charge-transfer characteristics compared to a reference compound, suggesting enhanced reactivity and stability relevant for hit identification. Finally, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling was conducted to assess the drug-likeness and pharmacokinetic safety of the lead compounds. These findings support them as promising orally active leads for further optimization. Our integrated approach shortlisted eight initial hits (A–H) that showed interesting scaffold diversity and finally identified two compounds, herein referred to as Compound A and Compound B, which demonstrated stable binding, favorable free energy, and an acceptable Absorption, Distribution, Metabolism, and Excretion, and Toxicity (ADMET) profile. These candidates emerge as promising starting points for developing novel anti-staphylococcal agents targeting the LtaS enzyme that cand be further proved by experimental validation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computer-Aided Design: Molecular Simulation Technology in Exploring Bioactive Compounds for Health Promotion)
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48 pages, 1173 KB  
Review
Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides
by Naveed Saleem, Naresh Kumar, Emad El-Omar, Mark Willcox and Xiao-Tao Jiang
Antibiotics 2025, 14(12), 1263; https://doi.org/10.3390/antibiotics14121263 (registering DOI) - 14 Dec 2025
Abstract
Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent [...] Read more.
Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent need for novel antimicrobial therapeutic strategies. Antimicrobial peptides (AMPs) function through diverse, often membrane-disrupting mechanisms that can address the latest challenges to resistance. However, the identification, prediction, and optimization of novel AMPs can be impeded by several issues, including extensive sequence spaces, context-dependent activity, and the higher costs associated with wet laboratory screenings. Recent developments in artificial intelligence (AI) have enabled large-scale mining of genomes, metagenomes, and quantitative species-resolved activity prediction, i.e., MIC, and de novo AMPs designed with integrated stability and toxicity filters. The current review has synthesized and highlighted progress across different discriminative models, such as classical machine learning and deep learning models and transformer embeddings, alongside graphs and geometric encoders, structure-guided and multi-modal hybrid learning approaches, closed-loop generative methods, and large language models (LLMs) predicted frameworks. This review compares models’ benchmark performances, highlighting AI-predicted novel hybrid approaches for designing AMPs, validated by in vitro and in vivo methods against clinical and resistant pathogens to increase overall experimental hit rates. Based on observations, multimodal paradigm strategies are proposed, focusing on identification, prediction, and characterization, followed by design frameworks, linking active-learning lab cycles, mechanistic interpretability, curated data resources, and uncertainty estimation. Therefore, for reproducible benchmarks and interoperable data, collaborative computational and wet lab experimental validations must be required to accelerate AI-driven novel AMP discovery to combat multidrug-resistant Gram-negative pathogens. Full article
(This article belongs to the Special Issue Novel Approaches to Prevent and Combat Antimicrobial Resistance)
23 pages, 6247 KB  
Article
Biochar Particle Size Modulates the Microbial Degradation of Petroleum Hydrocarbons in Contaminated Soil
by Yanjie Wang, Qiong Wang, Meijuan Wang, Haiqing Lei and Jiabo Chen
Agronomy 2025, 15(12), 2874; https://doi.org/10.3390/agronomy15122874 (registering DOI) - 14 Dec 2025
Abstract
Petroleum hydrocarbons are pervasive soil pollutants that detrimentally affect the soil structure, nutrients, and microbial ecosystems. However, the effect of biochar particle size on the remediation effectiveness remains a critical, unresolved parameter. Here, a soil remediation experiment was conducted to evaluate the synergy [...] Read more.
Petroleum hydrocarbons are pervasive soil pollutants that detrimentally affect the soil structure, nutrients, and microbial ecosystems. However, the effect of biochar particle size on the remediation effectiveness remains a critical, unresolved parameter. Here, a soil remediation experiment was conducted to evaluate the synergy between biochars of different particle sizes and nutrient addition. Total petroleum hydrocarbons (TPHs) were quantified gravimetrically, and specific hydrocarbon fractions were analysed via gas chromatography mass spectroscopy (GC‒MS) while the microbial community composition was analysed via high-throughput sequencing. The results revealed that granular bulrush straw biochar (0.85 mm) with nutrients achieved the greatest TPH degradation (73.35%), significantly outperforming both powder biochar and soybean straw biochar. This enhanced remediation was associated with a significant shift in the microbial community (p < 0.05), characterized by substantial increases in hydrocarbon-degrading bacteria, particularly Actinobacteria and the genus Mycobacterium. This study revealed that the synergistic application of granular biochar and nutrients is a highly effective, nature-based strategy for petroleum-contaminated soil, which functions by resolving a critical biochar parameter to enhance key microbial degraders. Full article
(This article belongs to the Special Issue Soil Degradation and Restoration: Challenges and Prospects for Agroecological Development)
26 pages, 10331 KB  
Article
STM-Net: A Multiscale Spectral–Spatial Representation Hybrid CNN–Transformer Model for Hyperspectral Image Classification
by Yicheng Hu, Jia Ge and Shufang Tian
Remote Sens. 2025, 17(24), 4031; https://doi.org/10.3390/rs17244031 (registering DOI) - 14 Dec 2025
Abstract
Hyperspectral images (HSIs) have been broadly applied in remote sensing, environmental monitoring, agriculture, and other fields due to their rich spectral information and complex spatial properties. However, the inherent redundancy, spectral aliasing, and spatial heterogeneity of high-dimensional data pose significant challenges to classification [...] Read more.
Hyperspectral images (HSIs) have been broadly applied in remote sensing, environmental monitoring, agriculture, and other fields due to their rich spectral information and complex spatial properties. However, the inherent redundancy, spectral aliasing, and spatial heterogeneity of high-dimensional data pose significant challenges to classification accuracy. Therefore, this study proposes STM-Net, a hybrid deep learning model that integrates SSRE (Spectral–Spatial Residual Extraction Module), Transformer, and MDRM (Multi-scale Differential Residual Module) architectures to comprehensively exploit spectral–spatial features and enhance classification performance. First, the SSRE module employs 3D convolutional layers combined with residual connections to extract multi-scale spectral–spatial features, thereby improving the representation of both local and deep-level characteristics. Second, the MDRM incorporates multi-scale differential convolution and the Convolutional Block Attention Module mechanism to refine local feature extraction and enhance inter-class discriminability at category boundaries. Finally, the Transformer branch equipped with a Dual-Branch Global-Local (DBGL) mechanism integrates local convolutional attention and global self-attention, enabling synergistic optimization of long-range dependency modeling and local feature enhancement. In this study, STM-Net is extensively evaluated on three benchmark HSI datasets: Indian Pines, Pavia University, and Salinas. Additionally, experimental results demonstrate that the proposed model consistently outperforms existing methods regarding OA, AA, and the Kappa coefficient, exhibiting superior generalization capability and stability. Furthermore, ablation studies validate that the SSRE, MDRM, and Transformer components each contribute significantly to improving classification performance. This study presents an effective spectral–spatial feature fusion framework for hyperspectral image classification, offering a novel technical solution for remote sensing data analysis. Full article
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16 pages, 1473 KB  
Article
Model for Optimizing Waste-Haulage Systems in Open-Pit Mines (Trucks vs. IPCC System)
by Ali Nasirinezhad, Dejan Stevanovic, Daniel Krzanovic and Mehdi Rahmanpour
Appl. Sci. 2025, 15(24), 13148; https://doi.org/10.3390/app152413148 (registering DOI) - 14 Dec 2025
Abstract
Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative [...] Read more.
Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative to truck haulage, as it demonstrates a higher degree of economic efficiency. In a theoretical and practical sense, due to its direct impact on the extraction plan, defining the optimal position of the crusher and consequently the system of conveyors is often the most challenging problem of this methodology. This paper introduces an innovative approach to determining the optimum waste haulage configuration by comparing conventional truck-based transport with IPCC systems. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem, explicitly incorporating spatial dimensions and the relocation costs of semi-mobile crushers. The model situates the crusher in a way that reduces transfer costs throughout production periods and it has been tested on a hypothetical open pit. Full article
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15 pages, 1192 KB  
Article
Females and Exercise Capacity Impairment in Heart Failure: A Sex-Focused Analysis
by Ainhoa Lorenzo, Raúl Ramos-Polo, Laia Lorenzo-Esteller, Xinying Lin, Emma Barragan, Paula Aranda, Èlia Boixader, Foix Regull, Nerea Martín, Ariana Ollé, Marc Llagostera, Núria José-Bazán, Pedro Moliner, Cristina Enjuanes and Josep Comin-Colet
J. Cardiovasc. Dev. Dis. 2025, 12(12), 494; https://doi.org/10.3390/jcdd12120494 (registering DOI) - 14 Dec 2025
Abstract
Heart failure (HF) is becoming increasingly common, especially in older females, and displays marked sex-related differences in pathophysiology, treatment, and outcomes. Submaximal exercise capacity (SEC), frequently measured by the six-minute walk test (6MWT), is an important marker of aerobic function, prognosis, and quality [...] Read more.
Heart failure (HF) is becoming increasingly common, especially in older females, and displays marked sex-related differences in pathophysiology, treatment, and outcomes. Submaximal exercise capacity (SEC), frequently measured by the six-minute walk test (6MWT), is an important marker of aerobic function, prognosis, and quality of life in HF. However, evidence regarding sex differences in SEC remains limited and inconsistent. This single-centre, prospective cohort study included 1069 patients with chronic HF enrolled between 2004 and 2014. SEC was assessed using the 6MWT, and extensive clinical and psychosocial data were collected. Multivariate models evaluated the independent association between sex and SEC. Results showed that females had significantly shorter 6MWT distances (155 ± 149 m) than males (265 ± 164 m; p < 0.001). Female sex was an independent predictor of impaired SEC in both unadjusted and adjusted analyses (odds ratios 2.226–3.609; p < 0.001). Additional determinants of reduced SEC included advanced age, higher NYHA class, elevated heart rate, diabetes, iron deficiency, dependence in activities of daily living, cognitive impairment, and depressive symptoms. These findings demonstrate that female sex is a strong, independent predictor of reduced functional capacity in chronic HF and emphasize the need for sex-specific strategies addressing both clinical and psychosocial factors to improve outcomes. Full article
(This article belongs to the Special Issue Epidemiological, Basic Science and Clinical Research Studies in Heart Failure)
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55 pages, 3942 KB  
Review
Latest Advancements and Mechanistic Insights into High-Entropy Alloys: Design, Properties and Applications
by Anthoula Poulia and Alexander E. Karantzalis
Materials 2025, 18(24), 5616; https://doi.org/10.3390/ma18245616 (registering DOI) - 14 Dec 2025
Abstract
High-entropy alloys (HEAs) are a class of multi-principal element materials composed of five or more elements in near-equimolar ratios. This unique compositional design generates high configurational entropy, which stabilizes simple solid solution phases and reduces the tendency for intermetallic compound formation. Unlike conventional [...] Read more.
High-entropy alloys (HEAs) are a class of multi-principal element materials composed of five or more elements in near-equimolar ratios. This unique compositional design generates high configurational entropy, which stabilizes simple solid solution phases and reduces the tendency for intermetallic compound formation. Unlike conventional alloys, HEAs exhibit a combination of properties that are often mutually exclusive, such as high strength and ductility, excellent thermal stability, superior corrosion and oxidation resistance. The exceptional mechanical performance of HEAs is attributed to mechanisms including lattice distortion strengthening, sluggish diffusion, and multiple active deformation pathways such as dislocation slip, twinning, and phase transformation. Advanced characterization techniques such as transmission electron microscopy (TEM), atom probe tomography (APT), and in situ mechanical testing have revealed the complex interplay between microstructure and properties. Computational approaches, including CALPHAD modeling, density functional theory (DFT), and machine learning, have significantly accelerated HEA design, allowing prediction of phase stability, mechanical behavior, and environmental resistance. Representative examples include the FCC-structured CoCrFeMnNi alloy, known for its exceptional cryogenic toughness, Al-containing dual-phase HEAs, such as AlCoCrFeNi, which exhibit high hardness and moderate ductility and refractory HEAs, such as NbMoTaW, which maintain ultra-high strength at temperatures above 1200 °C. Despite these advances, challenges remain in controlling microstructural homogeneity, understanding long-term environmental stability, and developing cost-effective manufacturing routes. This review provides a comprehensive and analytical study of recent progress in HEA research (focusing on literature from 2022–2025), covering thermodynamic fundamentals, design strategies, processing techniques, mechanical and chemical properties, and emerging applications, through highlighting opportunities and directions for future research. In summary, the review’s unique contribution lies in offering an up-to-date, mechanistically grounded, and computationally informed study on the HEAs research-linking composition, processing, structure, and properties to guide the next phase of alloy design and application. Full article
(This article belongs to the Special Issue New Advances in High Entropy Alloys)
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23 pages, 8185 KB  
Article
Multiparametric Cost–CO2 Optimization of Bored Reinforced-Concrete Piles Under Combined Loading in Cohesive Soils
by Primož Jelušič
Buildings 2025, 15(24), 4519; https://doi.org/10.3390/buildings15244519 (registering DOI) - 14 Dec 2025
Abstract
Laterally loaded slender piles present a classic soil–structure interaction problem where pile displacements and flexural demands are governed by the mobilized lateral resistance of the surrounding soil and the axial-bending capacity of the reinforced concrete section. In response to increasing pressure to reduce [...] Read more.
Laterally loaded slender piles present a classic soil–structure interaction problem where pile displacements and flexural demands are governed by the mobilized lateral resistance of the surrounding soil and the axial-bending capacity of the reinforced concrete section. In response to increasing pressure to reduce embodied emissions, this study develops LAVERCO, an optimization framework for cost- and CO2-efficient design of bored reinforced-concrete piles in cohesive soils subjected to combined lateral and axial actions. The framework integrates Eurocode-based geotechnical checks with full N–M section verification of the RC pile and applies a genetic algorithm over a multi-parametric grid of lateral load, vertical load, and undrained shear strength, using economic cost and embodied CO2 as alternative single objectives. Rank-based (Spearman) sensitivity analysis quantifies how actions, soil strength, and design variables influence the optimal solutions. The results reveal two consistent geometry regimes: CO2-optimal piles are systematically longer and slimmer, while COST-optimal piles are shorter and thicker. In both cases, the objective is dominated by pile length and is reduced by higher undrained shear strength; vertical load has a moderate direct effect, while horizontal load contributes mainly through deflection and bending checks. Feasibility improves significantly in stronger clays, and CO2-optimal geometries generally incur higher costs, clarifying the trade-off between economic and environmental performance. The framework provides explicit geometry-level guidance for selecting bored pile designs that balance cost and embodied CO2 across a wide range of soil and loading conditions and can be directly applied in both preliminary and detailed designs. Full article
(This article belongs to the Section Building Structures)
16 pages, 1391 KB  
Article
Effects of Lactiplantibacillus plantarum and Cellulase Inoculation on Silage Quality of Grape Branches and Leaves
by Changhao Li, Zhiwei Huo, Shuangming Li, Rongzheng Huang, Yingli Ji, Chunhui Ma, Shaoqi Cao and Fanfan Zhang
Microorganisms 2025, 13(12), 2842; https://doi.org/10.3390/microorganisms13122842 (registering DOI) - 14 Dec 2025
Abstract
To tackle grape branch and leaf waste and alleviate global feed shortages, this study tested silage made from Xinjiang ‘Seedless White’ grape foliage. Three treatments were established: CK (control, only grape branches and leaves), PL (inoculated with 5 × 106 CFU·g−1 [...] Read more.
To tackle grape branch and leaf waste and alleviate global feed shortages, this study tested silage made from Xinjiang ‘Seedless White’ grape foliage. Three treatments were established: CK (control, only grape branches and leaves), PL (inoculated with 5 × 106 CFU·g−1 fresh weight Lactiplantibacillus plantarum), and PLC (inoculated with 5 × 106 CFU·g−1 L. plantarum and 0.3% cellulase). Silages were fermented at 18–23 °C and analyzed on days 7, 15, 30, and 60. PLC reduced dry matter loss in the late fermentation stage, while lowering Neutral detergent fiber (NDF) and Acid detergent fiber (ADF) contents to solve the high-fiber issue of grape foliage silage. It also maintained a lower pH in the mid-to-late stage and higher Lactic acid (LA) content to ensure anti-spoilage. Microbiologically, PLC had the highest Lactiplantibacillus abundance on day 7; on day 60, its Simpson index was higher, meaning stronger microbial community stability. Firmicutes replaced Cyanobacteria as the new dominant phylum, with Lactiplantibacillus remaining the absolute dominant genus, and the growth of molds and yeasts was effectively inhibited. In conclusion, the combined application of L. plantarum and cellulase enhances the quality of grape branch and leaf silage. This study turns low-value grape branches and leaves into high-quality feed, providing support for grape branch and leaf resource utilization and helping alleviate global feed shortages. Full article
(This article belongs to the Special Issue Microorganisms in Silage)
21 pages, 3597 KB  
Article
An Integrated IoT- and Machine Learning-Based Smart Management and Decision Support System for Sustainable Oil Palm Production
by Kritsada Puangsuwan, Supattra Puttinaovarat, Natthaseth Sriklin, Weerapat Phutthamongkhon and Siriwan Kajornkasirat
Sustainability 2025, 17(24), 11204; https://doi.org/10.3390/su172411204 (registering DOI) - 14 Dec 2025
Abstract
Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. [...] Read more.
Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning. Full article
(This article belongs to the Special Issue Smart Sensors and IoT Solutions for Sustainable Agriculture and Aquaculture Practices)
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19 pages, 4782 KB  
Article
A Web-Based Learning Model for Smart Campuses: A Case in Landscape Architecture Education
by Gamze Altun and Murat Zencirkıran
Sustainability 2025, 17(24), 11203; https://doi.org/10.3390/su172411203 (registering DOI) - 14 Dec 2025
Abstract
This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried [...] Read more.
This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide. Full article
(This article belongs to the Special Issue Sustainability-Oriented Digital Transformation and Innovative Assessments in Higher Education)
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25 pages, 1371 KB  
Review
Extracellular Vesicle-Mediated Delivery of Antioxidant Enzymes: Emerging Insights and Translational Opportunities
by Junyu Wang, Yakun Li, Robin P. F. Dullaart, Peter Olinga and Han Moshage
Antioxidants 2025, 14(12), 1504; https://doi.org/10.3390/antiox14121504 (registering DOI) - 14 Dec 2025
Abstract
Oxidative stress is a key contributor to the onset and progression of diverse pathological conditions, including metabolic dysfunction-associated steatotic liver disease (MASLD), neurodegeneration, cardiovascular disorders, and cancer. Conventional antioxidant therapies, such as small-molecule scavengers or systemic enzyme administration, are limited by poor stability, [...] Read more.
Oxidative stress is a key contributor to the onset and progression of diverse pathological conditions, including metabolic dysfunction-associated steatotic liver disease (MASLD), neurodegeneration, cardiovascular disorders, and cancer. Conventional antioxidant therapies, such as small-molecule scavengers or systemic enzyme administration, are limited by poor stability, inefficient delivery, and off-target effects. Extracellular vesicles (EVs), particularly exosomes, are increasingly recognized as natural carriers of antioxidant enzymes (AOEs), including catalase, superoxide dismutases, glutathione peroxidases, peroxiredoxins, and thioredoxin. These vesicles not only protect enzymes from degradation but also enable targeted delivery to recipient cells, where they can actively modulate redox homeostasis. In this review, we summarize current evidence for AOEs as bona fide EV cargo, outline mechanisms that govern their selective packaging and transfer, and highlight their roles in intercellular communication under physiological and pathological conditions. We also discuss emerging therapeutic applications of both natural and engineered EVs for redox modulation, along with the challenges of quantifying enzymatic activity, ensuring reproducibility, and scaling clinical translation. By integrating insights from cell biology, redox signaling, and translational research, we propose that EV-mediated AOE delivery represents a promising next-generation strategy for combating oxidative stress-related diseases. Full article
(This article belongs to the Section Antioxidant Enzyme Systems)
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22 pages, 6432 KB  
Article
Minerals as Windows into Habitability on Lava Tube Basalts: A Biogeochemical Study at Lava Beds National Monument, CA
by Dina M. Bower, Amy C. McAdam, Clayton S. C. Yang, Feng Jin, Maeva Millan, Clara Christiann, Mathilde Mussetta, Christine Knudson, Jamielyn Jarvis, Sarah Johnson, Zachariah John, Catherine Maggiori, Patrick Whelley and Jacob Richardson
Minerals 2025, 15(12), 1303; https://doi.org/10.3390/min15121303 (registering DOI) - 14 Dec 2025
Abstract
Lava tubes on Earth provide unique hydrogeological niches for life to proliferate. Orbital observations of the Martian surface indicate the presence of lava tubes, which could hold the potential for extant life or the preservation of past life within a subsurface environment protected [...] Read more.
Lava tubes on Earth provide unique hydrogeological niches for life to proliferate. Orbital observations of the Martian surface indicate the presence of lava tubes, which could hold the potential for extant life or the preservation of past life within a subsurface environment protected from harsh conditions or weathering at the surface. Secondary minerals in lava tubes form as a combination of abiotic and biotic processes. Microbes colonize the surfaces rich in these secondary minerals, and their actions induce further alteration of the mineral deposits and host basalts. We conducted a biogeochemical investigation of basaltic lava tubes in the Medicine Lake region of northern California by characterizing the compositional variations in secondary minerals, organic compounds, microbial communities, and the host rocks to better understand how their biogeochemical signatures could indicate habitability. We used methods applicable to landed Mars missions, including Raman spectroscopy, X-ray diffraction (XRD), Laser-Induced Breakdown Spectroscopy (LIBS), and gas chromatography–mass spectrometry (GC-MS), along with scanning electron microscopy (SEM) and metagenomic DNA/RNA sequencing. The main secondary minerals, amorphous silicates, and calcite, formed abiotically from the cave waters. Two types of gypsum, large euhedral grains with halites, and cryptocrystalline masses near microbial material, were observed in our samples, indicating different formation pathways. The cryptocrystalline gypsum, along with clay minerals, was associated with microbial materials and biomolecular signatures among weathered primary basalt minerals, suggesting that their formation was related to biologic processes. Some of the genes and pathways observed indicated a mix of metabolisms, including those involved in sulfur and nitrogen cycling. The spatial relationships of microbial material, Cu-enriched hematite in the host basalts, and genetic signatures indicative of metal cycling also pointed to localized Fe oxidation and mobilization of Cu by the microbial communities. Collectively these results affirm the availability of bio-essential elements supporting diverse microbial populations on lava tube basalts. Further work exploring these relationships in lava tubes is needed to unravel the intertwined nature of abiotic and biotic interactions and how that affects habitability in these environments on Earth and the potential for life on Mars. Full article
(This article belongs to the Special Issue Exploring Novel Interactions Between Microbes and Minerals)
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14 pages, 258 KB  
Article
Is the Basic Structure of the Universe Simple?
by Martin Tamm
Axioms 2025, 14(12), 922; https://doi.org/10.3390/axioms14120922 (registering DOI) - 14 Dec 2025
Abstract
Can our extremely complicated world be explained, starting from simple conditions and laws? Such questions easily become metaphysical, but at the same time they have historically served science well by promoting new and fruitful ideas. Of particular interest in this paper is the [...] Read more.
Can our extremely complicated world be explained, starting from simple conditions and laws? Such questions easily become metaphysical, but at the same time they have historically served science well by promoting new and fruitful ideas. Of particular interest in this paper is the unification of general relativity and quantum mechanics. It is suggested that a way to find a common basis for these theories could be to view them both as stochastic theories which, among all possible macroscopic developments, promote the simplest ones. But with the difference that general relativity uses real probabilities, whereas quantum mechanics uses complex weights. In a certain sense this approach goes back to the Principle of Least Action, although the perspective on this principle in the present paper is different from the one which is commonly used in contemporary physics. It is also suggested that a more general principle, which applies to both theories and which is beyond both stationarity and minimizing, could give us a better starting point for the unification. Full article
(This article belongs to the Special Issue Recent Advances in Quantum Mechanics and Mathematical Physics)
15 pages, 2244 KB  
Article
Photochemical Rearrangements of Pyridine N-Oxides: Pathways to Oxaziridine Derivatives
by Cristian J. Guerra, Yeray A. Rodríguez-Núñez, Efraín Polo-Cuadrado, Mitchell Bacho, Jorge Soto-Delgado, Victor B. Fuentes-Guerrero, Eduardo I. Torres-Olguín, Cristopher A. Fica-Cornejo, Daniela Rodríguez-García, Manuel E. Taborda-Martínez, Leandro Ayarde-Henríquez and Adolfo E. Ensuncho
Molecules 2025, 30(24), 4776; https://doi.org/10.3390/molecules30244776 (registering DOI) - 14 Dec 2025
Abstract
The photochemical behavior of substituted pyridine N-Oxides is characterized by complex rearrangements culminating in the formation of valuable photoproducts. The CAS(10,8)/cc-pVDZ approach with NEVPT2 corrections is applied to investigate geometric distortions associated with the S1 excited state, conical intersections, and the ultimate [...] Read more.
The photochemical behavior of substituted pyridine N-Oxides is characterized by complex rearrangements culminating in the formation of valuable photoproducts. The CAS(10,8)/cc-pVDZ approach with NEVPT2 corrections is applied to investigate geometric distortions associated with the S1 excited state, conical intersections, and the ultimate transformation of pyridine N-Oxides into oxaziridine-like derivative formations. Our results reveal that the deactivation of the S1 excited state is driven by an out-of-plane rotation of the N-O oxygen atom, resulting in the formation of a lone pair over the nitrogen atom. Along this excited-state reaction pathway, the N-O bond undergoes significant weakening, while a C=C double bond emerges mainly in the excited state. The deactivation at the minimum-energy conical intersection leading to the ground state reveals the formation of an oxaziridine-like intermediate, which subsequently converts into a 1,2-oxazepine derivative. Full article
(This article belongs to the Special Issue Recent Advances in Charge-Transfer/Biomimetic Coordination Chemistry – Electrochemistry, Photochemistry, Catalysis, Biological Application and Theory)
27 pages, 3231 KB  
Review
Towards Greener Sample Preparation: A Review on Micro-QuEChERS Advances and Applications in Food, Environmental, and Biological Matrices
by Athina Papadopoulou, Vasiliki Boti and Christina Nannou
Separations 2025, 12(12), 339; https://doi.org/10.3390/separations12120339 (registering DOI) - 14 Dec 2025
Abstract
This review provides a comprehensive evaluation of recent advances in miniaturized Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) sample preparation techniques applied across food, environmental, and biological matrices. Covering developments within 2020–2025, it focuses on analytical performance, environmental impact, and alignment with [...] Read more.
This review provides a comprehensive evaluation of recent advances in miniaturized Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) sample preparation techniques applied across food, environmental, and biological matrices. Covering developments within 2020–2025, it focuses on analytical performance, environmental impact, and alignment with principles of sustainable and green analytical chemistry. Central to this review is the significant reduction in solvent and sample volumes achieved through miniaturization, thus decreasing the reagent consumption and hazardous waste generation. The integration of eco-friendly extraction solvents and sorbent materials enhances selectivity and reduces the environmental footprint. These methods are often coupled with high-resolution mass spectrometers, enabling sensitive, multi-residue, and suspect analysis. Challenges associated with complex matrices, low analyte concentrations, and the need for robust clean-up procedures are addressed through innovative hybrid workflows and advanced materials, e.g., polymeric electrospun fibers and deep eutectic solvents. The growing adoption of greener protocols is highlighted. Moreover, it underscores their potential to improve routine analytical workflows while reducing environmental burden. Future research should focus on the development of sustainable sample preparation with improved sensitivity, broader applicability, and minimal ecological impacts. This comprehensive assessment supports the ongoing transformation of analytical chemistry towards more sustainable practices without compromising analytical reliability and efficacy. Full article
(This article belongs to the Special Issue Optimization of Advanced Separation Technologies for the Analysis of Emerging Contaminants)
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25 pages, 1646 KB  
Review
Berberine: A Rising Star in the Management of Type 2 Diabetes—Novel Insights into Its Anti-Inflammatory, Metabolic, and Epigenetic Mechanisms
by Da Liu, Liting Zhao, Ying Wang, Lei Wang, Donglu Wu and Yangyang Liu
Pharmaceuticals 2025, 18(12), 1890; https://doi.org/10.3390/ph18121890 (registering DOI) - 14 Dec 2025
Abstract
Type 2 diabetes mellitus (T2DM) is a widespread metabolic disorder characterized by insulin resistance and pancreatic β-cell dysfunction, posing a substantial global health challenge. This review systematically summarizes the therapeutic potential of berberine, a natural isoquinoline alkaloid, in the management of T2DM. Berberine’s [...] Read more.
Type 2 diabetes mellitus (T2DM) is a widespread metabolic disorder characterized by insulin resistance and pancreatic β-cell dysfunction, posing a substantial global health challenge. This review systematically summarizes the therapeutic potential of berberine, a natural isoquinoline alkaloid, in the management of T2DM. Berberine’s pharmacological activities are discussed from multiple perspectives, including enhancing insulin sensitivity and regulating glucose metabolism—encompassing glycogen synthesis, gluconeogenesis, and glucose transport. The review also highlights berberine’s anti-inflammatory, antioxidant, and epigenetic enzyme-targeting actions and its involvement in key T2DM-related signaling pathways such as AKT, AMPK, and GLUTs. These findings collectively elucidate the multi-targeted and multi-pathway molecular mechanisms underlying berberine’s efficacy against T2DM. Additionally, the review covers the pharmacological activities and molecular mechanisms of berberine in treating T2DM complications—including diabetic nephropathy, retinopathy, cardiomyopathy, neuropathy, and diabetic foot ulcers—as well as its clinical and preclinical applications and the synergistic benefits of combination therapy with agents such as metformin, ginsenoside Rb1, and probiotics. By systematically reviewing the literature retrieved from PubMed and Web of Science up to 2025, this article provides a comprehensive summary of current research, offering a theoretical foundation for the clinical use of berberine in T2DM therapy. Full article
(This article belongs to the Special Issue Advances in Natural Products: Basic, Therapeutic, and Computational Approaches in Chronic and Infectious Diseases)
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17 pages, 4921 KB  
Article
Effect of Immersion in Quaternary Ammonium Methacryloxy Silane Mixed Monomer on the Mechanical Properties and Antibacterial Activity of a 3D-Printed Urethane Dimethacrylate Denture Base Resin
by Kun-Min Kim, Yeseul Park, Jimin Kim, Mu-Yeol Cho and Jee-Hwan Kim
J. Funct. Biomater. 2025, 16(12), 462; https://doi.org/10.3390/jfb16120462 (registering DOI) - 14 Dec 2025
Abstract
Denture base resins are susceptible to microbial colonization, and current antibacterial additives often lose effectiveness and may weaken material properties. This study evaluated whether immersion in a quaternary ammonium methacryloxy silane (QAMS)-containing monomer can enhance antibacterial activity without compromising the mechanical properties of [...] Read more.
Denture base resins are susceptible to microbial colonization, and current antibacterial additives often lose effectiveness and may weaken material properties. This study evaluated whether immersion in a quaternary ammonium methacryloxy silane (QAMS)-containing monomer can enhance antibacterial activity without compromising the mechanical properties of digital light processing–printed urethane dimethacrylate denture base resin. Specimens of printed denture base resin were immersed in mixtures of denture base resin and a QAMS-containing monomer at ratios of 10:0 (Control), 7:3 (K3), 5:5 (K5), 3:7 (K7), and 0:10 (K10), followed by post-curing. Flexural strength and modulus were measured by three-point bending, and surface hardness was assessed by Vickers microhardness testing. Antibacterial activity against Streptococcus mutans was assessed by inhibition-zone and colony-counting assays. All QAMS-treated groups preserved flexural strength, with a slight reduction in modulus in K5 (p < 0.05), while hardness remained unchanged. Antibacterial activity improved in all QAMS-treated groups; K5 and K7 showed the strongest results. Surface analyses using scanning electron microscopy and energy-dispersive X-ray spectroscopy verified formation of a Si-rich modified layer. QAMS immersion followed by post-curing produced a stable, contact-active antibacterial surface without reducing mechanical properties. Among the formulations, K7 (~21 wt% QAMS) provided the most favorable balance of antibacterial activity and mechanical performance. Full article
(This article belongs to the Special Issue Advanced Biomaterials and Biotechnology: Applications in Dental Medicine—2nd Edition)
17 pages, 2597 KB  
Article
Optimization of Dynamic Scheduling for Flexible Job Shops Using Multi-Agent Deep Reinforcement Learning
by Jianqi Wang, Renwang Li and Qiang Wang
Processes 2025, 13(12), 4045; https://doi.org/10.3390/pr13124045 (registering DOI) - 14 Dec 2025
Abstract
This study proposes an optimization framework based on Multi-agent Deep Reinforcement Learning (MADRL), conducting a systematic exploration of FJSP under dynamic scenarios. The research analyzes the impact of two types of dynamic disturbance events—machine failures and order insertions—on the Dynamic Flexible Job Shop [...] Read more.
This study proposes an optimization framework based on Multi-agent Deep Reinforcement Learning (MADRL), conducting a systematic exploration of FJSP under dynamic scenarios. The research analyzes the impact of two types of dynamic disturbance events—machine failures and order insertions—on the Dynamic Flexible Job Shop Scheduling Problem (DFJSP). Furthermore, it integrates process selection agents and machine selection agents to devise solutions for handling dynamic events. Experimental results demonstrate that, when solving standard benchmark problems, the proposed multi-objective DFJSP scheduling method, based on the 3DQN algorithm and incorporating an event-triggered rescheduling strategy, effectively mitigates disruptions caused by dynamic events. Full article
(This article belongs to the Section Process Control and Monitoring)
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28 pages, 1874 KB  
Article
Effect of Conventional and Ultrasound-Assisted Extraction Conditions on the Physicochemical Properties, Phytochemical Content, Antioxidant Activity and Functional Properties of Alfalfa Protein Concentrates
by Angela Gurev, Viorica Bulgaru, Iana Ciugureanu, Natalia Netreba, Veronica Dragancea, Irina Dianu, Iuliana Sandu, Mihail Mazur, Tatiana Mitina, Nadejda Bandarenco and Aliona Ghendov-Mosanu
Foods 2025, 14(24), 4309; https://doi.org/10.3390/foods14244309 (registering DOI) - 14 Dec 2025
Abstract
Alfalfa (Medicago sativa L.) is an underutilized source of phytonutrients and easily digestible protein, containing all essential amino acids, highlighting its potential for food applications. This study aimed to produce alfalfa protein concentrates (APC) from frozen aerial parts and evaluate how conventional [...] Read more.
Alfalfa (Medicago sativa L.) is an underutilized source of phytonutrients and easily digestible protein, containing all essential amino acids, highlighting its potential for food applications. This study aimed to produce alfalfa protein concentrates (APC) from frozen aerial parts and evaluate how conventional extraction and ultrasound-assisted extraction (UAE) affect the extraction yield, physicochemical properties, functional attributes, color parameters, phytochemical composition and antioxidant activity. The influence of extraction pH and the type of acid used for isoelectric precipitation was also evaluated. Paired t-tests (p ≤ 0.05) showed that UAE (37 kHz, 25 °C, 15 min) increased the extraction yield by 20.5–39.7%, the protein content in APC by 2.5–12.1% and the in vitro protein digestibility by 5.6–11.03%, depending on the extraction conditions. Ultrasound treatment decreased the levels of chlorophyll and carotenoids, modified the color parameters and increased the total polyphenols and flavonoids content. Improvements in the textural, foaming and emulsifying properties of APC were also observed. UAE also reduced the scavenging capacity of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals. However, the 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) scavenging activity significantly increased in aqueous APC extracts, reaching 3118.8 mg TE/100 g DW. Overall, UAE proved effective in improving the yield and functionality of APC, supporting its application in the development of alfalfa-based protein ingredients. Full article
(This article belongs to the Special Issue Advances in Plant-Based Foods: From Sustainable Production to Functional Applications)
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13 pages, 2380 KB  
Case Report
First Latin American Case of MLASA2 Caused by a Pathogenic Variant in the Anticodon-Binding Domain of YARS2
by José Rafael Villafán-Bernal, Jhonatan Rosas-Hernández, Humberto García-Ortiz, Angélica Martínez-Hernández, Cecilia Contreras-Cubas, Israel Guerrero-Contreras, Hane Lee, Go Hun Seo, Alessandra Carnevale, Francisco Barajas-Olmos and Lorena Orozco
Int. J. Mol. Sci. 2025, 26(24), 12039; https://doi.org/10.3390/ijms262412039 (registering DOI) - 14 Dec 2025
Abstract
MLASA2 is a rare mitochondrial disorder with limited geographic representation in published medical literature. Here, we report the first confirmed case of MLASA2 in a Latin American 16-year-old male harboring a homozygous pathogenic variant p.(Asp311Glu) in the YARS2 gene. The patient presented with [...] Read more.
MLASA2 is a rare mitochondrial disorder with limited geographic representation in published medical literature. Here, we report the first confirmed case of MLASA2 in a Latin American 16-year-old male harboring a homozygous pathogenic variant p.(Asp311Glu) in the YARS2 gene. The patient presented with sideroblastic anemia and short stature, accompanied by other skeletal dysplasia features not previously associated with MLASA2, including epiphyseal dysplasia, rib edge widening, and poorly defined vertebral structures, but without lactic acidosis. Notably, the patient did not present exercise intolerance but recently exhibited reduced muscle strength. The p.(Asp311Glu) variant, located in the anticodon-binding domain of the mitochondrial tyrosyl-tRNA synthetase (Mt-TyrRS), was consistently predicted to be pathogenic by multiple in silico tools. Molecular modeling revealed that this variant destabilizes the ‘KMSKS’ motif, potentially compromising tRNA recognition fidelity and aminoacylation efficiency. Analysis of runs of homozygosity (ROH) revealed significantly elevated consanguinity (ROH: 31.93%), consistent with a consanguineous mating between biological parents. This case expands the geographic distribution of MLASA2, documents previously unreported phenotypes, suggests a novel pathogenic mechanism, and demonstrates the utility of genomic approaches for diagnosing rare mitochondrial disorders in the absence of complete clinical information and family history. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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14 pages, 198 KB  
Article
Ethics and Theism
by Bruce A. Russell
Religions 2025, 16(12), 1575; https://doi.org/10.3390/rel16121575 (registering DOI) - 14 Dec 2025
Abstract
In this essay I argue that there are necessarily true synthetic a priori moral propositions whose truth does not depend on the existence of God. To make my case, I appeal to an analogy with arithmetic truths such as 2 + 2 = [...] Read more.
In this essay I argue that there are necessarily true synthetic a priori moral propositions whose truth does not depend on the existence of God. To make my case, I appeal to an analogy with arithmetic truths such as 2 + 2 = 4 whose truth does not depend on the existence of God. I criticize views like Peter Railton’s that hold that moral truths are like truths about natural kinds such as water and heat, and non-cognitivists who hold that there are no robust moral truths. The point of my criticisms is to answer challenges to my view that there are necessarily true synthetic a priori moral propositions and, in the case of Railton, to block an argument by Robert Adams for a Divine Command Theory of ethics. Second, I argue by example that there can be conflicts between what is best for me and those for whom I care and what is morally required that cannot be reconciled by a theistic ethics. It can be rational to violate moral requirements that have the same contents as the commands of a loving God even if there would be most reason to adhere to those requirements IF God exists, just as it can be rational to leave your umbrella at home even if there would be most reason to take it IF it rained. This will be true regardless of whether the reason to adhere to God’s commands, IF God exists, is because our greatest good is the love of God (and that requires adhering to his commands) or because God will punish you if you do not and reward you if you do. The problem of evil is the primary reason to believe that God does not exist, and so to believe that there are no divine commands that there would be most reason to follow if God did exist. Full article
(This article belongs to the Special Issue Is an Ethics without God Possible?)
14 pages, 3483 KB  
Article
The Influence of Annealing on the Structural, Optical and Electrical Properties of Copper Selenite Nanocrystals Synthesized by the Chemical Deposition Method
by Gulnaz Sarsekhan, Abay Usseinov, Aiman Akylbekova, Abdirash Akilbekov, Alma Dauletbekova, Diana Junisbekova, Ainash Abdrakhmetova, Gulnara Aralbayeva, Leila Kassenova and Zein Baimukhanov
Crystals 2025, 15(12), 1060; https://doi.org/10.3390/cryst15121060 (registering DOI) - 14 Dec 2025
Abstract
This work presents a study of copper selenite nanocrystals, obtained for the first time by chemical deposition (template synthesis) in a SiO2/Si track template, and investigates their properties. The obtained nanostructures were subjected to structural, optical, and electrical analysis. After deposition, [...] Read more.
This work presents a study of copper selenite nanocrystals, obtained for the first time by chemical deposition (template synthesis) in a SiO2/Si track template, and investigates their properties. The obtained nanostructures were subjected to structural, optical, and electrical analysis. After deposition, X-ray diffraction (XRD) analysis confirmed the formation of the orthorhombic phase CuSeO3. Subsequent annealing in a vacuum at 800 °C and 1000 °C led to successive phase transformations: to the monoclinic phase and, finally, to the triclinic polymorph of copper selenite. Photoluminescence (PL) analysis showed that the intensity and spectral position of the emission peaks vary depending on the crystal structure, which is associated with changes in defects and bandgap width as a result of heat treatment. Current–voltage characteristic (CVC) measurements showed that the phase composition significantly affects electrical conductivity. In particular, the transition to the triclinic phase after annealing at 1000 °C led to noticeable changes in optical and electrical properties compared to the initial material. Thus, a direct relationship has been established between heat treatment conditions, crystal structure, and functional properties of CuSeO3-based materials, opening up possibilities for their application in photonics and electronics. Full article
(This article belongs to the Special Issue Electronic Phenomena of Transition Metal Oxides Volume II)
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16 pages, 848 KB  
Review
What to Know About Falls in Older Adults? Risk Factors, Predictors, and Therapeutic Interventions
by Fernanda Bueno Pilastri, Julia Fantim Lopez, Eric Nkansah Boateng and Nise Ribeiro Marques
Int. J. Environ. Res. Public Health 2025, 22(12), 1863; https://doi.org/10.3390/ijerph22121863 (registering DOI) - 14 Dec 2025
Abstract
Background: Approximately one quarter of community-dwelling older adults experience at least one fall each year. Falls can result in soft tissue injuries, fractures, or even death. Given this high prevalence, it is essential to identify fall-related risk factors, develop predictive models, and prescribe [...] Read more.
Background: Approximately one quarter of community-dwelling older adults experience at least one fall each year. Falls can result in soft tissue injuries, fractures, or even death. Given this high prevalence, it is essential to identify fall-related risk factors, develop predictive models, and prescribe effective exercise-based interventions to prevent falls. Objective: To analyze risk factors, predictors, and therapeutic interventions for falls in older adults. Methods: A literature search was conducted in SCIELO, PUBMED, and PEDro databases between 15–20 October 2025. Inclusion criteria comprised peer-reviewed, open-access studies in English published from 2020 onward. Findings were categorized into three domains: (1) fall risk factors, (2) predictive models, and (3) exercise-based interventions. Twenty studies met the inclusion criteria. Results: Falls among older adults arise from multifactorial interactions involving physical, clinical, cognitive, and social factors such as impaired mobility, comorbidities, polypharmacy, and cognitive decline. Lower-limb strength and functional performance are key determinants of fall risk. Current predictive models show limited accuracy, with fall history as the strongest predictor. Exercise-based interventions, particularly multicomponent and home-based programs, improve balance, strength, and mobility but show variable effects on fall rates. The absence of standardized parameters for exercise prescription limits the development of evidence-based guidelines. Conclusions: Falls in older adults are multifactorial events influenced by physical and cognitive decline. Predictive models remain imprecise, and although exercise interventions improve functional outcomes, their impact on reducing falls is inconsistent. Standardized exercise protocols are needed to optimize fall prevention strategies. Full article
(This article belongs to the Special Issue Achieving Effective Fall Prevention and Intervention for Older Adults)
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20 pages, 2825 KB  
Article
Dementia Detection via Retinal Hyperspectral Imaging and Deep Learning: Clinical Dataset Analysis and Comparative Evaluation of Multiple Architectures
by Wen-Shou Lin, Chia-Ling Chen, Shih-Wun Liang and Hsiang-Chen Wang
Bioengineering 2025, 12(12), 1362; https://doi.org/10.3390/bioengineering12121362 (registering DOI) - 14 Dec 2025
Abstract
This study aimed to detect dementia using intelligent hyperspectral imaging (HSI), which enables the extraction of detailed spectral information from retinal tissues. A total of 3256 ophthalmoscopic images collected from 137 participants were analyzed. The spectral signatures of selected retinal regions were reconstructed [...] Read more.
This study aimed to detect dementia using intelligent hyperspectral imaging (HSI), which enables the extraction of detailed spectral information from retinal tissues. A total of 3256 ophthalmoscopic images collected from 137 participants were analyzed. The spectral signatures of selected retinal regions were reconstructed using hyperspectral conversion techniques to examine wavelength-dependent variations associated with dementia. To assess the diagnostic capability of deep learning models, four convolutional neural network (CNN) architectures—ResNet50, Inception_v3, GoogLeNet, and EfficientNet—were implemented and benchmarked on two datasets: original ophthalmoscopic images (ORIs) and hyperspectral images (HSIs). The HSI-based models consistently demonstrated superior accuracy, achieving 84% with ResNet50, 83% with GoogLeNet, and 82% with EfficientNet, compared with 80–81% obtained from ORIs. Inception_v3 maintained an accuracy of 80% across both datasets. These results confirm that integrating spectral information enhances model sensitivity to dementia-related retinal changes, highlighting the potential of HSI for early and noninvasive detection. Full article
(This article belongs to the Special Issue Retinal Biomarkers: Seeing Diseases in the Eye)
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