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16 pages, 12939 KB

Open AccessArticle

Strategic Carbon Source Selection Enhances Biomass and Paramylon Yields in Mixotrophic Euglena gracilis Cultivation

by Xue Xiao, Rui He, Xinyue Guo, Xinxin Zhao, Zhengfei Yang, Yongqi Yin, Minato Wakisaka and Jiangyu Zhu

Microorganisms 2025, 13(10), 2339; https://doi.org/10.3390/microorganisms13102339 (registering DOI) - 11 Oct 2025

Abstract

Euglena gracilis’s mixotrophic metabolism offers biotechnological potential. This study investigated how glucose, sodium acetate, ethanol, and propanetriol regulate its growth, photosynthesis, and paramylon production. All carbon sources boosted paramylon yield versus photoautotrophic controls. Ethanol and glucose were both highly effective, supporting the [...] Read more.

Euglena gracilis’s mixotrophic metabolism offers biotechnological potential. This study investigated how glucose, sodium acetate, ethanol, and propanetriol regulate its growth, photosynthesis, and paramylon production. All carbon sources boosted paramylon yield versus photoautotrophic controls. Ethanol and glucose were both highly effective, supporting the highest biomass accumulation (5.71 and 4.42-fold increases, respectively) and paramylon content without a significant difference between them. Ethanol supplementation enhanced chlorophyll b via coupled TCA cycle/glyoxylate shunt activity, while glucose showed the strongest tendency for high paramylon and the highest carotenoid content (13.36-fold higher). Sodium acetate triggered alkaline stress (pH 8.5), suppressing pigments and inducing spherical cells. Propanetriol reduced biomass but enhanced PSII efficiency (Fv/Fm). These results demonstrate carbon source-driven metabolic partitioning: ethanol and glucose both excel in promoting growth and storage, while additionally directing carbon toward chlorophyll b or carotenoids, respectively. These findings enable targeted bioprocess optimization: selection between ethanol or glucose can be based on the value of co-products, advancing E. gracilis as a sustainable cell factory. Full article

(This article belongs to the Special Issue Microbial Interactions and Growth Enhancement Strategies in Microalgal Cultivation Systems)

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7 pages, 1052 KB

Open AccessBrief Report

A New Variant in the NALCN Channel Is Responsible for Cerebellar Ataxia and Cognitive Impairment

by Rute Luísa Cabrita Pinto, Roberto Fancellu, Tiziana Benzi Markushi, Silvia Viaggi, Barbara Testa, Giuseppina Conteduca, Lane Fitzsimmons, Domenico Coviello and Angela Elvira Covone

Genes 2025, 16(10), 1181; https://doi.org/10.3390/genes16101181 (registering DOI) - 11 Oct 2025

Abstract

Background/Objectives: CLIFAHDD syndrome (OMIM # 616266) is a rare neurodevelopmental disorder caused by mutations in the NALCN gene. It is characterized by hypotonia, developmental delay, and congenital contractures of the limbs and face. We report a 33-year-old Italian woman with a mild form [...] Read more.

Background/Objectives: CLIFAHDD syndrome (OMIM # 616266) is a rare neurodevelopmental disorder caused by mutations in the NALCN gene. It is characterized by hypotonia, developmental delay, and congenital contractures of the limbs and face. We report a 33-year-old Italian woman with a mild form of CLIFAHDD who exhibited early-onset language difficulties and mild intellectual disability and later developed gait and balance impairments in adulthood. Methods and Results: Whole Exome Sequencing (WES) identified a novel missense variant c.1514A>T; p.(Lys505Met) in the NALCN gene. The allele frequency of this variant is not detected (MAF = 0.0), the variant is classified as likely pathogenic according to ACMG criteria, and predicted to be probably damaging by PolyPhen-2. It affects a critical residue within the second pore-forming domain of the NALCN channel, potentially altering lipid interactions and channel regulation. Sanger sequencing and segregation analysis confirmed the variant to be heterozygous and de novo. Conclusions: The patient’s milder symptoms and later onset, compared to severe pediatric cases, suggest that the clinical spectrum of CLIFAHDD syndrome may be broader than previously recognized. These findings underscore the potential influence of mutation location on disease presentation and severity. Full article

(This article belongs to the Section Genetic Diagnosis)

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18 pages, 1413 KB

Open AccessArticle

Enhanced Ocular Retention and Anti-Allergic Efficacy of a Novel HA–Ectoine–CMC Eye Drop for Dry Eye Disease Management

by Jon Andrade del Olmo, Alejandro Melero, Ander Pino, Nagore Martínez de Cestafe, Oihane Gartziandia, Miguel Ucelay López de Heredia, Josune Torrecilla, Laura Gómez, Sandra Benito Cid, José María Alonso and Raúl Pérez González

J. Pharm. BioTech Ind. 2025, 2(4), 16; https://doi.org/10.3390/jpbi2040016 (registering DOI) - 11 Oct 2025

Abstract

Dry eye disease (DED) is a multifactorial ocular surface disorder that significantly affects vision and quality of life. While artificial tears are the standard first-line therapy, their effectiveness is limited by the complex pathophysiology of DED. This study evaluated DayDrop^® Triple Action, [...] Read more.

Dry eye disease (DED) is a multifactorial ocular surface disorder that significantly affects vision and quality of life. While artificial tears are the standard first-line therapy, their effectiveness is limited by the complex pathophysiology of DED. This study evaluated DayDrop^® Triple Action, a novel formulation combining hyaluronic acid (HA), ectoine, and carboxymethylcellulose (CMC), designed to enhance tear film stability and ocular surface protection. Physicochemical and rheological properties were assessed, including viscosity, pseudoplasticity, and viscoelastic behaviour under dynamic conditions, along with ectoine release over 24 h. An in vitro allergic conjunctivitis model using conjunctival fibroblasts exposed to a pro-allergic cytokine cocktail was employed to examine immunomodulatory effects. DayDrop^® Triple Action demonstrated high viscosity with pronounced pseudoplasticity and stable viscoelasticity, supporting improved mucoadhesion. The formulation provided sustained ectoine release and exhibited a positive immunomodulatory effect, likely linked to ectoine’s preferential hydration mechanism, which stabilizes membranes and reduces inflammatory signalling. These findings suggest that DayDrop^® Triple Action integrates viscoelastic optimization, osmoprotection, and targeted anti-inflammatory action, offering a promising non-pharmacological strategy for managing DED and allergic ocular surface disorders. Full article

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17 pages, 9241 KB

Open AccessArticle

Machine Learning Applications for Earthquake Magnitude Prediction in Western Turkey

by Ilknur Kaftan

Appl. Sci. 2025, 15(20), 10909; https://doi.org/10.3390/app152010909 (registering DOI) - 11 Oct 2025

Abstract

Earthquakes are unpreventable natural disasters that result in many casualties and economic losses in the regions where they occur. Earthquake prediction and seismic risk assessments are essential in minimising these losses. Due to the complex nature of seismic events, it is necessary to [...] Read more.

Earthquakes are unpreventable natural disasters that result in many casualties and economic losses in the regions where they occur. Earthquake prediction and seismic risk assessments are essential in minimising these losses. Due to the complex nature of seismic events, it is necessary to use a cutting-edge methodology to predict earthquake occurrence effectively. Machine learning methods have been among the most efficient and current methods for solving complex nonlinear problems and analysing big datasets. Because of this feature, they are widely used for predicting earthquakes and earthquake parameters. This study focuses on applying machine learning methods to analyse seismic events in Western Turkey from 1975 to 2024. The aim is to compare the effectiveness of five machine learning approaches for predicting earthquake magnitudes: Long Short-Term Memory (LSTM), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Tree (DT), Random Forest (RF), and Convolutional Neural Network (CNN). The outcomes of these applied methods are encouraging in terms of the prediction of magnitude. Among all the results, the LSTM method is slightly more successful than the other methods, with a Root Mean Square Error (RMSE) of 0.1391, Mean Square Error (MSE) of 0.0193, Mean Absolute Error (MAE) of 0.1046 and Mean Absolute Percentage Error (MAPE) of 3.0631%, respectively. Full article

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5 pages, 146 KB

Open AccessEditorial

Computer Vision and Artificial Intelligence Driving the Advancement of Agricultural Intelligence in Dynamic Environments

by Xiuguo Zou, Xiaochen Zhu, Wentian Zhang, Yan Qian and Yuhua Li

Agriculture 2025, 15(20), 2112; https://doi.org/10.3390/agriculture15202112 (registering DOI) - 11 Oct 2025

Abstract

The rise of agricultural digitalization is progressively reshaping the conventional extended management model through the profound integration of intelligent sensing technology and artificial intelligence [...] Full article

(This article belongs to the Special Issue Application of Vision Technology and Artificial Intelligence in Smart Farming—2nd Edition)

23 pages, 2499 KB

Open AccessReview

Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review

by Hillary Kwame Ofori, Kwame Bell-Dzide, William Leslie Brown-Acquaye, Forgor Lempogo, Samuel O. Frimpong, Israel Edem Agbehadji and Richard C. Millham

Symmetry 2025, 17(10), 1704; https://doi.org/10.3390/sym17101704 (registering DOI) - 11 Oct 2025

Abstract

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep [...] Read more.

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Artificial Intelligence for Cybersecurity)

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15 pages, 606 KB

Open AccessSystematic Review

Artificial Intelligence for Risk–Benefit Assessment in Hepatopancreatobiliary Oncologic Surgery: A Systematic Review of Current Applications and Future Directions on Behalf of TROGSS—The Robotic Global Surgical Society

by Aman Goyal, Michail Koutentakis, Jason Park, Christian A. Macias, Isaac Ballard, Shen Hong Law, Abhirami Babu, Ehlena Chien Ai Lau, Mathew Mendoza, Susana V. J. Acosta, Adel Abou-Mrad, Luigi Marano and Rodolfo J. Oviedo

Cancers 2025, 17(20), 3292; https://doi.org/10.3390/cancers17203292 (registering DOI) - 11 Oct 2025

Abstract

Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its [...] Read more.

Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)

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28 pages, 3979 KB

Open AccessReview

Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction

by David De León Pérez, Sergio Salazar-Galán and Félix Francés

Water 2025, 17(20), 2932; https://doi.org/10.3390/w17202932 (registering DOI) - 11 Oct 2025

Abstract

This Scoping Review methodically synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological modeling-based forecasting. The analysis encompasses 572 studies from 2017 to 2024, with the objective of addressing the central question: What are the emerging trends, best practices, and gaps [...] Read more.

This Scoping Review methodically synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological modeling-based forecasting. The analysis encompasses 572 studies from 2017 to 2024, with the objective of addressing the central question: What are the emerging trends, best practices, and gaps in this field? In accordance with the six-stage protocol that is aligned with PRISMA-ScR standards, 92 studies were selected for in-depth evaluation. The results of the study indicate the presence of three predominant patterns: (1) exponential growth in the applications of machine learning and artificial intelligence; (2) geographic concentration in Chinese, North American, and European watersheds; and (3) persistent operational barriers, particularly in data-scarce tropical regions with limited flood and streamflow forecasting validation. Hybrid statistical-AI modeling frameworks have been shown to enhance forecast accuracy and PU quantification; however, these frameworks are encumbered by constraints in computational demands and interpretability, with inadequate validation for extreme events highlighting critical gaps. The review emphasizes standardized metrics, broader validation, and adaptive postprocessing to enhance applicability, advocating robust frameworks integrating meteorological input to hydrological output postprocessing for minimizing uncertainty chains and supporting water management. This study provides an updated field mapping, identifies knowledge gaps, and prioritizes research for the operational integration of advanced PU quantification. Full article

(This article belongs to the Section Hydrology)

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18 pages, 1644 KB

Open AccessArticle

Composting Poultry Feathers with Keratinolytic Bacillus subtilis: Effects on Degradation Efficiency and Compost Maturity

by Justyna Sobolczyk-Bednarek, Anna Choińska-Pulit and Wojciech Łaba

Materials 2025, 18(20), 4667; https://doi.org/10.3390/ma18204667 (registering DOI) - 11 Oct 2025

Abstract

The continuous advancement of the food industry is accompanied by increased generation of animal waste, including poultry feathers. Composting presents a sustainable alternative to disposal methods such as incineration by converting waste into valuable fertilizer products. This study aimed to evaluate the impact [...] Read more.

The continuous advancement of the food industry is accompanied by increased generation of animal waste, including poultry feathers. Composting presents a sustainable alternative to disposal methods such as incineration by converting waste into valuable fertilizer products. This study aimed to evaluate the impact of inoculation with the keratinolytic strain Bacillus subtilis P22 on the quality and maturity of compost produced from feathers combined with organic additives (wood shavings and lignite). The experiment involved evaluation of the keratinolytic potential of the tested strain, and characterization of its proteolytic enzymes, solid-state cultures and composting conducted at semi-technical scale. The B. subtilis P22 strain demonstrated the ability to solubilize 78% of feather material within 7 days of cultivation. The keratinolytic enzyme complex was likely dominated by polycatalytic alkaline serine proteases, i.e., subtilisins. The effectiveness of the inoculum was confirmed in laboratory solid-state cultures, where the dry mass loss in inoculated samples was twice that of the control containing only endogenous microflora. At the semi-technical scale, inoculation with B. subtilis P22 significantly accelerated compost maturation and mineralization (C/N = 10.2; N-NH₄⁺/N-NO₃⁻ = 0.4; Cw/Corg = 0.9) compared to the control. The final compost’s mineral composition indicates its potential for use as an organic soil amendment. Full article

(This article belongs to the Section Green Materials)

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16 pages, 3417 KB

Open AccessArticle

Roll Angular Velocity and Lateral Overturning Tendency of a Small-Tracked Forestry Tractor Under No-Sideslip Dynamic Driving Conditions

by Yun-Jeong Yang, Moon-Kyeong Jang and Ju-Seok Nam

Forests 2025, 16(10), 1568; https://doi.org/10.3390/f16101568 (registering DOI) - 11 Oct 2025

Abstract

In this study, a driving test was conducted using a small-tracked forestry tractor with a scale of 1/11 in the shape of an actual tractor to assess safety under dynamic conditions. The driving conditions resulting in lateral overturning were derived. Additionally, an angular [...] Read more.

In this study, a driving test was conducted using a small-tracked forestry tractor with a scale of 1/11 in the shape of an actual tractor to assess safety under dynamic conditions. The driving conditions resulting in lateral overturning were derived. Additionally, an angular velocity sensor was used to analyze the variation in roll angular velocity with driving conditions. Driving condition variables comprised obstacle height, ground slope angle, and driving speed. Obstacle height had five levels between 0 and 40 mm in 10 mm intervals, and ground slope angle had 11 levels at 5° intervals from 0° to 50°. Driving speed had three levels: 0.07, 0.11, and 0.13 m/s. The ground slope angle resulting in lateral overturning in the driving scenario was lower than that in non-driving under all conditions. Roll angular velocity increased as obstacle height and tractor driving speed increased. However, ground slope angle did not significantly affect angular velocity. Roll angular velocity at the moment of lateral overturning was about 90 deg/s regardless of driving conditions. A certain critical angular velocity was found to induce lateral overturning, and adjusting the driving method such as reducing driving speed and making turns when the roll angular velocity of the tractor approached the critical value improved safety. However, the quantitative results from the small tractor cannot be directly applied to full-size tractors. Although numerical values may differ, this study focused on capturing the overall trends in lateral overturning considering various driving conditions. Future studies can improve the practical applicability of these findings by determining the critical angular velocity of various full-size tractors. Full article

(This article belongs to the Section Forest Operations and Engineering)

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17 pages, 1397 KB

Open AccessArticle

Activity-Based Profiling of Papain-like Cysteine Proteases During Late-Stage Leaf Senescence in Barley

by Igor A. Schepetkin and Andreas M. Fischer

Plants 2025, 14(20), 3132; https://doi.org/10.3390/plants14203132 (registering DOI) - 11 Oct 2025

Abstract

Leaf senescence is a developmental process that allows nutrients to be remobilized and transported to sink organs. Previously, papain-like cysteine proteases (PLCPs) have been found to be highly expressed during leaf senescence in different plant species. In this study, we analyzed active PLCPs [...] Read more.

Leaf senescence is a developmental process that allows nutrients to be remobilized and transported to sink organs. Previously, papain-like cysteine proteases (PLCPs) have been found to be highly expressed during leaf senescence in different plant species. In this study, we analyzed active PLCPs in barley (Hordeum vulgare L.) leaves during the terminal stage of natural senescence. Anion exchange chromatography of protein extracts from barley leaves, harvested six weeks after anthesis, followed by activity assays using the substrates Z-FR-AMC and Z-RR-AMC, revealed a single prominent peak corresponding to active PLCPs. This hydrolytic activity was completely inhibited by E-64, a potent and irreversible inhibitor of cysteine proteases. Fractions enriched for PLCP activity were affinity-labeled with DCG-04 and subjected to SDS-PAGE fractionation, separating two major bands at 43 and 38 kDa. These bands were analyzed using tandem mass spectrometry, allowing the identification of eleven PLCPs. Identified enzymes belong to eight PLCP subfamilies, including CTB/cathepsin B-like (HvPap-19 and -20), RD19/cathepsin F-like (HvPap-1), ALP/cathepsin H-like (HvPap-12 or aleurain), SAG12/cathepsin L-like A (HvPap-17), CEP/cathepsin L-like B (HvPap-14), RD21/cathepsin L-like D (HvPap-6 and -7), cathepsin L-like E (HvPap-13 and -16), and XBCP3 (HvPap-8). Among the identified PLCPs, HvPap-6 was the most abundant. Peptides corresponding to HvPap-6 were identified in both the 43 kDa and 38 kDa bands in approximately the same quantity based on total spectral count. Thus, our results indicate that two active HvPap-6 isoforms can be isolated from barley leaves at late senescence. Full article

(This article belongs to the Section Plant Development and Morphogenesis)

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25 pages, 738 KB

Open AccessReview

Diabetic Retinopathy, a Comprehensive Overview on Pathophysiology and Relevant Experimental Models

by Kate Gettinger, Deokho Lee, Yohei Tomita, Kazuno Negishi and Toshihide Kurihara

Int. J. Mol. Sci. 2025, 26(20), 9882; https://doi.org/10.3390/ijms26209882 (registering DOI) - 11 Oct 2025

Abstract

Diabetic retinopathy (DR) is a serious complication of diabetes, leading to vision loss worldwide. The prevalence of DR has increased in recent decades. To understand the pathophysiology of DR, various experimental models have been developed and used. In this review article, we first [...] Read more.

Diabetic retinopathy (DR) is a serious complication of diabetes, leading to vision loss worldwide. The prevalence of DR has increased in recent decades. To understand the pathophysiology of DR, various experimental models have been developed and used. In this review article, we first outline what is currently known of the general pathology of DR, including the mechanisms involved in hyperglycemia, vascular dysfunction, retinal ischemia, retinal inflammation, and retinal degeneration. We next summarize various pathologies detected in experimental models in vivo, such as in chemically and genetically induced murine, rat, and monkey models, surgical methods in larger animals like cats, and a novel murine DR model using occlusion of the carotid artery under early diabetic conditions. A general overview of the in vitro models, including cell monocultures, co-cultures, and 3D models, is also provided. This current summary enables further research to obtain a more thorough understanding of DR pathogenesis and develop appropriate treatment measures. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Treatment of Retinal Diseases)

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16 pages, 1803 KB

Open AccessReview

Valve-in-Valve Transcatheter Aortic Valve Implantation Versus Redo SAVR for Degenerated Biological Prosthesis: A Narrative Review Stating Our Experience

by Salvatore Torre, Laura Asta, Adriana Sbrigata, Sebastiano Castrovinci, Enrico Amoncelli, Antonio Segreto, Giuseppe Maria Raffa, Gioachino Agostino Giarratana, Vincenzo Argano and Calogera Pisano

J. Clin. Med. 2025, 14(20), 7158; https://doi.org/10.3390/jcm14207158 (registering DOI) - 11 Oct 2025

Abstract

Surgical aortic valve replacement (SAVR) is still the gold-standard treatment for aortic stenosis. However, the increasing use of biological prostheses, even in young patients, makes Valve-in-Valve (ViV) transcatheter aortic valve implantation (TAVI) an attractive option compared to redo SAVR, thanks to its lower [...] Read more.

Surgical aortic valve replacement (SAVR) is still the gold-standard treatment for aortic stenosis. However, the increasing use of biological prostheses, even in young patients, makes Valve-in-Valve (ViV) transcatheter aortic valve implantation (TAVI) an attractive option compared to redo SAVR, thanks to its lower invasiveness and sometimes greater safety. However, there are several technical and anatomical aspects to consider. Therefore, the aim of our review is to examine the main mechanisms responsible for the degeneration of biological prostheses and, subsequently, to analyze the hemodynamic (transvalvular gradients, patient–prosthesis mismatch, paravalvular leakage) and technical (risk of coronary obstruction, prosthetic implantation strategy) aspects that most influence the procedure’s success and long-term outcomes. To this end, we present a case we treated in order to enhance our readers’ experience with this procedure. Currently, ViV TAVI is approved for patients at high surgical risk, but it could become a valid option compared to redo SAVR; however, more clinical trials are needed to better analyze the survival differences between these two procedures. Furthermore, it remains a therapeutic strategy reserved for highly specialized centers due to the technical difficulties involved in its execution. Full article

(This article belongs to the Special Issue Recent Advances in Transcatheter Aortic Valve Replacement: Second Edition)

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10 pages, 224 KB

Open AccessArticle

Longitudinal Comparison of Burnout and Anxiety Among Healthcare and Non-Healthcare Workers During COVID-19 in Turkey

by Ibrahim Gün, Kadriye Serap Karacalar and Rasim Onur Karaoğlu

COVID 2025, 5(10), 171; https://doi.org/10.3390/covid5100171 (registering DOI) - 11 Oct 2025

Abstract

The COVID-19 pandemic has placed a considerable psychological burden on healthcare workers, potentially leading to increased burnout and anxiety. This study aimed to evaluate burnout and anxiety levels among healthcare workers compared to non-healthcare professionals during the pandemic. We initially recruited 438 adults; [...] Read more.

The COVID-19 pandemic has placed a considerable psychological burden on healthcare workers, potentially leading to increased burnout and anxiety. This study aimed to evaluate burnout and anxiety levels among healthcare workers compared to non-healthcare professionals during the pandemic. We initially recruited 438 adults; 351 (217 HCWs and 134 non-HCWs) provided complete responses across all three survey waves and were analyzed. Burnout was assessed using the Maslach Burnout Inventory, and anxiety with the State–Trait Anxiety Inventory. Data were collected through an online self-administered survey at three different time points during the pandemic, and analyzed with non-parametric tests and effect sizes. Healthcare workers exhibited significantly higher levels of emotional exhaustion, depersonalization, overall burnout, and anxiety compared to non-healthcare workers across all three periods (p < 0.05). Of 438 consented individuals, 351 (80.1%) completed all waves, allowing within-population longitudinal comparisons. Within the healthcare worker group, women, individuals living alone, those working night shifts, and those considering a career change had notably higher burnout and anxiety scores. No significant differences were observed in personal accomplishment scores. Healthcare workers experienced greater psychological distress than non-healthcare workers during the COVID-19 pandemic. Identifying vulnerable subgroups and implementing supportive strategies are essential to protect the mental health and well-being of healthcare professionals during pandemics and similar crises. Full article

(This article belongs to the Special Issue COVID and Public Health)

12 pages, 1926 KB

Open AccessArticle

Tracking False Lumen Remodeling with AI: A Variational Autoencoder Approach After Frozen Elephant Trunk Surgery

by Anja Osswald, Sharaf-Eldin Shehada, Matthias Thielmann, Alan B. Lumsden, Payam Akhyari and Christof Karmonik

J. Pers. Med. 2025, 15(10), 486; https://doi.org/10.3390/jpm15100486 (registering DOI) - 11 Oct 2025

Abstract

Objective: False lumen (FL) thrombosis plays a key role in aortic remodeling after Frozen Elephant Trunk (FET) surgery, yet current imaging assessments are limited to categorical classifications. This study aimed to evaluate an unsupervised artificial intelligence (AI) algorithm based on a variational autoencoder [...] Read more.

Objective: False lumen (FL) thrombosis plays a key role in aortic remodeling after Frozen Elephant Trunk (FET) surgery, yet current imaging assessments are limited to categorical classifications. This study aimed to evaluate an unsupervised artificial intelligence (AI) algorithm based on a variational autoencoder (VAE) for automated, continuous quantification of FL thrombosis using serial computed tomography angiography (CTA). Methods: In this retrospective study, a VAE model was applied to axial CTA slices from 30 patients with aortic dissection who underwent FET surgery. The model encoded each image into a structured latent space, from which a continuous “thrombus score” was developed and derived to quantify the extent of FL thrombosis. Thrombus scores were compared between postoperative and follow-up scans to assess individual remodeling trajectories. Results: The VAE successfully encoded anatomical features of the false lumen into a structured latent space, enabling unsupervised classification of thrombus states. A continuous thrombus score was derived from this space, allowing slice-by-slice quantification of thrombus burden across the aorta. The algorithm demonstrated robust reconstruction accuracy and consistent separation of fully patent, partially thrombosed, and completely thrombosed lumen states without the need for manual annotation. Across the cohort, 50% of patients demonstrated an increase in thrombus score over time, 40% a decrease, and 10% remained unchanged. Despite these individual differences, no statistically significant change in overall thrombus burden was observed at the group level (p = 0.82), emphasizing the importance of individualized longitudinal assessment. Conclusions: The VAE-based method enables reproducible, annotation-free quantification of FL thrombosis and captures patient-specific remodeling patterns. This approach may enhance post-FET surveillance and supports the integration of AI-driven tools into personalized aortic imaging workflows. Full article

(This article belongs to the Special Issue New Insights into Personalized Medicine for Cardiac Surgery: Clinical Advances in Diagnosis, Treatment, and Prognosis)

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19 pages, 7242 KB

Open AccessArticle

Influence of Fe Vacancy on the Bonding Properties of γ-Fe (111)/α-Al₂O₃ (0001) Interfaces: A Theoretical Study

by Xiaofeng Zhang, Renwei Li, Qicheng Chen, Dehao Kong and Haifeng Yang

Materials 2025, 18(20), 4666; https://doi.org/10.3390/ma18204666 (registering DOI) - 11 Oct 2025

Abstract

Here, the effects of Fe vacancy defects on the bonding properties of γ-Fe (111)/α-Al₂O₃ (0001) interfaces are studied in depth at the atomic and electronic levels using first-principles calculations. The first (V₁), second (V₂), third (V [...] Read more.

Here, the effects of Fe vacancy defects on the bonding properties of γ-Fe (111)/α-Al₂O₃ (0001) interfaces are studied in depth at the atomic and electronic levels using first-principles calculations. The first (V₁), second (V₂), third (V₃), and fourth (V₄) layers of vacancy structures within the Fe substrate, as well as the ideal Fe/Al₂O₃ interface structure, are proposed and contrasted, including their thermodynamic parameters and atomic/electronic properties. The results demonstrate that the presence of vacancies in the first atomic layer of Fe deteriorates the interfacial bonding strength, whereas vacancies situated in the third layer enhance the interfacial bonding strength. The effect of vacancy beyond the third layer becomes negligible. This occurs mainly because vacancy defects at different positions induce the relaxation behavior of atoms, resulting in bond-breaking and bond-forming reactions at the interface. Following that, the formation process of vacancies can cause the transfer and rearrangement of the electrons at the interface. This process leads to significant changes in the charge concentration of the interfaces, where V₃ is the largest and V₁ is the smallest, indicating that the greater the charge concentration, the stronger the bonding strength of the interface. Furthermore, it is discovered that vacancy defects can induce new electronic orbital hybridization between Fe and O at the interface, which is the fundamental reason for changes in the properties of the interface. Interestingly, it is also found that more electronic orbital hybridization will strengthen the bonding performance of the interface. It seems, then, that the existence of vacancy defects not only changes the electronic environment of the Fe/Al₂O₃ interface but also directly affects the bonding properties of the interface. Full article

(This article belongs to the Section Materials Simulation and Design)

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13 pages, 1000 KB

Open AccessArticle

Shrinkage, Degree of Conversion, Water Sorption and Solubility, and Mechanical Properties of Novel One-Shade Universal Composite

by Long Ling, Theresa Lai, Pei-Ting Chung and Raj Malyala

Polymers 2025, 17(20), 2728; https://doi.org/10.3390/polym17202728 (registering DOI) - 11 Oct 2025

Abstract

This study aims to evaluate the shrinkage, degree of conversion, water sorption and solubility, and mechanical properties of a newly developed one-shade universal composite and compare it with five other commercially available universal composites with one or multiple shades. Our proprietary resin and [...] Read more.

This study aims to evaluate the shrinkage, degree of conversion, water sorption and solubility, and mechanical properties of a newly developed one-shade universal composite and compare it with five other commercially available universal composites with one or multiple shades. Our proprietary resin and filler technologies developed the experimental one-shade universal composite (Experimental). Volumetric shrinkage was determined using the AcuVol video imaging method (n = 5). Degree of conversion was measured using FTIR (n = 5). Water sorption and solubility (15 × 1 mm, n = 5) and flexural strength and modulus (2 × 2 × 25 mm, n = 5) were measured according to ISO-4049. Diametral tensile strength (6 × 3 mm, n = 8) was tested according to ANSI/ADA-Specification #27. The data were analyzed using one-way ANOVA and post hoc Tukey tests (p ≤ 0.05). Like Clearfil Majesty ES-2, Experimental showed lower or significantly lower volumetric shrinkage than other composites. Experimental exhibited a considerably higher degree of conversion and high flexural modulus compared to the others. However, there are no significant differences in flexural strength among these universal composites except for Omnichroma. Experimental also displayed significantly higher diametral tensile strength than the others, except similar to Filtek Supreme Ultra. Experimental has the lowest values of water sorption and solubility among the composites tested. The experimental universal composite demonstrated improved or comparable physical and mechanical properties compared to commercially available one-shade universal composites or multi-shade conventional universal composites, which is of significance for the clinical performance of dental restorations. Full article

(This article belongs to the Section Polymer Composites and Nanocomposites)

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24 pages, 4004 KB

Open AccessArticle

Genetic Monitoring of the Endangered Acipenser dabryanus Using a High-Resolution MNP System

by Lu Cai, Wei Jiang, Zhiwei Fang, Hai Peng, Hao Chen, Renjing Wan, Lifen Gao, Baolong Zhang, Zilan Xiao, Sha Li, Lun Li, Lihong Chen, Huiyin Song, Tiantian Li and Junfei Zhou

Diversity 2025, 17(10), 704; https://doi.org/10.3390/d17100704 (registering DOI) - 11 Oct 2025

Abstract

Acipenser dabryanus, once abundant in China’s freshwater ecosystems, is now extinct in the wild. Effective genetic tools are urgently needed to support conservation efforts under the Yangtze River Protection Law and the 10-year fishing ban. Traditional molecular markers (e.g., COI, SSR, [...] Read more.

Acipenser dabryanus, once abundant in China’s freshwater ecosystems, is now extinct in the wild. Effective genetic tools are urgently needed to support conservation efforts under the Yangtze River Protection Law and the 10-year fishing ban. Traditional molecular markers (e.g., COI, SSR, SNP) often lack sufficient resolution for fine-scale population assessment. Here, we developed a high-resolution Multiple-Nucleotide Polymorphism (MNP) system for A. dabryanus, comprising 424 newly developed, highly polymorphic markers optimized for multiplex PCR and high-throughput sequencing. The MNP system demonstrated excellent performance in individual fin tissue samples, successfully distinguishing Acipenser sinensis and Acipenser ruthenus individuals from the A. dabryanus population. In addition, 41 characteristic alleles specific to A. dabryanus were further identified. Across samples, it achieved >90% MNP locus detection rate, with an average of 7.48 alleles per locus, 66.5% heterozygosity, >98% reproducibility, and 99% accuracy. A strong correlation was observed between DNA concentration and spike-in-based copy numbers (R² > 0.99), and sensitivity analysis confirmed reliable detection at ~1 copy/reaction. Application of the system across 97 samples, including 51 A. dabryanus tissue samples and 46 water environmental samples, revealed clear population structure with an average genetic differentiation of 70.45%, highlighting substantial genetic diversity within the sampled populations. Based on the above experimental results, the high-resolution MNP system has the potential to enable construction of population-specific allelic genotypes to distinguish wild individuals from released ones and, when applied to tissue and eDNA samples, to facilitate monitoring of migration pathways and habitat connectivity. Such applications could provide essential genetic information to evaluate release programs, guide conservation strategies, and inform habitat restoration for the recovery of A. dabryanus. Full article

(This article belongs to the Special Issue Genetic Diversity, Ecology and Conservation of Endangered Species—2nd Edition)

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18 pages, 2155 KB

Open AccessArticle

PRV gD-Based DNA Vaccine Candidates Adjuvanted with cGAS, UniSTING, or IFN-α Enhance Protective Immunity

by Xinqi Shi, Shibo Su, Yongbo Yang, Liang Meng, Wei Yang, Xinyu Qi, Xuyan Xiang, Yandong Tang, Xuehui Cai, Haiwei Wang, Tongqing An and Fandan Meng

Pathogens 2025, 14(10), 1026; https://doi.org/10.3390/pathogens14101026 (registering DOI) - 11 Oct 2025

Abstract

Pseudorabies virus (PRV), a major swine pathogen, causes severe neurological, respiratory, and reproductive disorders, resulting in substantial economic losses to the global swine industry. Previous studies have shown that the gD glycoprotein of PRV has an effective protective effect. In this study, we [...] Read more.

Pseudorabies virus (PRV), a major swine pathogen, causes severe neurological, respiratory, and reproductive disorders, resulting in substantial economic losses to the global swine industry. Previous studies have shown that the gD glycoprotein of PRV has an effective protective effect. In this study, we constructed a plasmid DNA vaccine (pVAX1-GD-Fc) encoding a gD protein fused with pig IgG Fc and evaluated the adjuvant effects of porcine cGAS, the universal STING complex mimic (UniSTING), or IFN-α in mice. The mice were immunized three times (days 0, 14, and 21) with pVAX1-GD-Fc in the presence or absence of an adjuvant, followed by lethal challenge with PRV-HLJ8 3 days after the final immunization. The results revealed that the pVAX1-GD-Fc group exhibited 20% mortality (1/5 mice) on day 7 postchallenge, and all adjuvanted groups achieved 100% survival during the 14-day observation period. Flow cytometric analysis of splenocytes one week after the second immunization revealed significantly greater CD8+ T cell proportions in the adjuvant groups than in both the mock and pVAX1-GD-Fc-only control groups (p < 0.01). Furthermore, T cell proliferation assays demonstrated a significantly increased stimulation index in the adjuvant-treated mice, confirming enhanced cellular immunity. These findings demonstrate that cGAS, UniSTING, and IFN-α can serve as effective vaccine adjuvants to rapidly enhance cellular immune responses to PRV, highlighting their potential application in veterinary vaccines. Full article

(This article belongs to the Special Issue Advancing Vaccine Strategies and Technologies for Controlling Swine Infectious Diseases)

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19 pages, 7846 KB

Open AccessArticle

Prediction of the Quantitative Biodistribution of Inhaled Titanium Dioxide Nanoparticles Using the Physiologically Based Toxicokinetic Modelling Method

by Jintao Wang, Zhangyu Liu, Bin Wan and Xinguang Cui

Toxics 2025, 13(10), 858; https://doi.org/10.3390/toxics13100858 (registering DOI) - 11 Oct 2025

Abstract

The present study aimed to establish a physiologically based toxicokinetic (PBTK) model to investigate the absorption, retention, and transport of inhaled nano-sized titanium dioxide (TiO₂-NPs) particles in rats, thereby providing a basis for understanding the absorption, distribution, and elimination mechanisms of [...] Read more.

The present study aimed to establish a physiologically based toxicokinetic (PBTK) model to investigate the absorption, retention, and transport of inhaled nano-sized titanium dioxide (TiO₂-NPs) particles in rats, thereby providing a basis for understanding the absorption, distribution, and elimination mechanisms of TiO₂-NPs in various organs. A detailed respiratory module and the Hill coefficient equation were adopted in the PBTK model. Calibration and validation of the model were conducted using the only two available inhalation biodistribution datasets for TiO₂-NPs found in the literature, encompassing different doses and exposure conditions. The overall fit with both datasets was acceptable with R² value of 0.95 in respiratory system and 0.88 in the secondary organs. The sensitivity analysis indicated that the alveolar–interstitial transfer rate (K_{alv_inter}) and tissue–blood distribution coefficients (P_lu, P_li, P_ki) significantly influenced the retention of TiO₂-NPs in pulmonary regions and distribution to secondary organs, with these parameters exhibiting time-dependent behavior. The PBTK model demonstrates a good predictive performance for TiO₂-NPs content in all rat organs, with simulated values consistently ranging within 0.5- to 2-fold of the measured data. In last, we developed a PBTK model that can well predict the in vivo distribution of inhaled TiO₂-NPs and provided a novel computational tool for cross-species extrapolation of human inhalation exposure and subsequent biodistribution. Full article

(This article belongs to the Special Issue Effects of Air Pollutants on Cardiorespiratory Health)

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14 pages, 13425 KB

Open AccessArticle

Evaluation of Wood Decay and Identification of Fungi Found in the USS Cairo, a Historic American Civil War Ironclad Gunboat

by Robert A. Blanchette, Benjamin W. Held, Claudia Chemello and Paul Mardikian

J. Fungi 2025, 11(10), 732; https://doi.org/10.3390/jof11100732 (registering DOI) - 11 Oct 2025

Abstract

Studies of microbial degradation of historic woods are essential to help protect and preserve these important cultural properties. The USS Cairo is a historic Civil War gunboat and one of the first steam-powered and ironclad ships used in the American Civil War. Built [...] Read more.

Studies of microbial degradation of historic woods are essential to help protect and preserve these important cultural properties. The USS Cairo is a historic Civil War gunboat and one of the first steam-powered and ironclad ships used in the American Civil War. Built in 1861, the ship sank in the Yazoo River of Mississippi in 1862 after a mine detonated and tore a hole in the port bow. The ship remained on the river bottom and was gradually buried with sediments for over 98 years. After recovery of the ship, it remained exposed to the environment before the first roofed structure was completed in 1980, and it has been displayed under a tensile fabric canopy with open sides at the Vicksburg National Military Park in Vicksburg, Mississippi. Concerns over the long-term preservation of the ship initiated this investigation to document the current condition of the wooden timbers, identify the fungi that may be present, and determine the elemental composition resulting from past wood-preservative treatments. Micromorphological characteristics observed using scanning electron microscopy showed that many of the timbers were in advanced stages of degradation. Eroded secondary cell walls leaving a weak framework of middle lamella were commonly observed. Soft rot attack was prevalent, and evidence of white and brown rot degradation was found in some wood. DNA extraction and sequencing of the ITS region led to the identification of a large group of diverse fungi that were isolated from ship timbers. Soft rot fungi, including Alternaria, Chaetomium, Cladosporium, Curvularia, Xylaria and others, and white rot fungi, including Bjerkandera, Odontoefibula, Phanerodontia, Phlebiopsis, Trametes and others, were found. No brown rot fungi were isolated. Elemental analyses using induced coupled plasma spectroscopy revealed elevated levels of all elements as compared to sound modern types of wood. High concentrations of boron, copper, iron, lead, zinc and other elements were found, and viable fungi were isolated from this wood. Biodegradation issues are discussed to help long-term conservation efforts to preserve the historic ship for future generations. Full article

(This article belongs to the Special Issue Mycological Research in Cultural Heritage Protection)

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25 pages, 3977 KB

Open AccessArticle

Multi-Sensor Data Fusion and Vibro-Acoustic Feature Engineering for Health Monitoring and Remaining Useful Life Prediction of Hydraulic Valves

by Xiaomin Li, Liming Zhang, Tian Tan, Xiaolong Wang, Xinwen Zhao and Yanlong Xu

Sensors 2025, 25(20), 6294; https://doi.org/10.3390/s25206294 (registering DOI) - 11 Oct 2025

Abstract

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging [...] Read more.

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

(This article belongs to the Special Issue Advances in Sensors for Online Condition Monitoring and Fault Diagnosis)

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17 pages, 2716 KB

Open AccessArticle

Enhancing Flare Gas Treatment: A Systematic Evaluation of Dual-Stage (Amine, CO₂ Supercritical) and Hybrid Approaches Using HYSYS

by Sulafa Abdalmageed Saadaldeen Mohammed, Khaled Elraies, M. Basheer Alameen and Mohammed Awad

ChemEngineering 2025, 9(5), 110; https://doi.org/10.3390/chemengineering9050110 (registering DOI) - 11 Oct 2025

Abstract

The flaring of associated gas in oil and gas operations contributes significantly to greenhouse gas emissions and represents a loss of valuable hydrocarbon resources. While amine absorption is widely applied for acid gas removal, the use of supercritical carbon dioxide (sc-CO₂) [...] Read more.

The flaring of associated gas in oil and gas operations contributes significantly to greenhouse gas emissions and represents a loss of valuable hydrocarbon resources. While amine absorption is widely applied for acid gas removal, the use of supercritical carbon dioxide (sc-CO₂) for flare gas treatment remains largely unexplored, despite its proven selectivity for hydrocarbons in other industries such as natural product extraction and polymer processing. Conventional flare gas treatment methods face trade-offs: amine absorption achieves high acid gas removal efficiency but offers limited selectivity for heavier hydrocarbons, whereas sc-CO₂ extraction enables efficient recovery of higher hydrocarbons but does not fully remove acid gases. This study addresses these gaps by evaluating three two-stage flare gas treatment configurations—dual-stage amine absorption, dual-stage sc-CO₂ absorption, and a hybrid of sc-CO₂ followed by amine absorption—using Aspen HYSYS V12.1 simulations, with recycling processes considered in each case. The dual-stage sc-CO₂ process achieved nearly complete hydrocarbon recovery (100%) and complete H₂S removal, but CO₂ remained at elevated concentrations in the treated gas. The dual-stage amine process completely removed CO₂ and H₂S, though with higher energy demand for solvent regeneration. The hybrid configuration combined the advantages of both approaches, achieving complete H₂S removal, 100% hexane recovery, 95.02% methane recovery, and a drastic reduction in CO₂ concentration (to 0.0012 mole fraction). These results demonstrate that integrating sc-CO₂ with amine absorption resolves the trade-off between hydrocarbon selectivity and acid gas removal, establishing a technically viable pathway for flare gas utilization with potential application in gas-to-liquids (GTL) and carbon management strategies Full article

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19 pages, 3509 KB

Open AccessSystematic Review

by Arun Vamadevan, Vijesh Vijayan, Fellisha Marwein and Nishad Yoosuf

Geriatrics 2025, 10(5), 130; https://doi.org/10.3390/geriatrics10050130 (registering DOI) - 11 Oct 2025

Abstract

Background: Older adults are at elevated risk of falls, especially when prescribed AEDs (AEDs) for neuropathic pain. The sedative and neuropsychiatric effects of these agents contribute significantly to fall-related morbidity. However, existing studies often lack stratification by age and dose. Objective: To systematically [...] Read more.

Background: Older adults are at elevated risk of falls, especially when prescribed AEDs (AEDs) for neuropathic pain. The sedative and neuropsychiatric effects of these agents contribute significantly to fall-related morbidity. However, existing studies often lack stratification by age and dose. Objective: To systematically evaluate the incidence and drug-specific risk of falls and fall-related adverse events (AEs) in older adults prescribed AEDs for neuropathic pain. Methods: A systematic search was performed across PubMed, Scopus, CINAHL, ScienceDirect, and Cochrane Library databases up to May 2025. Studies were selected using PICOS criteria and included RCTs and controlled cohort studies reporting on AED-related AEs among participants aged ≥60 years. The methodological quality was assessed using RoB 2, ROBINS-I, and GRADE frameworks. Meta-analyses were performed using logit event rates and fixed-effects modeling via Comprehensive Meta-Analysis v3.7. Publication bias was evaluated using Begg’s and Egger’s tests. Results: Twenty-three studies met the inclusion criteria. The pooled logit event rate for falls was −1.693 (95% CI: −1.993 to −1.393), corresponding to a 15.5% incidence. Gabapentin showed the lowest fall risk (~10%), while pregabalin and carbamazepine were associated with higher rates of dizziness (up to 21.6%), sedation (~15.5%), and ataxia (~17.8%). Heterogeneity was low (I² = 0–22.3%) across outcomes. Conclusions: AEDs carry a clinically significant fall risk in older adults, with dose-dependent patterns. Gabapentin may present a safer profile, while pregabalin and carbamazepine warrant cautious use and monitoring. These findings inform individualized prescribing and fall prevention strategies in geriatric neuropathic pain management. Full article

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20 pages, 2594 KB

Open AccessArticle

Evaluating the Generalization Gaps of Intrusion Detection Systems Across DoS Attack Variants

by Roshan Jameel, Khyati Marwah, Sheikh Mohammad Idrees and Mariusz Nowostawski

J. Cybersecur. Priv. 2025, 5(4), 85; https://doi.org/10.3390/jcp5040085 (registering DOI) - 11 Oct 2025

Abstract

Intrusion Detection Systems (IDS) play a vital role in safeguarding networks, yet their effectiveness is often challenged, as cyberattacks evolve in new and unexpected ways. Machine learning models, although very powerful, usually perform well only on data that closely resembles what they were [...] Read more.

Intrusion Detection Systems (IDS) play a vital role in safeguarding networks, yet their effectiveness is often challenged, as cyberattacks evolve in new and unexpected ways. Machine learning models, although very powerful, usually perform well only on data that closely resembles what they were trained on. When faced with unfamiliar traffic, they often misclassify. In this work, we examine this generalization gap by training IDS models on one Denial-of-Service (DoS) variant, DoS Hulk, and testing them against other variants such as Goldeneye, Slowloris, and Slowhttptest. Our approach combines careful preprocessing, dimensionality reduction with Principal Component Analysis (PCA), and model training using Random Forests and Deep Neural Networks. To better understand model behavior, we tuned decision thresholds beyond the default 0.5 and found that small adjustments can significantly affect results. We also applied Shapley Additive Explanations (SHAP) to shed light on which features the models rely on, revealing a tendency to focus on fixed components that do not generalize well. Finally, using Uniform Manifold Approximation and Projection (UMAP), we visualized feature distributions and observed overlaps between training and testing datasets, but these did not translate into improved detection performance. Our findings highlight an important lesson: visual or apparent similarity between datasets does not guarantee generalization, and building robust IDS requires exposure to diverse attack patterns during training. Full article

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18 pages, 5417 KB

Open AccessArticle

¹H Time Domain Nuclear Magnetic Resonance and Oscillatory Rheology as a Tool for Uncovering the Impact of UV-C Radiation on Polypropylene

by Jessica Caroline Ferreira Gimenez, Sophia Helena Felisbino Bonatti, Marcos Vinícius Basaglia, Rodrigo Henrique dos Santos Garcia, Alef dos Santos, Lucas Henrique Staffa, Mazen Samara, Silvia Helena Prado Bettini, Eduardo Ribeiro de Azevedo, Emna Helal, Nicole Raymonde Demarquette, Manoel Gustavo Petrucelli Homem and Sandra Andrea Cruz

Polymers 2025, 17(20), 2727; https://doi.org/10.3390/polym17202727 (registering DOI) - 11 Oct 2025

Abstract

UV-C radiation has emerged as a germicidal agent against pathogens, particularly following the COVID-19 pandemic. While UV-C effectively reduces cross-contamination in hospitals, it induces photodegradation in polymer devices, potentially damaging and posing risks to patient safety. Therefore, it is crucial to detect the [...] Read more.

UV-C radiation has emerged as a germicidal agent against pathogens, particularly following the COVID-19 pandemic. While UV-C effectively reduces cross-contamination in hospitals, it induces photodegradation in polymer devices, potentially damaging and posing risks to patient safety. Therefore, it is crucial to detect the effects of UV-C photodegradation on early stages, as well as the effects of prolonged UV-C exposure. In this study, we investigated the UV-C photodegradation (254 nm, 471 kJ/mol) of isotactic polypropylene homopolymer (PP), commonly used in medication packaging. The impact of UV-C on PP was evaluated through rheology and infrared spectroscopy. Surface energy was measured by the contact angles formed by drops of water and diiodomethane. The effects of photodegradation on the polymer’s morphology were examined using scanning electron microscopy, and the melting temperature and crystallinity by differential scanning calorimetry. Lastly, the effect of UV-C on molecular mobility was studied using ¹H Time Domain Nuclear Magnetic Resonance (¹H TD-NMR). These techniques proved to be valuable tools for identifying the early stages of UV-C photodegradation, and ¹H TD-NMR was a sensitive method to identify the chain branching as a photodegradation product. This study highlights the impact of UV-C on PP photodegradation and hence the importance of understanding UV-C-induced degradation. Full article

(This article belongs to the Special Issue Degradation and Stabilization of Polymer Materials 2nd Edition)

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32 pages, 781 KB

Open AccessArticle

Navigating Emotional Barriers and Cognitive Drivers in Mobile Learning Adoption Among Greek University Students

by Stefanos Balaskas, Vassilios Tsiantos, Sevaste Chatzifotiou, Dionysia Filiopoulou, Kyriakos Komis and George Androulakis

Knowledge 2025, 5(4), 23; https://doi.org/10.3390/knowledge5040023 (registering DOI) - 11 Oct 2025

Abstract

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as [...] Read more.

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

(This article belongs to the Special Issue Knowledge Management in Learning and Education)

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