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Background/Objectives: Postoperative sensitivity remains a common challenge following direct composite restorations, especially in Class II cavities with deep proximal boxes. The snow-plow and resin-coating techniques have been proposed to improve marginal adaptation and reduce postoperative discomfort; however, comparative clinical data remain limited. This prospective, split-mouth, quasi-experimental study aimed to compare postoperative pain associated with Class II restorations placed using either the snow-plow or resin-coating technique. Methods: This prospective, split-mouth study followed 83 adult patients (aged 18–45 years) who received bilateral Class II composite restorations for one week. The study received ethical approval. Each participant received one restoration using the snow-plow technique and another using the resin-coating approach. Pain intensity was evaluated using a 10-point visual analog scale (VAS) at baseline, 24-h, 72-h, and 1-week postoperatively. Analyses included Wilcoxon signed-rank, Friedman, Chi-square, McNemar, and two-way repeated-measures ANOVA tests. Results: Pain intensity peaked at 24-h for both techniques and declined significantly by 72-h and 1 week (p < 0.001). The snow-plow technique showed slightly lower mean pain scores at 24 and 72 h (p = 0.026 and p = 0.004, respectively), though categorical analyses revealed no significant difference in pain-free or minimal-pain proportions at any interval (p > 0.05). Both techniques showed significant within-group reductions in pain over time (p < 0.001). Conclusions: Both restorative approaches demonstrated similar postoperative pain trajectories, with substantial improvement by one week. While minor differences in early mean pain intensity were observed, these were not clinically significant. The findings suggest that either technique can be effectively employed to achieve satisfactory postoperative comfort when modern adhesive protocols are applied. Clinicians can therefore select either technique based on preference and clinical circumstances, with the expectation of comparable short-term postoperative comfort outcomes. Full article

(This article belongs to the Section Dentistry, Oral Surgery and Oral Medicine)

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39 pages, 8925 KB

Open AccessReview

Rainfall-Induced Landslide Prediction Models, Part I: Empirical–Statistical and Physically Based Causative Thresholds

by Kyrillos Ebrahim, Sherif M. M. H. Gomaa, Tarek Zayed and Ghasan Alfalah

Water 2025, 17(22), 3273; https://doi.org/10.3390/w17223273 (registering DOI) - 16 Nov 2025

Abstract

Introduction and Problem Statement: Landslides represent a significant geological hazard worldwide. One of the primary triggers for these landslides is rainfall, which is becoming more intense as a result of climate change. The available literature has produced extensive research. However, this largely overlooks the use of mixed methodologies. Furthermore, a comprehensive review combining empirical, physically based, deterministic, and phenomenological models is still rare. Objective and Method: This study (Part I of a two-part review) addresses this gap by employing a mixed review that integrates quantitative scientometric analysis with a qualitative systematic review. The primary objective of Part I is to deliver a critical assessment, focusing on empirical and physically based causative threshold models. Main Results and Validation: Macroscopically, our analysis reveals that antecedent rainfall is a more robust indicator than classical intensity–duration (I-D) thresholds, though the latter remains widely used due to its simplicity. Physically based models provide a critical bridge when geotechnical data is scarce, correlating rainfall with internal slope responses like displacement. At a microscopic level, hybrid artificial intelligence (AI) models consistently demonstrate superior predictive accuracy by capturing complex, nonlinear relationships missed by simpler models. These findings are validated through a systematic evaluation of performance metrics across the reviewed literature. Main Conclusions and Significance: We conclude that while empirical thresholds offer operational simplicity, the future of accurate prediction lies in sophisticated hybrid AI models trained on extensive monitoring data. This review synthesizes fragmented knowledge into a unified framework, providing a clear roadmap for model selection. Full article

(This article belongs to the Special Issue Rainfall-Induced Landslides: Influencing, Modelling and Hazard Assessment: 2nd Edition)

15 pages, 3132 KB

Open AccessArticle

Visibility-Based Calibration of Low-Cost Particulate Matter Sensors: Laboratory Evaluation and Theoretical Analysis

by Ayala Ronen

Sensors 2025, 25(22), 6995; https://doi.org/10.3390/s25226995 (registering DOI) - 16 Nov 2025

Abstract

Low-cost optical sensors for particulate matter (PM) monitoring, such as the SDS011, are widely used due to their affordability and ease of deployment. However, their accuracy strongly depends on aerosol properties and environmental conditions, necessitating reliable calibration. This study presents a theoretical and laboratory evaluation of a practical calibration method based on visibility sensors, which measure atmospheric light extinction and are readily available at many meteorological stations. Experiments were conducted in a controlled aerosol chamber, using SDS011 sensors, visibility sensors (FD70 and SWS250), and gravimetric samplers. The mass extinction coefficient was determined through parallel measurements of visibility and mass concentration, enabling conversion of optical signals into accurate PM values. The calibrated SDS011 sensors demonstrated consistent response with a stable normalization factor (dependent on aerosol type, wavelength, and particle size), allowing their deployment as a spatially distributed sensor network. Comparison with manufacturer calibration revealed substantial deviations due to differences in aerosol optical properties, highlighting the importance of application-specific calibration. The visibility-based approach enables real-time, continuous calibration of low-cost sensors with minimal equipment, offering a scalable solution for PM monitoring in resource-limited or remote environments. The method’s robustness under varying environmental conditions remains to be explored. Nevertheless, the results establish visibility-based calibration as a reliable and accessible framework for enhancing the accuracy of low-cost PM sensing technologies. The method enables scalable calibration with a single gravimetric reference and is suited for future field deployment in resource-limited settings, following additional validation under real atmospheric conditions. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)

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26 pages, 2510 KB

Open AccessArticle

A Three-Machine Flowshop Scheduling Problem with Linear Fatigue Effect

by Weiping Xu, Zehou Sun, Xiaotian Ai, Baoyun Zhao, Jingyi Lu, Hanyu Zhou, Xinqi Mao, Xiaoling Wen, Chin-Chia Wu and Shufeng Liu

Mathematics 2025, 13(22), 3670; https://doi.org/10.3390/math13223670 (registering DOI) - 16 Nov 2025

Abstract

Highly customized requirements in smart manufacturing result in the unavoidable manual execution of complex operational procedures. Physical and mental fatigue from long work periods for assembly-line operators induces production issues, such as defective work-in-processes or equipment failure. An effective production schedule should account for worker fatigue. This study investigates a three-machine flowshop scheduling problem with the objective of makespan minimization, in which a linear fatigue effect function provides an approximate mathematical representation of fatigue and recovery processes in workers. A mixed integer programming (MIP) model is developed to optimize the integration of automated and human-operated production in manufacturing systems. Given its NP-hardness, an improved tabu search (ITS) algorithm is designed to obtain high-quality solutions, incorporating multiple initial solutions, a well-designed encoding-decoding strategy, and a tabu-based adaptive search mechanism to enhance efficiency. Numerical simulations indicate the veracity of the MIP model and the effectiveness of the ITS algorithm. Full article

(This article belongs to the Special Issue Recent Advances in Planning and Scheduling for Supply Chain Optimization)

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21 pages, 2624 KB

Open AccessArticle

Hypersphere-Guided Reciprocal Point Learning for Open-Set Industrial Process Fault Diagnosis

by Shipeng Li, Qi Wen, Binbin Zheng and Xinhua Wang

Processes 2025, 13(11), 3698; https://doi.org/10.3390/pr13113698 (registering DOI) - 16 Nov 2025

Abstract

Deep neural networks (DNNs) have achieved superior performance in diagnosing process faults, but they lack robustness when encountering novel fault types absent from training sets. Such unknown faults commonly appear in industrial settings, and conventional DNNs often misclassify them as one of the known fault types. To address this limitation, we formulate the concept of open-set fault diagnosis (OSFD), which seeks to distinguish unknown faults from known ones while correctly classifying the known faults. The primary challenge in OSFD lies in minimizing both the empirical classification risk associated with known faults and the open space risk without access to training data for unknown faults. In order to mitigate these risks, we introduce a novel approach called hypersphere-guided reciprocal point learning (SRPL). Specifically, SRPL preserves a DNN for feature extraction while constraining features to lie on a unit hypersphere. To reduce empirical classification risk, it applies an angular-margin penalty that explicitly increases intra-class compactness and inter-class separation for known faults on the hypersphere, thereby improving discriminability among known faults. Additionally, SRPL introduces reciprocal points on the hypersphere, with each point acting as a classifier by occupying the extra-class region associated with a particular known fault. The interactions among multiple reciprocal points, together with the deliberate synthesis of unknown fault features on the hypersphere, serve to lower open-space risk: the reciprocal-point interactions provide an indirect estimate of unknowns, and the synthesized unknowns provide a direct estimate, both of which enhance distinguishability between known and unknown faults. Extensive experimental results on the Tennessee Eastman process confirm the superiority of the proposed method compared to state-of-the-art OSR algorithms, e.g., an 82.32% AUROC score and a 71.50% OSFDR score. Full article

(This article belongs to the Special Issue Fault Detection Based on Deep Learning)

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37 pages, 13106 KB

Open AccessArticle

Extend the Lifetime of Power Components in Series DC Motor Drives Using ANN-Based Adaptive Switching Frequency Optimization

by Erkan Eren, Hakan Kaya and Salih Baris Ozturk

Sensors 2025, 25(22), 6996; https://doi.org/10.3390/s25226996 (registering DOI) - 16 Nov 2025

Abstract

This study presents an Artificial Neural Network (ANN)-based adaptive switching frequency control strategy for series Direct current (DC) motor drives used in battery-powered mining locomotives, aiming to extend the lifetime of critical power-electronic components such as Insulated Gate Bipolar Transistors (IGBTs) and DC bus capacitors. In embedded systems for electric traction, two dominant degradation factors, motor current ripple and IGBT temperature fluctuation, significantly shorten component lifetimes. Conventional fixed switching frequencies impose a trade off: higher frequencies reduce current ripple but increase IGBT losses and temperature, while lower frequencies yield the opposite effect. Consequently, an adaptive variable switching frequency control algorithm is proposed to perform real-time decision making by predicting the optimal switching frequency that minimizes both motor current ripple and IGBT thermal fluctuations. The proposed algorithm was trained with a dataset acquired from current sensors, NTC temperature sensors, and a potentiometer defining the target current (PWM duty). Performance comparisons with a fixed frequency demonstrate that the ANN-driven approach maintains an average current ripple of less than 5% (average) and 10% (maximum), while the lifetime of the IGBT and capacitors improves. A fairness index was defined to quantify the relative lifetime improvement of the IGBT and capacitor, revealing that the proposed variable frequency switching model enhances the overall system performance by up to 13 times compared to fixed-frequency operation. These results confirm that the integration of embedded machine learning and adaptive control algorithms can substantially enhance the durability and efficiency of power-electronic systems in real-time industrial applications. Full article

(This article belongs to the Section Electronic Sensors)

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29 pages, 2146 KB

Open AccessArticle

A Lightweight Training Approach for MITM Detection in IoT Networks: Time-Window Selection and Generalization

by Yi-Min Yang, Ko-Chin Chang and Jia-Ning Luo

Appl. Sci. 2025, 15(22), 12147; https://doi.org/10.3390/app152212147 (registering DOI) - 16 Nov 2025

Abstract

The world has adopted so many IoT devices but it comes with its own share of security vulnerabilities. One such issue is ARP spoofing attack which allows a man-in-the-middle to intercept packets and thereby modify the communication. Also, this allows an intruder to gain access to the user’s entire local area network. The ACI-IoT-2023 dataset captures ARP spoofing attacks, yet its absence of specified extracted features hinders its application in machine learning-aided intrusion detection systems. To combat this, we present a framework for ARP spoofing detection which improves the dataset by extracting ARP-specific features and evaluating their impact under different time-window configurations. Beyond generic feature engineering and model evaluation, we contribute by treating ARP spoofing as a time-window pattern and aligning the window length with observed spoofing persistence from the dataset timesheet—turning window choice into an explainable, repeatable setting for constrained IoT devices; by standardizing deployment-oriented efficiency profiling (inference latency, RAM usage, and model size) reported alongside accuracy, precision, recall and F1-scores to enable edge-feasible model selection; and by providing an ARP-focused, reproducible pipeline that reconstructs L2 labels from public PCAPs and derives missing link-layer indicators, yielding a transparent path from labeling to windowed features to training evaluation. Our research systematically analyzes five models with multiple time-windows, including Decision Tree, Random Forest, XGBoost, CatBoost, and K-Nearest Neighbors. This study shows that XGBoost and CatBoost provide maximum performance at the 1800 s window that corresponds to the longest spoofing duration in the timesheet, achieving accuracy greater than 0.93%, precision above 0.95%, recall near 0.91%, and F1-scores above 0.93%. Although Decision Tree has the least inference latency (∼0.4 ms.), its lower recall risks missed attacks. By contrast, XGBoost and CatBoost sustain strong detection with less than 6$ ms inference and moderate RAM, indicating practicality for IoT deployment. We also observe diminishing returns beyond (∼1800 s) due to temporal over-aggregation. Full article

(This article belongs to the Special Issue Machine Learning and Its Application for Anomaly Detection)

17 pages, 637 KB

Open AccessArticle

Multicast Covert Communication in PA-Assisted ISAC Systems

by Bingtao He, Yuxiang Ding, Lu Lv, Long Yang, Yuchen Zhou and Jian Chen

Electronics 2025, 14(22), 4464; https://doi.org/10.3390/electronics14224464 (registering DOI) - 16 Nov 2025

Abstract

A covert communication scheme is designed for pinching antenna (PA)-enabled integrated sensing and communication (ISAC) systems. The base station (BS) emits sensing signals to detect the potential eavesdropper while opportunistically performing covert multicast transmissions. To enhance covertness, the inherent power uncertainty of the sensing signals is exploited to confuse eavesdroppers, thereby creating protective coverage for the legitimate transmission. For the considered systems, we design an alternating optimization framework to iteratively optimize the baseband, beamforming, and PA positionson the two waveguides, in which successive convex approximation and particle swarm optimization methods are introduced. Simulated results confirm that the proposed scheme achieves the highest covert communication rates with different numbers of multicast users compared to benchmark methods. Furthermore, increasing the transmit power and the number of PAs can further improve the covertness performance. Full article

(This article belongs to the Special Issue Security for Integrated Sensing and Communication in Next-Generation Wireless Communication)

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18 pages, 5256 KB

Open AccessArticle

Cotton Yield Map Prediction Using Sentinel-2 Satellite Imagery in the Brazilian Cerrado Production System

by Carlos Manoel Pedro Vaz, Ednaldo José Ferreira, Eduardo Antônio Speranza, Júlio César Franchini, João de Mendonça Naime, Ricardo Yassushi Inamasu, Ivani de Oliveira Negrão Lopes, Sérgio das Chagas, Mathias Xavier Schelp, Leonardo Vecchi and Rafael Galbieri

AgriEngineering 2025, 7(11), 390; https://doi.org/10.3390/agriengineering7110390 (registering DOI) - 16 Nov 2025

Abstract

Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in large-scale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha⁻¹ and R² of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha⁻¹, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring. Full article

(This article belongs to the Special Issue Remote Sensing for Enhanced Agricultural Crop Management)

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15 pages, 2779 KB

Open AccessCommunication

Synthesis of 1,2,4-Triazole-3-Thiol Derivatives from Thiosemicarbazides and Carboxylic Acids Using Polyphosphate Ester

by Bogdan A. Tretyakov, Viktoria I. Tikhonova, Svyatoslav Y. Gadomsky and Nataliya A. Sanina

Molecules 2025, 30(22), 4422; https://doi.org/10.3390/molecules30224422 (registering DOI) - 16 Nov 2025

Abstract

Conditions have been established for the direct reaction of thiosemicarbazides with carboxylic acids in the presence of polyphosphate ester (PPE) to synthesize 1,2,4-triazole-3-thiol derivatives. The synthesis involves two consecutive steps: (i) acylation of the thiosemicarbazide with a carboxylic acid in chloroform in the presence of PPE at 90 °C using a hydrothermal reaction vessel, followed by (ii) cyclodehydration of the acylation product by treatment with an aqueous alkali solution. Using this new synthetic approach, 15 derivatives of 1,2,4-triazole-3-thiol were obtained, five of which were synthesized for the first time. The structures of the synthesized compounds were confirmed by NMR spectroscopy and mass spectrometry. Full article

(This article belongs to the Special Issue Heterocyclic Compounds: Synthesis, Characterization and Biological Evaluation)

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27 pages, 2567 KB

Open AccessArticle

Economic Sustainability of Selected Individual On-Site Systems of Rural Sanitation Under Conditions in Poland

by Marcin K. Widomski and Anna Musz-Pomorska

Sustainability 2025, 17(22), 10241; https://doi.org/10.3390/su172210241 (registering DOI) - 16 Nov 2025

Abstract

The sustainability of rural areas depends on effective wastewater management to reduce human impact on the environment, including the risk of pollution to surface water, groundwater, and soil from human waste. However, organized sanitation systems, which include pipeline networks and wastewater treatment plants in rural communities with low population densities, often have very low profitability and cost-efficiency, which greatly reduces their acceptance and residents’ willingness to pay. This study examines the economic profitability and cost-efficiency of selected on-site household sewage collection and treatment systems operating under real economic conditions in Poland. An evaluation was conducted on seven contemporary models of individual bioreactors, as well as a standard anaerobic septic tank equipped with drainage filters. Additionally, all options were tested on permeable and poorly permeable soils. For each variant, investment costs, as well as operation and maintenance expenses, were calculated. Financial evaluation utilized indicators of economic profitability and cost-efficiency, including the Payback Period, Net Present Value, Benefits–Cost Ratio, and Dynamic Generation Costs. The potential financial benefits included savings from avoiding the use of holding septic tanks and sewage transport by slurry wagons. All the studied designs of on-site sanitary sewage management showed significant economic feasibility and cost-efficiency. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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19 pages, 4260 KB

Open AccessArticle

Safety and Functional Properties of Rapeseed Honey Regarding Its Geographical Origin

by Monika Tomczyk, Monika Lewczuk, Michał Miłek, Magdalena Surma, Anna Sadowska-Rociek and Małgorzata Dżugan

Appl. Sci. 2025, 15(22), 12146; https://doi.org/10.3390/app152212146 (registering DOI) - 16 Nov 2025

Abstract

Rapeseed (Brassica napus) honey is a popular monofloral honey produced in Poland and is often suspected of pesticide-residue contamination due to the extensive use of pesticides in oilseed rape cultivation. Moreover, because of the presence of fatty acids, it can absorb hydrophobic polycyclic aromatic hydrocarbons (PAHs) that occur as environmental pollutants. Thus, the aim of the study was to assess the safety of rapeseed honey in terms of pesticide residues and PAHs contamination in relation to its functional properties, including antioxidant properties, polyphenol profile, protein content, and enzymatic activity. Local honey samples originating from Lublin (five) and Podkarpackie (five) Voivodeships were compared with five samples purchased from commercial sources. None of 58 pesticides, including carbamates, organophosphorus, organochlorines, pyrethroids, and neonicotinoids, were detected in the tested honey samples. All samples were also completely free of four major harmful PAHs legally limited in food (benzo[a]pyrene, benz[a]anthracene, chrysene, and benzo[b]fluoranthene). Among other PAH compounds, seven were detected accidentally in samples of various origins. The total phenolic content and antioxidant activity determined by DPPH^•, FRAP, and CUPRAC assays were relatively uniform among the groups studied. High-performance thin-layer chromatography (HPTLC) revealed characteristic fingerprints including kaempferol, ferulic acid, and caffeic acid, providing a specific profile that can be considered a marker of rapeseed honey authenticity and used to detect adulteration. Protein content ranged from 18 to 85 mg/100 g, remaining within the range typical for light honeys, while α-glucosidase activity was significantly reduced in commercial products, reflecting the effects of processing and storage. The study confirmed the high functional value and safety of rapeseed honey offered on the South-Eastern Poland market, which confirm the cleanliness of the bees’ habitat in terms of pesticide residues and PAHs pollution. Nevertheless, regular monitoring of pesticide residues and PAHs in honeys from agricultural areas remains advisable. Full article

(This article belongs to the Special Issue The World of Bees: Diversity, Ecology and Conservation)

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26 pages, 5214 KB

Open AccessArticle

Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers

by Kanat Suleimenov, Akim Kapsalyamov, Beibit Abdikenov, Aiman Ozhikenova, Yerbolat Igembay and Kassymbek Ozhikenov

Mathematics 2025, 13(22), 3669; https://doi.org/10.3390/math13223669 (registering DOI) - 16 Nov 2025

Abstract

The control of tendon-driven robotic fingers presents significant challenges due to their inherent underactuation, coupled with complex non-linear dynamics arising from tendon elasticity, friction, and external disturbances. Therefore, achieving precise control of finger motion and contact interactions necessitates advanced modeling, estimation, and control strategies capable of addressing uncertainties in tendon tension, routing, and elasticity. This paper presents a comprehensive comparative study of three distinct control paradigms: feedback linearization with Proportional-Derivative (FBL-PD) control, feedback linearization with super-twisting sliding-mode algorithm (FBL-STA), and deep-deterministic reinforcement learning (DDPG-RL), for the precise trajectory tracking of a three-link tendon-driven robotic finger. Through extensive simulations, the performance of each controller is rigorously evaluated based on trajectory-tracking accuracy and robustness to varying disturbances. The results indicate that under disturbance-free conditions, the FBL-PD and FBL-STA controllers, when properly tuned, achieve precise tracking of the reference trajectory; however, they produce noticeably noisy control signals. When subjected to external disturbances, these controllers exhibit increased sensitivity, producing even noisier responses. In contrast, the DDPG-RL maintains smooth control dynamics and achieves sufficiently accurate tracking in both scenarios. This comparative analysis elucidates the strengths and weaknesses of each control strategy, offering critical insights and practical guidelines for the design and implementation of advanced control systems for dexterous tendon-driven robotic fingers. Full article

(This article belongs to the Special Issue Applications of Mathematical Methods in Robotic Systems)

18 pages, 2534 KB

Open AccessArticle

Vegetation and Landscape Shift After Beaver Settlement in a Mountainous Area

by Rita Rakowska and Alina Stachurska-Swakoń

Biology 2025, 14(11), 1603; https://doi.org/10.3390/biology14111603 (registering DOI) - 16 Nov 2025

Abstract

Beavers are classified as ecosystem engineers because their activities can significantly alter environmental conditions. Vegetation and landscape changes, based on a series of vegetation maps and satellite images between 1994 and 2022, were studied in a mountain valley of a protected area in the Polish part of the Eastern Carpathians. Eighteen plant communities were identified before the beavers were released, with moist and wet communities covering 76.8% of the area. After 25 years of beaver presence, the vegetation changed: fresh communities decreased from 23% to 10%, and communities with grey alder disappeared. At the same time, the moist and wet communities expanded and new ones developed. Overall, the share of these communities increased to 89% of the area, with the dominant tall herb Filipendulo-Geranietum and Menyanthes trifoliata community. A distinctive feature was an increase in vegetation patchiness with a corresponding decrease in the evenness index. Landscape analysis revealed a 9.5% increase in the length of the streambed and fluctuations in the number of beaver ponds (11–25) and migration corridors (4–20). The number of corridors increased as the availability of grey alder decreased. The total area of the ponds exceeded 2200 m², indicating their significant role in water retention and modifying microclimatic conditions. Full article

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17 pages, 609 KB

Open AccessReview

RhoA/Rho-Kinase Signaling in Vascular Smooth Muscle and Endothelium: Mechanistic Insights and Translational Implications in Hypertension

by Stephanie Randar, Diana L. Silva-Velasco, Fernanda Priviero and R. Clinton Webb

Biomolecules 2025, 15(11), 1607; https://doi.org/10.3390/biom15111607 (registering DOI) - 16 Nov 2025

Abstract

The small GTPase RhoA and its downstream effector Rho-kinase (ROCK) have emerged as pivotal regulators of vascular smooth muscle cell (VSMC) contraction, endothelial function, and vascular remodeling. Activation of the RhoA/ROCK pathway enhances calcium (Ca²⁺) sensitivity by inhibiting myosin light chain phosphatase (MLCP), thereby promoting sustained vascular tone independent of intracellular Ca²⁺ levels. In endothelial cells (ECs), RhoA/ROCK signaling contributes to nitric oxide (NO) dysregulation, oxidative stress, cytoskeletal reorganization, and inflammatory activation. Cumulative evidence implicates this pathway in the development and progression of hypertension and other cardiovascular diseases, where maladaptive vascular remodeling, VSMC proliferation, and endothelial dysfunction drive increased vascular resistance. Translational studies have identified ROCK inhibitors and indirect modulators such as statins as promising therapeutic strategies. This review integrates recent mechanistic insights into RhoA/ROCK regulation of vascular function with clinical and translational perspectives on targeting this pathway in hypertension. Full article

(This article belongs to the Special Issue Calcium Signaling and Transport in Health and Disease: Recent Developments and New Insights)

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