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by Sofia Gabellone, Giovanni Carotenuto, Manuel Arcieri, Paolo Bottoni, Giulia Sbanchi, Tiziana Castrignanò, Davide Piccinino, Chiara Liverani and Raffaele Saladino

Int. J. Mol. Sci. 2025, 26(17), 8728; https://doi.org/10.3390/ijms26178728 (registering DOI) - 7 Sep 2025

Abstract

The mammalian target of rapamycin pathway, mTOR, is a crucial signaling pathway that regulates cell growth, proliferation, metabolism, and survival. Due to its dysregulation it is involved in several ailments such as cancer or age-related diseases. The discovery of mTOR and the understanding of its biological functions were greatly facilitated by the use of rapamycin, an antibiotic of natural origin, which allosterically inhibits mTORC1, effectively blocking its function. In this entirely computational study, we investigated mTOR’s interaction with seven ligands: two clinically established inhibitors (everolimus and rapamycin) and five lignin-derived oligomers, a renewable natural polyphenol recently used for the drug delivery of everolimus. The seven complexes were analyzed through all-atom molecular dynamics simulations in explicit solvent using a high-performance computing platform. Trajectory analyses revealed stable interactions between mTOR and all ligands, with lignin-derived compounds showing comparable or enhanced binding stability relative to reference drugs. To evaluate the stability of the molecular complex and the behavior of the ligand over time, we analyzed key parameters including root mean square deviation, root mean square fluctuation, number of hydrogen bonds, binding free energy, and conformational dynamics assessed through principal component analysis. Our results suggest that lignin fragments are a promising, sustainable scaffold for developing novel mTOR inhibitors. Full article

(This article belongs to the Special Issue The Application of Machine Learning to Molecular Dynamics Simulations)

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23 pages, 6875 KB

Open AccessArticle

Precision-Controlled Bionic Lung Simulator for Dynamic Respiration Simulation

by Rong-Heng Zhao, Shuai Ren, Yan Shi, Mao-Lin Cai, Tao Wang and Zu-Jin Luo

Bioengineering 2025, 12(9), 963; https://doi.org/10.3390/bioengineering12090963 (registering DOI) - 7 Sep 2025

Abstract

Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3L/min, and the response time is under 200ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100mL/cmH

_{2}

O and airway resistance from 2.01 to 14.67

\sqrt{{cmH}_{2} O} / (L / s)

, with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human–machine interaction studies, and simulating pathological respiratory states. Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

28 pages, 1651 KB

Open AccessArticle

Temporal Dynamics of Vaccination Decision-Making: How Trust and Risk Perception Evolved During COVID-19 in Germany

by Lisa Herbig and Brady Wagoner

COVID 2025, 5(9), 150; https://doi.org/10.3390/covid5090150 (registering DOI) - 7 Sep 2025

Abstract

The COVID-19 pandemic created unprecedented conditions for examining how vaccination willingness evolves during prolonged health crises. This longitudinal mixed-methods study examines temporal dynamics in COVID-19 vaccination willingness across three phases of Germany’s vaccination campaign (N = 1063 survey respondents; n = 40 interview participants). Using mixed-effects models and thematic analysis, we tested whether institutional trust and personal risk perception predict vaccination willingness and how their relative importance changes over time. Results reveal that trust in scientific institutions emerges as the strongest predictor, outperforming political trust and becoming more influential over time, while risk perceptions become less predictive with time. Qualitative analysis identified a multitude of different argumentative themes for and against COVID-19 vaccination (as well as conditional acceptance), with 30% of participants expressing both. The themes complement the quantitative analysis by demonstrating a shift from analytical, risk-focused decision-making to heuristic, trust-based processing as vaccination campaigns progress, with important implications for adaptive public health communication strategies. Full article

(This article belongs to the Section COVID Public Health and Epidemiology)

14 pages, 3176 KB

Open AccessArticle

Acoustic Emission Assisted Inspection of Punching Shear Failure in Reinforced Concrete Slab–Column Structures

by Xinchen Zhang, Zhihong Yang and Guogang Ying

Buildings 2025, 15(17), 3226; https://doi.org/10.3390/buildings15173226 (registering DOI) - 7 Sep 2025

Abstract

Slab–column structures are susceptible to sudden punching shear failure at connections due to the absence of traditional beam support, prompting the need for effective damage monitoring. This study employs an acoustic emission (AE) technique to investigate the failure process of reinforced concrete slab–column specimens, analyzing basic AE parameters (hits, amplitude, energy), improved b-value (Ib-value), and RA–AF correlation, while introducing a Gaussian Mixture Model (GMM) to establish a unified index integrating crack type identification and energy information. Experimental results show that AE parameters can effectively track different stages of crack development, with Ib-value reflecting the transition from micro-crack to macro-crack growth. The correlation between AE energy and structural strain energy enables quantitative damage assessment, while RA–AF analysis and GMM clustering reveal the shift from bending-dominated to shear-dominated failure modes. This study provides a comprehensive framework for real-time damage evaluation and failure mode prediction in slab–column structures, demonstrating that AE-based multi-parameter analysis and data-driven clustering methods can characterize damage evolution and improve the reliability of structural health monitoring. Full article

(This article belongs to the Special Issue The Application of Intelligence Techniques in Construction Materials)

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21 pages, 18328 KB

Open AccessArticle

Physiological Variation in Jarillo Peach Across Altitudinal Gradients

by Enrique Quevedo-García, Javier de León and José Alejandro Cleves-Leguízamo

Agronomy 2025, 15(9), 2145; https://doi.org/10.3390/agronomy15092145 (registering DOI) - 7 Sep 2025

Abstract

Environmental factors affect plant physiological processes. Understanding these factors can increase productivity, especially in tropical mountain ecosystems, where they vary with altitude. This study aimed to analyze the physiological variations related to water vapor and gas exchange in Prunus persica L. Batsch according to the altitudinal gradient in North Santander. One plant was selected per altitude, and six leaves were selected per plant and per branch across three phenological stages. Conductance (gs), stomatal resistance (SR), and transpiration (E) were determined using a calibrated portable porometer over two cycles. Linear mixed-effects models with repeated measurements over time, phenological effects, altitude, and light conditions were used. At higher altitudes, gs and E decreased and SR increased, possibly due to higher ultraviolet radiation and lower temperatures with increasing altitude. Maximum values were reached at EF6. gs and E exhibited diurnal patterns, decreasing at the end of the day to minimize water loss during periods of lower solar radiation. The cultivar adjusted its stomatal and water regulation mechanisms according to altitude. These findings provide advanced insights into plant acclimatization strategies in mountain ecosystems and inform the sustainable management practices needed in the face of impending global climate variability. Full article

(This article belongs to the Special Issue Agricultural Innovation and Sustainable Development in Tropical Cropping Systems)

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16 pages, 1071 KB

Open AccessArticle

Microwave–Assisted OSA–Faba Bean Starch Production for Probiotic Microencapsulation

by Mayra Esthela González-Mendoza, Fernando Martínez-Bustos, Eduardo Castaño-Tostado, María del Carmen Cortez-Trejo and Silvia Lorena Amaya-Llano

Polysaccharides 2025, 6(3), 81; https://doi.org/10.3390/polysaccharides6030081 (registering DOI) - 7 Sep 2025

Abstract

Probiotics offer significant health benefits; however, their efficacy is often compromised by low survival rates in stressful conditions. Microencapsulation using modified starches presents a promising strategy to enhance probiotic viability. This study aimed to evaluate microwave-assisted octenyl succinic anhydride (OSA) modification of faba bean starch to provide a protective matrix for the microencapsulation of Lactobacillus rhamnosus GG (LGG) through spray drying. Starch was extracted from faba beans and hydrolyzed, and a factorial design was employed for OSA esterification (3% w/w) using a conventional microwave (30 or 60 s at power levels of 2 or 10). The starches were characterized, and the most effective treatment was selected for the microencapsulation of LGG, varying the inlet temperature (120 and 140 °C) and flow rate (7 and 12 mL/min) at 30% solids content. Microwaves significantly reduced the processing time for starch esterification. Microwave-assisted OSA modification produced starches with low viscosity (<0.015 Pa·s), high amylose and resistant starch content, and good solubility, making them suitable for probiotic encapsulation. The microencapsulation of LGG resulted in a powder yield of 41–55%, with particle sizes ranging from 5 to 20 µm and survival rates of 81–90%. This study presents an effective method of producing OSA-modified starch from faba beans using microwave energy, demonstrating strong potential for probiotic delivery applications. Full article

16 pages, 2652 KB

Open AccessArticle

Preparation of Pt/xMnO₂-CNTs Catalyst and Its Electrooxidation Performance in Methanol

by Guang Chen, Zhijun Teng, Hanqiao Xu and Hongwei Li

Catalysts 2025, 15(9), 864; https://doi.org/10.3390/catal15090864 (registering DOI) - 7 Sep 2025

Abstract

In this study, MnO₂-CNTs composite support was prepared by citric acid reduction method, and then, Pt nanoparticles were loaded on the surface by ethylene glycol reduction method to obtain a series of Pt/xMnO₂-CNTs catalysts. Structural characterization (TEM, XRD, HRTEM) showed that Pt nanoparticles were uniformly dispersed on the surface of the catalyst with an average particle size of 3.6 nm. Electrochemical tests show that when the content of MnO₂ is 20 wt.%, the Pt/_20wt._%MnO₂-CNTs catalyst has the best methanol oxidation performance, and its mass activity and long-term stability are 4.0 times and 5.41 times that of commercial Pt/C, respectively. The in situ FTIR results showed that MnO₂ promoted the dissociation of water through synergistic effect, generated abundant OH species, accelerated the oxidation of CO intermediates, and inhibited the poisoning of Pt sites. In this study, it is clear that the excellent performance of Pt/xMnO₂-CNTs is due to multiple synergistic effects. Modified carbon nanotubes facilitate proton conduction, Pt nanoparticles effectively activate methanol, and MnO₂ modulates reaction intermediates via its bifunctional mechanism. This comprehensive mechanism understanding provides a theoretical basis for the design of high-performance catalysts for direct methanol fuel cells. Full article

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17 pages, 7701 KB

Open AccessArticle

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

by Methun Kamruzzaman, Jorge S. Salinas, Hemanth Kolla, Kenneth L. Sale, Uma Balakrishnan and Kunal Poorey

Sensors 2025, 25(17), 5586; https://doi.org/10.3390/s25175586 (registering DOI) - 7 Sep 2025

Abstract

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics. Full article

(This article belongs to the Special Issue Recent Advances in Wearable and Non-Invasive Sensors)

19 pages, 3398 KB

Open AccessArticle

Polynucleotides Enhance Collagen Synthesis via Modulating Phosphoenolpyruvate Carboxykinase 1 in Senescent Macrophages: Experimental Evidence

by Kyung-A Byun, Hyun Jun Park, Seyeon Oh, Kuk Hui Son and Kyunghee Byun

Int. J. Mol. Sci. 2025, 26(17), 8720; https://doi.org/10.3390/ijms26178720 (registering DOI) - 7 Sep 2025

Abstract

Polynucleotide (PN), a high-molecular-weight DNA fragment derived from salmon and other fish sources, shows promising anti-aging and regenerative effects on the skin. This study investigated how PN enhances collagen synthesis, focusing on its effect on phosphoenolpyruvate carboxykinase 1 (PCK1) in senescent macrophages and its downstream effects on fibroblasts. Using in vitro senescent cell models and in vivo aged animal models, PN significantly upregulated the adenosine 2A receptor (A2AR), adenylate cyclase (AC), cyclic AMP (cAMP), protein kinase A (PKA), and cAMP response element-binding protein (CREB) in senescent macrophages. This led to increased PCK1 expression, which reduced oxidative stress and promoted M2 macrophage polarization, associated with elevated levels of interleukin-10 and tumor growth factor-β. Conditioned media from PN-treated macrophages enhanced SMAD family member 2 and signal transducer and activator of transcription 3 phosphorylation in senescent fibroblasts, increasing collagen I and III synthesis and reducing nuclear factor-κB activity. In vivo, PN administration elevated expression of the A2AR/AC/PKA/CREB/PCK1 pathway, reduced oxidative stress, increased M2 macrophage markers, and significantly improved collagen density and skin elasticity over time. Use of a PCK1 inhibitor attenuated these effects, highlighting the pivotal role of PCK1. Overall, PN modulates macrophage-fibroblast interactions via the CREB/PCK1 axis, enhancing collagen synthesis and counteracting age-related skin changes. PN has emerged as a promising therapeutic agent for skin rejuvenation by targeting cellular senescence and promoting extracellular matrix restoration. Full article

(This article belongs to the Section Biochemistry)

15 pages, 323 KB

Open AccessReview

Trifecta of CD-19 Receptor, IgG4 Disease and the Mitigate Trials

by Rahul Jain, Bipneet Singh, Palak Grover, Jahnavi Ethakota, Sakshi Bai, Gurleen Kaur and Merritt Bern

BioChem 2025, 5(3), 29; https://doi.org/10.3390/biochem5030029 (registering DOI) - 7 Sep 2025

Abstract

IgG4-related disease (IgG4-RD) is a subacute, progressive, multisystemic autoinflammatory condition which presents with nonspecific symptoms like weight loss, fatigue and myalgia, and is marked by lymphoplasmacytic infiltrates rich in IgG4-positive plasma cells. IgG4-RD can involve various organs including the pancreas, bile ducts, thyroid, salivary and lacrimal glands, retroperitoneum, kidneys, lungs and CNS, often mimicking malignancy. A rigorous literature review was conducted. Articles on IgG4 disease, CD-19 and the MITIGATE trials were studied and included in the review. Glucocorticoids remain first-line therapy, but adverse effects and relapses are common. Rituximab, an anti-CD20 agent, is effective but may leave CD20-negative plasmablasts intact, contributing to relapse. In contrast, CD19-targeting therapies like inebilizumab offer more comprehensive B-cell depletion, including plasmablasts, potentially reducing relapses, fibrosis progression and long-term organ damage. MITIGATE trials showed promise in the use of an anti-CD-19 agent in preventing IgG4 disease flares and prolonging the time to first flare. Full article

13 pages, 3168 KB

Open AccessArticle

Production and Storage of Male-Sterile Somatic Embryos of Sugi (Japanese Cedar, Cryptomeria japonica) at Temperatures Above Freezing

by Tsuyoshi E. Maruyama, Momi Tsuruta, Saneyoshi Ueno and Yoshinari Moriguchi

Forests 2025, 16(9), 1431; https://doi.org/10.3390/f16091431 (registering DOI) - 7 Sep 2025

Abstract

Sugi-pollinosis poses a significant socioeconomic and public health concern in Japanese society. Consequently, the use of male-sterile plants (pollen-free plants or PFPs) is anticipated in reforestation efforts. In this context, we developed an improved, simplified method for efficiently propagating sugi PFPs. In the present study, we compared the efficiency of different embryogenic cell lines (ECLs) in producing somatic embryos and examined how effectively these embryos germinate and convert into plantlets. We also evaluated the germination potential of somatic embryos stored for various durations at temperatures above freezing and room temperature. The production efficiency of somatic embryos ranged from 129.6 to 504.1 per plate, with an average of 349.8 across the ECLs tested. The overall average germination and conversion rates of somatic embryos were found to be 93.9% and 92.4%, respectively. Furthermore, although differences were observed among the evaluated genotypes, our five-year study demonstrated that sugi somatic embryos could be stored at 25 °C, 15 °C, or 5 °C for 6, 12, or 24 months, respectively, without a notable decline in germination capacity. The developed method enhances flexibility in plant production scheduling and facilitates the optimal timing for transferring somatic seedlings to the field. Full article

(This article belongs to the Section Genetics and Molecular Biology)

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14 pages, 1414 KB

Open AccessArticle

Stable and Convergent High-Order Numerical Schemes for Parabolic Integro-Differential Equations with Small Coefficients

by Lolugu Govindarao, Khalil S. Al-Ghafri, Jugal Mohapatra and Thȧi Anh Nhan

Symmetry 2025, 17(9), 1475; https://doi.org/10.3390/sym17091475 (registering DOI) - 7 Sep 2025

Abstract

Singularly perturbed integro-partial differential equations with reactionâ€“diffusion behavior present significant challenges due to boundary layers arising from small perturbation parameters, which complicate the development of accurate and efficient numerical schemes for physical and engineering models. In this study, a uniformly convergent higher-order method is proposed to address these challenges. The approach applies the implicit Euler method for temporal discretization on a uniform mesh and central differences on a Shishkin mesh for spatial approximation, and utilizes the trapezoidal rule for evaluating integral terms; further, extrapolation techniques are incorporated in both time and space to increase accuracy. Numerical analysis demonstrates that the base scheme achieves first-order convergence, while extrapolation enhances convergence rates to second-order in time and fourth-order in space. Theoretical results confirm uniform convergence with respect to the perturbation parameter, and comprehensive numerical experiments validate these analytical claims. Findings indicate that the proposed scheme is reliable, efficient, and particularly effective in attaining fourth-order spatial accuracy when solving singularly perturbed integro-partial differential equations of reactionâ€“diffusion type, thus providing a robust numerical tool for complex applications in science and engineering. Full article

(This article belongs to the Section Mathematics)

19 pages, 9786 KB

Open AccessArticle

Maize Kernel Batch Counting System Based on YOLOv8-ByteTrack

by Ran Li, Qiming Liu, Miao Wang, Yuchen Su, Chen Li, Mingxiong Ou and Lu Liu

Sensors 2025, 25(17), 5584; https://doi.org/10.3390/s25175584 (registering DOI) - 7 Sep 2025

Abstract

In recent years, the application of deep learning technology in the field of food engineering has developed rapidly. As an essential food raw material and processing target, the number of kernels per maize plant is a critical indicator for assessing crop growth and predicting yield. To address the challenges of frequent target ID switching, high falling speed, and the limited accuracy of traditional methods in practical production scenarios for maize kernel falling count, this study designs and implements a real-time kernel falling counting system based on a Convolutional Neural Network (CNN). The system captures dynamic video streams of kernel falling using a high-speed camera and innovatively integrates the YOLOv8 object detection framework with the ByteTrack multi-object tracking algorithm to establish an efficient and accurate kernel trajectory tracking and counting model. Experimental results demonstrate that the system achieves a tracking and counting accuracy of up to 99% under complex falling conditions, effectively overcoming counting errors caused by high-speed motion and object occlusion, and significantly enhancing robustness. This system combines high intelligence with precision, providing reliable technical support for automated quality monitoring and yield estimation in food processing production lines, and holds substantial application value and prospects for widespread adoption. Full article

(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)

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14 pages, 428 KB

Open AccessArticle

Instrumented Functional Mobility Assessment in Elderly Patients Following Total Knee Arthroplasty: A Retrospective Longitudinal Study Using the Timed Up and Go Test

by Andrei Machado Viegas da Trindade, Leonardo Pinheiro Rezende, Helder Rocha da Silva Araújo, Rodolfo Borges Parreira and Claudia Santos Oliveira

Life 2025, 15(9), 1409; https://doi.org/10.3390/life15091409 (registering DOI) - 7 Sep 2025

Abstract

In the context of the rising demand for total knee arthroplasty (TKA) in older adults and persistent uncertainty about the quality of long-term functional recovery, this study evaluated elderly patients’ mobility after unilateral TKA via a transquadriceps approach using instrumented Timed Up and Go (TUG) tests. A total of 20 patients treated between 2022 and 2024 at a tertiary hospital were invited to participate in this observational, retrospective, descriptive study, and 19 met the inclusion criteria (age 50–80 and Kellgren–Lawrence ≥ 4). The participants performed two TUG trials at two postoperative time points (18 and 53 months), with an inertial measurement unit (G-sensor) capturing 15 kinematic variables. When comparing the postoperative time points, it was found that the total TUG duration remained stable (14.97 ± 3.48 vs. 15.47 ± 2.93 s; p = 0.58), while the mid-turning peak velocity increased significantly (106.44 ± 30.96 vs. 132.77 ± 30.82°/s; p = 0.0039; r = 0.88). The end-turning velocity and sit-to-stand parameters showed small-to-moderate effect size gains without statistical significance. These findings suggest that, in the first year following surgery, patients continue to experience difficulties with movement fluidity and motor control—especially during turning—underscoring the value of segmented, sensor-based assessments and the need for extended rehabilitation protocols that emphasize rotational control and balance. These findings provide clinically relevant parameters that can support future interventional studies and help guide rehabilitation planning after TKA. Full article

(This article belongs to the Special Issue Knee Bracing in Human Movement: Biomechanics, Artificial Intelligence, and Optimization)

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19 pages, 7978 KB

Open AccessArticle

A Local Thresholding Algorithm for Image Segmentation by Using Gradient Orientation Histogram

by Lijie Dong, Kailong Zhang, Mingyue He, Shenxin Zhong and Congjie Ou

Appl. Sci. 2025, 15(17), 9808; https://doi.org/10.3390/app15179808 (registering DOI) - 7 Sep 2025

Abstract

This paper proposes a new local thresholding method to further explore the relationship between gradients and image patterns. In most studies, the image gradient histogram is simply divided into K bins that have the same intervals in angular space. This kind of empirical approaches may not fully capture the correlation information between pixels. In this paper, a variance-based idea is applied to the gradient orientation histogram. It clusters pixels into subsets with different angular intervals. Analyzing these subsets with similar common patterns respectively will help to assist in achieving the optimal thresholds for image segmentation. For the result assessments, the proposed algorithm is compared with other 1-D and 2-D histogram-based thresholding methods, as well as hybrid local–global thresholding methods. It is shown that the proposed algorithm can effectively recognize the common features of the images that belong to the same category, and maintain the stable performances when the number of thresholds increases. Furthermore, the processing time of the present algorithm is competitive with those of other algorithms, which shows the potential application in real-time scenes. Full article

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