Search Results (2,130)

Three-dimensional cell culture systems are relevant in vitro models for studying cellular behavior. In this regard, this present study investigates the interaction between human osteoblast-like cells and 3D-printed scaffolds mimicking physiological and osteoporotic bone structures under simulated microgravity conditions. The objective is to assess the effects of scaffold architecture and dynamic culture conditions on cell adhesion, proliferation, and metabolic activity, with implications for osteoporosis research. Polylactic acid scaffolds with physiological (P) and osteoporotic-like (O) trabecular architectures were 3D-printed by means of fused deposition modeling technology. Morphometric characterization was performed using micro-computed tomography. Human osteoblast-like SAOS-2 and U2OS cells were cultured on the scaffolds under static and dynamic simulated microgravity conditions using a rotary cell culture system (RCCS). Scaffold biocompatibility, cell viability, adhesion, and metabolic activity were evaluated through Bromodeoxyuridine incorporation assays, a water-soluble tetrazolium salt assay, and an enzyme-linked immunosorbent assay of tumor necrosis factor-α secretion. Both scaffold models supported osteoblast-like cell adhesion and growth, with an approximately threefold increase in colonization observed on the high-porosity O scaffolds under dynamic conditions. The dynamic environment facilitated increased surface interaction, amplifying the effects of scaffold architecture on cell behavior. Overall, sustained cell growth and metabolic activity, together with the absence of detectable inflammatory responses, confirmed the biocompatibility of the system. Scaffold microstructure and dynamic culture conditions significantly influence osteoblast-like cell behavior. The combination of 3D-printed scaffolds and a RCCS bioreactor provides a promising platform for studying bone remodeling in osteoporosis and microgravity-induced bone loss. These findings may contribute to the development of advanced in vitro models for biomedical research and potential countermeasures for bone degeneration. Full article

Incorporation of lanthanide (Ln) and actinide (An) ions into the human body poses significant chemotoxic and radiotoxic risks, necessitating effective decorporation strategies. This study investigates the displacement of biologically relevant ligands from trivalent ions of europium, Eu(III), and curium, Cm(III), in artificial biofluids by various complexing agents, i.e., ethylenediaminetetraacetic acid (EDTA), ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), diethylenetriaminepentaacetic acid (DTPA), and spermine-based hydroxypyridonate chelator 3,4,3-LI(1,2-HOPO) (HOPO). Utilizing a modified unified bioaccessibility method (UBM) to simulate gastrointestinal conditions, we conducted concentration-dependent displacement experiments at both room and body temperatures. Time-resolved laser-induced fluorescence spectroscopy (TRLFS) supported by ²H nuclear magnetic resonance (NMR) spectroscopy and thermodynamic modelling revealed the complexation efficacy of the agents under physiological conditions. Results demonstrate that high affinity, governed by complex stability constants and ligand pK_a values, is critical to overcome cation and anion competition and leads to effective decorporation. Additionally, there is evidence that cyclic ligands are inferior to linear ligands for this application. HOPO and DTPA exhibited superior displacement efficacy, particularly in the complete gastrointestinal tract simulation. This study highlights the utility of in vitro workflows for evaluating decorporation agents and emphasizes the need for ligands with optimal binding characteristics for enhanced chelation therapies. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

There is a lack of systematic research on the comprehensive regulatory effects of urea and organic fertilizer application on soil quality and cotton yield in summer direct-seeded cotton fields in the Yangtze River Basin. Additionally, there is a redundancy of indicators in the cotton field soil quality evaluation system and a lack of reports on constructing a minimum dataset to evaluate the soil quality status of cotton fields. We aim to accurately and efficiently evaluate soil quality in cotton fields and screen nitrogen application measures that synergistically improve soil quality, cotton yield, and nitrogen fertilizer utilization efficiency. Taking the summer live broadcast cotton field in Jiangxi Province as the research object, four treatments, including CK without nitrogen application, CF with conventional nitrogen application, N1 with nitrogen reduction, and N2 with nitrogen reduction and organic fertilizer application, were set up for three consecutive years from 2022 to 2024. A total of 15 physical, chemical, and biological indicators of the 0–20 cm plow layer soil were measured in each treatment. A minimum dataset model was constructed to evaluate and verify the soil quality status of different nitrogen application treatments and to explore the physiological mechanisms of nitrogen application on yield performance and stability from the perspectives of cotton source–sink relationship, nitrogen use efficiency, and soil quality. The minimum dataset for soil quality evaluation in cotton fields consisted of five indicators: soil bulk density, moisture content, total nitrogen, organic carbon, and carbon-to-nitrogen ratio, with a simplification rate of 66.67% for the evaluation indicators. The soil quality index calculated based on the minimum dataset (MDS) was significantly positively correlated with the soil quality index of the total dataset (TDS) (R² = 0.904, p < 0.05). The model validation parameters RMSE was 0.0733, nRMSE was 13.8561%, and the d value was 0.9529, all indicating that the model simulation effect had reached a good level or above. The order of soil quality index based on MDS and TDS for CK, CF, N1, and N2 treatments was CK < N1 < CF < N2. The soil quality index of N2 treatment under MDS significantly increased by 16.70% and 26.16% compared to CF and N1 treatments, respectively. Compared with CF treatment, N2 treatment significantly increased nitrogen fertilizer partial productivity by 27.97%, 31.06%, and 21.77%, respectively, over a three-year period while maintaining the same biomass, yield level, yield stability, and yield sustainability. Meanwhile, N1 treatment had the risk of significantly reducing both boll density and seed cotton yield. Compared with N1 treatment, N2 treatment could significantly increase the biomass of reproductive organs during the flower and boll stage by 23.62~24.75% and the boll opening stage by 12.39~15.44%, respectively, laying a material foundation for the improvement in yield and yield stability. Under CF treatment, the cotton field soil showed a high degree of soil physical property barriers, while the N2 treatment reduced soil barriers in indicators such as bulk density, soil organic carbon content, and soil carbon-to-nitrogen ratio by 0.04, 0.04, 0.08, and 0.02, respectively, compared to CF treatment. In summary, the minimum dataset (MDS) retained only 33.3% of the original indicators while maintaining high accuracy, demonstrating the model’s efficiency. After reducing nitrogen by 20%, applying 10% total nitrogen organic fertilizer could substantially improve cotton biomass, cotton yield performance, yield stability, and nitrogen partial productivity while maintaining soil quality levels. This study also assessed yield stability and sustainability, not just productivity alone. The comprehensive nitrogen fertilizer management (reducing N + organic fertilizer) under the experimental conditions has high practical applicability in the intensive agricultural system in southern China. Full article

Some herbicides, such as saflufenacil, can persist as residues in sprayer tanks even after cleaning, causing phytotoxicity in sensitive crops. This study aimed to simulate potential injury caused by saflufenacil residues, applied alone or combined with glyphosate, on soybean. The field experiment was conducted using a randomized complete block design with four replicates. The treatments included glyphosate (1440 g ha⁻¹), eight saflufenacil doses ranging from 1.09 to 70.00 g ha⁻¹, each tested alone or combined with glyphosate, and a weed-free control, totaling 18 treatments. Phytotoxicity was assessed at 7, 14, 21, 28, and 35 days after treatment (DAT). Physiological variables were measured at 21 DAT, and grain yield components were evaluated at harvest. Saflufenacil caused increasing phytotoxicity at doses exceeding 4.38 g ha⁻¹ when applied alone and above 2.17 g ha⁻¹ when combined with glyphosate. The highest doses negatively affected soybean physiology and grain yield components. Soybean tolerated up to 2.17 g ha⁻¹ saflufenacil alone and up to 1.09 g ha⁻¹ combined with glyphosate without significant yield loss. These results highlight the importance of thorough and correct cleaning of the sprayer tank and suggest limit residue levels that avoid crop damage, helping to prevent unexpected damage to soybean in crop rotations. Full article

We present a novel method to estimate blood cortisol concentration from sweat cortisol measurements, incorporating a kinetic model to simulate cortisol transport dynamics. Cortisol dysregulation is observed in conditions like Cushing’s syndrome, characterized by excessive cortisol production, and stress-related disorders, which can lead to metabolic disturbances, anxiety, and impaired overall health. Sweat-sensing technology offers a non-invasive and continuous alternative to blood sampling. However, the limited research exploring the sweat–blood cortisol relationship in patients shows a moderate correlation ($R<0.6$), hindering its clinical application for long-term monitoring. In this paper, we propose a novel kinetic model describing cortisol transport from blood to sweat. The model was validated using data from 44 patients before and after cardiac surgery. A high Pearson correlation coefficient of 0.95 (95% CI: 0.92–0.97) was observed between our model’s estimated and experimental blood cortisol concentrations. Moreover, the method enables personalized estimation of physiological parameters, accurately reflecting patients’ status under varying clinical conditions. The method paves the way for the clinical application of long-term, non-invasive monitoring of cortisol using sweat-sensing technology. Enabling the personalized estimation of physiological parameters could potentially support clinical decision-making, helping doctors diagnose and monitor patients with health conditions involving cortisol dysregulation. Full article

This study investigates the development of silver (Ag)-doped zirconia (ZrO₂) coatings deposited on 316LVM stainless steel via the unbalanced magnetron sputtering technique. The oxygen content in the Ar/O₂ gas mixture was systematically varied (12.5%, 25%, 37.5%, and 50%) to assess its influence on the resulting coating properties. In response to the growing demand for biomedical implants with improved durability and biocompatibility, the objective was to develop coatings that enhance both wear and corrosion resistance in physiological environments. The effects of silver incorporation and oxygen concentration on the structural, tribological, and electrochemical behavior of the coatings were systematically analyzed. X-ray diffraction (XRD) was employed to identify crystalline phases, while atomic force microscopy (AFM) was used to characterize surface topography prior to wear testing. Wear resistance was evaluated using a ball-on-plane tribometer under simulated prosthetic motion, applying a 5 N load with a bone pin as the counter body. Corrosion resistance was assessed through electrochemical impedance spectroscopy (EIS) in a physiological solution. Additionally, tribocorrosive performance was investigated by coupling tribological and electrochemical tests in Ringer’s lactate solution, simulating dynamic in vivo contact conditions. The results demonstrate that Ag doping, combined with increased oxygen content in the sputtering atmosphere, significantly improves both wear and corrosion resistance. Notably, the ZrO₂-Ag coating deposited with 50% O₂ exhibited the lowest wear volume (0.086 mm³) and a minimum coefficient of friction (0.0043) under a 5 N load. This same coating also displayed superior electrochemical performance, with the highest charge transfer resistance (38.83 kΩ·cm²) and the lowest corrosion current density (3.32 × 10⁻⁸ A/cm²). These findings confirm the high structural integrity and outstanding tribocorrosive behavior of the coating, highlighting its potential for application in biomedical implant technology. Full article

Among marine organisms, baleen whale species like the humpback whale (Megaptera novaeangliae) are a case for which individual-based models are necessary to study population changes because individual trait variabilities predominate over average demographic rates to govern population dynamics. These models require quantification of individual organisms’ dynamics with respect to local conditions, which implies optimal strategy frameworks cannot be used. Instead, to quantify how individuals perform according to the environmental conditions they encounter, we formulated a model linking individual mechanical characteristics of swimming and diving with their aerobic metabolism and behavior. The model simulates the dynamics of swimming and diving for the reported range of whale sizes (1000 to 50,000 kg). Additional processes simulate foraging events including rapid accelerations and water engulfment, which modifies whale shape, weight and drag. Simulations show how the energy cost of swimming at equilibrium increases geometrically with velocity and linearly with mass. The duration and distance covered under apnea vary monotonically with mass but not with velocity; hence, there is a positive mass-dependent optimal velocity that maximizes the distance and duration of apnea. The dive limit was explored with a combination of the physiological state, mechanical force produced and distance to return to surface. This combination is imposed as an inequality constraint on the whale individual. The inequality constraint, transformed as a multi-layer perceptron, which continuously processes information about oxygen, depth and relative velocity, provides the whale individual with autonomous decision-making about whether or not to continue the dive. The results also highlight where missing metabolic information is needed to simulate the dynamics of a population of autonomous individuals at the scale of the Global Ocean. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

The objective of this study was to evaluate the physiological, biochemical, and productive effects of the foliar application of bioregulators, based on auxin, cytokinin, and gibberellic acid, on yellow melon, cultivar DALI^®, plants subjected to different salinity levels in a protected environment to simulate Brazil’s semi-arid conditions. The experiment was conducted using a completely randomized block design, in a 4 × 3 factorial scheme, with four salinity levels (0, 2, 4, and 6 dS m⁻¹) and three doses of the bioregulator, Stimulate^® (0%, 100%, and 150% of the recommended dose), with six weekly applications. The physiological variables (chlorophyll a fluorescence and gas exchange) and biochemical parameters (antioxidant enzyme activity and lipid peroxidation) were evaluated at 28 and 42 days after transplanting, and the agronomic traits (fresh fruit mass, physical attributes, and post-harvest quality) were evaluated at the end of the experiment. The results indicated that salinity impaired the physiological and productive performance of the plants, especially at higher levels (4 and 6 dS m⁻¹), causing oxidative stress, reduced photosynthesis, and decreased yield. However, the application of the bioregulator at the 100% dose mitigated the effects of salt stress under moderate salinity (2 dS m⁻¹), promoting higher CO₂ assimilation rates of up to 31.5%, better water-use efficiency, and reduced lipid peroxidation. In addition, the fruits showed a greater mass of up to 66%, thicker pulp, and higher soluble solids (> 10 °Brix) content, making them suitable for sale in the market. The 150% dose did not provide additional benefits and, in some cases, resulted in inhibitory effects. It is concluded that the application of Stimulate^® at the recommended dose is effective in mitigating the effects of moderate salinity, up to ~3 dS m⁻¹, in yellow melon crops; however, its effectiveness is limited under high salinity conditions, requiring the use of complementary strategies. Full article

Seeds in soil are often exposed to cycles of hydration and dehydration, which can prime them by triggering physiological activation without leading to germination. While this phenomenon has been scarcely studied in wild species, it may play a critical role in enhancing drought resilience and maintaining seed viability under the warmer conditions predicted by climate change. In this study, I investigated the effects of hydration–dehydration cycles on germination response under water stress in eight Cistus species typical of Mediterranean shrublands. First, seeds were exposed to a heat shock to break physical dormancy, simulating fire conditions. Subsequently, they underwent one of two hydration–dehydration treatments (24 or 48 h) and were germinated under a range of water potentials (0, –0.2, –0.4, –0.6, and –0.8 MPa). Six out of eight species showed enhanced germination responses following hydration–dehydration treatments, including higher final germination percentages, earlier germination onset (T₀), or increased tolerance to water stress. These findings highlight the role of water availability as a key factor regulating germination in Cistus species and evidence a hydration memory mechanism that may contribute in different ways to post-fire regeneration in Mediterranean ecosystems. Full article

Two-dimensional culture systems have been used for a long time in the research field but their disadvantages make it difficult to reproduce the in vivo environment. Three-dimensional culture systems overcome these limitations, simulating the physiological context of an organism, from the molecular level to the cellular, tissue, and organ complexity levels. This review focuses on 3D cellular models, such as spheroids and tumoroids, which reproduce tumor heterogeneity and microenvironments. It also includes 3D cultures of mesenchymal stem cells (MSCs), particularly those derived from teeth. In conclusion, 3D models are profoundly impacting the biomedical field by offering more accurate in vitro platforms for drug development and disease modeling, thereby significantly reducing the reliance on animal testing and leading to the advancement of personalized and regenerative medicine. Full article

Background/Objectives: Understanding the molecular interactions between small bioactive compounds and serum albumins is essential for drug development and pharmacokinetics. Noraucuparin, a biphenyl-type phytoalexin with promising pharmacological properties, has shown a strong binding affinity to bovine serum albumin (BSA), a model protein for drug transport. This study aims to elucidate the structural and energetic characteristics of the noraucuparin–BSA complex under physiological and slightly elevated temperatures. Methods: Microsecond-scale molecular dynamics (MD) simulations and Molecular Mechanics Generalized Born Surface Area (MMGBSA)-binding-free energy calculations were performed to investigate the interaction between noraucuparin and BSA at 298 K and 310 K. Conformational flexibility and per-residue energy decomposition analyses were conducted, along with interaction network mapping to assess ligand-induced rearrangements. Results: Noraucuparin preferentially binds to site II of BSA, near the ibuprofen-binding pocket, with stabilization driven by hydrogen bonding and hydrophobic interactions. Binding at 298 K notably increased the structural mobility of BSA, affecting its global conformational dynamics. Key residues, such as Trp213, Arg217, and Leu237, contributed significantly to complex stability, and the ligand induced localized rearrangements in the protein’s intramolecular interaction network. Conclusions: These findings offer insights into the dynamic behavior of the noraucuparin–BSA complex and enhance the understanding of serum albumin–ligand interactions, with potential implications for drug delivery systems. Full article

Background/Objectives: Cooling strategies are critical for optimizing athlete recovery and performance in team sports, yet practical, accessible solutions remain underexplored. This study evaluated the efficacy of a cool vest during a 15 min recovery period following 45 min of simulated soccer match play on core and skin temperature regulation. Methods: Forty-seven physically active males completed an exercise protocol replicating the physiological demands of a soccer half-match. Participants were randomized into an experimental group using a cool vest during recovery (IG, n = 24) or a control group (CG, n = 23) without cooling intervention. Results: Compared to CG, IG exhibited small but significant reductions in skin temperature (31.46 ± 0.67 °C vs. 32.40 ± 1.04 °C; ES = −0.24, 95% CI: −0.40 to −0.08; p = 0.003) and tympanic temperature (35.99 ± 0.45 °C vs. 36.54 ± 0.66 °C; ES = −0.43, 95% CI: −0.67 to −0.18; p < 0.001) 10 min post-exercise. These differences intensified to small and moderate effects at 15 min post-exercise for skin (31.07 ± 0.67 °C vs. 32.51 ± 0.91 °C; ES = −0.37, 95% CI: −0.53 to −0.21; p < 0.001) and tympanic temperatures (35.70 ± 0.42 °C vs. 36.65 ± 0.58 °C; ES = −0.71, 95% CI: −0.96 to −0.46; p < 0.001), respectively, with IG maintaining consistently lower values. No temperature changes were observed in CG. Conclusions: These findings demonstrate that a 15 min cool vest application during halftime effectively reduces core and skin temperatures, compared to passive recovery. This supports its utility as a practical, low-cost strategy for thermoregulatory management in soccer, warranting further investigation into its long-term performance benefits. Full article