Search Results (2,358)

Magnesium alloys are promising biodegradable orthopedic implant materials, but their clinical translation is hindered by rapid, unregulated corrosion in physiological environments. Polyvinylidene fluoride (PVDF) coating has attracted substantial attention for addressing the issue above. However, it suffers from insufficient interfacial adhesion to Mg alloy substrates. In this work, we propose a Zr-based pretreatment strategy to enhance PVDF coatings. The pretreatment was performed via a chemical conversion deposition method, which fabricated a Zr-based film on AZ31 magnesium alloy and greatly promoted the adhesion of the following PVDF coating. Interface analysis showed that coating adhesion was improved from 0.44 MPa to 2.48 MPa. In light of this, corrosion protection performance was significantly improved. Electrochemical tests in simulated body fluid revealed the enhanced PVDF coating shifted the corrosion potential from −1.594 V to −1.392 V and reduced the corrosion current density by over five orders of magnitude. Immersion tests also showed stable pH level, low weight loss, and good hydrophobicity with the enhanced PVDF coating. In summary, the enhanced PVDF coating provides excellent corrosion protection for magnesium alloys, thus boosting their biomedical use. Full article

Urban environments, particularly university campuses, are increasingly exposed to thermal discomfort due to the Urban Heat Island (UHI) effect and intense solar radiation. This study evaluates the effectiveness of passive and hybrid cooling strategies, specifically sun-sail shading and mist cooling, in enhancing outdoor thermal comfort (OTC) in a university courtyard. The Van Dyck courtyard at the American University of Beirut, located on the East Mediterranean coast, was selected due to its heavy use between 10 am and 2 pm during summer, when ambient temperatures ranged between 32 and 36 °C and relative humidity between 21 and 33%. Thermal variations across four seating areas were analyzed using ENVI-met, a high-resolution microscale model validated against on-site data, achieving Mean Absolute Percentage Errors of 4% for air temperature and 5.2% for relative humidity. Under baseline conditions, Physiological Equivalent Temperature (PET) exceeded 58 °C, indicating severe thermal stress. Several mitigation strategies were evaluated, including three shading configurations, two mist-cooling setups, and a combined system. Results showed that double-layer shading reduced PET by 17.1 °C, mist cooling by 1.2 °C, and the combined system by 20.7 °C. Shading minimized radiant heat gain, while mist cooling enhanced evaporative cooling, jointly bringing thermal sensations closer to slightly warm–comfortable conditions. These cooling interventions also have sustainability value by reducing dependence on mechanically cooled indoor spaces and lowering campus air-conditioning demand. As passive or low-energy measures, shading and mist cooling support climate-adaptive outdoor design in heat-stressed Mediterranean environments. Full article

The widespread use of portable electronic devices has increasingly promoted the prolonged maintenance of non-physiological postures, particularly anterior and downward head flexion. Therefore, this study aimed to analyze the condylar and incisor relationship displacement induced by this improper posture. A total of 20 adult subjects (9 F, 11 M; mean age 27 ± 5) were recruited at the Department of Dentistry, Vita-Salute San Raffaele University, Milan, Italy. Mandibular kinematics was recorded using JMA-Optic AG (Zebris Medical GmbH, Isny, Germany). The protocol adopted consisted of three phases: (1) Habitual occlusion with light clenching, (2) Neuromuscular rest position (RP) verified by surface electromyography (sEMG), (3) Anterior head flexion (40–60°) (HF), simulating the posture typically observed during portable digital device use. Millimetric measurements of condylar displacement from RP to HF and incisal plane changes were collected. Data were analyzed descriptively with Microsoft Excel, and inferentially with StatPlus Pro (AnalystSoft, StatPlus: mac Pro, version 8). The right condyle exhibited a mean displacement of 1.9 mm in the downward direction (p < 0.001), while the left condyle showed a downward displacement of 1.5 mm (p < 0.001). No significant difference was observed between the two sides. At the dental level, the lower incisor revealed a mean shift of 1.0 mm superiorly (p < 0.001) and 0.7 mm anteriorly (p < 0.001). The HF determines a significant condylar and incisal plane displacement, and may predispose individuals to TMJ disorders, supporting the hypothesis of an emerging cranio-cervico-mandibular condition linked to prolonged use of high-tech display terminals, here proposed as ED-TMD (Electronic Device-Induced Temporomandibular Disorder). Full article

Supervised ECG-based sleep apnea detection typically depends on large and fully annotated datasets, yet the rarity and cost of labeling apneic events often lead to substantial annotation scarcity in practice. This study provides a controlled evaluation of how such scarcity degrades classification performance and, as a key contribution, investigates whether a constrained, morphology-preserving ECG augmentation framework can compensate for reduced apnea-label availability. Using the PhysioNet Apnea–ECG dataset, we simulated seven levels of label retention ($r=5$–$100\%$) and trained a lightweight CNN–BiLSTM model under both subject-dependent (SD) and subject-independent (SI) five-fold protocols. Offline augmentation was applied only to apnea segments and consisted of simple, physiologically motivated time-domain perturbations designed to retain realistic cardiac and respiratory dynamics. Across both evaluation settings, augmentation substantially mitigated performance loss in the low- and mid-scarcity regimes. Under SI evaluation, the mean F1-score improved from 0.57 to 0.72 at $r=5\%$ and from 0.63 to 0.76 at $r=10\%$, with scores at $r=10$–$40\%$ (0.75–0.77) approaching the full-label baseline of 0.79. Temporal and spectral analyses confirmed preservation of P–QRS–T morphology and respiratory modulation without distortion. These results demonstrate that simple and interpretable ECG augmentations provide an effective and reproducible baseline for data-efficient apnea screening and offer a practical path toward scalable annotation and robust single-lead deployment under label scarcity. Full article

This study aimed to evaluate, through finite element analysis (FEA), the biomechanical behavior of edentulous maxillary overdentures supported on 2, 3, and 4 implants with conometric connections, and to determine the minimum implant number that maintains stresses within physiological limits. A 3D finite element model of a resorbed edentulous maxilla was generated from CT images and processed in ANSYS v19.0. Subsequently, six models were simulated according to implant number (2, 3, or 4) and cortical bone thickness (0.5 mm or 1 mm). Conical connection implants and cobalt–chromium-reinforced overdentures with Equator attachments were modeled. Bilateral axial loads were applied and Von Mises equivalent stresses were calculated for implants and abutments, while maximum and minimum principal stresses were analyzed in bone. Results showed that the highest deformation and stress concentrations were observed in the two-implant models, with trabecular stresses ranging from 6.5 to 8.4 MPa, exceeding the 5 MPa safety threshold. In contrast, both three- and four-implant models maintained trabecular stresses below 3 MPa, while keeping cortical bone stresses within physiological limits. The three-implant tripod configuration demonstrated a comparable stress distribution to the four-implant models. From a biomechanical perspective, overdentures supported on four implants with 1 mm cortical thickness showed the most favorable performance. Nevertheless, the three-implant model represented a biomechanically acceptable and potentially cost-effective alternative, suggesting its viability as a simplified clinical option that warrants further investigation. Full article

Leaf area index (LAI) is a pivotal biophysical parameter linking vegetation physiological processes and macro-ecological functions. Accurate large-scale LAI estimation is indispensable for agricultural management, climate change research, and ecosystem modeling. However, existing methods fail to efficiently extract integrated spatial-spectral-temporal features and lack targeted modeling of spatio-temporal dependencies, compromising the accuracy of LAI products. To address this gap, we propose STC-DeepLAINet, a Transformer-GCN hybrid deep learning architecture integrating spatio-temporal correlations via the following three synergistic modules: (1) a 3D convolutional neural networks (CNNs)-based spectral-spatial embedding module capturing intrinsic correlations between multi-spectral bands and local spatial features; (2) a spatio-temporal correlation-aware module that models temporal dynamics (by “time periods”) and spatial heterogeneity (by “spatial slices”) simultaneously; (3) a spatio-temporal pattern memory attention module that retrieves historically similar spatio-temporal patterns via an attention-based mechanism to improve inversion accuracy. Experimental results demonstrate that STC-DeepLAINet outperforms eight state-of-the-art methods (including traditional machine learning and deep learning networks) in a 500 m resolution LAI inversion task over China. Validated against ground-based measurements, it achieves a coefficient of determination (R²) of 0.827 and a root mean square error (RMSE) of 0.718, outperforming the GLASS LAI product. Furthermore, STC-DeepLAINet effectively captures LAI variability across typical vegetation types (e.g., forests and croplands). This work establishes an operational solution for generating large-scale high-precision LAI products, which can provide reliable data support for agricultural yield estimation and ecosystem carbon cycle simulation, while offering a new methodological reference for spatio-temporal correlation modeling in remote sensing inversion. Full article

Poultry transport represents a significant animal welfare challenge, particularly when birds are exposed to heat stress during travel, a condition that can compromise physiological stability, performance, and survival. Despite the relevance of this issue, research on engineering improvements to poultry transport crates remains limited. In this study, four virtual models of poultry transport crates were evaluated to assess their potential to improve the thermal comfort internal airflow conditions. Computational Fluid Dynamics (CFD) simulations were conducted under three transport speeds, complemented by wind tunnel experiments using reduced-scale prototypes fabricated by additive manufacturing. The results demonstrated that the alternative crate 3 (AC3) model presented exhibited superior internal average airflow velocities (IAFV) across all speeds, including a 32.85% increase compared to the conventional crate at 60 km/h. Wind tunnel testing confirmed significant differences among crate designs. AC3 showed lower air temperature than AC1 and reduced relative humidity compared to CC and AC2. Thermal comfort indices supported these findings, with AC3 presenting the lowest THI and enthalpy, indicating a less stressful microclimate. In terms of airflow, AC2 and AC3 achieved higher IAFV (19.27 ± 8.49 m/s and 19.30 ± 4.80 m/s) than CC and AC1. AC3 also had the lowest dynamic pressure, suggesting reduced airflow resistance and more efficient aerodynamics. Therefore, improved crate geometry and increased ventilation surface can enhance airflow distribution, potentially reduce heat accumulation and improve animal welfare. However, further studies involving live birds, realistic stocking densities, and full-scale trailer simulations are required to validate these benefits under commercial transport conditions. Full article

Background/Objectives: This study explores the integration of Design of Experiments (DoE) with Physiologically Based Biopharmaceutics Modeling (PBBM) to streamline the development of extended-release (XR) formulations. Using donepezil (DPZ) as a model drug, we developed an optimized XR formulation exhibiting a dissolution profile comparable to the reference product, Aricept^® (Eisai GmbH, Frankfurt, Germany). Methods: A Box–Behnken experimental design was applied to systematically evaluate how formulation variables—HPMC 100, HPMC 4000, and NaCMC—affect drug release kinetics, tablet hydration, and erosion. This strategy enabled the identification of optimal excipient concentrations with minimal experimental effort. Results: The in vitro dissolution data were then integrated into a PBBM framework to simulate drug release and pharmacokinetics, enabling virtual bioequivalence (VBE) assessments. The combined approach provided robust predictive insights into formulation performance, substantially reducing reliance on resource-intensive in vivo studies. Beyond its successful application with DPZ, this integrated methodology offers a scalable and generalizable strategy for efficiently developing bioequivalent XR formulations for various clinically relevant drugs. Conclusions: Our findings highlight the importance of leveraging advanced statistical methods and in silico modeling to overcome contemporary pharmaceutical development challenges, paving the way for innovative, cost-effective solutions that significantly accelerate time-to-market. Full article

The development of implantable neural interfaces is essential for enabling bidirectional communication between the nervous system and prosthetic devices, yet their evaluation still relies primarily on in vivo models which are costly, variable, and ethically constrained. Here, we report a bio-inspired artificial nerve simulator engineered as a reproducible ex vivo platform for pre-implantation testing of plug-type electrodes. The simulator is fabricated from a conductive hydrogel composite based on reduced graphene oxide (rGO), polyaniline (PANI), agarose, sucrose, and sodium chloride, with embedded conductive channels that replicate the fascicular organization and conductivity of peripheral nerves. The resulting construct exhibits impedance values of ~2.4–2.9 kΩ between electrode needles at 1 kHz, closely matching in vivo measurements (~2 kΩ) obtained in Sus scrofa domesticus nerve tissue. Its structural and electrical fidelity enables systematic evaluation of electrode–nerve contact properties, signal transmission, and insertion behavior under controlled conditions, while reducing reliance on animal experiments. This bio-inspired simulator offers a scalable and physiologically relevant testbed that bridges materials engineering and translational neuroprosthetics, accelerating the development of next-generation implantable neural interfaces. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

Background/Objectives: Acute stress is known to influence food-related motivation and decision-making, often promoting a preference for energy-dense, palatable foods. However, traditional laboratory paradigms have limited ecological validity. This study examined the relationship between stress-induced physiological changes, eating behavior traits, and food cravings using a virtual reality (VR) adaptation of the Trier Social Stress Test (VR-TSST) followed by a VR supermarket task in adolescents. Methods: Thirty-eight adolescents (mean age 15.8 ± 0.6 years) participated in the study. Physiological parameters (HR, QT, PQ intervals) were recorded pre- and post-stress using a portable ECG device (WIWE). Perceived stress and eating behavior traits were evaluated with the Perceived Stress Scale (PSS) and the Three-Factor Eating Questionnaire (TFEQ-R21C), respectively. Immediately after the VR-TSST, participants performed a VR supermarket task in which they rated cravings for sweet, fatty, and healthy foods using visual analog scales (VAS). Paired-samples t-tests examined pre–post changes in physiological parameters, partial correlations explored associations between ECG responses and eating traits, and a 2 × 3 mixed-model Repeated Measures ANOVA assessed the effects of food type (sweet, fatty, healthy) and uncontrolled eating (UE) group (low vs. high) on post-stress cravings. Results: Acute stress induced significant increases in HR and QTc intervals (p < 0.01), confirming a robust physiological stress response. The ANOVA revealed a strong main effect of food type (F(1.93, 435.41) = 168.98, p < 0.001, η²p = 0.43), indicating that stress-induced cravings differed across food categories, with sweet foods rated highest. A significant food type × UE group interaction (F(1.93, 435.41) = 16.49, p < 0.001, η²p = 0.07) showed that adolescents with high UE exhibited greater cravings for sweet and fatty foods than those with low UE. Overall, craving levels did not differ significantly between groups. Conclusions: The findings demonstrate that acute stress selectively enhances cravings for high-reward foods, and that this effect is modulated by baseline uncontrolled eating tendencies. The combined use of VR-based stress induction and VR supermarket simulation offers an innovative, ecologically valid framework for studying stress-related eating behavior in adolescents, with potential implications for personalized nutrition and the prevention of stress-induced overeating. Full article

The aim of this study was to investigate the molecular interactions of heparin, diclofenac, and their supramolecular complexes with cyclooxygenase enzymes (COX-1 and COX-2) using computational docking techniques. Diclofenac is a widely used nonsteroidal anti-inflammatory drug (NSAID) that inhibits COX isoforms, whereas heparin is a polyanionic glycosaminoglycan with established anticoagulant and emerging anti-inflammatory properties. Supramolecular association between these agents may modulate their physicochemical behavior and target engagement. Molecular modeling, dual-drug docking, and molecular dynamics (MD) simulations were employed to characterize the interactions of heparin, diclofenac, and pre-formed heparin–diclofenac complexes with COX-1 and COX-2. Geometry optimization and lipophilicity (logP) estimates were obtained using HyperChem, while protein–ligand docking was performed in HEX using crystallographic COX structures from the Protein Data Bank. Docking poses were analyzed in Chimera, and selected complexes were refined through short MD simulations. Pre-formed heparin–diclofenac assemblies exhibited markedly enhanced docking scores toward both COX isoforms compared with single ligands. Binding orientation strongly influenced affinity: for COX-1, the heparin–diclofenac configuration yielded the most favorable interaction, whereas for COX-2 the diclofenac–heparin configuration was preferred. Both assemblies adopted binding modes distinct from free diclofenac, suggesting cooperative electrostatic and hydrophobic contacts at the enzyme surface. Supramolecular complexation also altered calculated logP values relative to the individual compounds. MD simulations supported the relative stability of the top-ranked complex–COX assemblies. These findings indicate that heparin–diclofenac assemblies may enhance and reorganize predicted COX interactions in a configuration-dependent manner and illustrate the utility of dual-drug docking for modeling potential synergistic effects. Such insights may inform the design of localized or topical formulations, potentially incorporating non-anticoagulant heparin derivatives, to achieve effective COX inhibition with reduced systemic exposure. However, the results rely on simplified heparin fragments, legacy docking tools, and short MD simulations, and should therefore be interpreted qualitatively. Experimental studies will be essential to confirm whether such supramolecular assemblies form under physiological conditions and whether they influence COX inhibition in vivo. Full article

The replacement of ionizable functional groups that are predominantly charged at physiological pH with neutral bioisosteres is a common strategy in medicinal chemistry; however, its impact on binding affinity is often context-dependent. Here, we investigated a series of amide derivatives of a glycomimetic E-selectin ligand, in which the carboxylate group of the lead compound is substituted with a range of amide and isosteric analogs. Despite the expected loss of the salt-bridge interaction with Arg97, several amides retained or even improved the binding affinity. Co-crystal structures revealed conserved binding poses across the series, with consistent interactions involving the carbonyl oxygen of the amide and the key residues Tyr48 and Arg97. High-level quantum chemical calculations ruled out a direct correlation between carbonyl partial charges and affinity. Instead, a moderate correlation was observed between ligand binding and the out-of-plane pyramidality of the amide nitrogen, suggesting a favorable steric adaptation within the binding site. Molecular dynamics (MD) simulations revealed that high-affinity ligands exhibit enhanced solution-phase pre-organization toward the bioactive conformation, likely reducing the entropic penalty upon binding. Further analysis of protein–ligand complexes using Molecular mechanics/Generalized born surface area (MM-GB/SA) decomposition suggested minor lipophilic contributions from amide substituents. Taken together, this work underscores the importance of geometric and conformational descriptors, beyond classical electrostatics, in driving affinity in glycomimetic ligand design and provides new insights into the nuanced role of amides as carboxylate isosteres in protein–ligand recognition. Full article

Color in urban public spaces is often approached as an aesthetic issue, yet it also governs psychological responses, legibility and safety, place identity, and environmental performance. Despite three decades of research, planners and designers still lack measurable, audit-ready guidance that links color decisions to verifiable outcomes. This paper presents a systematic review that consolidates evidence from environmental psychology, architecture and urban design, cultural studies, and building and urban physics. Studies were screened for outdoor or outward-facing settings and for explicitly reported color variables and performance indicators. The findings are organized into four domains in which color operates as a system variable: psychological and physiological effects; cultural expression and place identity; functional zoning and wayfinding; and sustainability and environmental adaptation. Across these domains, the review identifies robust patterns—such as the central role of luminance and saturation in shaping affect, attention, and visibility—while highlighting where outcomes are strongly conditioned by cultural, climatic, and material context. On this basis, the paper proposes an Objective–Strategy–Metric–Validation (OSMV) framework that connects design objectives to color strategies, quantitative metrics (e.g., color difference, contrast, and reflectance measures), and procedures for simulation or field validation. Framed in this way, color emerges not as a decorative accessory but as a measurable design variable that can be integrated into performance-based planning, regulation, and multi-objective optimization of urban public spaces. Full article

Background/Objectives: Accurate absorbed dose estimation is essential for optimising targeted radionuclide therapy (TRT) in metastatic castration-resistant prostate cancer, where kidney toxicity is dose-limiting. [¹⁷⁷Lu]Lu-rhPSMA-10.1 is a novel PSMA-targeted radioligand with favourable tumour-to-kidney uptake ratios; however, inter-patient pharmacokinetic variability can lead to differences in organ and tumour absorbed doses under fixed-activity administration. Personalised dosimetry offers a means to address this variability. This work aims to create mouse PBPK model-based digital twins for [¹⁷⁷Lu]Lu-rhPSMA-10.1 to test the model’s resistance to noise and evaluate its impact on accuracy and absorbed dose calculations. Methods: Five CB-17 SCID mice bearing LNCaP tumour xenografts received 2.6–3.1 MBq [¹⁷⁷Lu]Lu-rhPSMA-10.1 intravenously. Biodistribution was assessed 24 h post-injection by organ weighing and gamma counting. The PBPK model, implemented in MATLAB SimBiology (R2023a), was fitted to individual biodistribution data using mouse-specific physiological parameters. Digital twins—combining the model with fitted parameters—were used to generate time–activity curves (TACs) for kidneys, tumours, and the whole body. Gaussian noise ($\sigma$ = 0–0.35) was added to TACs to simulate measurement error. The model was refitted, and absorbed doses from time-integrated activities (TIAs) were compared to digital twin references. Results: The digital twin approach reproduced experimental data with physiologically plausible parameters. Absorbed dose estimates remained consistent and robust, deviating by <2.3% in kidneys and <1.0% in tumours. Conclusions: PBPK-based digital twins enable reliable, individualised dosimetry, even under substantial measurement uncertainty. Full article