Search Results (9,923)

Nanomedicine has yielded clinically useful platforms, including liposomes, albumin-bound nanoparticles, and lipid nanoparticles; yet, many systems translate poorly because of nonspecific biodistribution, limited target-site accumulation, inefficient cellular uptake and intracellular delivery, immune clearance, and off-target toxicity. These bottlenecks are often shaped at cell membrane interfaces, where therapeutic materials are recognized, retained, internalized, or cleared and may elicit unsafe responses. Here, we frame cell membrane biophysics as a therapeutic interface for nanomedicine. We examine how lipid organization and fluidity, mechanics, electrochemical state, glycocalyx architecture, and membrane protein identity shape recognition, adhesion, endocytosis, fusion, trafficking, immune responses, and drug release. We assess how disease-associated membrane remodeling can create candidate therapeutic entry points and delivery barriers across cancer, neurodegeneration, inflammation, infection, and vascular disease. We then analyze receptor-mediated targeting, lipid-domain-associated uptake, membrane-coated nanocarriers, engineered extracellular vesicles, and hybrid platforms, with explicit context-of-use definitions and design boundaries. Finally, we propose translational qualification through function-linked critical quality attributes, mechanism-relevant potency assays, context-matched models, in vivo pharmacology and immune safety, scalable manufacturing, and regulatory evaluation. Progress will depend less on descriptive membrane mimicry than on measurable, reproducible, and qualified membrane-dependent functions. Full article

The increasing prevalence of antimicrobial resistance in bacteria highlights the need for alternative membrane-active compounds with favorable safety profiles. β-Caryophyllene (BCP), a bicyclic sesquiterpene, has demonstrated antimicrobial effects; however, its biological responses in Gram-negative bacteria and associated membrane interactions remain insufficiently characterized. In this study, the growth inhibitory effect of BCP against E. coli ATCC 25922 was evaluated through OD595 growth kinetics, while hemocompatibility was assessed using sheep erythrocytes, and cannabidiol (CBD) was included as a comparative control. To investigate membrane-associated effects, differential scanning calorimetry (DSC) was performed using DPPE/DPPG (8:2) bilayers as a simplified phospholipid membrane model. BCP inhibited bacterial growth with an IC₅₀ of 0.83 mg/mL and exhibited low hemolytic activity (2.92% at 1 mg/mL). DSC analyses revealed concentration-dependent shifts in phase transition temperature and reductions in transition enthalpy (ΔH kJ/mol) 36% and 82% for BCP-5 and CBD-10 according to the control, indicating alterations in lipid organization and membrane thermotropic behavior. In contrast, CBD showed greater growth inhibitory potency (IC₅₀ of 0.042 mg/mL) but more pronounced disruption of membrane organization. Overall, these findings suggest that BCP exhibits moderate growth inhibition associated with membrane related effects and low hemolytic activity, providing insights into the relationship between physicochemical properties, membrane interactions, and biological responses. Full article

In vitro neurovascular unit (NVU) models are essential for reproducing blood–brain barrier (BBB) transport and neurovascular cell interactions. However, the literature remains fragmented: biomaterial chemistry, fabrication parameters and organ-on-a-chip architecture are commonly evaluated in isolation, while inconsistent reporting of matrix properties, processing history, cell source, flow and barrier readouts prevents head-to-head comparison and the extraction of transferable design rules. To address this gap, this review integrates biomaterials, manufacturing technologies and organ-on-a-chip engineering within a unified material–process–structure–function framework. We translate endothelial junctions, basement-membrane components and perivascular cells into experimentally actionable material requirements; compare natural, synthetic, semisynthetic and decellularized extracellular-matrix hydrogels; and examine crosslinking, peptide functionalization, stimuli responsiveness, composite-network formation and preparation methods. Findings from Transwell, microfluidic, tubular, self-assembled and 3D-bioprinted BBB systems are used to relate matrix stiffness, degradability, ligand density, permeability, device-body material and fabrication route to barrier maturation, analytical access and reproducibility. By defining matched controls and minimum reporting requirements for chemistry, mechanics, transport and processing, this review provides a practical basis for next-generation BBB models that can improve permeability and efficacy screening in drug discovery, reproduce disease- and patient-specific barrier dysfunction, and support individualized response testing with iPSC- or patient-derived cells. Full article

Background: Glioblastoma (GBM) is the most aggressive subtype of malignant glioma. Current therapeutic options remain limited, highlighting the need for the development of novel compounds capable of improving the efficacy of standard treatments. This study aimed to evaluate the biological activity of a series of pyrazolone-based hydrazone compounds (TPPs) in vitro GBM cell lines. Methods: The eight TPPs were synthesized by a nucleophilic addition reaction of different substituted hydrazines with 1-(5-hydroxy-3-methyl-1-phenyl-1H-pyrazol-4-yl)-2-phenylethan-1-one and tested on two human GBM cell lines, T98 and U87. The cytotoxic effects were evaluated via MTT assay. The most active compound was further investigated at IC₅₀ and IC₂₅ concentrations to evaluate mechanisms of cellular damage, including reactive oxygen species (ROS) production and mitochondrial membrane potential (ΔΨ_m) changes. Additional assays included colony formation, cell cycle analysis, and evaluation of DNA damage and apoptosis markers. Results: TPP25 exhibited the highest activity with IC₅₀ values of 11.01 μM (95% CI: 10.42 to 11.64) and 13.12 μM (95% CI: 10.23 to 16.87) on T98 and U87 lines, respectively. Treatment induced early ROS production and mitochondrial depolarization, along with a significant reduction in colony formation. Cell cycle analysis revealed accumulation in the sub-G0 phase, consistent with increased cell death, supported by propidium iodide uptake. Furthermore, the results suggest the involvement of an apoptotic-like mechanism as supported by Annexin V positivity, γ-H2AX upregulation and transient caspase-3 activation. Conclusions: TPP25 demonstrates significant in vitro cytotoxicity, likely driven by a pro-apoptotic mechanism. This profile positions it as a potential lead compound for further preclinical evaluation, supporting its future transition into in vivo GBM models. Full article

The Weibei Plain, characterized by its complex stratified aquifer system and extensive brine resources, faces severe groundwater salinization. Unraveling the precise evolutionary mechanisms of diverse hydrochemical types across varying depths and geomorphological zones remains a significant challenge. This study synthesizes a multi-batch hydrochemical dataset with multi-isotopic tracers (δ²H, δ¹⁸O, δ¹¹B, δ⁸¹Br, δ³⁷Cl) to establish a comprehensive groundwater evolutionary model from the piedmont plain to the coastal marine plain. The results indicate distinct hydrochemical zonation governed by geographic geomorphology and historical marine transgressions. Salinization in transitional waters is primarily driven by physical mixing and reverse cation exchange rather than extreme evaporative fractionation. Crucially, isotopic mass balance definitively reveals that deep brine (depth > 60 m) originates not from modern seawater intrusion, but from the extreme surface evaporation of ancient paleo-seawater. This paleo-brine underwent profound isotopic exchange during its gravity-driven downward migration, evidenced by intense clay mineral adsorption (yielding extreme δ¹¹B enrichment up to 64.42‰) and secondary evaporite dissolution. Furthermore, the regional cone of depression formed by intensive brine extraction has profoundly altered deep hydrodynamics, inducing overflow and membrane ultrafiltration across massively thick clay aquitards. This process distinctly drives the isotopic fractionation observed in deep brackish waters. The analysis process in this study combines the isotope method with the regional geomorphological zoning, which can provide a reference for the analysis of groundwater evolution characteristics in other coastal aquifers. Full article

Depression is often associated with functional disturbances in the visual system. However, the fundamental features underlying these visual system aberrations in depression remain to be fully elucidated. In particular, in the first stage of visual processing, how different retinal output neuron types change their intrinsic properties and output features in response to specific light stimulation remains unclear. Here, by adopting a mouse model of depression induced by chronic unpredictable stress (CUS), we found that depression is associated with reduced blood perfusion in the retinal inner plexiform layer (IPL). The hypoperfusion in the IPL is paralleled by a remarkable reduction of retinal ganglion cell (RGC) density, with more cell loss in ipRGCs than in the general RGCs. The surviving RGCs—particularly, ipRGCs—changed their intrinsic electrical properties, exhibiting decreased membrane input resistance, more depolarized resting membrane potential, and altered spiking properties. Additionally, these cells showed stimulus-size-dependent increases in light-evoked responses. Together, our findings demonstrate that depression is associated with the retinal IPL hypoperfusion and RGC impairments (particularly ipRGCs) and suggest retinal layer- and RGC-type-specific susceptibilities, furthering our understanding of retinal pathophysiology in depression. Full article

This study investigates machine learning techniques for predicting the behavior of proton exchange membrane electrolyzers, which are vital for sustainable hydrogen production. This work addresses these challenges by integrating artificial neural networks to develop predictive models capable of capturing the performance of proton exchange membrane electrolyzers with high accuracy. This research utilizes a multi-layer perceptron network architecture, optimized through rigorous data preprocessing, parameter tuning, and error minimization strategies. The dataset used was based on published PEME numerical simulation datasets and encompasses key performance indicators, including stack voltage, water transport, and electrochemical reactions. The trained artificial neural networks models achieved mean squared error values of 3.66 × 10⁻⁵ and 9.75 × 10⁻⁶, with correlation coefficients of 0.99996 and 0.99958, demonstrating near-perfect predictive accuracy. A comparative benchmarking study against alternative regression algorithms revealed that the proposed MLP models significantly outperformed Gradient Boosting and Random Forest by several orders of magnitude, thereby establishing a higher level of persuasiveness and reliability for the developed framework. Average deviation rates of 0.11% and −0.01% further validated model reliability. The novelty of this work lies in its comprehensive approach, which goes beyond isolated metrics by addressing interactions across system parameters. This integrated framework enables enhanced prediction, control, and optimization of proton exchange membrane electrolyzer’s performance, setting a new benchmark for leveraging machine learning in hydrogen energy systems. These findings pave the way for scalable, cost-effective solutions to improve proton exchange membrane electrolyzers’ efficiency and operational reliability. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

This paper presents a mathematical model to investigate how waves and currents interact with a flexible perforated floating membrane in finite water depth within the framework of linear wave theory. The perforated flexible membrane is modeled based on Darcy’s law and the one-dimensional string equation. The complex dispersion relation in the presence of current velocity is derived from the Green’s function technique using a fundamental source potential solution. The dispersion curve is analyzed by comparing the phase and group velocities for different water depths. Further, a physical model associated with the effect of current on a moored finite floating perforated flexible membrane integrated with a vertical wall is formulated. Then, the theoretical solution of a physical boundary value problem near a vertical rigid wall is obtained using the matching technique and the roots of the dispersion relation derived from the Green’s function technique. Numerical simulations are provided to verify the convergence of the series solution and the accuracy of the obtained analytical findings are evaluated against previously published analytical and experimental datasets. Further, several numerical results on the membrane deflection, hydrodynamic coefficients, and horizontal force on the wall for various structural parameters, mooring stiffness, and current velocities are analyzed. It is observed that the present analysis with this perforated membrane breakwater will be helpful to coastal and marine engineers to understand the influence of current velocity. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

Humic acid, as a natural macromolecular aggregate rich in functional groups, offers abundant modification sites and tunable chemical functionality, making it a promising building block for three-dimensional network construction. In this study, glutaraldehyde (GA) was employed as a crosslinking agent to incorporate sodium humate (NaHA, sodium salt of humic acid from alkaline treatment) into a polyvinyl alcohol (PVA) matrix, yielding composite membranes with enhanced structural stability and performance. The results demonstrate that NaHA effectively modulates the crosslinked network, and the physical and mechanical characteristics can be readily tailored by varying the PVA/NaHA/GA ratio. Compared with pristine PVA/GA hydrogel, the inclusion of NaHA significantly influences the mechanical response, with optimal comprehensive performance achieved at a NaHA content of 7.5 wt%, corresponding to a tensile strength of 60.4 MPa and an elongation at break of 74.2%. Furthermore, the cumulative release of NaHA after two days reached 64.4%, confirming that NaHA supramolecular aggregates were stably entrapped within the PVA/GA crosslinked matrix. The release kinetics were well described by the Korsmeyer–Peppas model. Overall, the covalent crosslinking of PVA with GA, together with hydrogen-bonding associations and physical entrapment mediated by NaHA, constructed a composite membrane network. This network exhibited tunable dry-state mechanical properties and sustained NaHA release, supporting its further evaluation as a prospective candidate for agricultural mulching films. Full article

Proton exchange membrane fuel cell (PEMFC) parameter identification is a nonlinear computational modeling problem involving strongly coupled electrochemical parameters and partially unobservable polarization processes. Its experimental teaching is further constrained by the cost of fuel cell stacks and the safety requirements associated with hydrogen operation. To address these challenges, this study develops a computational modeling and intelligent simulation framework for a virtual teaching experiment on PEMFC parameter identification. A semi-empirical output-voltage model is established, and the sum of squared errors (SSE) between measured and simulated voltages is formulated as the optimization objective. An enhanced Logistic–Tent reverse snow ablation optimizer (LRSAO), termed RLFDB-LRSAO, is introduced by integrating roulette-wheel-selection-enhanced fitness-distance balance and Lévy flight perturbation. Its methodological novelty lies in the stage-wise coordination of population-diversity enhancement, candidate-selection guidance, and search perturbation within the LRSAO framework, rather than in the individual component strategies themselves. The framework organizes the identification process into mechanism interpretation, model construction, algorithm implementation, parameter configuration, visualization, comparative evaluation, and reflective analysis. Case studies using the NedStack PS6 and Modular SR-12 stacks yield best SSE values of 1.2173340 and 6.13503904, respectively. A small-scale qualitative teaching evaluation involving 20 postgraduate students indicated that the framework supported programming practice, strengthened conceptual understanding of PEMFC parameter identification and intelligent optimization, and provided useful support for research-oriented skills such as engineering problem analysis, technical writing, and innovation-oriented project development. These results provide preliminary evidence of technical and educational feasibility while supporting a cautious, problem-dependent interpretation of the optimizer. Full article

The global emergence of multidrug-resistant fungal pathogens, including Candida auris, Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Pneumocystis jirovecii, poses a growing threat to public health, particularly among immunocompromised individuals. The limited number of available antifungal drug classes and the rapid evolution of resistance mechanisms, including target-site mutations, efflux pump activation, biofilm formation, metabolic adaptation, and stress-response signaling, have substantially reduced treatment efficacy. This review provides a comprehensive overview of current antifungal therapies, their limitations, and emerging molecular targets for next-generation antifungal drug discovery. We highlight promising targets involved in fungal cell wall biosynthesis, membrane integrity, mitochondrial metabolism, virulence regulation, and host–pathogen interactions, emphasizing their interconnected roles within adaptive resistance networks. Attention is given to small-molecule isothiazolone-based inhibitors, including phosphoglucomutase-targeting compounds, as novel candidates capable of disrupting multiple fungal survival pathways. We further discuss advances in combination therapies, anti-virulence approaches, nanotechnology-based delivery systems, and artificial intelligence-driven drug discovery pipelines that integrate multi-omics data, structural modeling, molecular docking, and virtual screening to accelerate therapeutic development. These advances support a transition from conventional single-target strategies toward systems-level, precision-guided antifungal therapies, providing a framework for overcoming multidrug resistance and improving clinical outcomes in invasive fungal infections. Full article

To address the challenges of structural deformation and limited load-bearing capacity in morphing wings, this paper proposes a novel rigid–flexible composite morphing wing based on a foldable membrane–skeleton structure with a high folding ratio. Inspired by the deployment mechanics of biological wings and the cooperative support principle of multi-bar mechanisms, an optimization model was established to resolve hinge interference in the skeletal design. Through geometric reconstruction of the skeleton, the design achieves compact stowage in the folded state and maximizes wing area in the deployed configuration. Furthermore, an integrated design model for the membrane–skeleton interface was established based on rigid–flexible hybrid connection principles, followed by an analysis of the wing’s static structural characteristics via finite element simulation. A prototype was fabricated to experimentally validate its morphing functionality and out-of-plane load-bearing performance. Results demonstrate that the mechanism attains an effective folding ratio of approximately 7.19. Additionally, the influence of membrane prestress on the overall structural load capacity was systematically investigated. Full article

Osteoarthritis (OA) is increasingly recognized as a mechanically driven whole-joint disease in which abnormal mechanotransduction initiates a cascade of mitochondrial dysfunction, chronic inflammation, and progressive cartilage degeneration. Among the mechanosensitive molecules identified to date, Piezo1 has emerged as a key mechanosensitive regulator linking pathological mechanical loading to intracellular calcium signaling and downstream cellular responses. Growing evidence indicates that persistent Piezo1 activation promotes mitochondrial calcium overload, excessive reactive oxygen species production, ATP depletion, mitochondrial membrane depolarization, and impaired mitophagy, ultimately amplifying chondrocyte dysfunction and extracellular matrix degradation. In parallel, mitochondrial damage triggers immunometabolic reprogramming through activation of the cGAS–STING pathway and the NLRP3 inflammasome. It also promotes pro-inflammatory cytokines, including interleukin-1β, tumor necrosis factor-α, and interleukin-6. Together, these responses may contribute to a self-perpetuating cycle of inflammation and tissue destruction. This review provides a comprehensive synthesis of recent advances regarding the role of Piezo1 in OA, focusing on the mechanistic links between mechanotransduction, mitochondrial dysfunction, mitophagy, and immunometabolic dysregulation. We further discuss the contribution of mitochondrial quality-control pathways, including PINK1/Parkin-, BNIP3-, and FUNDC1-mediated mitophagy, as well as alterations in mitochondrial dynamics involving DRP1, MFN1, MFN2, and OPA1. Emerging experimental models are discussed as valuable tools for accelerating therapeutic discovery. Finally, we critically evaluate current therapeutic strategies targeting the Piezo1–mitochondria axis, including mechanosensitive channel modulation, mitochondrial protection, mitophagy enhancement, gene therapy, biomaterial-assisted delivery, and nanomedicine. Collectively, current evidence supports the Piezo1–mitochondria–immune axis as an important mechanistic framework contributing to OA pathogenesis and as a potential therapeutic target. Integrating mechanobiology, mitochondrial medicine, and precision-engineered experimental models may facilitate the development of next-generation disease-modifying therapies capable of slowing or preventing osteoarthritis progression. Full article