Search Results (2,107)

Background: Targeting cancer tumors using PLGA (Poly(D, L-lactide-co-glycolide)) nanoparticles (NPs) requires clathrin-mediated endocytosis (CME) and lysosomal degradation to provide release within cancer cells. However, Caveolae-mediated endocytosis (CavME) provides lysosomal escape, which is favorable in oral applications. Macropinocytosis (MPC) is a non-targeted way of endocytosis, used by immune cells. Methods: In this proof-of-concept study, we investigated how polysaccharide surface coatings modulate the endocytic uptake of FITC-labeled PLGA nanomicelles (FPM) in MCF-7 breast cancer cells using spectrophotometry. This research involved the surface modification of FPM using polysaccharides: cellulose (FPCM) as a polyglucan and Halomonas Levan (FPLM) as a polyfructan, to modify the NP and cell-surface association. Results: MPC was found to be the major internalization pathway for the nanomicelles ~200 nm. However, after surface modification, FPCM and FPM remained highly MPC-dependent with additional CavME/CME involvement, whereas FPLM showed relatively reduced MPC dependence and a higher CME contribution. Conclusion: Overall, the results indicate that simple polysaccharide coatings can bias the relative use of MPC, CME, and CavME for PLGA nanomicelles in MCF-7 cells, providing a basis for pathway-oriented nanocarrier design. Validation by flow cytometry, studies in additional breast cancer cell lines, and transporter-level investigations will be needed to generalize and refine these findings. Full article

Continuous-flow separation of magnetically labeled cells according to surface-marker expression levels is increasingly needed to study phenotypic heterogeneity and support downstream assays. Here, we present a microfluidic platform that uses spatially engineered soft magnetic strips (SMS) to sculpt lateral magnetic deflection fields for quantitative, label-guided cell fractionation. Under a uniform bias field, the SMS generates controllable magnetic gradients within the microchannel, producing distinct lateral velocities among EpCAM-labeled tumor cells that carry different Dynabead loads, which indirectly report membrane protein expression. Multi-outlet collection converts these “race-based” trajectory differences into discrete expression-level-resolved fractions. A COMSOL–MATLAB framework and a force-equivalent metric |(H·∇)H| are used to optimize key structural parameters of the magnetic interface, including strip thickness, width, and vertical spacing from the flow channel. Three journey nodes at 1.5, 3, and 9 mm along the flow path define a three-stage cascade that partitions MDA-MB-231, Caco-2, and A549 cells into four EpCAM-related magnetic subgroups: high (H), medium (M), low (L), and near-negative (N). Experiments show that the sorted fractions follow the expected expression trends reported in the literature, while maintaining high cell recovery (>90%) and viability retention of 98.2 ± 1.3%, indicating compatibility with downstream whole-blood assays and culture. Rather than introducing a new biomarker, this work establishes a quantitative magnetic-field design strategy for continuous microfluidic sorting, in which the spatial configuration of soft magnetic elements is exploited to implement expression-level-dependent fractionation in next-generation magneto-fluidic separation systems. Full article

Accurate and sensitive antibody detection remains critical for advanced COVID-19 diagnostics and monitoring SARS-CoV-2 immunity. This study presents a highly sensitive technique for detecting anti-SARS-CoV-2 antibodies in human serum using an integrated photonic sensing platform. The platform utilizes disposable one-dimensional photonic crystal biochips engineered to sustain Bloch Surface Waves. The biochips are integrated into a custom-made optical set-up, which is capable of dual-mode detection: label-free refractometry and label-based fluorescence. Tests on human serum, including negative controls and positive samples from a recovered COVID-19 patient, confirmed the platform’s effective performance. In fluorescence mode, clear discrimination between positive and negative samples was achieved down to a 1:10⁴ serum dilution, with an optimal operating range centered around 1:10³ dilution. These results demonstrate the potential of the technique as a highly sensitive and versatile platform for antibody detection, with significant relevance for advanced COVID-19 diagnostics. Full article

Surface Plasmon Resonance imaging (SPRi) is a powerful label-free technique for high-throughput biochemical analysis. Wavelength modulation is particularly suitable for SPRi due to its wide dynamic range and robustness to fabrication tolerances. However, conventional systems relying on tunable filters (e.g., AOTF, LCTF) suffer from high cost, complexity, and limited temporal resolution. To overcome these drawbacks, we developed a rapid wavelength-modulation SPRi system using a multi-LED source and an adaptive second-order fitting (ASF) algorithm. The system covers the 730–805 nm spectrum with five LEDs. The ASF algorithm first performs a coarse full-spectrum scan to locate the resonance wavelength, then dynamically selects an optimal three-LED subset for fast second-order fitting, enabling accurate reconstruction of resonance wavelength without mechanical scanning. This approach significantly reduces cost and complexity while achieving a scanning cycle of 105 ms, RI resolution of 5.54 × 10⁻⁶ RIU, dynamic range of 0.0241 RIU, and excellent multi-channel consistency. The system has been successfully applied to monitor multi-channel antibody–antigen interactions in real time. Furthermore, it was used to detect cartilage oligomeric matrix protein (COMP) in synovial fluid, where an elevated concentration in an osteoarthritis sample versus a control aligned with its role as a cartilage catabolism marker. This work validates a practical and reliable platform for early diagnosis of osteoarthritis. Full article

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration. Full article

The leading cause of post-surgical hospital readmission is the emergence of hospital-acquired infections (HAIs), where surgical site infections (SSIs) constitute a substantial negative impact on patient outcome and contribute annual direct costs estimated to range from $28.4 billion to $45 billion in the U.S. To address the need for novel antimicrobial coating strategies, previous research has demonstrated that certain microbes can degrade poly(aspartic acid) (PAA)-based coatings, suggesting potential limitations of single-compound approaches that must be considered when designing antimicrobial surfaces. In this proof-of-concept study, we investigated whether ordered sequential coatings combining thermally synthesized PAA (tPAA) and oleic acid (OleA) might produce enhanced antimicrobial effects compared to individual compounds. Despite concerns regarding PAA biodegradability, the benefits of using PAA include low cytotoxicity and an ability to chelate metals such as calcium and facilitate bone mineralization and growth post-surgery. Using simple yet effective methods of surface coating applications which utilize tPAA and OleA, we investigated the potential of these ordered coatings to attenuate planktonic and sessile (biofilm) growth and development in Pseudomonas aeruginosa and Escherichia coli in vitro. Application of these ordered coatings resulted in up to 62% reduction in bacterial carrying capacity for P. aeruginosa and up to 43% reduction in biofilm mass relative to untreated controls. Further, confocal imaging via immunohistochemical labeling revealed methods for evaluating the impact of treatments targeting biofilm development through extracellular DNA quantification. Additionally, these coatings show dose-dependent cytotoxic effects against 3T3 mouse fibroblast cells. These preliminary findings, along with results derived from cytotoxicity assessment and physicochemical characterization via dynamic light scattering, suggest that ordered tPAA-OleA coating systems warrant further investigation as potential antimicrobial strategies, though additional validation, including testing against diverse clinical isolates, mechanistic studies, and in vivo evaluation, would be required before clinical application. Full article

In this study, the authors focus on optimizing the processing parameters for the fabrication of biodegradable polymer filaments intended for subsequent 3D printing of biomedical structures and implants. Following extrusion and additive manufacturing, the produced materials underwent a comprehensive evaluation that included mechanical, microbiological, biofilm formation, and electron microscopy analyses. The complexity of these tests aimed to determine the potential of the developed materials for biomedical applications, particularly in the field of scaffold fabrication. At the initial stage, three types of filaments (technical designations 111, 145, and 146) were produced using Fused Filament Fabrication (FFF) technology. These filaments were based on a PLA/PHB matrix with varying types and concentrations of plasticizers. Standardized destructive tensile and compressive mechanical tests were conducted using an MTS Insight 1 kN testing system equipped with an Instron 2620-601 extensometer. Among the tested samples, the filament labeled 111, composed of PLA/PHB thermoplastic starch and a plasticizer, exhibited the most favorable mechanical performance, with a Young’s modulus of elasticity of 4.63 MPa for 100% infill. The filament labeled 146 had a Young’s modulus of elasticity of 4.53 MPa for 100% infill and the material labeled 145 had a Young’s modulus of elasticity of 1.45 MPa for 100% infill. Microbiological assessments were performed to evaluate the capacity of bacteria and fungi to colonize the material surfaces. During bacterial activity assessment, we observed biofilm formation on the examined sample surfaces of each material from the smooth and rough sides. The colony-forming units (CFUs) increased directly with the exposure time. For all samples from each material, the Log10 (CFU) value reached above 9.41 during 72 h of incubation for the activity of each type of bacteria (Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans). Scanning electron microscopy provided insight into the surface quality of the material and revealed its local quality and purity. Surface defects were eliminated by this method. Overall, the results indicate that the designed biodegradable filaments, especially formulation 111, have promising properties for the development of scaffolds intended for hard tissue replacement and could also be suitable for regenerative applications in the future after achieving the desired biological properties. Full article

The growing interest in plant-based ingredients in food production has increased the demand for effective alternatives to animal-derived emulsifiers. In this study, the physicochemical and functional properties of selected commercial plant protein preparations as natural emulsifiers in food emulsions were assessed. Emulsifying activity and stability (EA, ES), foaming capacity and stability (FC, FS), water and oil absorption (WAC, OAC), color (CIE Lab*), viscosity, surface tension, and zeta potential were analyzed. Pea (PP1–PP4), rice (RP1, RP2) and chickpea (CP1) proteins showed the most favorable properties, characterized by high EA values (58.3–62.5%) and emulsion stability during storage (62–65%) after 6 days. Emulsions formulated with these proteins were significantly lighter (L* > 69). PP1 exhibited more than twice the viscosity of the other samples. The lowest surface tension values (<45 mN/m) were observed for RP2 and PP1, indicating strong surface activity. Pea proteins PP1, PP2, and PP4 showed the highest system stability, with zeta potential values below –35 mV. Overall, the selected plant protein preparations, particularly pea, rice, and chickpea proteins, showed promising functional properties, confirming their potential use as natural emulsifiers in clean-label plant-based formulations and providing a basis for further product development. Full article

This paper presents Hy-LIFT (Hybrid LLM-Integrated Fault Diagnosis Toolkit), a multi-stage framework for interpretable and data-efficient fault diagnosis in 5G/6G networks that integrates a high-precision interpretable rule-based engine (IRBE) for known patterns, a semi-supervised classifier (SSC) that leverages scarce labels and abundant unlabeled logs via consistency regularization and pseudo-labeling, and an LLM Augmentation Engine (LAE) that generates operator-ready, context-aware explanations and zero-shot hypotheses for novel faults. Evaluations on a five-class, imbalanced Dataset-A and a simulated production setting with noise and label scarcity show that Hy-LIFT consistently attains higher macro-F1 than rule-only and standalone ML baselines while maintaining strong per-class precision/recall (≈0.85–0.93), including minority classes, indicating robust generalization under class imbalance. IRBE supplies auditable, high-confidence seeds; SSC expands coverage beyond explicit rules without sacrificing precision; and LAE improves operational interpretability and surfaces potential “unknown/novel” faults without altering classifier labels. The paper’s contributions are as follows: (i) a reproducible, interpretable baseline that doubles as a high-quality pseudo-label source; (ii) a principled semi-supervised learning objective tailored to network logs; (iii) an LLM-driven explanation layer with zero-shot capability; and (iv) an open, end-to-end toolkit with scripts to regenerate all figures and tables. Overall, Hy-LIFT narrows the gap between brittle rules and opaque black-box models by combining accuracy, data efficiency, and auditability, offering a practical path toward trustworthy AIOps in next-generation mobile networks. Full article

Detecting fine, weak-textured defects with discontinuous boundaries on complex industrial surfaces is challenging due to interference from background textures and characters, as well as the scarcity of labeled data. To address this issue, we propose YOLO-SR, an engineering modification of YOLO11 tailored to defect segmentation on smart-card surfaces. Rather than introducing a new detection architecture, YOLO-SR reuses the backbone–neck–head design of YOLO11 and only adjusts a few modules to better capture elongated, low-contrast defects. The approach comprises two key components: first, embedding Strip Pooling (SP) within the C3K2 module to form C3K2_SP; second, a Rectangular Self-Calibration Module (RCM) is interposed after the top-level semantic layer. RCM generates rectangular gates to spatially recalibrate local responses, suppressing interference from complex textures and characters. To mitigate data scarcity and distributional bias, a texture-adaptive procedural defect synthesis strategy was developed. This strategy generates defect samples that conform to the background texture statistics of high-quality backgrounds. Experiments on the integrated circuit chip (ICChip) and signature plate (SignPlate) datasets show that YOLO-SR outperforms the YOLO11 baseline. Results indicate that SP and RCM complement each other by integrating directional priors from mid-to-high layers with top-level shape self-calibration. This enhances the visibility and localization stability of elongated defects while maintaining efficient inference. Full article

Osteogenesis imperfecta (OI) and Ehlers–Danlos syndrome (EDS) are inherited connective tissue disorders caused by diverse genetic defects, many of which affect collagen biosynthesis. However, the identified genetic variants do not always fully explain the clinical heterogeneity observed in patients, highlighting the need for advanced models and imaging techniques to assess collagen structure and fibroblast behavior at the microscopic level. In this study, we employed 5-week three-dimensional (3D) dermal fibroblast cultures derived from patients with haploinsufficient (HI) and dominant-negative (DN) OI, EDS, and healthy controls. Using label-free higher harmonic generation microscopy (HHGM), we visualized and quantified secreted collagen fibers and fibroblast morphology in situ. We analyzed fibroblast 3D orientation, collagen fiber diameter, collagen amount per cell, and the spatial alignment between fibroblasts and collagen fibers. HI OI fibroblasts secreted significantly less collagen than both control and EDS-derived cells, while EDS samples exhibited thinner collagen fibers compared to controls. Across all groups, collagen fiber orientation was strongly correlated with fibroblast alignment, in line with the role of fibroblasts in matrix organization. In healthy controls and HI OI samples, we observed a depth-dependent, counterclockwise rotation in fibroblast orientation from the culture bottom to the surface—a pattern that was less prominent in DN OI and EDS samples, potentially reflecting altered matrix guidance in diseased tissues. Overall, the quantity and quality of collagen, as well as fibroblast morphology and organization, were markedly altered in the OI and EDS model systems. These alterations may mirror tissue-level manifestations of the diseases, demonstrating the physiological relevance of patient-derived 3D fibroblast models for OI and EDS, as well as the power of harmonic generation microscopy in probing the cellular and extracellular consequences of disease-related gene defects in collagen or its biosynthetic pathways. Extensions of this methodological approach provide a way towards deeper understanding of tissue-level manifestations of collagen dysregulation in connective tissue disorders. Full article

The need to choose appropriate and meaningful names for the objects of scientific inquiry, in the spirit of Michael Faraday and, on a different level, of the ancient Chinese doctrine of rectification of names (正名, Zhèngmíng), is illustrated here in the case of the so-called Planet Nine. Since before the discovery of Neptune, the fascinating hypothesis of the possible existence of a new, distant planet in the solar system, yet to be discovered, has regularly surfaced in the pages of astronomy journals in various guises. Its most recent incarnations have been tentatively given names such as Planet X, Planet Y, and, most famously, Planet Nine. Such labels are unsatisfactory because they reveal no significant physical or orbital properties of the object which they are attributed to. I propose here the name Telisto, from the ancient Greek word τήλɩστoς for ‘farthest, most remote’ which captures a feature common to all versions of this scenario that seems destined to remain at the forefront of astronomical research for a long time to come: its supposedly great heliocentric distance, estimated at several hundred astronomical units. By exploring the history of astronomy, I also respond to some criticisms that might be leveled at this proposal. Among other things, I also draw a comparison with the naming of the so-called axions, which are hypothetical elementary particles proposed almost fifty years ago and which continue to be an active object of research. Full article

Subsea environments are vital for global biodiversity, climate regulation, and human activities such as fishing, transport, and resource extraction. Accurate mapping and monitoring of these ecosystems are essential for sustainable management. Airborne LiDAR bathymetry (ALB) provides high-resolution underwater data but produces large and complex datasets that make efficient analysis challenging. This study employs deep learning (DL) models for the multi-class classification of ALB waveform data, comparing two recurrent neural networks, i.e., Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). A preprocessing pipeline was developed to extract and label waveform peaks corresponding to five classes: sea surface, water, vegetation, seabed, and noise. Experimental results from two datasets demonstrated high classification accuracy for both models, with LSTM achieving 95.22% and 94.85%, and BiLSTM obtaining 94.37% and 84.18% on Dataset 1 and Dataset 2, respectively. Results show that the LSTM exhibited robustness and generalization, confirming its suitability for modeling causal, time-of-flight ALB signals. Overall, the findings highlight the potential of DL-based ALB data processing to improve underwater classification accuracy, thereby supporting safe navigation, resource management, and marine environmental monitoring. Full article

The objective of this study was to develop a pectin–tetraethoxysilane (TEOS) hybrid gel with improved functional properties and biocompatibility. The sol–gel process was used to create pectin–TEOS hydrogels containing 0.75, 1.00, 1.25, and 1.50 M TEOS, which were labeled AP-T0.75, AP-T1.00, AP-T1.25, and AP-T1.50. The pectin–TEOS hydrogel AP-T1.50 exhibited a hardness of 631 kPa, a Young’s modulus of 1588 kPa, and an elasticity of 1.95 mm. The degree of swelling decreased as the TEOS content increased. The pectin–TEOS hydrogel AP-T1.25 exhibited the highest strength of adhesion to serosa of 60.6 mN. Serum protein and bovine serum albumin (BSA) adsorption by pectin–TEOS gels was recorded in the range of 2–43 µg/mg after 6 h of incubation at pH 5.0, 7.4, and 8.0. Pectin–TEOS gels demonstrated low rates of hemolysis and complement activation. Leukocyte adhesion on the surface of pectin–TEOS gels depends on TEOS content. Consequently, the mechanical characteristics, serosal adherence, and biocompatibility of pectin–TEOS gel position it as a strong contender for the advancement of smart biomaterials. Full article

This study investigated the effects of heat treatment on the surface properties, structure, and Pb(II) sorption capacity of natural clinoptilolite zeolite (Nat-CLI). For this purpose, 10 g samples of Nat-CLI were heated separately at 300, 400, and 500 °C for two hours. The resulting samples were labeled CLI-300, CLI-400, and CLI-500, respectively. The samples were characterized using thermogravimetric analysis (TGA), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) analysis, and point of zero-charge (pH_pzc) measurements. XRD results confirmed that the crystal structure of Nat-CLI remained unchanged after heat treatment, a finding supported by FT-IR and TGA analyses. BET analysis revealed that the heating temperature altered both the specific surface area (S_BET) and the mean pore diameter. The values were as follows: S_BET = 16.5, 14.1, 14.4, and 14.9 m²/g and mean pore diameter = 38.1, 36.0, 48.6, and 36.0 Å for Nat-CLI, CLI-300, CLI-400, and CLI-500, respectively. The results of the kinetic study showed that the pseudo-second order model best agreed with the experimental data for all adsorbents. The maximum sorption capacities of Pb(II) were 28.26, 32.96, 34.69, and 33.85 mg/g for Nat-CLI and CLI-300, CLI-400, and CLI-500, respectively. These results suggest that the sample treated at 400 °C (CLI-400) achieved the highest sorption capacity due to its larger mean pore diameter. Full article