Search Results (2,438)

Background/Objectives: NANOBODY^® molecules (VHHs) are attractive vectors for radiopharmaceuticals due to their small size and high target affinity, but rapid clearance and pronounced kidney retention limit their therapeutic applicability. Binding to serum albumin is a widely used strategy to prolong circulation, yet the respective contributions of albumin-binding affinity and molecular format remain insufficiently defined. This study aimed to systematically evaluate how affinity and valency modulate VHH pharmacokinetics. Methods: Four monovalent albumin-binding VHHs spanning nanomolar to micromolar affinities and two bivalent constructs were engineered, generated by fusing an albumin-binding VHH to an irrelevant non-binding VHH. All constructs incorporated a site-specific cysteine for DFO* conjugation, enabling uniform zirconium-89 labeling with high radiochemical purity. Pharmacokinetics were assessed in healthy mice using serial blood sampling and positron emission tomography. Blood and kidney exposure were quantified by non-compartmental analysis. Results: All albumin-binding constructs showed increased systemic exposure and reduced kidney uptake relative to a non-binding control. Nanomolar-affinity binders reached maximal exposure, and further affinity increases (K_D < ~100 nM) did not improve pharmacokinetics, suggesting a threshold. The micromolar binder showed intermediate exposure but still reduced renal retention compared with control. Valency effects were affinity-dependent. They were negligible at high affinity but pronounced at low affinity, where bivalency reduced systemic exposure and increased kidney uptake toward control levels. Conclusions: Albumin binding enables tuning of VHH pharmacokinetics in an affinity-dependent manner. Above an apparent affinity threshold, pharmacokinetics become format independent, whereas below this threshold, molecular format substantially influences systemic and renal disposition. Full article

Foreign object intrusion severely threatens railway safety. Existing methods struggle with open-set categories, high annotation costs, and poor label-efficient generalization. To address these issues, we propose UG-Net, an unsupervised-guided label-efficient detection framework. The core idea is a two-stage strategy: first, a masked autoencoder (MAE) learns “normality” priors from unlabeled data and generates a spatial attention mask via a deep feature difference strategy; then, this mask is fused as a fourth channel into a lightweight YOLOv8n detector. This approach effectively alleviates reliance on manual annotations. On a self-constructed railway dataset, UG-Net achieved 94.56% mAP@0.5 using only 200 labeled samples, significantly outperforming the YOLOv8n baseline (86.91%). The framework provides a label-efficient solution for industrial anomaly detection. Full article

While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we propose $\mathbf{SplitML}$, a secure and privacy-preserving framework for Federated Split Learning (FSL). By integrating $IND-CP{A}^D$ secure Fully Homomorphic Encryption (FHE) with Differential Privacy (DP), $\mathbf{SplitML}$ establishes a defense-in-depth strategy that minimizes information leakage and thwarts reconstructive inference attempts. The framework accommodates heterogeneous model architectures by allowing clients to collaboratively train only the common top layers while keeping their bottom layers exclusive to each participant. This partitioning strategy ensures that the layers closest to the sensitive input data are never exposed to the centralized server. During the training phase, participants utilize multi-key CKKS FHE to facilitate secure weight aggregation, which ensures that no single entity can access individual updates in plaintext. For collaborative inference, clients exchange activations protected by single-key CKKS FHE to achieve a consensus derived from Total Labels (TL) or Total Predictions (TP). This consensus mechanism enhances decision reliability by aggregating decentralized insights while obfuscating soft-label confidence scores that could be exploited by attackers. Our empirical evaluation demonstrates that $\mathbf{SplitML}$ provides substantial defense against Membership Inference (MI) attacks, reduces temporal training costs compared to standard encrypted FL, and improves inference precision via its consensus mechanism, all while maintaining a negligible impact on federation overhead. Full article

This study investigates a mechanically aerated, yeast-free bread technology incorporating apple-derived plant ingredients (juice, purée, and powder) in response to the growing demand for clean-label bakery products. The global bakery sector represents one of the largest food markets worldwide, with the baking yeast segment alone accounting for several billion USD annually, while interest in yeast-free and yeastless-dough products continues to expand. To address technological limitations associated with yeast exclusion, dough aeration was achieved using a two-stage whipping protocol (1000 rpm for 4 min, followed by 500 rpm for 1 min and stabilization at 500 rpm for 1 min under 4.0 ± 0.1 MPa gauge pressure), forming a stable protein–carbohydrate foam system. Rheological evaluation using Mixolab 2 showed that formulations containing 3–5% apple purée exhibited the most favorable dough development characteristics, with stability increasing from 3.30 ± 0.15 min in the control to 8.90 ± 0.20 min. Texture profiling using a CT-2 analyzer equipped with a cylindrical probe (50% compression, 60 mm/min, slices 25 mm thick, n = 5) revealed a significant reduction in crumb firmness, from 3.01 ± 0.15 N in the control to 2.12 ± 0.10 N in the purée- and powder-enriched samples (p < 0.05). Nutritional assessment indicated improvements in vitamin C content (up to 2.23 mg/100 g) and protein quality: the amino acid score, calculated according to FAO/WHO reference patterns on a mg/g-protein basis, increased from 76.5 ± 1.8% to 89.2 ± 2.3%. Microbiological analysis showed reduced total aerobic mesophilic counts after 72 h of storage—4.7 × 10³ CFU/g in the control versus 1.8–3.4 × 10³ CFU/g in apple-enriched breads. Overall, the results demonstrate that mechanical aeration combined with apple-derived ingredients enhances the structural, nutritional, and microbiological quality of yeast-free bread, offering a promising clean-label approach for functional bakery products. Full article

Background: The traditional formula Liqi Yangyin (LQYY) has shown clinical and preclinical efficacy for depression with constipation, yet its molecular mechanisms remain incompletely defined. This study aimed to elucidate its mechanisms using an integrative approach. Methods: Constituents of LQYY were profiled by UPLC-MS/MS and integrated with network pharmacology and molecular docking to identify brain-accessible components and putative targets. A chronic unpredictable mild stress (CUMS) model was used for experimental validation. Outcomes included behavioral tests (sucrose preference test, open field test, and forced swimming test), gastrointestinal indices, including fecal water content, time of first black stool, and intestinal propulsion rate, histopathology of the prefrontal cortex (PFC) and colon, TUNEL staining, NeuN immunofluorescence, Western blotting, and qRT-PCR. Results: LQYY attenuated CUMS-induced weight loss and depressive-like behaviors and improved intestinal transit metrics. It reduced neuronal apoptosis in the PFC and ameliorated colonic injury. Mechanistically, docking and enrichment analyses highlighted hub targets (STAT3, AKT1, ESR1, IL-6, TNF, TP53) and the JAK/STAT pathway. In vivo, LQYY decreased IL-6, TNF-α, ESR1, TP53, and STAT3, and increased AKT1 in the PFC and colon; it also reduced the TUNEL-positive rate and restored NeuN labeling, upregulated Bcl-2, and downregulated p-JAK2/JAK2 and p-STAT3/STAT3 ratios, and the expression of Bax and cleaved-caspase-3 in the PFC, consistent with the suppression of pro-inflammatory and apoptotic signaling. Conclusions: LQYY exerts antidepressant and pro-motility effects in CUMS mice by modulating JAK2/STAT3-centered networks and inhibiting neuronal apoptosis, thus supporting a multi-component, multi-target strategy for treating depression with constipation, and providing a defined molecular hypothesis for future investigation. Full article

This study investigated the effectiveness of basil (Ocimum basilicum L.) extract obtained by hydrodistillation (EO) and lipid extract (LE) obtained via supercritical fluid extraction in preserving the quality of ground fish patties during refrigerated storage. Gilthead sea bream (Sparus aurata) patties were formulated with varying concentrations of EO and LE and evaluated over three days at 4 °C. The chemical composition of the extracts, analyzed by GC-MS, revealed linalool, eucalyptol, and τ-cadinol as dominant bioactive compounds, with EO richer in monoterpenes and LE in sesquiterpenes. Both extracts significantly reduced lipid oxidation (TBARS) and protein oxidation (thiol content), with the strongest antioxidative effect observed in patties containing 0.150 µL/g of LE. Color parameters (L*, a*, b*, ΔE) were moderately influenced, without adverse effects on product appearance. pH and water activity values remained stable across treatments, while total volatile basic nitrogen (TVB-N) levels confirmed delayed spoilage in extract-treated patties. Results highlight the potential of basil extracts, especially LE obtained by SFE, as effective natural antioxidants in fish-based products. These findings support the development of clean-label, health-promoting products tailored to individual needs, and show that ground fish porridge has promise as a viable material for the production of innovative seafood products. Full article

Heterogeneous expression of a single surface protein within one cell population can drive major functional differences, yet low-expression subtypes remain difficult to isolate. Conventional tube-based immunomagnetic separation collapses all labelled cells into one positive fraction and thus cannot resolve small differences in marker abundance. Here, we present MagSculptor, a microfluidic platform for high-resolution magnetic fractionation of low-expression EpCAM-defined subtypes within one immunomagnetically labelled population at a time. Arrays of soft-magnetic strips create localized high-gradient zones that map modest differences in bead loading onto distinct capture positions, yielding High (H), Medium (M), Low (L), and Negative (N) fractions. Finite element method simulations of coupled magnetic and hydrodynamic fields quantify the field gradients and define an operating window. Experimentally, epithelial cancer cell lines processed sequentially under identical settings show reproducible subtype partitioning. In a low-EpCAM model (MDA-MB-231), conventional flow cytometry, under standard EpCAM staining conditions, did not yield a robust EpCAM-positive gate, whereas MagSculptor still revealed graded subpopulations. Western blotting confirms a monotonic decrease in EpCAM abundance from H to N, and doxorubicin assays show distinct in vitro drug sensitivities, while viability remains above 95%. MagSculptor thus helps extend immunomagnetic separation from binary enrichment to multi-level isolation of low-expression subtypes and provides a convenient front-end for downstream functional and molecular analyses. Full article

Background: Patients with end-stage renal disease (ESRD) on hemodialysis are at increased risk for severe influenza, and underlying immune dysfunction may limit vaccine-induced protection. Methods: This observational open-label study evaluated immune responses in 93 hemodialysis patients vaccinated with seasonal inactivated influenza vaccine (IIV) during the 2019–2020 (n = 22) and 2023–2024 (n = 71) seasons. Immune responses were comprehensively assessed using hemagglutination inhibition and microneutralization assays to measure antibody levels, together with flow cytometry analysis of key immune cell populations, including plasmablasts, T-follicular helper cells (Tfh), and effector memory T cells (Tem). Results: During the 2019–2020 season, antibody responses in hemodialysis patients were comparable to those in healthy volunteers in both younger (18–60 years) and older (over 60) age groups. By day 7 post-vaccination, there was a pronounced increase in activated Tfh1 cells, coinciding with a surge in plasmablasts and a rise in antigen-specific B cells. This was accompanied by a T-cell response mediated by IFNγ-producing and polyfunctional CD4+ Tem cells. In the 2023–2024 season, revaccination was associated with higher baseline antibody levels but did not alter subsequent response kinetics to A/H1N1pdm, A/H3N2, and B/Yamagata antigens. In contrast, responses to B/Victoria were higher in revaccinated patients throughout the entire observation period. Conclusions: Our findings confirm that standard-dose IIV vaccination is beneficial for hemodialysis patients, inducing robust and adequate humoral and T-cell immune responses. Full article

Deep anomaly detection in multilingual environments remains challenging due to limited labeled data, semantic inconsistency across languages, and the unstable distribution of rare abnormal patterns. These challenges are particularly severe in low-resource scenarios—characterized by scarce labeled anomaly data and non-standardized terminology—where conventional supervised or transfer-based models suffer from semantic drift and feature mismatch. To address these limitations, a data-driven cross-lingual anomaly detection framework, LR-SSAD, is proposed. Targeting paired text and behavioral data without requiring parallel translation corpora, the framework is built upon the joint optimization of complementary self-supervised objectives. A cross-lingual masked prediction module is designed to capture language-invariant semantic structures to align semantic spaces, while a Mamba-based sequence reconstruction module leverages its linear computational complexity ($\mathcal{O}\left(N\right)$) to efficiently model long-range dependencies in transaction histories, overcoming the computational bottlenecks of quadratic attention mechanisms. To further enhance robustness under noisy supervision, a noise-aware pseudo-label refinement mechanism is introduced. Evaluated on a newly constructed real-world financial dataset (spanning January–June 2023) comprising 1.2 million multilingual texts and 420,000 transaction sequences, experimental results demonstrate that LR-SSAD achieves substantial improvements over state-of-the-art baselines. The model achieves an accuracy of 0.932, a precision of 0.914, a recall of 0.891, and an F1-score of 0.902, with the Area Under the Curve (AUC) reaching 0.948. The proposed framework provides a scalable and data-efficient solution for anomaly detection in real-world multilingual environments. Full article

Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics—stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent representations. Unsupervised clustering identified latent basins (“attractors”). A neural ordinary differential equation (neural-ODE) approximated latent dynamics. All dynamics were inferred from cross-sectional population variability rather than longitudinal follow-up, serving as a proof-of-concept approximation of morphologic continuity. Voxel-level perturbation quantified local morphodynamic sensitivity, and proof-of-concept control was explored by adding small inputs to the neural-ODE using both a deterministic controller and a reinforcement learning agent based on soft actor–critic (SAC). Survival analyses (Kaplan–Meier, log-rank, ridge-regularized Cox) assessed associations with outcomes. Results: The learned latent manifold was smooth and clinically organized. Three dominant attractor basins were identified with significant survival stratification (χ² = 31.8, p = 1.3 × 10⁻⁷) in the static model. Dynamic attractor basins derived from neural-ODE endpoints showed modest and non-significant survival differences, confirming that these dynamic labels primarily encode the morphodynamic structure rather than fixed prognostic strata. Dynamic basins inferred from neural-ODE flows were not independently prognostic, indicating that the inferred morphodynamic field captures geometric organization rather than additional clinical risk information. The latent stability index showed a weak but borderline significant negative association with survival (ρ = −0.13 [−0.26, −0.01]; p = 0.0499). In multivariable Cox models, age remained the dominant covariate (HR = 1.30 [1.16–1.45]; p = 5 × 10⁻⁶), with overall C-indices of 0.61–0.64. Voxel-level sensitivity maps highlighted enhancing rims and peri-necrotic interfaces as influential regions. In simulation, deterministic control redirected trajectories toward lower-risk basins (≈57% success; ≈96% terminal distance reduction), while a soft actor–critic (SAC) agent produced smoother trajectories and modest additional reductions in terminal distance, albeit without matching the deterministic controller’s success rate. The learned attractor classes were internally consistent and clinically distinct. Conclusions: Learning a latent attractor landscape links generative AI, dynamical systems theory, and clinical outcomes in GBM. Although limited by the cross-sectional nature of BraTS and modest prognostic gains beyond age, these results provide a mechanistic, controllable framework for tumor morphology in which inferred dynamic attractor-like flows describe latent organization rather than a clinically predictive temporal model, motivating prospective radiogenomic validation and adaptive therapy studies. Full article

Instance segmentation of cotton leaves in complex field environments presents challenges including low accuracy, high computational complexity, and costly data annotation. This paper presents GS-BiFPN-YOLO, a lightweight instance segmentation method that integrates SAM for semi-automatic labeling and enhances YOLOv11n-seg with GSConv, BiFPN, and CBAMs to reduce annotation cost and improve accuracy. To streamline parameters, the YOLOv11-seg architecture incorporates the lightweight GSConv module, utilizing group convolution and channel shuffle. Integration of a Bidirectional Feature Pyramid Network (BiFPN) enhances multi-scale feature fusion, while a Convolutional Block Attention Module (CBAM) boosts discriminative focus on leaf regions through dual-channel and spatial attention mechanisms. Experimental results on a self-built cotton leaf dataset reveal that GS-BiFPN-YOLO achieves a bounding box and mask mAP@0.5 of 0.988 and a recall of 0.972, maintaining a computational cost of 9.0 GFLOPs and achieving an inference speed of 322 FPS. In comparison to other lightweight models (YOLOv8n-seg to YOLOv12n-seg), the proposed approach achieves superior segmentation accuracy while preserving high real-time performance. This research offers a practical solution for precise and efficient cotton leaf instance segmentation, thereby facilitating the advancement of intelligent monitoring systems for cotton production. Full article

This study re-examines generational differences in media literacy and news consumption within the evolving digital landscape. It expands on the well-known dichotomy of Digital Natives and Digital Immigrants by proposing a new conceptual framework that introduces the terms Analog Anchors and Digital Floaters. These terms aim to reflect the heterogeneity and fluidity more accurately, the adaptive nature of users’ engagement with digital media. A quantitative survey was conducted using an online questionnaire distributed to Greek participants (N = 1020) through a non-probability convenience sampling method. The analysis revealed significant variations in digital literacy, news consumption habits, and skepticism toward the media across generations. Findings indicate that the relationships with technology and information are not linear or age-bound but are shaped by cultural, cognitive, and social parameters. High levels of media skepticism observed across all age groups further challenge traditional divides. As a result, this study argues for a paradigm shift that captures the complexity of media literacy in the platform era, moving from static generational labels towards a more dynamic understanding of users as Analog Anchors and Digital Floaters. Full article

Defect engineering has been demonstrated to be an attractive strategy to improve the catalytic performance of g−C₃N₄−based catalysts. Herein, three graphite carbon nitrides (labeled “CN”) containing a certain number of cyano groups and nitrogen vacancies are prepared successfully by calcination of the dicyandiamide−based CN in the presence of KOH, and the performances of the corresponding Pd−based catalysts are evaluated by using the formic acid (FA) dehydrogenation as a probe reaction. The characterizations of X−ray diffraction (XRD), scanning transmission electron microscopy (STEM), X−ray photoelectron spectra (XPS), hydrogen temperature−programmed desorption (H₂−TPD), and hydrogen spillover experiments indicate that the high catalytic activity of Pd/CNK−0.5 is mainly attributed to its high efficient hydrogen spillover, relatively high dispersity of Pd species, and basicity due to the introduction of a proper amount of cyano groups and nitrogen vacancies. The low initial activity of Pd/CNK−0.75 may mainly be ascribed to its low hydrogen spillover ability and the strongly chemisorbed hydrogen on Pd single atoms or small clusters. Full article

Background/Objectives: Within public health and preventive nutrition, food labeling plays a critical role in supporting healthier dietary behaviors. This study aimed to evaluate the behaviors, perceptions, and nutritional literacy of young adults from Iași, Romania, regarding simple carbohydrates (SCHO) consumption and food label-reading habits. Materials and Methods: A cross-sectional survey was conducted between May–June 2023 using 20-item Likert-scale questionnaire completed by 150 participants aged 18–30 years. Statistical analysis included descriptive metrics, Chi-square tests, and Pearson’s correlation, with significance set at p ≤ 0.05. Results: The cohort consisted of 72% females (N = 108) and 28% males (N = 42), with 42.7% (N = 64) holding university degrees. Although 22% (N = 33) considered SCHO consumption highly important, only 13.3% (N = 20) frequently read nutrition labels (p ≤ 0.05). Dietary patterns showed that 27.3% primarily consumed sweets, while others combined sweets with carbonated beverages, dairy products, or whole grains; overall, 44% (N = 66) reported frequent sweet consumption. Label reading was highest for sweets (40.7%), lower for dairy products (19.3%) and soft drinks (9.3%). Additionally, 30.7% (N = 46) checked only expiration dates, whereas just 11.3% (N = 17) reviewed nutritional content. Trust in label accuracy was low: 48% (N = 72) expressed neutrality and 14% (N = 21) disagreed. Although 77.3% (N = 116) recognized the link between sugar intake and dental caries, only 23.3% (N = 35) felt well informed about oral health risks. Taste dominated food selection (68.7%), while nutritional value was cited by 16.7% (N = 25). Conclusions: Young adults from Iași demonstrated notable gaps in nutritional literacy and suboptimal dietary behaviors, emphasizing the need for structured educational strategies to improve preventive practices relevant to systemic and oral health. Full article

Background/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 ± 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications. Full article