Agro-industrial valorization of Satsuma mandarin peels is typically limited to pectin or essential oils, often neglecting valuable non-volatile bioactives. This study investigates high-pressure homogenization (HPH) as an intensive microstructural disruption strategy to recover matrix-bound bioactives and obtain physically stable multi-phase plant-derived fluid systems using water or oil-in-water (o/w) mixtures. HPH treatment (up to 10 passes) effectively disrupted the cellular matrix, achieving a similar to 2.6-fold particle size reduction. In aqueous systems, 5 passes maximized the recovery of total polyphenols and carotenoids. Remarkably, this water-only process achieved total carotenoid analytical accessibility statistically comparable to exhaustive conventional solvent extraction with acetone, indicating that intense physical disruption can effectively liberate sequestered lipophilic compounds without the primary use of organic solvents. In o/w systems, processing enabled a passive physical partitioning of compounds driven by their respective chemical polarities. Mass balance tracking across the physical fractions indicated that lipophilic carotenoids and polymethoxylated flavones (PMFs) preferentially partitioned into the lipid-rich cream phase, while hydrophilic flavonoids (e.g., narirutin) remained in the continuous serum layer. Chromatographic phase profiles and serum depletion data supported these distinct distribution trends for PMFs (e.g., nobiletin) and carotenoids. Isothermal calorimetry validated the functional protective capacity of the resulting colloidal system: the incorporation of 1% HPH-treated peel powder significantly delayed lipid autooxidation in exogenous linseed oil model, yielding an induction time superior to a purified resveratrol standard. These findings demonstrate that HPH effectively releases and distributes mandarin peel bioactives into distinct, multi-phase fluid matrices, offering a sustainable, solvent-free processing pathway for the valorization of citrus by-products.
In this paper, we analyze a linearized model for incompressible transversely isotropic dispersive fiber-reinforced materials in which both the elastic and dispersive properties depend on the direction of the fibers. We investigate the motions in bodies made of these materials that are subjected to prescribed shear or normal action on part of the rigid boundaries. In the case where the motion is induced by shear stresses acting at the boundary, we provide the analytical solution for the displacement field from which we derive the components of the stress tensor. In the case where the motion is due to the action of normal stresses at the boundary, the initial and boundary value problem is solved using a projection algorithm based on spectral methods. In both cases, we quantify how anisotropic dispersion and material stiffness affect the displacement field.
Diabetic retinopathy (DR) grading from fundus images remains challenging because severity labels are ordinal, class distributions are imbalanced, and neighboring stages are often visually ambiguous. This study presents a unified pipeline for five-class DR grading that integrates controlled CNN/Transformer backbone benchmarking, ensemble evaluation, computational-cost analysis, external validation, and proof-of-concept attribution reporting. APTOS 2019 dataset was used to benchmark six backbone architectures under stratified five-fold cross-validation with fixed fold assignments, identical preprocessing, imbalance-aware training, and model selection based on quadratic weighted kappa (QWK). Hard voting, weighted soft voting, stacking, and hybrid class-level fusion were investigated to combine complementary models, while trainable parameters, model size, inference latency, and throughput were reported to quantify deployment cost. Model generalization was assessed by directly evaluating APTOS-trained models on Messidor-2 without retraining, fine-tuning, or recalibration. Safety-filtered summaries generated with a vision-language model were restricted to non-diagnostic model-attribution descriptions, and fundus-masked Grad-CAM++ maps were paired with these summaries for attribution reporting. Among individual backbones, ResNet-50 and ConvNeXt-Tiny achieved the strongest performance, while weighted soft voting provided the most stable ensemble performance. External validation confirmed the difficulty of cross-dataset five-class DR grading, with lower performance than that observed during internal cross-validation. The generated summaries adhered to predefined safety constraints but were not intended for lesion localization, diagnosis, or clinical validation. Overall, the study demonstrates that controlled benchmarking and weighted ensembling can provide stable performance for ordinal DR grading, while computational overhead, domain shift, uncertainty calibration, and expert clinical validation remain important considerations for real-world deployment.
Cancer therapy is increasingly shifting towards targeted strategies capable of maximizing therapeutic efficacy while minimizing off-target toxicity. Extracellular vesicles (EVs), including exosomes and microvesicles, have emerged as promising natural nanocarriers due to their characteristics like biocompatibility, stability in biological fluids, and capacity for selective cargo delivery. EVs participate in intercellular communication through highly regulated biological processes that control their formation, cargo selection, cellular uptake, and downstream signaling activity. This mini-review highlights how regulated sorting processes, surface-associated tropism, and diverse internalization pathways determine EVs specificity and functional impact in recipient tumor cells. Furthermore, current advances in engineering EVs for drug and RNA delivery, emphasizing their potential to enhance therapeutic precision while minimizing systemic toxicity, are summarized here. In conclusion, by linking fundamental molecular mechanisms to translational applications, EVs emerge as a promising platform for the development of targeted and biologically compatible cancer therapies.
Atmospheric aerosols modulate Earth's radiation balance through direct effects and through their role as cloud condensation nuclei (CCN), contributing to variability in near-surface temperature (NST). Galactic cosmic rays (GCRs) further influence aerosol-cloud interactions by enhancing particle formation and growth, but combined aerosol optical depth (AOD)-GCR effects on NST remain poorly constrained across climates. Using satellite and reanalysis data, we examine joint influences on NST anomalies at three neutron-monitoring stations, Oulu, Newark, and Hermanus, during 2000-2022. The sites share similar geomagnetic cutoffs but contrasting climates, enabling separation of ionization from geomagnetic shielding. Multiple linear regression (MLR) captures AOD effects and their modulation by GCR flux. Adding an interaction term (AOD & times; GCR) improves fit, raising adjusted R2 from 0.22 -> 0.31 (Oulu), 0.37 -> 0.52 (Newark), and 0.69 -> 0.78 (Hermanus). ECMWF reanalysis shows hydrophilic organic matter aerosol (OMA) dominates (0.19, 0.29, 0.41 & micro;g kg-1 at Oulu, Newark and Hermanus), with sulphate elevated at Oulu/Newark and coarse sea salt at Hermanus. Elevated OMA and sulphate at Oulu/Newark imply GCR-enhanced fine CCN and cooling, whereas humid, sea-salt-rich Hermanus favors ion-mediated growth of larger hygroscopic particles that increase longwave trapping and warming. Findings provide site-specific evidence that GCR ionization modulates aerosol processes and contributes to regional NST variability, informing improved parameterizations in climate models.
