Search Results (433)

Cholesterol blood levels in low-density lipoproteins (LDLs) are a key parameter for assessing the risk of cardiovascular diseases. Direct quantification of LDL cholesterol at the point of care would be possible if all other lipoproteins, particularly the high-density lipoproteins (HDLs), could be removed prior to measurement. Here, we investigated whether a molecularly imprinted membrane (MIM) could be used for the solid-phase affinity extraction (SPAE) of HDL in a paper-based lateral flow test. Samples traveled by capillarity through the MIM before reaching a detection zone where LDL cholesterol was quantified enzymatically. MIMs were produced by impregnation of the membrane with a dispersion of molecularly imprinted polymers (MIPs) selective for HDL. MIPs were synthesized using precipitation polymerization and exhibited good selectivity for HDL compared with LDL and an uptake capacity of 5.0–7.0 µg of HDL-C/mg of MIP. The MIM enabled the removal of HDL with an efficiency of typically 68%. However, quantification of LDL cholesterol suffered from strong non-specific binding of LDL, likely due to its inherent colloidal instability. Overall, our results highlight the challenges associated with SPAE of colloidal particles. Furthermore, our study demonstrates a novel, efficient, and potentially generic modality to integrate SPAE into paper-based POC diagnostic tests. Full article

This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials. Full article

The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R² = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially. Full article

Background: Glaucoma is a multifactorial optic neuropathy and the leading cause of irreversible blindness worldwide. Vascular mechanisms, including impaired perfusion of the optic nerve head, are increasingly recognized as contributors to disease progression. Optical coherence tomography angiography (OCTA) enables non-invasive assessment of retinal and choroidal microvasculature, including peripapillary microvasculature dropout (MvD), which may serve as a marker of glaucomatous damage. Methods: A cross-sectional case–control study was conducted, including patients with primary open-angle glaucoma (OAG) and healthy controls. All participants underwent a comprehensive ophthalmic evaluation and OCTA imaging using the PLEX Elite 9000 system. Peripapillary vessel density (pVD), flow index (pFI), peripapillary choroidal thickness (PCT), β-zone parapapillary atrophy (β-PPA), and choroidal vascular indices were measured. MvD was defined as the complete absence of microvasculature within the β-PPA boundary. Statistical analyses included univariate and multivariate regression models to examine variables associated with PCT and to assess the association between MvD and visual field mean defect (MD), as well as other glaucoma characteristics. ROC curve analysis was performed to evaluate the ability of MvD to discriminate between different levels of visual field defects. Results: A total of 87 eyes (41 glaucomatous, 46 controls) were analyzed. Glaucoma patients exhibited significantly lower pVD, pFI, PCT, and choroidal vascular indices compared to the controls. MvD was detected in 10 glaucomatous eyes and was associated with a larger β-PPA area, smaller choroidal luminal and stromal areas, and worse mean deviation (MD) values. Multivariate regression showed that the number of ocular hypotensive treatments and StructureIndex variables were significantly associated with PCT (adjusted R² = 0.14). Logistic regression analysis identified MD, MD slope, and β-PPA area as variables significantly associated with the presence of MvD. ROC analysis showed that the presence of MvD had good discriminatory ability for visual field mean defects (MDs) (AUC = 0.77, 95% CI: 0.69–0.87; p = 0.005). Conclusions: Peripapillary MvD detected by OCTA is associated with reduced choroidal vascularity, increased β-PPA, and greater visual field deterioration in glaucoma patients. MvD may serve as a structural marker associated with functional deterioration in glaucoma patients. Full article

Rift Valley fever (RVF) is a re-emerging vector-borne zoonotic disease that causes outbreaks in humans and animals across Africa. To better understand RVF at human–animal interfaces, a prospective longitudinal survey of people, livestock, and mosquitoes was conducted from 2016 to 2018, in two regions of Tanzania, with distinct climatic zones (Iringa and Morogoro). Molecular and serological tools for testing (RT-qPCR and IgM/IgG ELISA) for RVF virus (RVFV) were used to assess infection and exposure in people and animals. Mosquitoes were collected quarterly from 10 sentinel locations. In total, 1385 acutely febrile humans, 4449 livestock, and 3463 mosquito pools were tested. In humans, IgM seroprevalence was 3.75% (n = 52/1385), and overall seroprevalence (IgM and/or IgG positive) was 8.30% (n = 115/1385). People from Iringa had a higher exposure risk than those from Morogoro (aOR 2.63), and livestock owners had an increased risk compared to non-owners (aOR 2.51). In livestock, IgM seroprevalence was 1.09%, while overall seroprevalence was 10.11%. A total of 68.4% of herds had at least one seropositive animal. Sentinel animal follow-up revealed that the probability of seroconversion was significantly higher in Morogoro. Low-level RVFV RNA was detected in 8 human and 22 mosquito pools. These findings indicate active transmission among vectors, livestock, and people during the study period, highlighting the need for One Health surveillance approaches for RVFV and other arboviruses. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Aortic valve stenosis, is a prevalent cardiovascular disease, narrows the valve orifice and restricts blood flow, resulting in abnormal high velocities and shear stresses. The progression of these hemodynamic abnormalities and their link with stenosis severity remain incompletely understood, which are critical for early detection and intervention. Computational Fluid Dynamics (CFD) was employed to characterize aortic hemodynamics across healthy, mild, moderate, and severe stenosis using a 3D steady-state model with idealized leaflet geometries. Key flow parameters, including velocity distribution, wall shear stress (WSS), pressure loss coefficient, and helicity, were evaluated. Results show a non-linear increase in velocity and WSS with stenosis severity, with peak jet velocities of 1.08, 1.82, 2.73, and 4.7 m/s and peak WSS of 11, 35, 80, and 122 Pa at the aortic arch, respectively. Severe stenosis produced a highly eccentric jet along the anterior of aortic arch, accompanied by a narrower jet, increased turbulence intensity and expanded recirculation zones. A significant increase in helicity and pressure loss coefficient was also observed for higher stenosis severities. These findings highlight the influence of valve leaflets on aortic flow dynamics, providing physiologically relevant insights into stenosis-induced mechanical stresses that may drive endothelial dysfunction and support earlier detection of disease progression. Full article

Uveal melanoma is the most common primary intraocular malignancy in adults, most frequently arising from the choroid, followed by the ciliary body and iris. Its diagnosis and management require precise characterization of tumor morphology, localization, and associated complications to optimize visual and systemic outcomes. Recent advances in optical coherence tomography (OCT), anterior segment OCT (AS-OCT), and OCT angiography (OCTA) have expanded the ophthalmologist’s ability to non-invasively visualize structural and vascular changes associated with this disease. In fact, enhanced depth imaging (EDI) and swept-source (SS) OCT can provide detailed views of deep ocular structures, enabling early detection of hallmark features such as subretinal fluid, retinal pigment epithelium disruption, and dome- or mushroom-shaped choroidal elevations; AS-OCT improves evaluation of lesions of the anterior segment, revealing iris architecture distortion and angle involvement; OCTA facilitates the visualization of abnormal tumor vasculature and detection of radiation-induced microvascular changes, including capillary dropout and foveal avascular zone enlargement. Moreover, these imaging modalities have demonstrated utility in differentiating uveal melanoma from pseudomelanomas, such as choroidal nevi, hemangiomas, and metastases. The present review aims at objectively assessing the use of OCT and OCTA in the diagnosis, treatment, and follow up of ocular melanoma, emphasizing their crucial role in identifying pathologic biomarkers of this potentially fatal ocular disease. Full article

The ultrastructural organization of different cell types involved in homeostatic growth in the cerebellum of juvenile chum salmon (Oncorhynchus keta) was investigated using transmission and scanning electron microscopy. The organization of astrocytes, oligodendrocytes, dark cells, adult-type glial and non-glial progenitors, stellate neurons, and eurydendroid cells (EDCs) in the molecular and granular layers and granular eminences was characterized. The organization of dendritic bouquets of Purkinje cells and climbing fibers was studied for the first time at the ultrastructural level, and the ultrastructural features of mossy fibers and the rosettes they form were characterized. Scanning electron microscopy (SEM) revealed the presence of single and paired adult-type neural stem/progenitor cells (aNSPCs) on the cerebellar surface and stromal clusters of aNSPCs outside the dorsal matrix zone (DMZ). Immunohistochemical (IHC) verification of proliferating cell nuclear antigen (PCNA) revealed five types of proliferating cells in the cerebellum of juvenile chum salmon: neuroepithelial cells (NECs), glial aNSPCs, and non-glial aNSPCs. A glial fibrillary acidic protein-positive (GFAP) complex consisting of radial glial fibers and aNSPCs was detected in the DMZ. At the same time, a complex of GFAP+ cerebellar afferents, consisting of differentiating mossy and climbing fibers, was found to develop in the cerebellum of juvenile chum salmon. Nestin+ non-glial aNSPCs and small nestin+ resident cells were detected in the dorsal, lateral, and basal areas, as well as in the granular layer (GrL) and granular eminences (GrEm). These cell types may contribute to the homeostatic growth of the cerebellum by acting as both active participants (PCNA+) and resident (silent) aNSPCs. Studying vimentin-positive systems in the cerebellum revealed a widespread presence of proliferating glial aNSPCs that actively contribute to homeostatic growth, as well as small resident immunopositive cells throughout the cerebellum of juvenile chum salmon. Immunolocalization of the neuronal RNA-binding protein marker (HuCD) was detected in numerous molecular layer (ML) cells at the early stages of neuronal differentiation in the dorsal and lateral regions of the cerebellum of juvenile chum salmon. HuCD + EDCs were detected for the first time in the dorsal (DZ) and basal (BZ) zones, forming broad axonal arborization. Immunolabeling of HuCD in combination with transmission electron microscopy (TEM) allowed EDCs to be characterized in the cerebellum of juvenile chum salmon for the first time. Full article

Isolated permafrost is widely distributed in freeze–thaw transition zones, characterized by blurred boundaries and strong spatial variability. Traditional methods such as drilling and electrical resistivity surveys are often limited in achieving efficient and continuous boundary identification. This study focuses on a typical isolated permafrost region in Northeast China and proposes a boundary detection strategy based on multi-frequency electromagnetic (EM) measurements using the GEM-2 sensor. By designing multiple frequency combinations and applying joint inversion, a boundary identification framework was developed and validated against borehole data. Results show that the multi-frequency joint inversion method improves the spatial identification accuracy of permafrost boundaries compared to traditional point-based techniques. In areas lacking boreholes, the method still demonstrates coherent boundary imaging and strong adaptability to geomorphological conditions. The multi-frequency joint inversion strategy significantly enhances imaging continuity and effectively captures electrical variations in complex freeze–thaw transition zones. Overall, this study establishes a complete non-invasive technical workflow—“acquisition–inversion–validation–imaging”—providing an efficient and scalable tool for engineering site selection, foundation design, and permafrost degradation monitoring. It also offers a methodological paradigm for electromagnetic frequency optimization and subsurface electrical boundary modeling. Full article

Food production systems in Ecuador’s high Andean region are pivotal for food security, rural livelihoods, and agrobiodiversity, yet they are increasingly exposed to climate stress. We assessed four representative crops (tree tomato, quinoa, potato, and maize) across three Andean zones (North, Center, South) in 2015–2022 using monthly NASA POWER (MERRA-2) climate fields. After confirming non-normality, Spearman correlations and multiple linear regressions with leave-one-year-out validation were applied to quantify the influence of maximum/minimum temperature and precipitation on cultivated and harvested area, production, sales, and loss categories. To place monthly signals in a process context, daily extreme-event diagnostics (ETCCDI-style) were also computed: heat days ($\mathrm{TX}90$), ≥5-day dry spells, and the annual maximum consecutive dry days (${\mathrm{CDD}}_{max}$). Models explained a wide range of variability across crops and zones (approx. $R^2\sim 0.55$–0.99), with quinoa showing the most consistent fits (several outcomes $R^2>0.90$). Extremes provide an eye-catching, actionable picture: the Southern zone concentrated dryness hazards, with 1–5 dry spells $\ge 5$ days per year and ${\mathrm{CDD}}_{max}$ up to ∼8 days, while heat-day frequency showed non-significant declines across zones in 2015–2022. Reanalysis frost days were virtually zero—consistent with under-detection of local valley frosts at coarse resolution—so frost risk was interpreted via monthly signals and reported losses. Overall, the results show precipitation-driven vulnerabilities in the South and support quinoa’s role as a resilient option under increasing climate stress, offering concrete guidance for water management and climate-smart planning in mountain agroecosystems. Full article

Background: Skin mycoses affect approximately 10% of the global population, and the range of effective topical antifungal agents remains limited. Voriconazole (VRC) is a broad-spectrum triazole with proven efficacy against drug-resistant fungal infections. This study aimed to develop and optimize VRC-loaded microemulsion (ME) polymer gels (Carbopol^®-based) for cutaneous delivery. Selected formulations also contained menthol (2%) as a penetration enhancer and potential synergistic antifungal agent. Methods: A comprehensive screening was performed using pseudoternary phase diagrams to identify stable oil/surfactant/co-surfactant/water systems. Selected MEs were prepared with triacetin, Etocas™ 35, and Transcutol^®, then gelled with Carbopol^®. Formulations were characterized for pH, droplet size, polydispersity index (PDI), and viscosity. In vitro VRC release was assessed using diffusion cells, while ex vivo permeation and skin deposition studies were conducted on full-thickness human skin. Rheological behavior (flow curves, yield stress) and texture (spreadability) were evaluated. Antifungal activity was tested against standard strain of Candida albicans and clinical isolates including a fluconazole-resistant strain. Results: The optimized ME (pH ≈ 5.2; droplet size ≈ 2.8 nm) was clear and stable with both VRC and menthol. Gelation produced non-Newtonian, shear-thinning hydrogels with low thixotropy, favorable for topical application. In ex vivo studies, performed with human skin, both VRC-loaded gels deposited the drug in the epidermis and dermis, with no detectable amounts in the receptor phase after 24 h, indicating retention within the skin. Menthol increased VRC deposition. Antifungal testing showed that VRC-containing gels produced large inhibition zones against C. albicans, including the resistant isolate. The VRC–menthol gel exhibited significantly greater inhibition zones than the VRC-only gel, confirming synergistic activity. Conclusions: ME-based hydrogels effectively delivered VRC into the skin. Menthol enhanced drug deposition and demonstrated synergistic antifungal activity with voriconazole. Full article

We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R² ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts. Full article

Concrete pavements frequently develop subsurface voids between surface and base layers during long-term service due to cyclic loading, environmental effects, and subgrade instability, which compromise structural integrity and traffic safety. Ground-penetrating radar (GPR) has been widely used as a non-destructive method for detecting such voids. However, the presence of coarse aggregates with strong electromagnetic scattering properties often introduces pseudo-reflection signals in radar images, hindering accurate void identification. To address this challenge, this study develops a high-fidelity three-phase concrete model incorporating aggregates, mortar, and the interfacial transition zone (ITZ). The Finite-Difference Time-Domain (FDTD) method is used to simulate electromagnetic wave propagation in both voided and intact structures. Simulation results reveal that aggregate-induced scattering can blur or distort reflection interfaces, generating pseudo-hyperbolic anomalies even in the absence of voids. In cases of thin-layer voids, real echo signals may be masked by aggregate scattering, leading to missed detections. GPR systems can be broadly classified into impulse, continuous-wave, and multi-frequency types. To validate the simulations, field tests using multi-frequency 2D/3D GPR systems and borehole verification were conducted. The results confirm the consistency between simulated and actual radar anomalies and validate the proposed model. This work provides theoretical insight and modeling strategies to enhance the interpretation accuracy of GPR data for subsurface void detection in concrete pavements. Full article

Background and Clinical Significance: Purtscher-like retinopathy is a rare occlusive microangiopathy that causes sudden vision loss of varying severity. It presents with diverse retinal findings, such as cotton-wool spots, haemorrhages, and optic disc and macular edema, among others. A key characteristic is the absence of trauma. This condition has been observed in patients with acute pancreatitis, renal failure, preeclampsia, HELLP syndrome, childbirth, and other systemic disorders. Case Presentation: A 35-year-old male presented with complaints of seeing spots in front of both eyes, with a duration of ten days following the initiation of treatment for acute alcoholic pancreatitis. On examination, best-corrected visual acuity (BCVA) in both eyes was 5/6. Fundus examination revealed multiple cotton-wool spots and haemorrhages located in the posterior pole and around the optic disc, more pronounced in the left eye, where the optic disc had blurred margins and the macular reflex was absent. Perimetry showed paracentral scotomas, and optical coherence tomography (OCT) revealed thickening and disruption of the inner retinal layers in the papillomacular region of both eyes. Fundus fluorescein angiography demonstrated adequate perfusion of the vascular network, with hypofluorescent areas in the arteriovenous phase, peripapillary and in the papillomacular zone, due to masking by cotton-wool spots and haemorrhages. Treatment included systemic antiplatelet agents, anticoagulants, and vitamins, along with topical non-steroidal anti-inflammatory drugs. Two months after the initial presentation visual acuity improved to 6/6 in both eyes. Follow-up OCT scans showed atrophy of the inner retinal layers corresponding to the previous cotton-wool spot and the areas of reduced light sensitivity on perimetry had decreased in size. Conclusions: Acute pancreatitis is the most common systemic condition associated with the development of Purtscher-like retinopathy. Timely diagnosis and management of the underlying systemic disease are essential for preventing ocular complications. Ophthalmological evaluation is necessary in patients with acute pancreatitis who present with visual symptoms in order to detect this often-overlooked rare condition. Full article