Accurate intracranial artery segmentation from Digital Subtraction Angiography is critical for the diagnosis and interventional treatment of cerebrovascular diseases. However, traditional methods struggle to capture dynamic spatiotemporal dependencies, often leading to vascular discontinuity and the loss of fine distal vessels due to background interference and resolution loss. In this study, we propose the Wavelet-Enhanced Spatiotemporal Connectivity-Preserving Network (WESCP-Net), a novel framework designed to synergize physical priors with frequency-domain feature extraction. Specifically, we introduce a Physically-Guided Spatiotemporal Enhancement module that explicitly exploits hemodynamic flow variance to differentiate active vascular signals from static artifacts. To address the loss of high-frequency spatial details in standard downsampling, we incorporate a Wavelet-Integrated Encoder and a Topology-Aware Reconstruction module, which utilize discrete wavelet transforms to preserve sharp vessel boundaries and restore structural connectivity. Experimental results on the DIAS dataset demonstrate that WESCP-Net achieves state-of-the-art performance, yielding a Dice Similarity Coefficient of 0.7982 and an Intersection over Union score of 0.6422. Notably, its connectivity-preserving mechanism achieves a clDice metric of 0.7135, improving the continuity of vascular terminals. WESCP-Net provides a robust technological paradigm for precise cerebrovascular segmentation, facilitating reliable surgical navigation and quantitative diagnosis.

The emergence of 16S rRNA methyltransferase (16S-RMTases) poses a major therapeutic challenge, as they confer high-level resistance to all clinically relevant aminoglycosides. In Chile, data on these enzymes remain scarce. This study investigated the epidemiological distribution and genetic relatedness of Klebsiella pneumoniae strains carrying 16S-RMTase genes. Antibiotic susceptibility was assessed using the Kirby-Bauer method and complementary techniques. Gene detection was performed via conventional PCR, and genotyping via ERIC-PCR. Among 1,200 K. pneumoniae clinical strains collected between 1997 and 2023; 233 (19.4%) exhibited high-level resistance to both amikacin and gentamicin, suggesting 16S-RMTase gene carriage. All presumptive strains carried 16S-RMTase genes, with rmtG being the most prevalent (n = 174), followed by armA (n = 30), rmtC (n = 24), and rmtD (n = 5). Genotyping revealed 19 distinct clusters. The rmtG gene was widely distributed, whereas rmtC displayed regional clustering, particularly in Concepción, and was associated with broader resistance profiles, including resistance to carbapenems. In contrast, rmtG-positive strains retained partial susceptibility to these agents. Fisher's exact test revealed associations (p < 0.05) between specific 16S-RMTase genes and non-susceptibility antibiotic profiles. The spread of 16S-RMTase genes since 2007, from Iquique to Puerto Montt, highlights their growing clinical relevance. The recent rise of armA and rmtC suggests a shift in dominant resistance mechanisms.

Seagrasses are essential blue carbon ecosystems that are increasingly threatened by the accumulation of emerging global pollutants, such as microplastics (MPs). However, in regions like Sri Lanka, MP contamination within seagrass beds remains critically understudied. This study investigated MP abundance in seagrass blades (Thalassia hemprichii), sediments, surface water, and sea cucumbers (Holothuria atra) and characteristics of isolated MPs from six selected sites along the Southern and Eastern coastlines of Sri Lanka (Indian Ocean). The average abundances of MPs in seagrass blades, Holothuria atra, surface water, vegetated sediments, and non-vegetated sediments were 1.59 ± 0.11 items/blade, 177.19 ± 53.28 items/kg, 9.60 ± 0.97 items/m3, 69.72 ± 8.38 items/kg, and 33.61 ± 2.15 items/kg, respectively. In this study, fibers were the most prevalent morphological type of MPs, while blue was the most dominant color. Fourier Transform Infrared Spectroscopy (FTIR) analysis revealed the predominant presence of low-density polyethylene (LDPE) in all types of samples, followed by polyethylene terephthalate (PET), high-density polyethylene (HDPE), polypropylene (PP), and polyamide or commonly known as nylon (PA) in variable amounts. A strong positive correlation between MPs on seagrass blades and those in sediments and surface water suggests that MPs from both water column and seabed contribute to their accumulation on seagrass. Furthermore, the ingestion of MPs by sea cucumbers indicates the potential for fragmentation and trophic transfer within the seagrass ecosystem, enabling MPs to enter and move through the marine food chain.

Continuous glucose monitoring (CGM) enables automated detection of eating events via meal detection algorithms (MDAs); however, CGM-only MDAs have not been comprehensively evaluated using a shared dataset. We compared nine published CGM-only MDAs using standardized metrics by testing them with CGM data from 16 young, healthy, normal-weight adults under free-living conditions. We employed a per-participant holdout design, with separate training, validation, and testing sets, and assessed performance on the test set (216 meals) using sensitivity, false positives per day (FP/day), and detection time (Δt). Sensitivity ranged from 49 to 90%, FP/day from 0.12 to 2.42, and Δt from 37 to 61min. Fuzzy logic and simulation-based approaches showed the highest sensitivity (90% and 83%) but slower detection (> 59min) and higher FP rates (> 1.28/day). Pattern-recognition classifiers (82%, 0.39 FP/day, 44min; 77%, 0.33, 42min) and a glucose-insulin-model-based method (77%, 0.22, 41min) showed more balanced performance, while rate-of-change detectors were faster (37-38min) but less sensitive (70-72%). No single MDA consistently outperformed others across metrics. Pattern-recognition and physiological modeling approaches demonstrated the most balanced performance, whereas rate-of-change methods enabled faster detection with reduced accuracy. Algorithm choice should reflect application priorities, such as early detection versus reliability.

Extracellular vesicles (EVs) are nanoscale lipid bilayer carriers that mediate intercellular communication through the transport of bioactive cargo. Plant-derived nanovesicles (PDNVs) have emerged as sustainable platforms for therapeutic, nutraceutical, and biotechnological applications; however, their physicochemical stability under storage and handling conditions remains insufficiently characterized. Here, we present an integrated dual-omics framework combining untargeted metabolomics and lipidomics with ontology-based analyses to systematically evaluate PDNV stability. Vesicles isolated in a single batch were subjected to controlled variations in temperature (+ 25°C, + 4°C, -20°C, -80°C), storage duration (1 week-3 months), buffer composition (acidic, neutral, basic), lyophilization, and freeze-thaw (F/T) cycling (1x, 3x, 5x), followed by GC-MS and LC-MS analyses. Multivariate (PLS-DA) and univariate analyses revealed that - 80°C best preserved native metabolite and lipid signatures, whereas + 25°C induced pronounced remodeling, including ceramide and sugar acid accumulation and depletion of phosphatidylcholine, hexosylceramides, and N-acylethanolamines. Storage at -20°C maintained short-term stability (~ 1 week) but showed progressive molecular drift over time. Buffer composition exerted modest yet reproducible effects, with PBS maintaining near-native lipid profiles. Lyophilization caused immediate lipid reorganization that remained largely stable during storage, while F/T cycling emerged as the most disruptive stressor. Notably, although multiple conditions altered molecular composition, most changes remained within 10%, underscoring the relative robustness of PDNVs and supporting - 80°C storage and short-term lyophilization as preferred preservation strategies.

Cancer metastasis is a significant contributor to global morbidity and mortality rates. The urokinase type plasminogen activator (uPA) and its receptor (uPAR) play an essential role in facilitating abnormal cell metastasis and tumor progression. This research examines novel uPA/uPAR compounds as potential anticancer targets in various types of tumors. Several compounds were screened for their bioactivity and inhibitory effects on uPA and its receptor interactions, demonstrating the significant potential of a compound previously recognized as a promising uPA inhibitor, initially developed for the treatment of multiple sclerosis. The compounds exhibited notably low IC₅₀ values, as low as 1.4 μM, against the aggressive triple negative breast cancer cell line MDA-MB-231 compared to the chemotherapeutic drug Mitoxantrone. MDA-MB-231 cells treated with KCO241 and KCO246 exhibited selective anticancer activity, inducing apoptotic pathways and demonstrating selective anticancer efficacy. This study showed that these novel uPA inhibitors have potential therapeutic applications for the treatment of metastatic cancers and the modulation of disease progression. Subsequent preclinical in vivo investigations will further evaluate the safety as well as pharmacological characteristics and potential for translation.

Recent advances in generative modeling have enabled the creation of highly realistic deepfake facial images, posing significant risks to digital security, media integrity, and public trust. Although deep learning-based detection methods have achieved strong performance, they often suffer from limited cross-dataset generalization, sensitivity to manipulation-specific artifacts, and reduced interpretability. To address these limitations, this paper proposes a forensic-first hybrid deepfake face detection framework that integrates handcrafted local forensic descriptors with multi-CNN deep semantic representations. Specifically, manipulation-sensitive regions are captured using a Bag-of-Visual-Words (BoVW) model constructed from Histogram of Oriented Gradients (HOG) features extracted at salient keypoints detected via SURF, FAST, and BRISK. In parallel, high-level features are obtained from fine-tuned ResNet-50, MobileNet, and ShuffleNet models and fused at the feature level to capture complementary semantic information. The combined feature representation is classified using a Support Vector Machine (SVM), enabling stable decision boundaries and improved generalization. Extensive experiments on six benchmark datasets of varying scale and complexity demonstrate that the proposed approach consistently outperforms state-of-the-art methods, achieving up to 97.55% accuracy while maintaining robustness under cross-dataset and challenging forensic conditions. The results highlight the effectiveness of integrating explicit forensic features with deep representations to achieve a robust, interpretable, and generalizable solution for deepfake face detection.

How to accurately characterize the nonlinear characteristics of rock mass creep has long been a research hotspot. The traditional Kelvin model cannot accurately characterize the nonlinear variation process in the decay creep stage and the dual characteristics of power function and exponential function in the accelerated creep stage, so it needs to be modified. Based on nonlinear rheological theory and damage theory, this paper establishes a damage creep model that simultaneously considers instantaneous elastic strain, nonlinear viscoelastic strain, viscous strain, and nonlinear viscoplastic strain. The differential damage constitutive equations under one-dimensional and three-dimensional stress states are derived, the method for determining model parameters is presented, and the model is verified based on the triaxial compression creep test results of sandstone. Meanwhile, a sensitivity analysis is performed on the parameters of the modified model. The results show that the modified model can accurately reflect the entire creep process of sandstone under different confining pressures. In uniaxial compression creep tests, the peak strain is most sensitive to changes in the parameters η₂ and η₄. Under high confining pressure, the peak strain is most sensitive to changes in the parameters β, n, and η₄.

Classification of brain tumors is a difficult problem in medical imaging analysis. Over the past few years, various deep learning-based techniques have been employed for detecting and classifying tumors from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In this paper, a new model referred to as Multimodal Attention based Convolutional Neural Network (MANet) has been proposed for detection and classification of brain tumors. The proposed MANet architecture involves integrating multiple feature sets extracted from MRI images through attention techniques. These include wavelet features, edge features, and texture features. This network is divided into two modules, namely feature extraction and classification. The feature extraction module incorporates three CNN streams assisted by wavelet, edge, and texture attention, respectively. The three streams learn discriminative features from diverse feature sets. The classification module concatenates the features and makes predictions through fully connected layers. The proposed MANet models four classes of brain tumors. These classes include glioma, meningioma, pituitary, and non-tumor classes. The proposed MANet model has been tested using two publicly available datasets. These datasets include Contrast Enhanced Magnetic Resonance Imaging (CT-MRI) and Brain Tumor Magnetic Resonance Imaging (BT-MRI). Comparison has been made between MANet and several other existing methods. Experimental results show that the proposed MANet model outperforms other approaches. Comparisons have been made to many existing state-of-the-art approaches. The experimental findings suggest that the suggested MANet is more effective than other approaches. MANet model reaches the highest level of efficiency with an accuracy of 99.12%, a precision of 99.44%, a recall of 99.35%, a sensitivity of 99.1%, a specificity of 99.6%, and an F1 score of 99.5%. Furthermore, experiments show the efficacy of different attention mechanisms along with suggested multimodal fusion technique. In conclusion, the proposed MANet is very promising as a tool to classify brain tumors.

Contemplative practices (including meditation and yoga) are increasingly popular worldwide, especially as alternatives and/or complements to mental health treatment. However, nationally representative samples that allow for accurate estimates of prevalence and relation to mental health are scarce. We conducted a nationally representative, cross-sectional survey in Australia and New Zealand (N = 2640). Sampling was stratified by age, gender, ancestry/ethnicity, region and income. Two pre-registered questions were analysed to explore participants' engagement in contemplative practices and associations to psychological distress and mental healthcare use. We used posthoc regression analyses to further clarify relationships between variables. Overall, 70% of participants engaged in contemplative practices over the past year, most commonly meditation (31%), relaxation (25%), breathing techniques (24%), and yoga (21%). Improving health and wellbeing were the primary motivations for practice. All contemplative practice users reported significantly higher psychological distress and mental healthcare use than non-practitioners (p's<0.05). Adjusting for sociodemographic differences removed associations with distress among yoga and relaxation practitioners (βrelaxation = 0.02, βyoga = - 0.03, n.s.). For meditators, the association disappeared when we accounted for mental healthcare use (βmeditation = 0.01, n.s.). Breathing practice was associated with increased distress in all models (βbreathing = 0.04-0.11, p's<0.05). Notably, meditators and relaxation practitioners with unmet healthcare needs reported less distress than non-practitioners with unmet needs (dmeditation=0.38, drelaxation=0.35). Contemplative practices are widespread among Australians and New Zealanders and may play a complementary and/or alternative role in managing mental health. Further research is needed to study their complex associations with mental health, and which types of contemplative practices are beneficial and safe for whom.