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Advancing human skin models by integrating skin microbes for next-generation research

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability

A dual-branch deep learning framework for emotion recognition from EEG signals

Dinitrogen Cleavage and Hydrogenation by a Uranium Hydride Complex Enabled through Metal–Ligand Cooperativity

A crystal graph to vector approach for predicting magnetic properties

Patterned magnetic pole configurations in bonded magnets

Neutral Diboron-Containing Heterocumulenes

Cockroach sensitization and its hidden links to mite and food allergens

Abstract Cockroach allergy is a common trigger of allergic reactions and may be a cause or a result of cross-reactions with other allergens. The aim of this study was to assess the pattern of sensitization to arthropod allergens in perennial allergic rhinitis (PAR) patients with positive skin prick test to cockroach. A group of PAR patients with positive skin prick test (SPT) result with cockroach extract ( Blattella germanica ) was selected. In addition to SPTs for other inhalant allergens, such as house dust mites (Dermatophagoides pteronyssinus and Dermatophagoides farinae) , birch, grass, mugwort, cat, dog, and Alternaria , participants underwent the ALEX2 test which allowed for detection of sensitization to cockroach-specific and cross-reacting molecules. Forty-eight participants took part in the study, of whom forty-six underwent the ALEX2 test. Among PAR patients with positive SPT results to cockroach extract only 2 had elevated IgE levels to cockroach specific allergens (Bla g 1 and Bla g 4). However, in substantial number of patients sensitization to cross-reacting allergens was demonstrated. This was associated with frequent sensitization to other arthropod extracts. A correlation was observed between cockroach allergy and allergy to edible insects such as crickets, locusts, and mealworms; seafood; house dust mites and storage mites; and wasp species, depending on the cockroach species. In our population of PAR patients sensitization to cockroaches is associated with a broader spectrum of cross-reactive allergens. These findings deepen our understanding of potential cross-allergenicity and may form the basis for personalized risk assessment and allergy treatment in patients with AR.

Engineering Intermolecular Packing of Quinoid-Cyanine Scaffolds for Enhanced Afterglow Brightness and Activatable Imaging

Experimental evaluation of an advanced rooted tree optimization based super twisting sliding mode power control for variable-speed wind turbine systems

Modulating Intermediate Reactivity for Nitrogen Oxide Selective Catalytic Reduction through Dual Scaling Laws: A Synergetic <i>In Situ</i> Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Density Functional Theory Study

Spectrofluorimetric determination of serum homovanillic acid using horseradish peroxidase and its association with autism spectrum disorder

Hydrophobic Refinement of Polarity-Switchable Lipo-Xenopeptides Modulates Endosomal Escape and Enhances mRNA Delivery In Vitro and In Vivo

Antibodies induced by antigen-containing liposomes as immunogens preferentially recognize their antigens present on lipid vesicles

Abstract We established a flow cytometry method to detect interactions between antibodies against glycosphingolipids (GSLs) and liposomes composed of phospholipids, cholesterol, and GSLs. Using this system, we found that antibodies induced by antigen-containing liposomes as immunogens bound preferentially to the antigens present on liposomes. Anti-GSL antibodies obtained using GSL-containing liposomes as immunogens exhibited greater sensitivity in detecting GSLs present on liposomes compared with antibodies obtained using cells or glycoproteins as immunogens. Further analyses using the breast cancer–associated glycolipid Globo-H showed that antibodies capable of detecting extracellular vesicles secreted by MCF-7 breast cancer cells could be induced by immunizing mice with Globo-H–containing liposomes. Our results indicate that antibodies obtained using antigen-containing liposomes as immunogens are suitable for detecting antigens present on liposomes, and such antibodies can be applied to the detection of specific antigens present on extracellular vesicles.

Iron-Catalyzed <i>anti</i> -Markovnikov Allylzincation of Terminal Alkynes

DeepStackVEGF a stacking ensemble deep learning framework for vascular endothelial growth factor prediction

Electrooxidation of Ethylene Glycol to Glycolic Acid in a Neutral Electrolyte via Enhanced *OH Generation and Directional Spillover

Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients

Abstract The intratumoral and peritumoral architectural heterogeneities of papillary thyroid carcinoma (PTC) are important in preoperative prediction of gross extrathyroidal extension (Gross ETE). This study systematically evaluated and compared the predictive efficacies of deep learning, radiomics, and their combined approach (Deep Learning-Radiomics, DLR) in predicting Gross ETE in PTC patients using ultrasound imaging. This retrospective study from three hospitals, between 01/01/2018, and 12/31/2022, included 4,542 PTC patients, divided into training ( n  = 3,179) and testing ( n  = 1,363) sets in a 7:3 ratio. Preoperative ultrasound images and clinical data were collected to develop radiomics and deep learning models based on different tumor expansion regions (5/10/15/20 pixels). A nomogram prediction model was developed by integrating multi-regional radiomics features and key clinical parameters. Model performance was assessed using metrics such as the area under the curve (AUC), sensitivity, and specificity. Feature importance was evaluated using SHapley Additive exPlanations (SHAP) analysis, and model interpretability was analyzed with Gradient-weighted Class Activation Mapping (Grad-CAM). In the test cohort, the radiomics model with 15 pixel expansion (AUC: 0.796) and the ResNet101 deep learning model (AUC: 0.832) showed optimal performance. The DLR model incorporating 15 pixel peritumoral features (DLRexpand15) combined with clinical parameters achieved superior predictive performance (AUC: 0.849, accuracy: 0.857, and specificity: 0.888). SHAP analysis identified deep learning features as the primary predictors in the fusion model, while Grad-CAM visualization confirmed spatial concordances between model-activated regions and histopathological invasion patterns. The DLRexpand15-based nomogram integrating clinical indicators provided an effective tool for preoperative prediction of Gross ETE in PTC patients. Strategic incorporation of peritumoral information significantly enhanced the predictive capacity of both radiomics and deep learning models. This multimodal approach provided clinically useful insights for surgical planning and risk stratification.

A silicon chip that enables the creation of 4D cameras

Cancer Cells Degrade the Nanoparticle Protein Corona for Biosynthesis