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- Current advances of murine models for food allergy69
This review focuses on IgE-mediated food allergy, compares the differential approaches in developing appropriate murine models for food allergy and details specific findings for three major food allergens, peanut, milk and shellfish.
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- Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring31
A hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation is formulated.
- A review of recent advances in scanned topographic map processing31
The goal is to show the exist ideas in STM processing and provide some general knowledge on methods and patterns which are employed in those studies and new thoughts and ideas about STMprocessing are expected.
- Electrohydrodynamic Jet-Printed Ultrathin Polycaprolactone Scaffolds Mimicking Bruch’s Membrane for Retinal Pigment Epithelial Tissue Engineering25
It is suggested that the EHDJ printing can fabricate scaffolds that mimic Bruch’s membrane by promoting maturation of RPE cells to form a polarized and functional monolayered epithelium with potential as an in vitro model for studying retinal diseases and treatment methods.
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- A novel fast image segmentation algorithm for large topographic maps15
This work proposes a novel algorithm for segmenting large topographic maps based on the ideas of fuzzy theory, randomized sampling and multilevel image fusion, which provides a reliable image segmentation method for large topographical maps.
- SCTMS: Superpixel based color topographic map segmentation method12
A color topographic map segmentation method based on superpixel to overcome problems of misalignment in scanner and other disturbances like inappropriate preserving, false color, mixed color and color aliasing problems occur in the raster color maps.
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- Three-Dimensional Scaffolds for Intestinal Cell Culture: Fabrication, Utilization, and Prospects11
Four types of up-to-date 3D cell culture scaffolds fabricated by various materials and techniques are summarized for a better recapitulation of some essential physiological and functional characteristics of original intestines compared to conventional cell models.
- A contour-line color layer separation algorithm based on fuzzy clustering and region growing11
The spatial relationship and the fuzzy similarity of color features are used in SRGCL to overcome the inaccurate classification of ambiguous pixels, and the procedure focusing on single contour-line layer will improve the accuracy of contouring-line segmentation result ofSRGCL relative to general segmentation methods.
- Unveiling the Antiobesity Mechanism of Sweet Potato Extract by Microbiome, Transcriptome, and Metabolome Analyses in Mice10
Liver transcriptomic and metabolomic profiling revealed that SPE may exert antiobesity effects by modulating the bile-sphingolipid metabolism, which was closely correlated with the reshaped gut microbiomes and SCFAs.
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- Sweet potato extract alleviates high-fat-diet-induced obesity in C57BL/6J mice, but not by inhibiting pancreatic lipases10
6% SPE supplement significantly ameliorated HFD-induced obesity in mice, including body weight gain, fat accumulation, adipocyte enlargement, insulin resistance, and hepatic steatosis, indicating that SPE, as a dietary supplement, has the great potential for weight control and treating hepatic Steatosis.
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- Mollusk allergy: Not simply cross‐reactivity with crustacean allergens9
It is demonstrated for the first time that mollusk tropomyosin can independently elicit a strong primary Thelper Type 2 (Th2)mediated IgE response in a IPsensitized mouse model of food allergy, which was primarily due to tropomyOSin, without any prior sensitization to crustacean allergens.
- Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation8
A benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed, which has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues.
- Color topographical map segmentation Algorithm based on linear element features8
A color map segmentation algorithm, which is used to segment color maps into different layers based on linear element features, is proposed in this paper and outperforms other segmentation approaches that regarding pixels as the elementary units.
- Exosomal CCT3 as a biomarker for diagnosis and immune therapy response in patients diagnosed with hepatocellular carcinoma7
Exosomal CCT3 is a biomarker for diagnosis and ICB therapy of HCC via MYC pathway activation and immune infiltration via MYC pathway activation and immune infiltration.
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- An Activated Dendritic-Cell-Related Gene Signature Indicative of Disease Prognosis and Chemotherapy and Immunotherapy Response in Colon Cancer Patients7
A activated dendritic cell-related gene signature (aDCRS) and an aDCRS-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions, thus improving the survival outcomes of CC patients in the future.
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- Diffusion semantic segmentation model: A generative model for medical image segmentation based on joint distribution5
The mainstream semantic segmentation schemes in medical image segmentation are essentially discriminative paradigms based on conditional distributions, but the learned feature space exhibits inherent instability, which directly affects the precision of the model in delineating anatomical boundaries.
- Graphic-based character grouping in topographic maps5
This paper presents a novel character grouping method based on the graph model, where undirected graphs are used to describe different words, where the color and size of the characters are served as the properties of the nodes, while the distance and angle between the characters is served as a weights of the edges connecting pairs of characters.
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- PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound4
A novel Prostate Zonal Segmentation Network (PZS‐Net), based on U‐Net, which learns critical cross‐frame information and multi‐scale features from sequential frames, is proposed and demonstrates the effectiveness and competitiveness of its key components via comprehensive ablation studies.
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- XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Image4
XctDiff, an algorithm framework for reconstructing CT from a single radiograph, is presented, which decomposes the reconstruction process into two easily controllable tasks: feature extraction and CT reconstruction.
- An anatomy-based iteratively searching convolutional neural network for organ localization in CT images3
A novel multi-organ localization method based on an end-to-end 3D convolutional neural network to embed the anatomy of structures in a deep learning-based approach and outperforms state-of-the-art methods on accuracy and efficiency.
- Scheduling Strategy Design Framework for Cyber–Physical System with Non-Negligible Propagation Delay3
This work proposes a heuristic framework to obtain the optimal scheduling strategy that can minimize the long-term average control cost and obtains the lookup table-based optimal offline strategy and the neural network-based suboptimal online strategy.
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- Antrodia cinnamomea Residual Biomass-Based Hydrogel as a Novel UV-Protective and Antimicrobial Wound-Healing Dressing for Biomedical Use2
A novel one-pot gel formation method, utilizing cinnamomea cellulose-riched residues to create hydrogels as an effective wound-healing dressing that combine UV protection with antimicrobial activity, making them a promising candidate for medical applications, particularly as a wound-healing dressing.
- Anatomic attention regions via optimal anatomy modeling and recognition for DL-based image segmentation1
It is demonstrated that object shape and layout variations can be explicitly learned to create computational models that are suitable for each anatomic object, which opens new possibilities for advancements in medical image segmentation and analysis.
- Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs1
A 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of-the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.
- Auto-segmentation of thoracic brachial plexuses for radiation therapy planning1
An anatomy-guided deep learning hybrid intelligence approach for segmenting thoracic right and left brachial plexuses consisting of two key stages based on a previously created fuzzy anatomy model of the body region with its key organs relevant for the task at hand.
- Line separation from topographic maps using regional color and spatial information1
A novel line separation method using the concept of regional color confusion to reduce the influences of the confusing colors to line separation, and the spatial relations are utilized to solve the problems of the membership determination of the mixed color regions.
- LaG-DESIQUE: A Local-and-Global Blind Image Quality Evaluator Without Training on Human Opinion Scores1
This paper extends the previous DESIQUE algorithm to a local-and-global way (LaG-DESIQUE) to blindly measure image quality without training on human opinion scores and demonstrates that LaG- DesIQUE performs competitively well in predicting image quality.
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- Anatomically ROI and IOI informed hybrid U-Net model for abdominal object segmentation in CT images–
This study introduces the Hybrid U-Net (HUN) model that focuses on Regions of Interest (ROI) and Intensities of Interest (IOI) determined by NI, thus enhancing resource utilization efficiency and reducing irrelevant information.
- Hybrid foundation models: an investigation of foundation anatomy model and foundation segmentation model (FAM-FSM) on thoracic CT images for radiation therapy planning–
A novel hybrid foundation model (HFM) by combining a foundation anatomy model and a foundation segmentation model (FAM-FSM) for automatic object segmentation on thoracic CT images for radiation therapy planning purposes is investigated.
- Open area segmentation in CT images based on pixel displacement and multi‐view with application in the axillary and lower cervical regions–
The segmentation of open regions presents challenges and remains relatively unexplored because they typically lack clear boundaries, complicating the segmentation process.
- Diffusion semantic segmentation: a generative segmentation model based on joint distributions–
This work proposes a novel segmentation architecture based on joint distribution, called the Denoising Semantic Segmentation Model (DSSM), which optimizes probability maps based on pixel feature classification through Bayesian posterior probability and is shown to perform better than state-of-the-art discriminative models.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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