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Response of licorice to different sources of nitrogen in soil and soilless culture systems
Effects of β-casein A1/A2 milk and β- and κ-casein genotypes on the performance, body composition, and fecal score of dairy calves
AI-augmented intraoperative decision-making workflows in diffuse midline glioma biopsy using cryosection pathology
Abstract Cryosection pathology is essential for intraoperative diagnosis of diffuse midline gliomas, yet it often leads to diagnostic errors and may prompt unnecessary re-biopsies before completion of the formal molecular assessment. In this study, we propose an AI-augmented framework, CryoAID, for rapid molecular outcome prediction during surgery for patients with diffuse midline glioma. CryoAID integrates a generative model to correct cryosection artefacts and a pathology foundation model to predict molecular statuses directly from cryosection images. We validate CryoAID across multiple cohorts to predict tumoural molecular statuses in the internal ( n = 326), external multi-centre ( n = 52), and consecutive ( n = 68) datasets. In particular, CryoAID accurately predicts major molecular statuses (e.g., ATRX, H3K27M, and TP53) using cryosection images that were previously deemed disqualified for molecular examinations. Beyond tumour cells, CryoAID reveals highly differential clinical features, including glial cell proliferation, abundant cytoplasm, and localised endothelial proliferation. In the retrospective analyses, CryoAID reduces re-biopsy rates by 26.4% and 26.6% in the internal and consecutive datasets, respectively. Our findings demonstrate that the AI-augmented pathology workflow can extract diagnostic value from specimens previously considered non-viable by traditional histopathology. This approach represents a shift towards real-time molecular pathology, potentially reducing re-biopsies and improving diagnostic precision for patients with diffuse midline glioma.
Preliminary exploration of the role of fibrinogen-like protein 2 in neuroblastoma
Enhanced plasmonic third-harmonic generation in graphene layers via Anderson localization induced by disordered gold gratings
A biomimetic senotherapy replenishing MAT2A promotes wound regeneration in preclinical models
Exploring the relationship between condylar position and pharyngeal airway dimensions using 3D analysis across different skeletal patterns
How gender and content shape children’s bragging expectations
Transcription start sites experience a high influx of heritable variants fueled by early development
Abstract Mutations drive evolution and genetic diversity, with the most consequential mutations occurring in coding exons and regulatory regions. However, the impact of transcription on germline mutagenesis remains poorly understood. Here, we identify a mutational hotspot at transcription start sites (TSSs) in the human germline, spanning several hundred base pairs in both directions. Notably, the hotspot is absent in de novo mutation data. We reconcile this by showing that TSS mutations are significantly enriched with early mosaic variants, many of which are excluded from de novo mutation calls, indicating that the hotspot partly arises during early embryogenesis. We associate the TSS mutational hotspot with divergent transcription, RNA polymerase II stalling, R-loops, and mitotic—but not meiotic—double-strand breaks, suggesting a recombination-independent mechanism distinct from known processes. Our findings are reinforced by mutational signature analysis, which highlights alternative double-strand break repair and transcription-associated mutagenesis. These insights reveal a germline mutational phenomenon with evolutionary and biomedical implications, particularly affecting genes linked to cancer and developmental phenotypes.
Study on the relationship between the steady-state rolling resistance of radial tires and energy consumption of electric vehicles
Pathogenicity study of ascomycetous opportunistic yeasts from fresh feces of pigeons using Tenebrio molitor larvae as a host model
Continuous indices to assess the phenotypic spectrum of kidney transplant rejection
Computational and experimental design of l-amino acid-based alternatives to ketorolac
Improving efficiency in smart grid monitoring using hybrid classification and dimensionality reduction
Trans-scale crystal dynamics for controlling kinetic responses in organic molecular systems
Enhanced photocatalytic degradation of chlorpyrifos in wastewater using a TiO2/chitosan/Ag2CO3 nanocomposite: synthesis, characterization, and optimization
Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis
Abstract Despite being the standard-of-care treatment, neoadjuvant therapy (NAT) attains a complete response only in approximately half of the patients with triple negative breast cancer. Thus, methods to predict and optimize patient response to NAT are needed. Previously, we employed patient-specific MRI data to calibrate a biology-based mathematical model that describes cell movement, proliferation, and death due to drug at the tumor level and cell proliferation at an image voxel level. We now extend our approach by using MRI data to group voxels into “habitats” whereby tumor cells of a habitat share the same proliferation. With this approach, we now calibrate habitat-informed proliferation rates for each habitat rather than local proliferation rates. When comparing error in tumor cell number and volume at the time of calibration, the local calibration has significantly ( p < 0.05) lower error than the habitat-informed calibration. However, the habitat-informed predictions of a future timepoint have significantly lower error than the local predictions. Compared to the local calibration, the habitat-informed calibration also requires fewer parameters, reducing the calibration time by a factor of 17. These results suggest that a habitat-informed calibration can provide both accurate and efficient predictions of breast cancer response to NAT.
Wafer-scale uniform epitaxy of transferable 2D single crystals for gate-all-around nanosheet field effect transistors
Detection of brain network abnormalities by graph invariants in Alzheimer’s disease using MRI images
Abstract Alzheimer’s disease is a major cause of dementia in older adults. It involves gradual changes in brain function that result in cognitive decline, affecting memory, reasoning, and executive skills. The accurate detection of structural abnormalities in brain networks is crucial for early diagnosis and disease staging. This study presents a graph-based framework that analyzes abnormalities in brain networks of Alzheimer’s patients using six distance-based topological indices: Szeged index, Graovac-Ghorbani index, Padmakar–Ivan index, Mostar index, Wiener index, and Normalized Graovac-Ghorbani index. These indices effectively characterize the structural properties of brain networks and identify disruptions linked to disease progression. The proposed framework first constructs brain graphs from MRI images using the Brightness Distance Matrix method, which captures the spatial relationships between pixels. Then, the constructed brain graphs are modeled using the Watts and Strogatz small-world model to normalize the topological indices. The normalized indices serve as input features for various machine learning models, including decision trees, logistic regression, support vector machines, and a multi-layer neural network. Among these models, a refined neural network model achieves the highest classification accuracy of 89.45%, confirming the value of topological indices as interpretable biomarkers for disease staging. This framework demonstrates the potential of graph-theoretic approaches for detecting Alzheimer’s-related brain network alterations and offers a scalable, interpretable, and privacy-friendly solution.