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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Updated: Mar 24, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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A Zero-Inflated Hierarchical Generalized Transformation Model to Address Non-Normality in Spatially-Informed

Hunter J Melton, Jonathan R Bradley, Chong Wu

    Biorxiv : the Preprint Server for Biology
    |March 23, 2026
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    Summary
    This summary is machine-generated.

    This study introduces a new method, ZI-HGT + CARD, to accurately identify cell types in oral cancer (OSCC) spatial transcriptomics data. This improves understanding of the tumor microenvironment and aids in developing new cancer therapies.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Oncology

    Background:

    • Oral squamous cell carcinomas (OSCC) present diagnostic challenges, leading to poor survival rates.
    • Spatial transcriptomics is key to understanding the OSCC tumor microenvironment.
    • Existing cell-type deconvolution methods struggle with zero-inflated OSCC data.

    Purpose of the Study:

    • To develop a novel method for accurate cell-type deconvolution in zero-inflated spatial transcriptomics data from OSCC.
    • To improve the understanding of the OSCC tumor microenvironment.
    • To quantify uncertainty in cell-type proportion estimations.

    Main Methods:

    • Developed a zero-inflated hierarchical generalized transformation model (ZI-HGT).
    • Integrated ZI-HGT with Conditional AutoRegressive Deconvolution (CARD) for enhanced cell-type deconvolution.
    • Applied the ZI-HGT + CARD framework to OSCC spatial transcriptomics data.

    Main Results:

    • The ZI-HGT + CARD framework significantly improved cell-type deconvolution accuracy for OSCC data.
    • The method effectively handles high zero-inflation in spatial transcriptomics data.
    • Accurate mapping of diverse fibroblast populations within the OSCC tumor microenvironment was achieved.

    Conclusions:

    • The ZI-HGT + CARD framework offers a robust solution for cell-type deconvolution in challenging spatial transcriptomics datasets.
    • This approach enhances the understanding of tumor heterogeneity and immunosuppression in OSCC.
    • The findings are critical for advancing OSCC research and therapeutic strategies.