Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Immune checkpoint inhibitor therapy after tumor-infiltrating lymphocytes in unresectable melanoma.

Journal for immunotherapy of cancer·2026

Same author

Deconvolving SARS-CoV-2 mRNA vaccine impact on immunotherapy-related survival.

Cancer discovery·2026

Same author

Diagnostic delay in histiocytic neoplasms and its association with local resource deprivation.

Haematologica·2026

Same author

Enhanced Telehealth in Prostate Cancer.

JAMA network open·2026

Same author

T-Cell Clonal Expansion in Peripheral Blood Following Interventional Radiology Procedures for Metastatic Liver Cancer.

Cancers·2026

Same author

Perspective Editorial: Assessment of Survival Benefit from Sentinel Node Biopsy for Melanoma: A Systematic Review and Meta-analysis.

Annals of surgical oncology·2026

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Xiyu Peng^1,2, James W Smithy³, Mohammad Yosofvand¹

¹Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, 10065, NY, USA.

Biorxiv : the Preprint Server for Biology

|October 17, 2024

Summary

This summary is machine-generated.

SpatialTopic, a new spatial topic model, decodes tissue image architecture by integrating cell type and spatial data. This scalable method enhances understanding of tumor microenvironments and disease progression.

Keywords:

Cellular neighborhoods Multiplexed tissue imaging Spatial molecular profiling Topic models Tumor microenvironment

More Related Videos

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

Related Experiment Videos

Last Updated: Jun 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

Area of Science:

Computational pathology
Spatial biology
Bioinformatics

Background:

Multiplexed tissue imaging advances tumor microenvironment studies.
Cellular neighborhood analysis faces computational and integrative challenges.
Lack of principled strategies hinders precise spatial feature identification and tracking.

Purpose of the Study:

Introduce SpatialTopic, a spatial topic model for high-level spatial architecture decoding.
Integrate cell type and spatial information within a topic modeling framework.
Overcome computational demands and improve integrative analysis across images.

Main Methods:

Developed SpatialTopic, a spatial topic model adapting natural language processing techniques.
Incorporated spatial information using densely overlapping image regions as documents.
Employed an efficient collapsed Gibbs sampling algorithm for model inference.

Main Results:

SpatialTopic demonstrates high scalability on large datasets (millions of cells).
The model achieves high precision and interpretability in spatial feature identification.
Consistently identifies biologically significant spatial topics like tertiary lymphoid structures (TLSs).

Conclusions:

SpatialTopic offers computational efficiency and broad applicability across imaging platforms.
Enables precise identification and tracking of dynamic spatial features in tissue images.
Enhances the analysis of large-scale multiplexed tissue imaging datasets for disease research.