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

Classification of Epithelial Tissues: Overview

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...

Classification of Epithelial Tissues: Stratified Epithelium

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...

Tissue Homogenization and Cell Lysis

Tissue Homogenization and Cell Lysis

Tissue homogenization involves disintegrating tissue architecture and lysing cells, and is an early step in isolating and analyzing cellular components. The method used for homogenization depends on the sample type, the amount of sample available, the analyte to be obtained, and the sensitivity of the method. These methods are broadly classified as mechanical and non-mechanical methods.
Mechanical methods of tissue homogenization
These methods rely on applying external physical force to disrupt...

Classification of Epithelial Tissues: Simple Epithelium

Classification of Epithelial Tissues: Simple Epithelium

Simple epithelium consists of a single layer of cells that lines body cavities and blood vessels. The shape of the cells in the epithelium reflects the function of the tissue. Cells in simple squamous epithelium appear as thin scales with flat, elliptical nuclei that mirror the form of the cell.
Because of the thinness of the cells, simple squamous epithelium is present where the rapid passage of chemical compounds is observed. For example, the endothelium that lines the capillaries and vessels...

Classification of Connective Tissues

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.

Classification of Epithelial Tissues: Glandular Epithelium

Classification of Epithelial Tissues: Glandular Epithelium

The glandular epithelium is made of one or more epithelial cells modified to synthesize and secrete chemical substances. Glandular epithelia can be classified based on cell number. Unicellular glands have individual secretory cells scattered across the epithelial monolayer. In contrast, multicellular glands consist of a hollow tubular duct attached to the cluster of secretory cells located in the deep pockets.
Multicellular glands are formed during early development when epithelial budding...

You might also read

Related Articles

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

Sort by

Same author

Fanconi Anemia as a Window into Premalignant Field Cancerization of the Oral Mucosa.

medRxiv : the preprint server for health sciences·2026

Same author

Multi-level disparities in survival among patients diagnosed with oral cavity cancer in Texas between 1995 and 2020.

Cancer epidemiology·2026

Same author

Clinician perspectives on determinants for assessing and treating intimate partner violence-related traumatic brain injury: a mixed methods content analysis.

Brain injury·2026

Same author

Large-scale DNA organization analysis of primary breast cancers for prediction of axillary lymph node metastases.

Breast (Edinburgh, Scotland)·2026

Same author

Potential Missed Opportunities to Administer Intravenous Thrombolysis to Patients With Acute Ischemic Stroke.

Stroke·2026

Same author

Impact of Mobile Stroke Units on Patients With Large Vessel Occlusion Acute Ischemic Stroke: A Prespecified BEST-MSU Substudy.

Stroke (Hoboken, N.J.)·2026

Search research articles

Related Experiment Video

Updated: Jun 1, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Classifying tissue samples from measurements on cells with within-class tissue sample heterogeneity.

Jose-Miguel Yamal¹, Michele Follen, Martial Guillaud

¹Division of Biostatistics, The University of Texas School of Public Health, Houston, TX 77030, USA. jose-miguel.yamal@uth.tmc.edu

Biostatistics (Oxford, England)

|June 7, 2011

Summary

A new statistical method classifies patients using cell measurements, accounting for data variations. This approach shows promise for detecting cervical neoplasia (abnormal cervical cells) efficiently.

More Related Videos

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Related Experiment Videos

Last Updated: Jun 1, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Area of Science:

Biostatistics
Medical Informatics
Computational Biology

Background:

Hierarchical classification problems, where macro-level objects are classified based on micro-level data, lack comprehensive statistical understanding.
Existing heuristic and formal statistical models do not fully address heterogeneity within macro-level objects in such classification tasks.

Purpose of the Study:

To propose a novel model-based statistical methodology for classifying macro-level objects with inherent heterogeneity.
To apply this methodology to the detection of cervical neoplasia using quantitative cytology data.

Main Methods:

A latent-class variable model is employed to model the log-odds of macro-level object class membership, addressing heterogeneity.
Latent classes are estimated through clustering of macro-level object density estimates.
The method is validated using quantitative cytology measurements from Papanicolaou smears for cervical neoplasia detection.

Main Results:

The proposed method demonstrates effectiveness in classifying patients with cervical neoplasia.
Automated quantitative cytology using this method achieves performance comparable to clinical cytopathology.
Significant improvement is observed compared to statistical models that do not account for data heterogeneity.

Conclusions:

The developed statistical methodology effectively handles heterogeneity in hierarchical classification.
Automated quantitative cytology presents a cost-effective and time-efficient alternative for cervical neoplasia screening.
This approach offers a robust statistical framework for analyzing nested data structures in biomedical applications.