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

Introduction to the Human Microbiota

Introduction to the Human Microbiota

Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity,...

Development of Human Microbiota

Development of Human Microbiota

The human microbiota begins developing at birth and undergoes continual change as we age. Infancy marks a critical period of microbial sensitivity, offering a “window of opportunity” during which beneficial microbes help mature the immune system. By age three, children typically develop a more stable and diverse microbial community. Newborns acquire microbes from their immediate environment; vaginal delivery favors maternal vaginal microbes, while cesarean births favor microbes from...

Methods to Assess Microbial Communities

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...

Microbiota of the Large Intestine

Microbiota of the Large Intestine

The large intestine hosts the most densely populated microbial ecosystem in the human body. This complex community primarily consists of anaerobic bacteria, with Bacillota (formerly Firmicutes) and Bacteroidota (formerly Bacteroidetes) as the predominant groups. The distribution of these microbes varies along different sections of the large intestine, influenced by local environmental factors such as oxygen availability and nutrient composition.The cecum, located at the beginning of the large...

Automated Microbial Diagnostics

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

Microbiota of the Stomach and Small Intestine

Microbiota of the Stomach and Small Intestine

The human gastrointestinal (GI) tract is characterized by distinct physicochemical conditions that shape its microbial communities. Among these, the stomach presents a particularly challenging environment for microbial colonization due to its highly acidic pH, ranging from 1 to 3. This extreme acidity effectively limits microbial density. However, certain acid-tolerant microorganisms are capable of surviving in this niche. Notably, Helicobacter pylori can colonize the gastric mucosa,...

You might also read

Related Articles

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

Sort by

Same author

PhyloSystemX: Enhancing the Analysis of Interaction Networks.

Methods in molecular biology (Clifton, N.J.)·2026

Same author

Gene expression and co-expression heterogeneity patterns and biodemography analyses during the cell cycle encourage aging studies in archaea.

GeroScience·2025

Same author

Transcriptional landscape of the cell cycle in a model thermoacidophilic archaeon reveals similarities to eukaryotes.

Nature communications·2025

Same author

Pseudogymnoascus destructans invasion stage impacts the skin microbial functions of highly vulnerable Myotis lucifugus.

FEMS microbiology ecology·2024

Same author

Most genetic roots of fungal and animal aging are hundreds of millions of years old according to phylostratigraphy analyses of aging networks.

GeroScience·2024

Same author

Duration of rupture of membranes and microbiome transmission to the newborn: A prospective study.

BJOG : an international journal of obstetrics and gynaecology·2024

Search research articles

Related Experiment Video

Updated: Mar 29, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Self-tracking the microbiome: where do we go from here?

Carine Gimbert¹, François-Joseph Lapointe²

¹Département de sciences biologiques, Université de Montréal, CP. 6128, Succursale Centre-ville, Montréal, QC, H3C 3J7, Canada. carine.gimbert@gmail.com.

|December 15, 2015

Summary

The quantified self movement uses technology for health monitoring and data sharing. This study examines the scientific and ethical aspects of self-tracking, particularly for microbiome research and personalized medicine.

More Related Videos

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

Published on: July 31, 2019

Related Experiment Videos

Last Updated: Mar 29, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

Published on: July 31, 2019

Area of Science:

Biotechnology
Personalized Medicine
Bioethics

Background:

The quantified self (QS) movement involves individuals using technology to monitor personal health data.
QS enthusiasts are increasingly interested in microbiome monitoring and personal interventions.
This trend raises questions about data ownership, risk, and the validity of self-experimentation.

Purpose of the Study:

To evaluate the scientific validity of single-subject serial observations versus randomized clinical trials.
To explore the ethical considerations of self-tracking in health and microbiome research.
To assess the potential benefits of self-tracking for personalized medicine.

Main Methods:

Literature review and ethical analysis of self-tracking practices.
Comparison of single-subject study designs with traditional randomized clinical trials.
Examination of consumer genomics and microbiome data in the context of self-tracking.

Main Results:

Self-tracking offers valuable insights but raises scientific validity questions compared to RCTs.
Ethical challenges include data ownership, privacy, and risk assessment.
Potential benefits for personalized medicine and microbiome research are significant but require careful consideration.

Conclusions:

Self-tracking, especially in microbiome research, presents both opportunities and challenges for personalized medicine.
Further research is needed to establish robust methodologies and ethical guidelines for QS data.
Balancing individual empowerment with scientific rigor and ethical responsibility is crucial.