Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.0K
Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Margin of Error01:27

Margin of Error

7.6K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.6K
Standard Error of the Mean01:13

Standard Error of the Mean

12.4K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
12.4K
Contaminants and Errors01:16

Contaminants and Errors

373
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
373

You might also read

Related Articles

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

Sort by
Same author

Quantitative assessment of additive effects of mirabegron and solifenacin in overactive bladder: re-analysis of SYNERGY study.

Urology·2026
Same author

NOP56 is essential for mammalian generation and maintenance of multiple central nervous systems, associated with SCA36 pathology.

Acta neuropathologica communications·2026
Same author

Clinical relevance of hyperamylasemia and pancreatitis-like imaging linked with accidental hypothermia.

PloS one·2026
Same author

Exploratory Analysis of Serum IGF-I Levels and Symptom Trajectories in Long COVID During the Omicron Period.

Journal of clinical medicine·2026
Same author

Clinical Utility of SARS-CoV-2 Antibody Titers in the Management of Patients With Long COVID Infected With the Omicron Variant.

British journal of biomedical science·2026
Same author

Preoperative Gamma-Glutamyltransferase-to-Lymphocyte Ratio as an Independent Prognostic Biomarker in Patients Undergoing Radical Cystectomy for Bladder Cancer.

Medicina (Kaunas, Lithuania)·2026

Related Experiment Video

Updated: Feb 1, 2026

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons
11:48

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

Published on: July 13, 2011

35.8K

In-process evaluation of culture errors using morphology-based image analysis.

Yuta Imai1, Kei Yoshida1, Megumi Matsumoto2

  • 1Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8601, Japan.

Regenerative Therapy
|December 8, 2018
PubMed
Summary

Quality control in cell manufacturing can be improved using image analysis. Machine learning models predict cell culture errors from morphological data, achieving high accuracy within two days.

Keywords:
Cell manufacturingIn-process measurementMesenchymal stem cellsMorphological analysisNon-invasive image analysisQuality control

More Related Videos

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures
07:59

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures

Published on: March 21, 2012

19.1K
Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
11:41

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

34.3K

Related Experiment Videos

Last Updated: Feb 1, 2026

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons
11:48

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

Published on: July 13, 2011

35.8K
The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures
07:59

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures

Published on: March 21, 2012

19.1K
Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
11:41

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

34.3K

Area of Science:

  • Regenerative Medicine
  • Biotechnology
  • Cell Therapy Manufacturing

Background:

  • Industrial-scale cell therapy manufacturing requires robust quality control.
  • Current methods rely on manual microscopic observation, which is time-consuming and subjective.
  • Lack of effective in-process measurement technology hinders stable and efficient cell production.

Purpose of the Study:

  • To develop and validate machine learning models for in-process quality control in cell culture.
  • To analyze time-course cell morphological information for detecting manufacturing errors.
  • To establish quantitative methods for evaluating cellular quality during culture.

Main Methods:

  • Cultured human mesenchymal stem cells (MSCs) under standard and intentional error conditions.
  • Utilized time-course microscopic images to quantitatively measure cell morphology.
  • Applied modified principal component analysis (PCA) for visualization and linear regression/MT method for prediction modeling.

Main Results:

  • Modified PCA effectively visualized differences between cell lots and culture conditions in real-time.
  • Prediction models achieved >80% accuracy in discriminating error conditions within two days.
  • Demonstrated the MT method's utility for processes with limited failure data.

Conclusions:

  • Quantitatively acquired morphological information serves as a valuable in-process measurement tool.
  • This approach enhances quality control in cell manufacturing.
  • Image processing and machine learning offer a path to automated, real-time quality assessment.