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Related Concept Videos

Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
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Related Experiment Video

Updated: May 28, 2026

Precise, High-throughput Analysis of Bacterial Growth
09:00

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Published on: September 19, 2017

Microcomputer assisted growth assessment.

D P Wilson, D Schoenefeld

    Computers in Biology and Medicine
    |January 1, 1986
    PubMed
    Summary
    This summary is machine-generated.

    A new microcomputer program analyzes child growth patterns, comparing individual data to established norms. It calculates growth indices, offering printed summaries and graphic displays for better insights.

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    Area of Science:

    • Pediatrics
    • Medical Informatics
    • Biostatistics

    Background:

    • Accurate monitoring of child growth is crucial for identifying developmental abnormalities.
    • Traditional methods for growth analysis can be time-consuming and prone to error.

    Purpose of the Study:

    • To introduce a novel microcomputer program designed for the analysis of pediatric growth patterns.
    • To enhance the efficiency and accuracy of assessing normal and abnormal growth in children.

    Main Methods:

    • Development of a microcomputer software application.
    • Implementation of algorithms for comparing individual growth data against established pediatric growth standards.
    • Calculation of key growth indices.

    Main Results:

    • The program provides comprehensive printed summaries of growth analyses.
    • Graphic displays visualize individual growth trajectories and deviations from norms.
    • Facilitates the identification of potential growth disorders.

    Conclusions:

    • The described microcomputer program offers a valuable tool for pediatricians and researchers.
    • It streamlines the process of growth pattern analysis, improving diagnostic capabilities.
    • Supports evidence-based clinical decision-making in child development.