Papillae Density Measurement
Papillae Density Measurement - Related Videos
10:50
Denver Papillae Protocol for Objective Analysis of Fungiform Papillae
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Cited by 47 •
2015
Here we present a protocol to measure fungiform papilla density from digital photographs. This method builds prioritization and objective characteristic metrics into the original descriptive work on fungiform papillae by Miller & Reedy (1990).
10:29
Measuring Oral Fatty Acid Thresholds, Fat Perception, Fatty Food Liking, and Papillae Density in Humans
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Cited by 50 •
2014
Oral chemoreception of fatty acids and the association with diet and fatty food preferences may enable the identification of mechanisms involved with the development of obesity and why dietary changes may be difficult for many individuals.
09:39
Technique to Collect Fungiform (Taste) Papillae from Human Tongue
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Cited by 30 •
2010
Knowledge of molecular mechanisms underlying gustatory transduction has recently enjoyed significant advances, largely due to using animal models. However, the wide diversity in taste sensitivity and specificity among mammals warrants studies in human tissue. We describe a biopsy technique to collect living taste cells from the papillae on human tongue.
08:46
Isolation and Culture of Human Fungiform Taste Papillae Cells
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Cited by 20 •
2012
We aimed to develop a reproducible protocol for isolating and maintaining long-term cultures of human fungiform taste papillae cells. Cells from human fungiform papillae obtained by biopsy were successfully maintained in culture for more than eight passages (12 months) without loss of viability.
09:50
Measuring the Densities of Aqueous Glasses at Cryogenic Temperatures
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Cited by 4 •
2017
A protocol for determining the vitreous phase densities of micro- to pico-liter size drops of aqueous mixtures at cryogenic temperatures is described.
FAQs
Magnification determines the apparent scale of the tissue image, while sampling location determines which part of the tissue contributes to the count. If either varies between observations, measured differences may reflect imaging or sampling choices rather than biological variation. Keeping both factors consistent makes density values more suitable for comparing individuals, species, developmental stages, or experimental conditions.
A density value is meaningful only in relation to the defined tissue area from which it was obtained. Counting papillae within a standardized region supports calculation per unit area, whereas inconsistent regions can make values difficult to compare. The selected area therefore connects the raw count to a biologically interpretable measurement of tissue organization.
Microscopy provides the image needed to examine papillae on the tissue surface, while digital image analysis can support identification and counting within the selected region. These approaches convert visible structure into quantitative data. Their value lies in applying the same observation and counting framework across samples, allowing structural differences to be evaluated systematically.
The measurement can be used to examine variation among individuals, species, developmental stages, or experimental conditions. Such comparisons may show whether papillae organization changes with biological context, although the interpretation depends on consistent imaging and sampling. In biology, this makes density a quantitative structural variable rather than a purely descriptive observation.
A typical workflow begins by selecting a defined tissue region and obtaining a standardized image through microscopy or digital imaging. Individual papillae are then identified within that region, counted, and related to the area examined to calculate density per unit area. Recording magnification and sampling location alongside the result supports consistent comparison among samples.
Papillae density measurements can connect surface structure with sensory function and broader tissue organization. They also support investigations of changes associated with disease or injury by providing a quantitative way to compare tissue samples. Because the method produces a value tied to a defined area, it helps researchers assess structural variation alongside biological condition or context.