Uric Acid Quartiles
Uric Acid Quartiles - Related Videos
01:15
Quartile
0 Views •
2023
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set: 1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5 The median or second quartile is seven. The lower half of the...
10:48
Electrochemical Preparation of Poly(3,4-Ethylenedioxythiophene) Layers on Gold Microelectrodes for Uric Acid-Sensing Applications
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2021
We describe aqueous and organic solvent systems for the electropolymerization of poly(3,4-ethylenedioxythiophene) to create thin layers on the surface of gold microelectrodes, which are used for sensing low molecular weight analytes.
![Quantitative SERS Detection of Uric Acid via Formation of Precise Plasmonic Nanojunctions within Aggregates of Gold Nanoparticles and Cucurbit[<em>n</em>]uril](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F61682.jpg%3Fv%3D2026-09-01%252015%253A22%253A12&w=750&q=75)
10:02
Quantitative SERS Detection of Uric Acid via Formation of Precise Plasmonic Nanojunctions within Aggregates of Gold Nanoparticles and Cucurbit[n]uril
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Cited by 4 •
2020
A host-guest complex of cucurbit[7]uril and uric acid was formed in an aqueous solution before adding a small amount into Au NP solution for quantitative surface-enhanced Raman spectroscopy (SERS) sensing using a modular spectrometer.
01:29
Microsoft Excel: Median, Quartile range, and Box Plots
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2025
In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data. Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which measures data spread, is...
03:38
Polyprotic Acids
0 Views •
2020
Acids are classified by the number of protons per molecule that they can give up in a reaction. Acids such as HCl, HNO3, and HCN that contain one ionizable hydrogen atom in each molecule are called monoprotic acids. Their reactions with water are: Monoprotic acids: Reactions with water HCl (aq) + H2O (l) ⟶ H3O+ (aq) + Cl− (aq) HNO3 (aq) + H2O (l) ⟶ H3O+ (aq) + NO3− (aq) HCN (aq) + H2O (l) ⇌ H3O+ (aq) + CN− (aq) Even though it contains four hydrogen...
FAQs
The method lets researchers examine whether health outcomes change progressively across increasing uric acid groups. Instead of treating the population as one combined sample, investigators can compare outcomes at several points in the observed distribution. A consistent rise in an outcome across successive quartiles may support a graded association between uric acid concentration and that outcome.
Quartile analysis does not require researchers to select one concentration as the boundary between normal and abnormal. This is useful when the relationship with an outcome may develop gradually across measured values. Comparing distribution-based groups can therefore reveal patterns that a single cutoff might not show, particularly in epidemiological analyses and clinical research.
When disease associations become stronger from lower to higher quartiles, the findings indicate that increasing uric acid levels may correspond to increasing disease frequency or risk within the studied population. This pattern can help researchers evaluate dose-related relationships involving gout, kidney function, cardiovascular health, or metabolic outcomes, without establishing that uric acid alone causes those conditions.
The relevant measurement depends on whether the study examines blood or urine uric acid concentrations. After selecting the available measurement, researchers can rank those values and form comparison groups within the population being studied. Keeping the measurement type consistent within an analysis allows the resulting quartile comparisons to reflect the intended biological sample rather than mixed sources.
Researchers first collect measured blood or urine uric acid concentrations, arrange the observations from lowest to highest, and divide the distribution into four equal-sized groups. They then compare the medical outcome of interest across those groups. This workflow supports epidemiological analyses by showing whether an outcome differs between lower and higher portions of the observed population.
Studies can compare quartile groups when examining relationships between uric acid and hyperuricemia, gout, kidney function, cardiovascular health, or metabolic outcomes. The approach can also help identify groups with comparatively higher risk and assess whether associations become stronger as concentrations rise. Its value lies in organizing population data for clinically relevant outcome comparisons.