Detection Sensitivity
Detection Sensitivity - Related Videos
02:18
A Sensitive Visual Method for the Detection of Hydrogen Sulfide-Producing Bacteria
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2025
Source: Zhu, W., & Chu, W. Sensitive Visual Method for the Detection of Hydrogen Sulfide Producing Bacteria. J. Vis. Exp. (2022)This video demonstrates a sensitive visual method for the rapid detection of hydrogen sulfide–producing bacteria. A color change from yellow to black indicates bacterial activity through the formation of bismuth sulfide.
07:13
Detection of Modified Forms of Cytosine Using Sensitive Immunohistochemistry
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Cited by 8 •
2016
Herein we describe a sensitive immunochemical method for mapping the spatial distribution of 5mC oxidation derivatives based on the use of peroxidase-conjugated secondary antibodies and tyramide signal amplification.
12:31
Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry
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Cited by 102 •
2015
Cell-to-cell transfer of protein aggregates, or proteopathic seeds, may underlie the progression of pathology in neurodegenerative diseases. Here, a novel FRET flow cytometry assay is described that enables specific and sensitive detection of seeding activity from recombinant or biological samples.
07:19
High Sensitivity 5-hydroxymethylcytosine Detection in Balb/C Brain Tissue
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Cited by 56 •
2011
The EpiMark 5-hmC and 5-mC Analysis Kit can be used to analyze and quantitate 5-methylcytosine and 5-hydroxymethylcytosine within a spe cific locus. The kit distinguishes 5-mC from 5-hmC by the addition of glucose to the hydroxyl group of 5-hmC via an enzymatic reaction utilizing β-glucosyltransferase (T4-BGT). When 5-hmC occurs In the context of CCGG, this modification converts a cleavable MspI site to a non-cleavable site.
10:09
Detection of Detergent-sensitive Interactions Between Membrane Proteins
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Cited by 2 •
2018
We describe a protocol for detection of detergent-sensitive interactions between membrane proteins using binding of the sorting receptor, sortilin, to the first luminal loop of the glucose transporter protein, GLUT4, as an example.
FAQs
The detection threshold sets the point at which a test classifies a result as positive. Because sensitivity depends partly on that threshold, changing it can alter how many affected individuals are recognized and how many produce false-negative results. Threshold selection therefore directly shapes whether a test is suitable for screening or early detection.
Specimen quality, disease stage, and analytical method can each change whether the test detects the condition. A poorly representative specimen may limit the available signal, while testing at a different stage may produce a different result. Consequently, sensitivity reported for one specimen, stage, or method should not automatically be assumed for another.
Sensitivity does not by itself show how a result will behave in the tested population. Specificity, prevalence, and predictive values add that context, helping clinicians judge diagnostic reliability rather than treating a high sensitivity value as a complete assessment of test performance. Considering these measures together supports more informed interpretation of test results.
High sensitivity is particularly valuable for screening and early detection, especially when missing a disease or condition could delay treatment. In these settings, reducing false-negative results is a central priority. The measure therefore helps clinicians identify tests that are less likely to overlook affected people when initial detection has important clinical consequences.
Clinicians should treat sensitivity as dependent on the circumstances of testing rather than as an unchanging characteristic. They can ask whether the specimen quality, disease stage, and analytical method in the test match the intended use. This helps them interpret negative findings more carefully and choose a strategy suited to the clinical situation.
Clinicians can first consider whether the test is being used for screening or early detection, then examine sensitivity in relation to specificity, prevalence, and predictive values. They should also consider threshold, specimen quality, disease stage, and analytical method. This combined review supports a testing strategy aligned with the consequences of missed disease.