Lesion Burden Measurement
Lesion Burden Measurement - Related Videos
03:23
Measurement of the In Vivo Burden of Bacteria Expressing Luciferase in an Insect Larva
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2025
This video showcases the in vivo bacterial burden measurement using Bacillus Calmette-Guérin, BCG, lux-infected insect larvae. The process involves larval homogenization, treatment with a luciferase substrate, and bioluminescence measurement to determine relative light units that correlate with bacterial burden.
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Assessing Bacterial Burden in the Mouse Spleen for Infection Studies
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Source: Madenspacher, J. H. and Fessler, M. B. A Non-invasive and Technically Non-intensive Method for Induction and Phenotyping of Experimental Bacterial Pneumonia in Mice. J. Vis. Exp. (2016)This video demonstrates the process of quantifying bacterial load in the spleen of an infected mouse to assess the severity of infection. The homogenized spleen is serially diluted and plated to determine total bacterial burden. The resulting data provide insights into host-pathogen interactions within...
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Measuring Drug Binding to Caseum from Tuberculosis Lesions via the Rapid Equilibrium Dialysis Method
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2025
Source: Sarathy, J. P. et al. An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions. J. Vis. Exp. (2017)This video demonstrates an in vitro assay for measuring drug binding to caseum from tuberculosis lesions using rapid equilibrium dialysis. It outlines the steps for preparing caseum suspensions, performing dialysis, and calculating the unbound drug fraction to predict drug penetration into caseum.
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Malassezia-Induced Skin Inflammation and Fungal Burden Analysis in a Murine Model
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2025
This video demonstrates Malassezia-induced skin infection in mice. Malassezia oil suspension is applied over the wounded ear, triggering inflammation and swelling of the ear tissue. Later, the ears are dissected, homogenized, and spread on an agar plate for growth. The number of colonies indicates the fungal burden resulting from skin inflammation.
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Monitoring Neutrophil Recruitment Dynamics and Bacterial Burden at the Infection Site in a Murine Model
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This video demonstrates in vivo imaging to study the neutrophil recruitment dynamics at an infection site in an immunocompromised, transgenic mouse with GFP-expressing neutrophils. It involves injecting luminescent bacteria to cause an infection. This leads to neutrophil recruitment to the infection site, whose tracking is done by imaging.
FAQs
These measurements describe different aspects of abnormal tissue. Lesion count indicates how many abnormalities are present, while size and volume capture their physical extent. Distribution adds information about where lesions occur within the imaged anatomy. Considering these dimensions together gives a more complete assessment than relying on a single measurement, especially when disease patterns vary.
Segmentation identifies the boundaries of each lesion so that analysis software or a clinician can calculate its dimensions or volume. Inaccurate boundaries can change the estimated burden, even when the scan itself is unchanged. Consistent segmentation methods therefore help make measurements more comparable across examinations, care teams, and clinical research assessments.
Image quality and acquisition protocols influence how clearly lesions and their boundaries appear on a scan. Differences in these factors can affect lesion identification and segmentation, which may alter estimates of number, size, or volume. Maintaining consistent imaging conditions supports more dependable comparisons and helps distinguish actual change from measurement variability.
Serial measurements allow findings from one examination to be compared with later examinations using the same measurement concepts. Changes in lesion number, dimensions, volume, or distribution can then contribute to assessment of disease progression or therapeutic response. Standardized estimates make these comparisons easier to interpret and improve communication among members of the care team.
A typical workflow begins with obtaining medical images, identifying abnormal tissue, and delineating lesion boundaries through clinician review or analysis software. The selected lesions are then quantified by number, size, volume, or distribution, and the results can be compared with other examinations. Reliable interpretation depends on consistent protocols and segmentation practices throughout the process.
The approach can support diagnosis, risk assessment, treatment planning, and monitoring of therapeutic response. Its use extends across conditions in which abnormal tissue can be characterized on medical images, including tumors, vascular disease, and inflammatory disorders. The relevant measurements may differ by clinical context, but each provides an objective description of disease extent.
Clinical studies can use lesion burden estimates as objective endpoints for evaluating disease status or therapeutic response. Standardized measurements also support consistent comparisons between examinations and improve communication about findings. Their usefulness depends on controlling factors that affect accuracy, particularly image quality, acquisition protocols, and the methods used to segment lesions.