Mortality Outcome
Mortality Outcome - Related Videos
03:57
Assessing Pathogenic Bacteria–Induced Mortality in C. elegans Larvae
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2025
Source: Anderson, Q. L., et al. A High-throughput, High-content, Liquid-based C. elegans Pathosystem. J. Vis. Exp. (2018)The video demonstrates a liquid-based assay where genetically modified C. elegans larvae are exposed to pathogenic bacteria in a microplate. Bacterial toxins secreted into the medium induce host stress and death. A fluorescent dye stains dead worms, allowing imaging-based quantification of mortality to assess genetic susceptibility or resistance.
07:03
Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
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Cited by 5 •
2016
Here we present a protocol for assessing seed survivorship, germination and dormancy under field conditions using buried, labeled seed strips and tetrazolium chloride (TZ) viability testing.
03:56
Mosquito Feeding Assay: A Method to Evaluate Test Compound Effects on Mortality and Fecundity of Adult Female Mosquitoes
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2025
This video demonstrates a blood-feeding assay to evaluate the mortality and fecundity of test compounds with novel modes of action on female mosquitoes. This technique helps in screening new classes of insecticides to kill the resistant population of mosquitoes and control disease spread.
10:07
Using Learning Outcome Measures to assess Doctoral Nursing Education
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Cited by 2 •
2010
The structure and process for measuring student learning outcomes through customizable assessment rubrics is discussed and applied to a doctoral nursing education program.
03:55
Larval Contact Assay: A Procedure to Evaluate Mosquito Larval Mortality following Direct Contact with Test Larvicidal Compound
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2025
In this video, we describe the larval contact assay to evaluate the toxicity of the dopamine receptor antagonist amitriptyline on the third instar stage larvae of mosquitoes, known vectors of several human infectious diseases. Assessing the larval mortality upon incubation with different amitriptyline concentrations over different time intervals helps determine its lethal concentration 50.
FAQs
The timing of death determines what the measure represents. A study may assess deaths during a specified follow-up period, survival time, or risk over that interval, while cause classification identifies which deaths are attributed to the disease or condition under study. Stating both elements prevents results from being interpreted beyond the period and endpoint actually measured.
Overall mortality counts deaths from all causes, whereas cause-specific mortality focuses on deaths assigned to a particular cause. These measures answer different clinical and population-health questions: an intervention could be evaluated for its effect on total deaths or on deaths related to a defined disease. The selected measure should match the decision or hypothesis being examined.
In survival analysis, participants who remain alive at the end of follow-up are censored rather than treated as having experienced the mortality endpoint. Censoring preserves the distinction between survival beyond observation and death during observation. Consequently, researchers can analyze survival time or risk over the planned period without assigning an unsupported death time to living participants.
Mortality outcome comparisons depend on clearly specified populations and follow-up periods. Changing who is included, how long participants are observed, or which cause is counted can change the resulting death rate, risk estimate, or survival summary. For that reason, these design choices must be stated when comparing patient groups, interventions, or population-health patterns.
A mortality analysis begins by defining the population, endpoint timing, and cause-of-death rules, followed by selection of an appropriate summary such as an overall or cause-specific death rate, survival time, or risk. Researchers then account for participants who remain alive through censoring in survival analyses. This workflow links the reported result to its underlying study design.
In clinical trials, mortality outcome can serve as an endpoint for comparing interventions, provided the groups are evaluated over a defined follow-up period and the endpoint is specified consistently. The resulting comparison can inform treatment effectiveness, but interpretation depends on whether the analysis concerns all deaths, a particular cause, survival time, or risk during follow-up.
Medicine uses mortality outcomes across several levels of inquiry. Clinical researchers apply them to treatment evaluation and prognosis, epidemiologists use them for disease surveillance, and public health investigators examine population health. Across these applications, careful definitions allow results to be compared between interventions or patient groups while preserving the distinction between timing, cause, and the population observed.