Population-specific Screening
Population-specific Screening - Related Videos
06:17
Depletion of Specific Cell Populations by Complement Depletion
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Cited by 15 •
2010
To effectively study the function of immune cell populations their purification is often required. Complement depletion is a fast and inexpensive technique for the isolation of immune cell populations with high purity.
00:09
Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations
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Cited by 1 •
2019
The BSelex method to identify and recover individual antigen-specific antibodies from human peripheral blood mononuclear cells combines flow cytometry with single cell PCR and cloning.
09:42
Isolation of Specific Neuron Populations from Roundworm Caenorhabditis elegans
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Cited by 2 •
2019
Here, we present a protocol for simple isolation of specific groups of live neuronal cells expressing green fluorescent protein from transgenic Caenorhabditis elegans lines. This method enables a variety of ex vivo studies focused on specific neurons and has the capacity to isolate cells for further short-term culturing.
15:29
Purification of Specific Cell Population by Fluorescence Activated Cell Sorting (FACS)
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Cited by 137 •
2010
For many scientific studies requiring a biological and chemical analysis of cell populations the cells must be in a high state of purity. Fluorescence activated cell sorting (FACS) is a superior method in which to obtain pure cell populations.
14:47
Population Growth - Student Protocol
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2019
Exponential Population Growth Model ExpandNOTE: In these experiments you will use computer software to simulate different types of growth models. HYPOTHESES: The experimental hypothesis might be that if we increase either R, the reproductive rate or the initial population size, NT, that this will increase the population size after 10 generations in an exponential growth model compared to unmanipulated control populations. The null hypothesis may be that populations of different R and initial...
FAQs
These characteristics help distinguish groups with different expected cancer risks. Age can identify periods when screening may be more relevant, while family history and genetic susceptibility can indicate elevated risk within an otherwise broad population. Researchers use these differences to consider whether screening should begin earlier, occur more often, or be evaluated differently than in groups with lower expected risk.
Disease prevalence affects how screening outcomes may be distributed across a defined group. When prevalence differs between populations, applying the same strategy can produce different levels of false-positive results, missed disease, or unnecessary follow-up. Accounting for prevalence helps researchers align screening choices and schedules with population health data rather than assuming that one approach performs equally well everywhere.
Environmental exposure may contribute to different risk profiles among populations, making it relevant when researchers estimate who may benefit from screening. Access to care also shapes whether people can obtain recommended tests and follow-up. Including both factors helps cancer research evaluate not only potential detection benefits, but also whether a strategy can be implemented fairly and reach the intended group.
Researchers first examine characteristics that distinguish the target group, including age distribution, genetic susceptibility, family history, environmental exposure, disease prevalence, and access to care. They then use this information to estimate risk and consider suitable screening choices, timing, and frequency. Emerging evidence can refine the strategy as population health data reveal differences in outcomes or unmet needs.
Matching screening recommendations to estimated risk can avoid applying an identical testing pattern to people with substantially different likelihoods of disease. A strategy designed around population characteristics may reduce screening in settings where it is less informative while directing greater attention toward groups with higher expected risk. This can limit false-positive results and the unnecessary procedures that may follow.
In cancer research, this approach provides a framework for studying how screening performs across groups with different risks, disease prevalence, and access conditions. Its findings can guide prevention programs toward earlier detection while identifying populations that may be underserved or missed by uniform recommendations. It also allows emerging evidence and population health data to inform future screening policies.