Cdc Wonder
Cdc Wonder - Related Videos
07:25
In Vitro Methods for Comparing Target Binding and CDC Induction Between Therapeutic Antibodies: Applications in Biosimilarity Analysis
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Cited by 2 •
2017
This protocol describes the in vitro comparison of two key functional characteristics of rituximab: target binding and complement-dependent cytotoxicity (CDC) induction. The methods were employed for a side-to-side comparison between reference rituximab and a rituximab biosimilar. These assays can be employed during biosimilar development or as a quality control in their production.
04:39
In Vitro Generation of Plasmacytoid Dendritic Cells from Common Lymphoid Progenitors
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2025
This video demonstrates an in vitro assay to generate plasmacytoid dendritic cells (pDCs) from common lymphoid progenitor cells (CLPs). Upon co-culturing purified CLPs with γ-irradiated feeder cells in the presence of Fms-like tyrosine kinase-3 ligand (FL), the CLPs differentiate into non-adherent pDCs, the presence of which is confirmed using flow cytometry.
01:19
COVID-19 / Coronavirus Outbreak: A Rapidly Deployable Medical Facility Optimized for Epidemics (Smart Pod)
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2020
The Centers for Disease Control and Prevention (CDC) recommends that healthcare providers perform screening tests on patients with symptoms of COVID-19 in an isolated room to protect other patients from potential exposure to the virus. The Smart Pod, a rapidly deployable medical facility that was designed in the wake of the Ebola virus, can serve as a biocontainment unit for airborne pathogens. This emergency solution has been developed by Baylor College of Medicine as a nimble, standardized...
13:59
Studying Mitotic Checkpoint by Illustrating Dynamic Kinetochore Protein Behavior and Chromosome Motion in Living Drosophila Syncytial Embryos
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Cited by 1 •
2012
The kinetochore is where the SAC initiates its signal monitoring the mitotic segregation of the sister chromatids. A method is described to visualize the recruitment and turnover of one of the kinetochore proteins and its coordination with the chromosome motion in Drosophila embryos using a Leica laser scanning confocal system.
06:13
Enzyme-linked Immunospot Assay (ELISPOT): Quantification of Th-1 Cellular Immune Responses Against Microbial Antigens
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Cited by 3 •
2010
Identification of microbial targets of adaptive immunity in idiopathic diseases can be accomplished by the use of the enzyme-linked immunospot assay.
FAQs
The platform works by pairing a selected public health dataset with user-defined filters. Researchers can specify dimensions such as age, sex, race and ethnicity, location, time period, or cause of death, then request results for that combination. Changing one filter changes the population represented in the tabulation or statistical output, allowing focused examination of disease or mortality patterns.
Because CDC WONDER returns aggregated records, its results describe population-level patterns rather than the circumstances of one identified person. This structure supports comparisons across groups, places, and periods while keeping the analysis centered on distributions of disease, mortality, or other health measures. In medicine, that makes the system useful for surveillance and epidemiology, not an individual clinical record.
Standardized data make categories and results more comparable across analyses. Applying the same dimensions, such as location, time period, or cause of death, helps users examine whether observed patterns differ between populations or change over time. This comparability is important for health disparities research and trend analysis because conclusions depend on consistent group definitions.
To begin an analysis, choose the dataset that matches the health question, select relevant filters, and generate a tabulated or statistical result. A researcher might define a time period and location, then refine the query by age, sex, race and ethnicity, or cause of death. Reviewing the resulting categories and measures keeps the output aligned with the original question.
CDC WONDER can reveal patterns in disease and mortality across demographic groups, geographic areas, and time periods. Researchers can use those outputs to examine population health, identify differences relevant to health disparities, and track trends that may warrant further investigation. The findings support population-level interpretation rather than individual diagnosis or treatment decisions.
Clinicians and public health professionals can use the results to examine emerging patterns, compare population groups, and support disease surveillance. The information may also contribute to prevention planning, resource allocation, and health policy discussions. Because the system organizes standardized population data, it helps connect observed disease or mortality patterns with broader public health priorities.
Its value in medicine and epidemiology comes from linking health outcomes with characteristics such as age, sex, race and ethnicity, location, and time period. These comparisons help investigators study population-level risks, monitor mortality or disease trends, and evaluate disparities. The resulting evidence can inform prevention strategies and public health decisions without reducing analysis to individual patient records.