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Related Concept Videos

Gap Junctions01:37

Gap Junctions

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Multicellular organisms employ a variety of ways for cells to communicate with each other. Gap junctions are specialized proteins that form pores between neighboring cells in animals, connecting the cytoplasm between the two, and allowing for the exchange of molecules and ions. They are found in a wide range of invertebrate and vertebrate species, mediate numerous functions including cell differentiation and development, and are associated with numerous human diseases, including cardiac and...
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Gap Junctions01:27

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The cytoplasm of adjacent animal cells can exchange small molecules, ions, and secondary messengers via the communication channels which form the gap junctions. These junctions comprise a few hundred to thousands of molecular channels, each made of two halves, called the connexon hemichannel. A connexon is a hexamer of six transmembrane connexin proteins, which assemble radially, thus forming a pore or channel in the center. One connexon hemichannel docks with a corresponding connexon on the...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Updated: Feb 3, 2026

Analysis of the Gap Junction-dependent Transfer of miRNA with 3D-FRAP Microscopy
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Discerning Disparities: The Data Gap.

Henrie M Treadwell1, Marguerite Ro2, LaTonya Sallad3

  • 11 Morehouse School of Medicine, Director, Community Voices & Men's Health Initiative, Atlanta, GA, USA.

American Journal of Men'S Health
|October 23, 2018
PubMed
Summary
This summary is machine-generated.

Health disparities in race and gender lead to worse health outcomes and premature deaths, especially for men of color. Addressing these requires integrating social determinants and disaggregating data for marginalized groups.

Keywords:
gender issues and sexual orientationhealth inequality/disparityhealth policy issueshealth-care issuesmarginalizationmen of colorquantitative researchspecial populations

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Area of Science:

  • Public Health
  • Health Equity
  • Social Epidemiology

Background:

  • Health disparities persist based on gender, race, and ethnicity, leading to early illness and preventable deaths.
  • Limited representation among healthcare providers and policymakers exacerbates these disparities in underserved communities.
  • Traditional epidemiological analyses often neglect social determinants of health and upward mobility factors.

Purpose of the Study:

  • To examine health disparities through the intersecting lenses of race, ethnicity, and gender.
  • To highlight the unique health challenges faced by boys and men, particularly those from communities of color.
  • To underscore the importance of data disaggregation for understanding and addressing health inequities.

Main Methods:

  • Analysis of health disparities considering race, ethnicity, and gender as primary variables.
  • Review of traditional epidemiological approaches and their limitations in capturing social determinants.
  • Examination of how systemic factors and historical inequities impact health outcomes for marginalized groups.

Main Results:

  • Health disparities disproportionately affect poor men of color, American Indians, and enslaved Africans, contributing to their marginalization.
  • Lack of race and ethnicity disaggregation in data collection hinders a full understanding of health and social justice issues for groups like Asian Americans and Hispanics/Latinx.
  • The current system fails to fully report the health and social justice experiences of various racial and ethnic groups.

Conclusions:

  • Examining disparities through race, ethnicity, and gender offers critical insights into men's health, especially for men of color.
  • Integrating all populations into health disparity indices requires intentional action from decision-makers.
  • Addressing health inequities necessitates a comprehensive approach that includes social determinants and disaggregated data.