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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Mouse Kidney Transplantation: Models of Allograft Rejection
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Longitudinal Peripheral Blood Transcriptomics Reveal Novel Signatures During Cardiac Allograft Rejection.

Rachad Ghazal1, Min Wang2, Akshatha N Srinivas2

  • 1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.

Transplantation Direct
|January 22, 2026
PubMed
Summary
This summary is machine-generated.

Peripheral blood transcriptomics reveal dynamic changes during acute cellular rejection (ACR) after heart transplantation. This study identifies potential predictive genes for ACR, offering new avenues for noninvasive monitoring.

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Area of Science:

  • Immunology
  • Genomics
  • Transplantation Medicine

Background:

  • Acute cellular rejection (ACR) is a significant complication following heart transplantation.
  • Current immunosuppression therapies have limitations in preventing ACR.
  • Noninvasive biomarkers for monitoring ACR dynamics are lacking.

Purpose of the Study:

  • To explore whole genome transcriptomic profiling for understanding ACR mechanisms.
  • To identify longitudinal, noninvasive biomarkers for ACR.
  • To develop a gene-based classifier for predicting ACR.

Main Methods:

  • RNA sequencing of peripheral blood from heart transplant recipients.
  • Comparison of gene expression profiles before, during, and after ACR.
  • Pathway analysis of differentially expressed genes (DEGs).
  • Machine learning for ACR prediction using gene panels.

Main Results:

  • Identified 235 rejection-specific and 863 postrejection DEGs.
  • DEGs during ACR were enriched in T-cell activation, apoptosis, and B-cell signaling pathways.
  • A 71-gene panel captured longitudinal ACR transcriptomic dynamics.
  • DYNLL1 and SERF2 were identified as predictive genes with an AUC of 0.63.

Conclusions:

  • Peripheral blood transcriptomics reveal dynamic temporal responses in ACR.
  • T- and B-cell pathways are significantly altered during ACR.
  • Identified potential ACR predictive genes warranting further investigation.