Disease Monitoring
Disease Monitoring - Related Videos
15:09
The Use of Primary Human Fibroblasts for Monitoring Mitochondrial Phenotypes in the Field of Parkinson's Disease
0 Views •
Cited by 22 •
2012
Fibroblasts from patients carrying mutations in Parkinson's disease-causing genes represent an easily accessible ex vivo model to study disease-associated phenotypes. Live cell imaging gives the opportunity to study morphological and functional parameters in living cells. Here we describe the preparation of human fibroblasts and subsequent monitoring of mitochondrial phenotypes.
07:35
Use of Ultra-high Field MRI in Small Rodent Models of Polycystic Kidney Disease for In Vivo Phenotyping and Drug Monitoring
0 Views •
Cited by 14 •
2015
The use of ultra-high field MRI as a non-invasive way to obtain phenotypic information of rodent models for polycystic kidney disease and to monitor interventions is described. Compared with the traditional histological approach, MRI images can be acquired in vivo, allowing for longitudinal follow-up.
08:43
Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System
0 Views •
Cited by 26 •
2015
Here we demonstrate the use of a wireless enabling technology for electroencephalogram (EEG) in neonatal rodent models of human disease. With telemetry, there are no encumbering connections, thus allowing natural behaviors.
06:52
Behavioral and Locomotor Measurements Using an Open Field Activity Monitoring System for Skeletal Muscle Diseases
0 Views •
Cited by 226 •
2014
Open field activity levels are used to assess locomotive and behavioral activity levels. This protocol provides a well-designed, standardized protocol to use in preclinical trials for neuromuscular disorders.
10:29
Induction of Graft-versus-host Disease and In Vivo T Cell Monitoring Using an MHC-matched Murine Model
0 Views •
Cited by 4 •
2012
Murine bone marrow transplantation is a widely used technique to study immunological mechanisms governing graft-versus-host disease in humans. The ability to monitor T cell trafficking patterns in vivo allows for detailed analysis of the development and perpetuation of T cell responses during graft-versus-host disease.
FAQs
Interpretation depends on change over time rather than a single abnormal result. Clinicians compare current symptoms, physical findings, biomarkers, or imaging with baseline values and established clinical thresholds. A trend can reveal whether disease is progressing, stabilizing, or responding to therapy, while threshold crossings can signal that closer evaluation or a change in management is warranted.
Each measurement type contributes a different view of the patient’s status. Symptoms and physical findings describe clinical experience and observable condition, whereas laboratory biomarkers and imaging provide additional measurable evidence. Combining these sources allows healthcare teams to assess progression, treatment response, and complications through more than one indicator, supporting a more informed longitudinal judgment.
Repeated assessment is especially important when complications may emerge between clinical encounters. New symptoms, altered physical findings, changing biomarker values, or imaging results can add evidence that the patient’s condition is no longer following the expected course. Detecting such changes earlier gives the care team information for follow-up decisions and potential treatment adjustment.
A disease monitoring workflow begins by recording relevant baseline findings, then repeating selected assessments during follow-up. The team reviews symptoms, examination findings, laboratory biomarkers, imaging, or other measurements and compares them with the baseline or applicable clinical thresholds. Documenting these results as a sequence makes changes easier to recognize and connects findings with subsequent care decisions.
The approach is useful when clinicians need to judge whether treatment is producing the intended response or whether disease status is changing. Serial findings can support decisions about adjusting therapy, increasing follow-up, or evaluating possible complications. Its value comes from linking management decisions to observed patient trends rather than relying only on one assessment at one time.
In clinical research, repeated measurements serve as outcome data for evaluating therapies and describing how diseases evolve across patients and populations. Investigators can examine changes in symptoms, biomarkers, imaging, or other recorded measures over time, rather than treating each observation as isolated. This longitudinal evidence helps connect an intervention with observed responses and disease trajectories.