Clinical Phenotypes
Clinical Phenotypes - Related Videos
14:45
Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
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Cited by 14 •
2012
In this report, we demonstrate the staining and analysis steps of a phenotyping assay performed on fresh whole blood to enumerate major innate and adaptive leukocyte populations. We emphasize considerations for performing these procedures in the context of a multicenter clinical trial.
07:35
A Non-invasive Way to Isolate and Phenotype Cells from the Conjunctiva
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Cited by 5 •
2017
The exposed normal ocular surface consists of cornea and conjunctiva. Epithelial cells, goblet cells and immune cells are present in the conjunctiva. Here, a non-invasive, technique of impression cytology is described using an impression cytology device and flow cytometry to analyze immune cells in the conjunctiva.
09:40
RNAi Screening to Identify Postembryonic Phenotypes in C. elegans
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Cited by 9 •
2012
We describe a sensitized method to identify postembryonic regulators of protein expression and localization in C. elegans using an RNAi-based genomic screen and an integrated transgene that expresses a functional, fluorescently tagged protein.
12:43
On-Chip Endothelial Inflammatory Phenotyping
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Cited by 3 •
2012
Microfluidic flow chambers etched by photolithography and fabricated from PDMS are applied to probe functional outcomes associated with EC dysfunction and inflammation. In a representative experiment, the ability of differential shear stress to modulate monocytic cell adhesion to cytokine activated EC monolayers is demonstrated.
07:35
Use of Ultra-high Field MRI in Small Rodent Models of Polycystic Kidney Disease for In Vivo Phenotyping and Drug Monitoring
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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.
FAQs
Clinical phenotypes emerge from the combined influence of several factors rather than from a single characteristic. Genetic variation may interact with underlying biological mechanisms, environmental exposures, and treatment history to produce different symptoms, functional changes, or progression patterns. This framework helps explain why patients who share a diagnosis can still show substantially different clinical presentations and disease courses.
A shared diagnosis does not guarantee identical disease expression. Individuals may differ in symptoms, physical signs, functional changes, or progression patterns because the contributing genetic, biological, environmental, and treatment-related factors vary between them. Characterizing these differences can separate clinically meaningful patient groups and support more precise interpretation of prognosis or treatment response.
Systematic comparison of observable disease features across individuals or populations can identify recurring patterns that distinguish one group from another. These patterns may include differences in symptoms, functional effects, progression, or responses observed after treatment. Recognizing such clusters can suggest disease subtypes and provide clues for investigating the biological mechanisms associated with variable clinical expression.
Characterization combines multiple sources of clinical information rather than relying on one measurement. Clinicians may review the medical history, perform a physical examination, and evaluate laboratory tests, imaging, and other relevant measurements. Integrating these findings captures symptoms, signs, functional changes, and progression patterns, creating a more informative basis for distinguishing patients with similar diagnostic labels.
Phenotype-based classification helps clinicians organize patients according to clinically observable differences. Those distinctions can support diagnosis, provide information about likely disease progression, and guide patient stratification. They can also assist in selecting targeted therapies when particular clinical patterns are relevant to treatment decisions. The approach therefore connects bedside observations with individualized clinical assessment.
Phenotypic comparisons can improve clinical trial design by helping investigators define more meaningful patient groups instead of treating all participants with the same diagnosis as identical. They may also clarify why treatment responses vary and guide research into the mechanisms underlying those differences. This supports studies that connect observed clinical patterns with disease biology and therapeutic outcomes.