Patient-specific Response
Patient-specific Response - Related Videos
12:09
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Cited by 14 •
2013
A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.
01:25
Dose-Response Relationship: Selectivity and Specificity
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2023
Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...
10:10
Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana
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Cited by 5 •
2010
The molecular basis of spatial-specific phytochrome responses is being investigated using transgenic plants that exhibit tissue- and organ-specific phytochrome deficiencies. The isolation of specific cells exhibiting induced phytochrome chromophore depletion by Fluorescence-Activated Cell Sorting followed by microarray analyses is being utilized to identify genes involved in spatial-specific phytochrome responses.
07:30
3D Printing Model of a Patient's Specific Lumbar Vertebra
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Cited by 4 •
2023
This study aims to create a 3D-printed model of a patient-specific lumbar vertebra, which contains both the vertebra and spinal nerve models fused from high-resolution computed tomography (HRCT) and MRI-Dixon data.
11:48
Patient-Specific Electric Field Simulation In Spinal Metastasis Electrochemotherapy
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2026
This protocol describes a complete, reproducible workflow for patient-specific electric-field simulation and validation of electrochemotherapy (ECT) for spinal metastasis. The workflow integrates multimodal imaging, semi-automatic and manual segmentation, tissue conductivity modeling, linear finite-element electric-field simulation, and experimental validation via post-procedure MRI-based necrosis overlap analysis.
FAQs
Genetic variation can alter drug metabolism, therapeutic targets, or immune activity. Changes in metabolism may influence how an intervention is processed, while target-related differences can affect effectiveness. Variation in immune activity may also shape treatment effects or tolerability. Considering these pathways helps clinicians move beyond population averages when selecting therapy or interpreting an individual’s outcome.
Disease biology affects response because patients with the same clinical diagnosis may differ in the biological features driving treatment sensitivity. These differences can influence whether an intervention engages its intended therapeutic target and how strongly the disease changes after treatment. Incorporating disease biology supports more precise predictions of benefit and helps explain differing responses among patients.
Prior treatment can change how a patient responds to a later intervention, while comorbidities can influence safety and tolerability. A treatment history therefore provides important context when clinicians assess a new option, rather than assuming that population results apply unchanged. Reviewing these factors with disease biology and genetic variation can clarify expected benefit and potential adverse effects.
Population averages summarize outcomes across groups, but they may not predict an individual’s effectiveness, safety, or tolerability. Patient-specific evaluation uses characteristics such as genetic variation, disease biology, prior treatment, and comorbidities to refine that prediction. This approach does not discard population evidence; it applies that evidence with attention to factors that may shift a particular patient’s response.
Clinicians can review genetic variation, disease biology, prior treatment, comorbidities, and other clinical factors that may affect drug metabolism, therapeutic targets, or immune activity. The purpose is to connect patient characteristics with expected effectiveness, safety, and tolerability before choosing or adjusting an intervention. This structured assessment supports treatment selection based on individual context rather than averages alone.
Pharmacogenomic dosing uses genetic information to help tailor treatment, while biomarker-guided care can support selection of interventions more likely to benefit a particular patient. Both approaches translate patient characteristics into treatment decisions. In clinical care and research, they support more precise predictions and may help reduce adverse effects while preserving attention to therapeutic benefit.
Evaluating response can improve treatment outcomes, reduce adverse effects, and clarify why patients experience different levels of benefit or tolerability. In clinical research, patient-specific factors can be incorporated to identify which therapies are most likely to help particular patients. This makes response assessment useful for individual care and for developing more precise predictions in future studies.