In Vitro Experiment Model
In Vitro Experiment Model - Related Videos
08:25
Advanced 3D Liver Models for In vitro Genotoxicity Testing Following Long-Term Nanomaterial Exposure
0 Views •
Cited by 21 •
2020
This procedure was established to be used for developing advanced 3D hepatic cultures in vitro, which can provide a more physiologically relevant assessment of the genotoxic hazards associated with nanomaterial exposures over both an acute or long-term, repeated dose regimes.
06:08
Use of a Linear Accelerator for Conducting In Vitro Radiobiology Experiments
0 Views •
Cited by 7 •
2019
Clinical linear accelerators can be used to determine biologic effects of a wide range of dose rates on cancer cells. We discuss how to set up a linear accelerator for cell-based assays and assays for cancer stem-like cells grown as tumorspheres in suspension and cell lines grown as adherent cultures.
06:16
Preparation Of Gushukang (GSK) Granules for In Vivo and In Vitro Experiments
0 Views •
Cited by 4 •
2019
This article provides a detailed protocol for preparing a working solution of Gushukang granules for animal studies and GSK granule containing serum for in vitro experiments. This protocol can be applied to pharmacological investigations of herbal medicines as well as prescriptions for both in vivo and in vitro experiments.
08:03
An In Vitro Organ Culture Model of the Murine Intervertebral Disc
0 Views •
Cited by 22 •
2017
Whole organ culture of the intervertebral disc (IVD) preserves the native extracellular matrix, cell phenotypes, and cellular-matrix interactions. Here we describe an IVD culture system using mouse lumbar and caudal IVDs in their functional spinal units and several applications utilizing this system.
08:41
Modeling Chemotherapy Resistant Leukemia In Vitro
0 Views •
Cited by 15 •
2016
The current report summarizes a protocol that can be utilized to model the influence of the bone marrow microenvironment niche on leukemic cells with emphasis placed on enrichment of the most chemoresistant subpopulation.
FAQs
Its central advantage is selective control over the variables that govern a response. Researchers can regulate temperature, concentration, pressure, geometry, or applied forces while keeping other parts of the setup defined. This separation makes it easier to connect an observed change to a particular factor and to test whether a quantitative model captures the underlying mechanism.
The relevant variables depend on the process being studied, but temperature, concentration, pressure, geometry, and applied forces are explicitly important control factors. Changing one of these conditions can alter transport, fluid flow, soft-matter behavior, or interface interactions. Defining and regulating them allows researchers to examine how each condition affects the measured response.
The laboratory model uses selected components rather than the full complexity of an organism or natural environment. This reduction allows researchers to focus on specific interactions and regulate experimental conditions more precisely. The resulting measurements can clarify governing mechanisms, although their immediate scope is the controlled system represented by the selected components.
Interfaces provide a setting for examining interactions between adjoining parts of a system under defined conditions. In physics, such models can help investigate how transport, fluid flow, or soft-matter behavior responds to controlled geometry and applied forces. Measuring those responses supports quantitative analysis of the mechanisms operating at the boundary.
First, researchers select the components needed to represent the process of interest and place them in a defined laboratory setup. They then regulate relevant conditions, such as temperature, concentration, pressure, geometry, or applied forces. Finally, they measure observable responses and use the results to test hypotheses or evaluate a quantitative model.
Measurements reveal observable responses under specified conditions, allowing researchers to determine how a system changes when selected variables are regulated. In physics, the data can expose governing mechanisms and provide quantitative tests of models. This makes the approach useful not only for describing behavior, but also for judging whether a proposed representation is consistent with experiment.
They are useful when researchers need to examine transport, fluid flow, soft-matter behavior, or interactions at interfaces without including the complexity of a complete organism or environment. Findings can guide the design of experiments and materials, and they can contribute to biomedical technologies by connecting controlled physical behavior with practical design decisions.