Best Fit Line
Best Fit Line - Related Videos
01:58
Studying the Permeation of FITC and FITC-Ferritin through an In Vitro Blood-Brain Barrier Model
0 Views •
2025
This video demonstrates a method to study permeation through an in vitro blood-brain barrier (BBB) model using astrocytes and endothelial cells on a membrane insert, with free FITC and FITC-loaded ferritin. FITC-ferritin bound to receptors on endothelial cells and crossed the membrane barrier to the lower chamber while free FITC showed restricted permeation and remained in the upper chamber.
02:56
FITC-Dextran Feeding: A Method to Quantify Intestinal Permeability in C. elegans
0 Views •
2023
This video introduces a method of to visualize and measure the integrity of the C. elegans intestine lumen by feeding worms FITC-labeled dextran. The example protocol shows the assay done with 3,3’-diindolylmethane (DIM)-treated and pathogen-fed worms.
11:13
In Vitro Permeation of FITC-loaded Ferritins Across a Rat Blood-brain Barrier: a Model to Study the Delivery of Nanoformulated Molecules
0 Views •
Cited by 15 •
2016
A method to establish an in vitro model of blood-brain barrier based on a co-culture of rat brain microvascular endothelial cells and astrocytes is described and validated. This system proved to be a valid tool to study the effect of nanoformulation on the trans-barrier permeation of fluorescent molecules.
04:50
Imaging FITC-dextran as a Reporter for Regulated Exocytosis
0 Views •
Cited by 10 •
2018
Here we detail a method for live cell imaging of regulated exocytosis. This method utilizes FITC-dextran, which accumulates in lysosome-related organelles, as a reporter. This simple method also allows distinguishing between different modes of regulated exocytosis in cells that are difficult to manipulate genetically.
07:22
Assessment of Blood-brain Barrier Permeability by Intravenous Infusion of FITC-labeled Albumin in a Mouse Model of Neurodegenerative Disease
0 Views •
Cited by 15 •
2017
In this study, we present an easy and efficient procedure for evaluating the disruption of the blood-brain barrier under neurodegenerative conditions. To achieve our objective, we infused high molecular weight fluorescein isothiocyanate labelled (FITC)-albumin into mouse jugular vein and evaluated its leakage into the brain parenchyma by fluorescence microscopy.
FAQs
Squaring the vertical residuals keeps positive and negative deviations from canceling in the fitting criterion. Least squares therefore evaluates how far observed outcomes lie above or below their predicted values while selecting one slope and intercept for the line. This makes the fitted result reflect the overall scatter around the line rather than simply adding signed differences.
The slope communicates how the predicted outcome changes as the predictor changes, while the intercept supplies the line’s starting value on the fitted scale. Together, these two quantities specify the line used for estimation. Interpreting them should remain tied to the variables and data range shown in the scatterplot, not treated as proof of causation.
Points that sit far above or below the fitted line have large vertical residuals, meaning their observed values differ substantially from the line’s predicted values. Reviewing these observations helps analysts identify unusual cases and decide whether they deserve closer attention during interpretation. This check complements the overall summary by showing where individual data points do not follow the common pattern.
To apply the method, first display the two quantitative variables in a scatterplot and identify which variable will be predicted. Calculate the least-squares slope and intercept, then use the resulting equation to obtain predicted values. Comparing those predictions with observations through vertical residuals helps assess how closely the data follow the fitted linear pattern.
Researchers turn to this approach when they need both a concise description of association and an estimate of an outcome from a predictor. In statistics, it can summarize a scatterplot, evaluate whether observations follow a linear trend, and flag unusual points. These uses support applications in biology, economics, and engineering.
A close-fitting line does not establish that changing the predictor causes the outcome to change. It summarizes the observed association and can support estimation within the data being examined. Statistical interpretation therefore requires separating what the line shows about the relationship from any causal explanation, especially when using the result to discuss real-world systems.