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With a chopstick probe like the Millicell STX probe, the shorter electrode is placed into the upper compartment (apical side) containing the complete culture medium, and the other electrode in the lower compartment (basolateral side, see Fig. 9.3 ) containing the same medium. The shorter electrode should not be in contact with the cell layer. Ascertain that the electrode is held steady and at a 90° angle to the plate insert.

This is perhaps the most common way of measuring TEER. However, the readings with the STX electrodes may vary across the cell layer. An alternative is to use the Endohm electrode system developed for the EVOM2 Epithelial Voltohmmeter (Fig. 9.4 ) which measures resistance or voltage over the whole area covered by the cells. However, this alternative necessitates transfer of the inserts from their culture wells to the Endohm chamber for measurement rather than using the hand-held STX electrodes. The Endohm chamber and the cap each contain a pair of concentric electrodes, a voltage-sensing silver/silver chloride pellet in the center plus an annular current electrode. The height of the top electrode can be adjusted to fit cell culture inserts from different manufacturers. The circular disc electrodes, situated above and beneath the membrane, allow a more uniform current density to flow across the membrane than with STX electrodes.

Sterilize the Endohm chamber and electrode with 70 % ethanol for 15 min and let air dry. Measure the resistance of a blank culture cup with no cells that has been thoroughly soaked with an electrolyte solution similar to the culture medium. Add culture medium to the chamber and transfer the insert to the Endohm chamber and mount the upper electrode. The height of the culture medium outside and inside the insert should be at the same level. Measure resistance in ohms (Ω).

If using a chopstick electrode like the Millicell STX electrode, resistance should be measured several times at different places of a monolayer due to variability in the uniformity of the cell layer, and an average value calculated. The average value of the blank, or preferably blanks, should then be subtracted from the resistance reading of the sample (R sample  − R blank  = R cell layer ). In a typical example the values can be like: 450 Ω − 160 Ω = 290 Ω, meaning that the resistance of the monolayer is 290 Ω. Then a correction has to be made for the area covered by the monolayer. Different culture inserts may have different shapes, and the filter covers different areas. Also, data from experiments carried out in different plate formats, i.e. 6-, 12- or 24-well plates must be normalized for comparison. Typically, the area covered by a filter insert for a 24-well plate will be 0.4 cm 2 . Multiply the area covered by the cell monolayer with the resistance found in the experiments: 290 Ω × 0.4 cm 2  = 116 Ω cm 2 . This value is independent of the area of the membrane used.

Athanasiou said that the results might also apply to treatment of hip, knee and other problem areas. The animals that did not receive the cartilage implants saw a 300 percent increase in osteoarthritis, which many TMJ patients eventually develop, while the treated ones did not.

"This is the first time that cogent healing has been shown in the TMJ area, and, I daresay, the first time anyone has shown successful biomechanical healing in any joint. It's key that we can achieve regeneration of an ailing tissue with our engineered implant, one that's mechanically suited to withstand stresses," he said. "So we believe this represents an important first in all joint healing studies."

Explore further: Growing Cartilage from Stem Cells

More information: N. Vapniarsky el al., "Tissue engineering toward temporomandibular joint disc regeneration," Science Translational Medicine (2018). stm.sciencemag.org/lookup/doi/ … scitranslmed.aaq1802

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(PhysOrg.com) -- Damaged knee joints might one day be repaired with cartilage grown from stem cells in a laboratory, based on research by Professor Kyriacos Athanasiou, chair of the UC Davis Department of Biomedical Engineering ...
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Box-Cox Transformations: The Box-Cox transformation, below, can be applied to a regressor, a combination of regressors, and/or to the dependent variable (y) in a regression. The objective of doing so is usually to make the residuals of the regression more homoskedastic (ie., independently and identically distributed) and closer to a normal distribution:

You might like to use the Regression Analysis with Diagnostic Tools JavaScript to check your computations, and to perform some numerical experimentation for a deeper understanding of the concepts.

We wish to test the following test of hypothesis on the two means of the dependent variable Y1, and Y2:

H 0 : The difference between the two means is about a given value M. H a : The difference between the two means is quite different than it is claimed.

Since we are dealing with dependent variables, it's natural to investigate the linear regression coefficients of the two samples; namely, the slopes and the intercepts.

Suppose we are interested in testing the equality of two slopes. In other words, we wish to determine if two given lines are statistically parallel. Let m 1 represent the regression coefficient for explanatory variable X 1 in sample 1 with size n 1 . Let m 2 represent the regression coefficient for X 2 in sample 2 with size n 2 . The difference between the two estimated slopes has the following variance:

has a t-distribution with d.f. = n1 + n2 - 4.

This test and its generalization in comparing more than two slopes are called the Analysis of Covariance (ANOCOV). The ANOCOV test is the same as in the ANOVA test; however there is an additional variable called covariate. ANOCOV enables us to conduct and to extend the before-and-after test for two different populations. The process is as follows:

The variance of the residuals is:

The test statistic is:

Depending on the outcome of the last test, one may reject the null hypothesis.

For our numerical example, using the Alexander Wang Whiplash destroyed jeans Outlet Cheap pN5jt
JavaScripts, we obtained the following statistics: Slope 1 = 1.3513514; its standard error = 0.2587641 Slope 2 = 1.4883721; its standard error = 1.0793906

These indicate that there is no evidence against equality of the slopes. Now, we may test for any differences in the intercepts. Suppose we wish to test the null hypothesis that the vertical distance between the two parallel lines is about 4 units.

Using the second function in the Analysis of Covariance JavaScript, we obtained the statistics: Common Slope = 1.425, Intercept =5.655, providing a moderate evidence against the null hypothesis.