Laboratory of Molecular Neuroscience of Sergipe (LaNMSE), Department of Morphology, Federal University of Sergipe, Av. Marechal Rondon, S/N, Rosa Elze, 49000-100 São Cristóvão, Sergipe, Brazil.
Department of Statistic, Federal University of Sergipe, Av. Marechal Rondon, S/N, Rosa Elze, 49000-100 São Cristóvão, Sergipe, Brazil.
Biomed Res Int. 2021 Nov 8;2021:3060983. doi: 10.1155/2021/3060983. eCollection 2021.
There is not a described method to count the core label of c-Fos-positive neurons, avoiding false-positive and false-negative results. The aim of this manuscript is to provide guidelines for a secure and accurate method to calculate a threshold to select which core of c-Fos-positive neurons marked by immunofluorescence has to be scored. A background percentage was calculated by dividing the intensity value (0 to 255) of the core of c-Fos-positive neurons by its surrounding background from the 8-bit images obtained in a previous study. Using the background percentage from 20% up to 98%, raising 2% once for each score, as threshold to choose which core has to be counted, a script was written for the R program to count the number of the c-Fos-positive neurons and the comparison between control and experimental groups. The differences of the average number of the core counted c-Fos-positive neurons between control and experimental groups, at all thresholds studied, showed a rising value related to an increase of the background percentage threshold as well as a decrease of its value related to an increase of the threshold of background percentage. For the smallest thresholds (high intensity of label), the differences between groups are suppressed (false negative). However, for the biggest thresholds (nonspecific label), these differences are always the same (false positive). Therefore, to avoid the false-negative and the false-positive values, it was chosen as the threshold of 62% the inflection point of the linear regression, which is equally different from the biggest and smallest values of the differences between groups.
目前没有描述的方法来计算 c-Fos 阳性神经元的核心标记，以避免假阳性和假阴性结果。本文的目的是提供一个安全准确的方法来计算一个阈值，以选择哪些用免疫荧光标记的 c-Fos 阳性神经元的核心需要进行评分。通过将先前研究中获得的 8 位图像中 c-Fos 阳性神经元核心的强度值（0 到 255）除以其周围背景的强度值，计算出背景百分比。使用从 20%到 98%的背景百分比，每次提高 2%作为阈值，选择要计数的核心，为 R 程序编写了一个脚本，以计算 c-Fos 阳性神经元的数量以及对照组和实验组之间的比较。在所有研究的阈值下，对照组和实验组之间核心计数的 c-Fos 阳性神经元的平均数量差异显示出与背景百分比阈值增加相关的上升值，以及与背景百分比阈值增加相关的下降值。对于最小的阈值（标记的高强度），组间差异被抑制（假阴性）。然而，对于最大的阈值（非特异性标记），这些差异总是相同的（假阳性）。因此，为了避免假阴性和假阳性值，选择 62%的背景百分比阈值作为线性回归的拐点，这与组间差异的最大和最小值相等不同。
