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Increased Accuracy to c-Fos-Positive Neuron Counting.

Wellington José da SilvaJosé Rodrigo Santos Santos SilvaJullyana de Souza Siqueira QuintansWaldecy de Lucca Junior
Published in: BioMed research international (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 p 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.
Keyphrases
  • high intensity
  • spinal cord
  • machine learning
  • resistance training
  • convolutional neural network
  • optical coherence tomography