Immunohistochemistry, Quality Control, and Principles of Validation in the Central Nervous System.

The conventional hematoxylin and eosin stain (H&E) is vital for the histological diagnosis but the role of immunohistochemistry (IHC) in the central nervous system is undeniable. Immunohistochemical techniques detect antigens in tissue sections by immunological and chemical reactions. This chapter reviews the preanalytic, analytic, and postanalytic phases of immunohistochemistry, as well as the principles of quality control and validation.

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. METHODS: To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. RESULTS: The classifier with the highest metrics was the four antibody-based Perfecto-Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. CONCLUSIONS: Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.

A Postmortem Study of Frontal and Temporal Gyri Thickness and Cell Number in Human Obesity.

OBJECTIVE: This study aimed to compare cortex thickness and neuronal cell density in postmortem brain tissue from people with overweight or obesity and normal weight. METHODS: The cortex thickness and neuron density of eight donors with overweight or obesity (mean = 31.6 kg/m2 ; SD = 4.35; n = 8; 6 male) and eight donors with normal weight (mean = 21.8 kg/m2 ; SD = 1.5; n = 8; 5 male) were compared. All participants were Mexican and lived in Mexico City. Randomly selected thickness measures of different cortex areas from the frontal and temporal lobes were analyzed based on high-resolution real-size photographs. A histological analysis of systematic-random fields was used to quantify the number of neurons in postmortem left and right of the first, second, and third gyri of frontal and temporal lobe brain samples. RESULTS: No statistical difference was found in cortical thickness between donors with overweight or obesity and individuals with normal weight. A smaller number of neurons was found among the donors with overweight or obesity than the donors with normal weight at different frontal and temporal areas. CONCLUSIONS: A lower density of neurons is associated with overweight or obesity. The morphological basis for structural brain changes in obesity requires further investigation.