Prediction of chickenpox in adults

Abstract

Aim. To evaluate the potential for detecting of gene allelic polymorphism for chickenpox in adults.Materials and methods. The examination involved 201 conscripted soldiers of Caucasian race, aged between 18 and 24, who were born in and served in Zabaykalsky Krai. Determination of gene polymorphism was carried out via polymerase chain reaction. Genomic DNA was extracted from whole blood leukocytes using DNA-Express reagent. IBM SPSS Statistics 25.0 (International Business Machines Corporation, License No. Z125-3301-14, USA) was used for statistical processing of the results.Results. IL-10(G1082A) obtains the highest predictive value for chickenpox, while TLR9(T1237C) – the lowest. More commonly, chickenpox affects adults with IL-10(C819T), IL-10(G1082A) and TLR9(A2848G). TLR9(T1237C) is insignificant for chickenpox. TT and TC IL-10(G1082A), GA and AA TLR9(A2848G), AG and GG IL-10(C819T) are activators of chickenpox. AA IL-10(G1082A), CC IL-10(C819T), AA TLR9(A2848G) are chickenpox protectors. Android app was created for prognosis and early diagnosis of chickenpox in adults.Conclusion. Genes GA, AA IL-10(G1082A, and TT, CT IL-10(C819T), as well as AG, GG TLR9(A2848G) predispose to chickenpox, and gene AA TLR9(A2848G) reduces the probability of developing the disease. Considering that, the main prognostic markers for chickenpox in adults are polymorphisms of genes IL-10(G1082A), TLR9(A2848G), IL-10(C819T), the developed diagnostic model allows for the prediction and early diagnosis of the disease.

Dermatología en la Práctica Médica General Vol. 8

“…Se han comparado varios modelos, como el VGG-16, InceptionV3 y CapsuleNet, y un modelo personalizado basado en ResNet-18 logra una precisión del 84,59% en la clasificación [27] . Los enfoques del aprendizaje por transferencia, como el aprendizaje por transferencia basado en la generalización y la regularización (GRA-TLA), también se han utilizado para el diagnóstico de la viruela símica, con una precisión que oscila entre el 77 y el 99% [ 28] . Las funciones de la CNN previamente preparadas, combinadas con los clasificadores de aprendizaje automático, han arrojado resultados prometedores en la detección de la viruela símica .…”