Antibody Pattern: Correlation of Age and Gender in ANA and Anti-ds-DNA Prevalence

The study was designed to find the prevalence of ANA antibodies and anti-dsDNA antibodies in samples tested at AFIP Rawalpindi and their correlation with age and gender and to find positive and negative predictive values of ANA antibodies.For this purpose,twelve thousand nine hundred sixty-seven(12,967)patients were analyzed for ANA with four hundred sixty-eight(468)healthy samples tested as control and four thousand seven hundred three(4,703)patients tested for ds-DNA antibodies.Retrospective data of all samples tested by indirect immunofluorescence(IF)for ANA antibodies and dsDNA antibodies was collected.To address positive and negative predictive values another control group(autoimmunity not suspected)of serum samples was taken from the healthy population.For the first group,age,gender,ANA antibodies and ds-DNA antibodies results(both tests performed by IIF)data was collected from a computer record cell;for the second control group,ANA antibodies were performed by IIF.12,967 and 4,703 samples(Group 1)were tested for ANA antibodies and dsDNA antibodies,respectively,during this period.1,119(9%)and 99(2%)were found positive for ANA antibodies and ds DNA antibodies.Among these positive samples,850(76%)and 73(74%)were females respectively.Gender predisposition towards autoimmunity(ANA)was found significant with a P value of(P=0.001).Relation of age was also found significant with anti-ANA antibodies with a P value of(P=0.001).This study shows a negative correlation between age(P=0.025)and gender(P=0.001)with anti-dsDNA which is also significant.High prevalence was found below the mean age of 38 years(SD±16.635)for ANA antibodies and the mean age of 35 years(SD±15.066)for ds-DNA antibodies.The age of ANA antibodies and dsDNA antibodies positive patients ranged from 1 year old to 98 years old and 2 years old to 95 years old respectively.In the second(autoimmunity-free)control group,a total of 468 samples were tested for ANA antibodies and 9(2%)were found positive.Positive predictive value(PPV)was 8.6%and negative predictive value(NPV)was 98%.ANA is a sensitive test for autoimmunity and it is significantly related to female gender and increasing age.The low prevalence of ANA antibodies among clinically suspected cases suggests that rationalization of test prescriptions is needed.Anti-ds-DNA is also a sensitive test for diagnosis of SLE and it is significantly related to female gender and increasing age.

Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical sciences.The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases.The use of IIF for detecting autoimmune diseases is widespread in different medical areas.Nearly 80 different types of autoimmune diseases have existed in various body parts.The IIF has been used for image classification in both ways,manually and by using the Computer-Aided Detection(CAD)system.The data scientists conducted various research works using an automatic CAD system with low accuracy.The diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)approach.The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune diseases.The technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the body.In the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL.Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models.These models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,respectively.Therefore,DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases.The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts.Also,the proposed technique is used to detect autoimmune diseases with a minimal margin of errors and flaws.