The link between Graves' disease (GD) and Hashimoto's thyroiditis (HT) has been debated for decades due to the shared pathological and immunological components. Immune intolerance and inappropriate immune reaction against self-thyroid cells are distinctive features of both diseases, but definitive data for the clinical presentation of autoimmune thyroid disease remains unclear. To analyse the expression of T-regulatory cells, CD58, the CD4/CD8 ratio and the neutrophil/lymphocyte ratio and to determine if these parameters could be used as differentiating markers between auto- and non-immune thyroid diseases, 75 patients were enrolled in this study-40 with autoimmune thyroid disease (HT and GD ), 15 with non-immune thyroid disease, and 20 healthy controls. Multicolour flow cytometry was used to analyse CD58, T-regulatory cells (Treg) expressing CD4, CD25, HLA-DR and CD8 using different stained fluorescent labelled monoclonal antibodies. The neutrophils and lymphocyte ratio was also measured. Lower expression of Treg with higher expression of CD58 (LFA-3) was found in the autoimmune diseases when compared with the non-immune and control groups. ROC analysis showed that CD58 with sensitivity 88% and specificity 100% with cut-off value more than or equal to 29.9 indicates Hashimoto's disease, while lower value indicates colloid goitre, and higher or equal to 29.84 indicates Graves' disease and lower indicates colloid goitre with 100% sensitivity and specificity. CD58 could be used as differentiating marker between immune and non-immune thyroid disorders.
Keyphrases
- structural basis
- induced apoptosis
- poor prognosis
- nk cells
- flow cytometry
- end stage renal disease
- cell cycle arrest
- multiple sclerosis
- transcription factor
- cystic fibrosis
- magnetic resonance
- escherichia coli
- binding protein
- squamous cell carcinoma
- signaling pathway
- chronic kidney disease
- pseudomonas aeruginosa
- peritoneal dialysis
- locally advanced
- machine learning
- cell proliferation
- prognostic factors
- contrast enhanced
- quantum dots
- deep learning
- cell migration