TSHR169 Antigen Specifically Binds to the Thyroid-stimulating Autoantibody, Representing an Effective Biomarker for Graves’ Disease

Zulkarnain, Zulkarnain; Sujuti, Hidayat; Soeatmadji, Djoko Wahono; Utomo, Huswo; Aulanni’am, Aulanni’am

doi:10.7546/ijba.2019.23.1.51-60

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…100031579, Integrated DNA Technologies) consisting of 417 oligonucleotide bases that was optimized according to our previous work. 6 The construct was designed by adding BamH1 and Xho1 restriction sites at the N‐terminus and C‐terminus, respectively. The construct was then subcloned into a pET28a vector (Novagen).…”

Section: Methodsmentioning

confidence: 99%

Comparative performance of ELISA and dot blot assay for TSH‐receptor antibody detection in Graves’ disease

Zulkarnain

Ulhaq

Sujuti

et al. 2022

Clinical Laboratory Analysis

Self Cite

View full text Add to dashboard Cite

Background Graves’ disease (GD) is an autoimmune disease, and it accounts for major cases of hyperthyroidism. Antibody against thyroid‐stimulating hormone receptor/TSHR (TRAb) is responsible for hyperthyroidism and is considered as a diagnostic marker for GD. Therefore, we developed a recombinant protein of human TSHR‐169 (hTSHR‐169), which was specifically recognized TRAb in the serum of GD patients and then compare the diagnostic performance between ELISA and dot blot of TRAb tests for their ability to diagnose GD. Methods 20 GD patients and 20 healthy individuals from the Indonesian population were enrolled. TRAb concentration and density were quantified. Comparative analysis was performed using receiver‐operating curve (ROC) analysis. Results For dot blot assay, the minimum concentration to detect TRAb requiring 100 ng of antigen with antiserum diluted at 1:60. For diagnosing GD, the ELISA yielded a higher AUC compared with the dot blot assay (0.95 and 0.85, respectively). Using the recommended cutoff values, the efficiency of both assays was examined by comparing the specificity and sensitivity of the assays to the clinical diagnosis. The ELISA showed 80% and 95%, while the dot blot assay showed 70% and 95% sensitivity and specificity, respectively. Conclusion Although the dot blot assay exhibited lower performance than the ELISA method, the dot blot assay is a simple and rapid diagnostic assay that is suitable for diagnosing GD in rural areas, in which healthcare facilities sometimes are not accessible.

show abstract

Section: Methodsmentioning

confidence: 99%

Comparative performance of ELISA and dot blot assay for TSH‐receptor antibody detection in Graves’ disease

Zulkarnain

Ulhaq

Sujuti

et al. 2022

Clinical Laboratory Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the earliest ways to figure out what a new protein does is to compare its sequence to the sequences of proteins with known functions. If two proteins have similar sequences, they will likely have similar structures, which can help predict their functions [3].…”

Section: Introductionmentioning

confidence: 99%

Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier

Roslidar,

Brilianty,

Alhamdi

et al. 2024

IJEEI

View full text Add to dashboard Cite

The primary nutrient that is crucial for identifying biochemical processes and biological norms in living cells is protein. Proteins are usually centered around one or a few functions which are defined by their family type. Hence, identification and classification are needed to separate the proteins according to their structure and families. In this work, we built a model to classify families of protein sequences. We used the protein sequences dataset consists of various macromolecules of biological significance. The classifier is built up using deep learning of Bi-LSTM. We began the research by collecting the dataset from the Protein Data Bank of the Research Collaboratory for Structural Bioinformatics, pre-processing the data using tokenizing, and modeling the classifier based on deep learning network of Bi-LSTM. As we get the best accuracy rate of the trained model, we figure out the model performance using the evaluation metrics of learning curve, accuracy rate, and loss. The results show that Deep Bi-LSTM provides excellent performance with fit learning curve, 99% accuracy rate, and 0.042 loss.

show abstract

TSHR169 Antigen Specifically Binds to the Thyroid-stimulating Autoantibody, Representing an Effective Biomarker for Graves’ Disease

Cited by 2 publications

References 8 publications

Comparative performance of ELISA and dot blot assay for TSH‐receptor antibody detection in Graves’ disease

Comparative performance of ELISA and dot blot assay for TSH‐receptor antibody detection in Graves’ disease

Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier

Contact Info

Product

Resources

About