Diagnosis and Management of Nodular Thyroid Disease

Elucidating the role of phytocompounds from Brassica oleracea var. italic (Broccoli) on hyperthyroidism: an in-silico approach

Thyroid hormone (TH) plays a crucial role in regulating the metabolism in every cell and all organs in of the human body. TH also control the rate of calorie burning, body weight, and function of the heartbeat. Therefore, the aim of the present study is to investigate the role of phytocompounds from Brassica oleracea var. italic (Broccoli) against irregularities of TH biosynthesis (hyperthyroidism) through in silico molecular modelling. Initially, the genetic network was built with graph theoretical network analysis to find the right target to control excessive TH production. Based on the network analysis, the three-dimensional crystal structure of the mammalian enzyme lactoperoxidase (PDB id: 5ff1) was retrieved from the protein data bank (PDB), and the active site was predicted using BIOVIA Discovery studio. Sixty-three phytocompounds were selected from the IMPPAT database and other literature. Selected sixty-six phytocompounds were docked against lactoperoxidase enzyme and compared with the standard drug methimazole. Based on the docking scores and binding energies, the top three compounds, namely brassicoside (- 10.00 kcal × mol-1), 24-methylene-25-methylcholesterol (- 9.50 kcal × mol-1), 5-dehydroavenasterol (- 9.40 kcal × mol-1) along with standard drug methimazole (- 4.10 kcal × mol-1) were selected for further ADMET and molecular dynamics simulation analysis. The top-scored compounds were for their properties such as ADMET, physicochemical and drug-likeness. The molecular dynamics simulation analyses proved the stability of lactoperoxidase-ligand complexes. The intermolecular interaction assessed by the dynamic conditions paved the way to discover the bioactive compounds brassicoside, 24-methylene-25-methylcholesterol, and 5-dehydroavenasterol prevent the excessive production of thyroid hormones.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00180-2.

Knowledge, attitude, and practice towards thyroid nodules and cancer among patients: a cross-sectional study

Aim

This study aimed to explore the knowledge, attitude, and practice (KAP) towards thyroid nodules (TN) and thyroid cancer (TC) among patients.

Subject and methods

This cross-sectional study enrolled patients with TN or TC at the Second Affiliated Hospital of the University of South China between September 2022 and February 2023. A self-administered questionnaire was developed to collect demographic information of the participants, and their knowledge, attitude and practice (KAP) towards TN and TC.

Results

A total of 510 valid questionnaires were collected. Among the participants, 102 (20.00%) were male, and 197 (38.63%) had the diagnosis of TC. The knowledge, attitude and practice scores were 5.76 ± 3.09 (possible range: 0-12), 31.07 ± 2.73 (possible range: 9-45), and 18.97 ± 2.92 (possible range: 5-25), respectively. Multivariate logistic regression showed that age of above 50 years old (OR = 0.27, 95%CI: 0.12-0.64, p = 0.003), junior college or bachelor's degree and above (OR = 4.97, 95%CI: 1.74-14.20, p = 0.003), monthly income of 5,000-10,000 CNY (OR = 2.02, 95%CI: 1.09-3.74, p = 0.025) and > 10,000 CNY (OR = 5.67, 95%CI: 2.49-12.94, p < 0.001) were independently associated with knowledge. The good knowledge (OR = 3.87, 95%CI: 1.89-7.95, p < 0.001), high school or technical secondary school (OR = 0.52, 95%CI: 0.30-0.88, p = 0.016), and monthly income of 5,000-10,000 CNY (OR = 2.02, 95%CI: 1.13-3.63, p = 0.018) were independently associated with practice.

Conclusion

Patients demonstrated poor knowledge, moderate attitude, and proactive practice towards TN and TC.

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity.