Discovery a series of novel inhibitors of human dihydroorotate dehydrogenase: Biological activity evaluation and molecular docking.

Human dihydroorotate dehydrogenase (hDHODH) is a key enzyme that catalyzes the de novo synthesis of pyrimidine. In recent years, various studies have shown that inhibiting this enzyme can treat autoimmune diseases such as rheumatoid arthritis (RA) and cancer. This study designed and synthesized a series of novel thiazolidone hDHODH inhibitors. Through biological activity evaluation, Compound 14 was found to have high inhibitory activity, with an IC50 value reaching nanomolar level. Preliminary structure-activity relationship studies found that the carboxyl group in R1 and the naphthalene in R2 are key factors in improving activity. Through molecular docking, the binding mode between inhibitors and proteins was elucidated. This study provides an important reference for further optimizing hDHODH inhibitors.

Study on diagnosis of thyroid nodules based on convolutional neural network.

OBJECTIVE: An artificial intelligence (AI) algorithm based on convolutional neural networks was used in ultrasound diagnosis in order to evaluate its performance in judging the nature of thyroid nodules and nodule classification. METHODS: A total of 105 patients with thyroid nodules confirmed by surgery or biopsy were retrospectively analyzed. The properties, characteristics, and classification of thyroid nodules were evaluated by sonographers and by AI to obtain combined diagnoses. Receiver operating characteristic curves were generated to evaluate the performance of AI, the sonographer, and their combined effort in diagnosing the nature of thyroid nodules and classifying their characteristics. In the diagnosis of thyroid nodules with solid components, hypoechoic appearance, indistinct borders, Anteroposterior/transverse diameter ratio > 1(A/T > 1), and calcification performed by sonographers and by AI, the properties exhibited statistically significant differences. RESULTS: Sonographers had a sensitivity of 80.7%, specificity of 73.7%, accuracy of 79.0%, and area under the curve (AUC) of 0.751 in the diagnosis of benign and malignant thyroid nodules. AI had a sensitivity of 84.5%, specificity of 81.0%, accuracy of 84.7%, and AUC of 0.803. The combined AI and sonographer diagnosis had a sensitivity of 92.1%, specificity of 86.3%, accuracy of 91.7%, and AUC of 0.910. CONCLUSION: The efficacy of a combined diagnosis for benign and malignant thyroid nodules is higher than that of an AI-based diagnosis alone or a sonographer-based diagnosis alone. The combined diagnosis can reduce unnecessary fine-needle aspiration biopsy procedures and better evaluate the necessity of surgery in clinical practice.