Prediction models constructed for Hashimoto's thyroiditis risk based on clinical and laboratory factors.

Background: Hashimoto's thyroiditis (HT) frequently occurs among autoimmune diseases and may simultaneously appear with thyroid cancer. However, it is difficult to diagnose HT at an early stage just by clinical symptoms. Thus, it is urgent to integrate multiple clinical and laboratory factors for the early diagnosis and risk prediction of HT. Methods: We recruited 1,303 participants, including 866 non-HT controls and 437 diagnosed HT patients. 44 HT patients also had thyroid cancer. Firstly, we compared the difference in thyroid goiter degrees between controls and patients. Secondly, we collected 15 factors and analyzed their significant differences between controls and HT patients, including age, body mass index, gender, history of diabetes, degrees of thyroid goiter, UIC, 25-(OH)D, FT3, FT4, TSH, TAG, TC, FPG, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Thirdly, logistic regression analysis demonstrated the risk factors for HT. For machine learning modeling of HT and thyroid cancer, we conducted the establishment and evaluation of six models in training and test sets. Results: The degrees of thyroid goiter were significantly different among controls, HT patients without cancer (HT-C), and HT patients with thyroid cancer (HT+C). Most factors had significant differences between controls and patients. Logistic regression analysis confirmed diabetes, UIC, FT3, and TSH as important risk factors for HT. The AUC scores of XGBoost, LR, SVM, and MLP models indicated appropriate predictive power for HT. The features were arranged by their importance, among which, 25-(OH)D, FT4, and TSH were the top three high-ranking factors. Conclusions: We firstly analyzed comprehensive factors of HT patients. The proposed machine learning modeling, combined with multiple factors, are efficient for thyroid diagnosis. These discoveries will extensively promote precise diagnosis, personalized therapies, and reduce unnecessary cost for thyroid diseases.

Preparative Separation of High-Purity Dihydroartemisinic Acid from Artemisinin Production Waste by Combined Chromatography.

In order to make full use of artemisinin production waste and thus to reduce the production cost of artemisinin, we developed an efficient and scalable method to isolate high-purity dihydroartemisinic acid from artemisinin production waste by combining anion-exchange resin with silica-gel column chromatography. The adsorption and desorption characteristics of dihydroartemisinic acid on 10 types of anion-exchange resin were investigated, and the results showed that the 717 anion-exchange resin exhibited the highest capacity of adsorption and desorption to dihydroartemisinic acid. Adsorption isotherms were established for the 717 anion-exchange resin and they fitted well with both Langmuir and Freundlich model. Dynamic adsorption and desorption properties of 717 anion-exchange resin were characterized to optimize the chromatographic conditions. Subsequently, the silica-gel column chromatography was performed and dihydroartemisinic acid with a purity of up to 98% (w/w) was obtained. Finally, the scale-up experiments validated the preparative separation of high-purity dihydroartemisinic acid from industrial waste developed in the present work. This work presented for the first time an isolation of dihydroartemisinic acid with a purity of 98% from Artemisia annua (A. annua) by-product, which adds more value to this crop and has the potential to lower the prices of anti-malarial drugs.

Isolation of Dihydroartemisinic Acid from Artemisia annua L. By-Product by Combining Ultrasound-Assisted Extraction with Response Surface Methodology.

Malaria is the most devastating parasitic disease worldwide. Artemisinin is the only drug that can cure malaria that is resistant to quinine-derived drugs. After the commercial extraction of artemisinin from Artemisia annua, the recovery of dihydroartemisinic acid (DHAA) from artemisinin extraction by-product has the potential to increase artemisinin commercial yield. Here we describe the development and optimization of an ultrasound-assisted alkaline procedure for the extraction of DHAA from artemisinin production waste using response surface methodology. Our results using this methodology established that NaOH at 0.36%, extraction time of 67.96 min, liquid-solid ratio of 5.89, and ultrasonic power of 83.9 W were the optimal conditions to extract DHAA from artemisinin production waste. Under these optimal conditions, we achieved a DHAA yield of 2.7%. Finally, we conducted a validation experiment, and the results confirmed the prediction generated by the regression model developed in this study. This work provides a novel way to increase the production of artemisinin per cultivated area and to reduce artemisinin production costs by recycling its commercial waste to obtain DHAA, an immediate precursor of artemisinin. The use of this technology may reduce the costs of artemisinin-based antimalarial medicines.

Determination of dihydroartemisinic acid in Artemisia annua L. by gas chromatography with flame ionization detection.

Dihydroartemisinic acid (DHAA) is the direct precursor to artemisinin, an effective anti-malaria compound from Artemisia annua L. (A. annua), and it can be transformed to artemisinin without the catalysis of enzyme. A rapid and sensitive analysis of DHAA in A. annua is needed to screen excellent plant resources aimed to improve artemisinin production. In order to develop a rapid and sensitive determination method for DHAA in plant, the extraction and analysis conditions were extensively investigated in the present work. As a result, extraction of powdered A. annua leaves at 55°C for 50 min with chloroform resulted in the highest yield of DHAA, with a recovery of >98%. The precision of this gas chromatographic procedure ranged from 1.22 to 2.94% for intra-day and from 1.69 to 4.31% for inter-day, respectively. The accuracy was 99.55-103.02% for intra-day and 98.86-99.98% for inter-day, respectively. The measured LOQ and LOD values of the proposed method reached 5.00 and 2.00 µg/mL, respectively. Validation indicated the method was robust, quick, sensitive and adequate for DHAA analysis.