Rapid Analysis of Aristolochic Acid Analogues in Traditional Chinese Patent Medicine by LC-MS/MS

Aristolochic acids have been demonstrated to have renal toxicity, cause carcinogenesis, and may cause gene mutations. A series of risk control measurements have been adopted worldwide since 1990s. Some varieties of traditional Chinese medicine with high content of aristolochic acids have been banned in China. However, some species containing aristolochic acids in microscale are still in use. In recent years, with the continuous awareness of drug safety, the aristolochic acid analogues were generally considered to be of potential safety risks. Among these constituents, aristolochic acid I is still the one with most studies. Therefore, in addition to aristolochic acid I, it is necessary to establish an accurate and rapid method to determine other aristolochic acid analogues. LC-MS/MS methods based on multireaction monitoring mode was established to simultaneously determine 9 aristolochic acid analogues including 5 aristolochic acids and 4 aristolactams for the first time. Furthermore, the method was applied for Long dan Xie gan Pill, a traditional complex compound preparation with a long history for treatment of diseases including hepatochlic hygropyrexia, dizziness, tinnitus, and deafness. It has attracted widespread attention because of the aristolochic acid nephropathy. The crude drug Caulis Aristolochiae manshuriensis (Guanmutong) collected in the prescription was replaced by Akebiae Caulis (Mutong), and the established method helps to understand the product safety on market. As a result, aristolochic acid I, aristolochic acid Iva, and aristolactam I were detected and determined in one batch of Long dan Xie gan Pill among 25 batches of samples. It provided practical approach to demonstrate trace aristolochic acids and aristolactams. It was beneficial to control the safety of related traditional Chinese medicine products.

Research Trends in Pharmacopuncture Treatment for Lateral Epicondylitis

The aim of this study was to examine pharmacopuncture treatment for lateral epicondylitis, and to contribute to developing a standardized treatment regimen by reviewing trends in clinical trials. Five randomized controlled trials, 1 case-control study, and 8 cohort studies published after 1999, that involved pharmacopuncture for lateral epicondylitis, were selected from Korean and international online databases (<i>n</i> = 8). The type of pharmacopuncture, dose, frequency, efficacy, and adverse events were analyzed. Seven types of pharmacopuncture were used, namely Bee Venom, Illicium henryi Diels, Akebiae Caulis, Angelicae sinensis Diels, Ligusticum chuanxiong Hort, Hominis Placenta, and Salviae Miltiorrhizae Radix. Dose, treatment duration, and treatment frequency varied widely. One study assessed the treatment efficacy according to frequency. Nine studies lacked data on adverse events. The quality of 5 randomized controlled trials was low. Although pharmacopuncture treatment appeared to be effective for lateral epicondylitis, it was difficult to standardize the regimen for lateral epicondylitis.

Identification of Toxic Herbs Using Deep Learning with Focus on the Sinomenium Acutum, Aristolochiae Manshuriensis Caulis, Akebiae Caulis

Toxic herbs are similar in appearance to those known to be safe, which can lead to medical accidents caused by identification errors. We aimed to study the deep learning models that can be used to distinguish the herb Aristolochiae Manshuriensis Caulis (AMC), which contains carcinogenic and nephrotoxic ingredients from Akebiae Caulis (AC) and Sinomenium acutum (SA). Five hundred images of each herb without backgrounds, captured with smartphones, and 100 images from the Internet were used as learning materials. The study employed the deep-learning models VGGNet16, ResNet50, and MobileNet for the identification. Two additional techniques were tried to enhance the accuracy of the models. One was extracting the edges from the images of the herbs using canny edge detection (CED) and the other was applying transfer learning (TL) to each model. In addition, the sensitivity and specificity of AMC, AC, and SA identification were assessed by experts with a Ph.D. degree in herbology, undergraduates and clinicians of oriental medicine, and the ability was compared with those of MobileNet-TL′s. The identification accuracies of VGGNet16, ResNet50, and MobileNet were 93.9%, 92.2%, and 95.6%, respectively. After adopting the CED technique, the accuracy was 95.0% for VGGNet16, 63.9% for ResNet50, and 80.0% for MobileNet. After using TL without the CED technique, the accuracy was 97.8% for VGGNet16-TL, 98.9% for ResNet50-TL, and 99.4% for MobileNet-TL. Finally, MobileNet-TL showed the highest accuracy among three models. MobileNet-TL had higher identification accuracy than experts with a Ph.D. degree in herbology in Korea. The result identifying AMC, AC, and SA in MobileNet-TL has demonstrated a great capability to distinguish those three herbs beyond human identification accuracy. This study indicates that the deep-learning model can be used for herb identification.