Sinomenine in Cardio-Cerebrovascular Diseases: Potential Therapeutic Effects and Pharmacological Evidences

Cardio-cerebrovascular diseases, as a major cause of health loss all over the world, contribute to an important part of the global burden of disease. A large number of traditional Chinese medicines have been proved effective both clinically and in pharmacological investigations, with the acceleration of the modernization of Chinese medicine. Sinomenine is the main active constituent of sinomenium acutum and has been generally used in therapies of rheumatoid arthritis and neuralgia. Varieties of pharmacological effects of sinomenine in cardio-cerebrovascular system have been discovered recently, suggesting an inspiring application prospect of sinomenine in cardio-cerebrovascular diseases. Sinomenine may retard the progression of atherosclerosis by attenuating endothelial inflammation, regulating immune cells function, and inhibiting the proliferation of vascular smooth muscle cells. Sinomenine also alleviates chronic cardiac allograft rejection relying on its anti-inflammatory and anti-hyperplastic activities and suppresses autoimmune myocarditis by immunosuppression. Prevention of myocardial or cerebral ischemia-reperfusion injury by sinomenine is associated with its modulation of cardiomyocyte death, inflammation, calcium overload, and oxidative stress. The regulatory effects on vasodilation and electrophysiology make sinomenine a promising drug to treat hypertension and arrhythmia. Here, in this review, we will illustrate the pharmacological activities of sinomenine in cardio-cerebrovascular system and elaborate the underlying mechanisms, as well as give an overview of the potential therapeutic roles of sinomenine in cardio-cerebrovascular diseases, trying to provide clues and bases for its clinical usage.

Cannabisin G from Sinomenium acutum Induces Apoptosis in Human Glioblastoma Cells

Glioblastoma (GBM) is the most common and lethal primary malignant tumor in human central nervous system, current therapies depend on surgical resection, chemotherapy and radiotherapy. The poor prognosis drives us to discover more potential natural products. Cannabisin G is a lignanamide with different effects on different cancer cells, but its effects on GBM cells are still unclear. In this study, cannabisin G was isolated from dried stem of Sinomenium acutum (Thunb.) Rehd. et Wils. by solvent extraction and various chromatographic methods for the first time. It was characterized by 1H-NMR and 13C-NMR. The human GBM cell U87 and U251 were used to investigate the bioactivities of cannabisin G. By CCK-8 assay, cannabisin G was found to significantly inhibit cell viabilities in a concentration-dependent manner. The cell migration was also remarkably blocked by cannabisin G, which was determined by transwell migration assay. Further, apoptotic changes were observed in nucleus morphology upon the treatment with cannabisin G by DAPI staining. To explore the underling mechanisms, MAPKs phosphorylation was detected by western blotting and the activation of MAPKs was found to be involved in the inhibitory effect on GBM cells. In summary, cannabisin G isolated from Sinomenium acutum (Thunb.) Rehd. et Wils. for the first time, was found to induce apoptosis in GBM cells, partly through the activation of MAPKs.

Analgesic Mechanism of Sinomenine against Chronic Pain

Purified from the roots of the plant Sinomenium acutum, sinomenine is traditionally used in China and Japan for treating rheumatism and arthritis. Previously, we have demonstrated that sinomenine possessed a broad analgesic spectrum in various chronic pain animal models and repeated administration of sinomenine did not generate tolerance. In this review article, we discussed sinomenine’s analgesic mechanism with focus on its role on immune regulation and neuroimmune interaction. Sinomenine has distinct immunoregulative properties, in which glutamate, adenosine triphosphate, nitric oxide, and proinflammatory cytokines are thought to be involved. Sinomenine may alter the unbalanced neuroimmune interaction and inhibit neuroinflammation, oxidative stress, and central sensitization in chronic pain states. In conclusion, sinomenine has promising potential for chronic pain management in different clinical settings.

Pd–Ce/ZIF-8 Nanocomposite for Catalytic Extraction of Sinomenine from Sinomenium acutum

Sinomenine is a naturally occurring alkaloid and commonly used as one of the bioactive drug components in rheumatoid arthritis (RA) treatment in the clinic. Varying supported palladium-based catalysts have been synthesized and examined as heterogeneous catalysts for catalytic extraction of sinomenine from Sinomenium acutum. Among various examined supported catalysts, Pd–Ce/ZIF-8 (zeolitic imidazolate framework-8) demonstrates promising catalytic activity in the extraction reaction with an improved yield of 2.15% under optimized conditions. The catalyst composite can be recovered by centrifuging, and reused. A total of three catalyst recycling processes were performed with constant activity. The catalyst Pd–Ce/ZIF-8 has a particle size range of 2–12 nm and a total Pd–Ce loading amount of 5.1 wt% (ZIF-8).

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.