Traditional Chinese Medicine Formulae QY305 Reducing Cutaneous Adverse Reaction and Diarrhea by its Nanostructure.

Traditional Chinese medicine (TCM) is widely used in clinical practice, including skin and gastrointestinal diseases. Here, a potential TCM QY305 (T-QY305) is reported that can modulate the recruitment of neutrophil in skin and colon tissue thus reducing cutaneous adverse reaction and diarrhea induced by epidermal growth factor receptor inhibitors (EGFRIs). On another hand, the T-QY305 formula, through regulating neutrophil recruitment features would highlight the presence of N-QY305, a subunit nanostructure contained in T-QY305, and confirm its role as potentially being the biomaterial conferring to T-QY305 its pharmacodynamic features. Here, the clinical records of two patients are analyzed expressing cutaneous adverse reaction and demonstrate positive effect of T-QY305 on the simultaneous inhibition of both cutaneous adverse reaction and diarrhea in animal models. The satisfying results obtained from T-QY305, lead to further process to the isolation of N-QY305 from T-QY305, in order to demonstrate that the potency of T-QY305 originates from the nanostructure N-QY305. Compared to T-QY305, N-QY305 exhibits higher potency upon reducing adverse reactions. The data represent a promising candidate for reducing cutaneous adverse reaction and diarrhea, meanwhile proposing a new strategy to highlight the presence of nanostructures being the "King" of Chinese medicine formula as the pharmacodynamic basis.

Nanostructures in Chinese herbal medicines (CHMs) for potential therapy.

With its long clinical history, traditional Chinese medicine (TCM) has gained acceptance for its specific efficacy and safety in the treatment of multiple diseases. Nano-sized materials study of Chinese herbal medicines (CHMs) leads to an increased understanding of assessing TCM therapies, which may be a promising way to illustrate the material basis of CHMs through their processing and extraction. In this review, we provide an overview of the nanostructures of natural and engineered CHMs, including extracted CHMs, polymer nanoparticles, liposomes, micelles, and nanofibers. Subsequently, the applications of these CHM-derived nanostructures to particular diseases are summarized and discussed. Additionally, we discuss the advantages of these nanostructures for studying the therapeutic efficacy of CHMs. Finally, the key challenges and opportunities for the development of these nanostructures are outlined.

Rheumatic valvular heart disease treated with traditional Chinese medicine: A case report.

BACKGROUND: Rheumatic heart disease (RHD) is an autoimmune disease that leads to irreversible valve damage and heart failure. Surgery is an effective treatment; however, it is invasive and carries risks, restricting its broad application. Therefore, it is essential to find alternative nonsurgical treatments for RHD. CASE SUMMARY: A 57-year-old woman was assessed with cardiac color Doppler ultrasound, left heart function tests, and tissue Doppler imaging evaluation at Zhongshan Hospital of Fudan University. The results showed mild mitral valve stenosis with mild to moderate mitral and aortic regurgitation, confirming a diagnosis of rheumatic valve disease. After her symptoms became severe, with frequent ventricular tachycardia and supraventricular tachycardia > 200 beats per minute, her physicians recommended surgery. During a 10-day preoperative waiting period, the patient asked to be treated with traditional Chinese medicine. After 1 week of this treatment, her symptoms improved significantly, including resolution of the ventricular tachycardia, and the surgery was postponed pending further follow-up. At 3 -month follow-up, color Doppler ultrasound showed mild mitral valve stenosis with mild mitral and aortic regurgitation. Therefore, it was determined that no surgical treatment was required. CONCLUSION: Traditional Chinese medicine treatment effectively relieves symptoms of RHD, particularly mitral valve stenosis and mitral and aortic regurgitation.

Modelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learning.

BACKGROUND: Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. METHODS: Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets. RESULTS: A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained. CONCLUSIONS: The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.