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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. [ABSTRACT FROM AUTHOR]

Abstract Background There is a long history of coronary heart disease (CHD) diagnosis and treatment in Chinese medicine (CM), but a formalized description of CM knowledge is still unavailable. This study aims to analyze a set of CM clinical data, which is important and urgent. Methods Relative associated density (RAD) was used to analyze the one-way links between the symptoms or syndromes or both. RAD results were further used in symptom selection. Results Analysis of a dataset of clinical CHD diagnosis revealed some significant relationships, not only between syndromes but also between symptoms and syndromes. Using RAD to select symptoms based on different classifiers improved the accuracy of syndrome prediction. Compared with other traditional symptom selection methods, RAD provided a higher interpretability of the CM data. Conclusion The RAD method is effective for CM clinical data analysis, particular for analysis of relationships between symptoms in diagnosis and generation of compact and comprehensible symptom feature subsets.

Abstract 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.

Background: We developed a computer-assisted diagnosis model to evaluate the feasibility of automated classification of intrapapillary capillary loops (IPCLs) to improve the detection of esophageal squamous cell carcinoma (ESCC).Methods: We recruited patients who underwent magnifying endoscopy with narrow-band imaging for evaluation of a suspicious esophageal condition. Case images were evaluated to establish a gold standard IPCL classification according to the endoscopic diagnosis and histological findings. A double-labeling fully convolutional network (FCN) was developed for image segmentation. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior > 15 years; mid level 10 - 15 years; junior 5 - 10 years).Results: Of the 1383 lesions in the study, the mean accuracies of IPCL classification were 92.0 %, 82.0 %, and 73.3 %, for the senior, mid level, and junior groups, respectively. The mean diagnostic accuracy of the model was 89.2 % and 93.0 % at the lesion and pixel levels, respectively. The interobserver agreement between the model and the gold standard was substantial (kappa value, 0.719). The accuracy of the model for inflammatory lesions (92.5 %) was superior to that of the mid level (88.1 %) and junior (86.3 %) groups (P < 0.001). For malignant lesions, the accuracy of the model (B1, 87.6 %; B2, 93.9 %) was significantly higher than that of the mid level (B1, 79.1 %; B2, 90.0 %) and junior (B1, 69.2 %; B2, 79.3 %) groups (P < 0.001).Conclusions: Double-labeling FCN automated IPCL recognition was feasible and could facilitate early detection of ESCC. [ABSTRACT FROM AUTHOR]