Project description:PurposeThe purpose of this study is to study the effects of heat-clearing Traditional Chinese Medicine (TCM) in the stable stage of bronchiectasis via N-of-1 trials.MethodsThe N-of-1 trials in this study were randomized and double-blinded with crossover comparisons consisting of three pairs. Each pair was of two 4-week periods. Each patient took the individualized decoction in the experimental period and the individualized decoction was removed of heat-clearing drugs, mainly including heat-clearing and detoxifying drugs, in the control period for three weeks. After three weeks, the patients stopped taking the decoction for one week. The primary outcome was from patients' self-reporting symptoms scores on a 1-7-point Likert scale. Mixed-effects models were used to conduct statistical analysis on these N-of-1 trials.ResultsOf the 21 patients enrolled, 15 completed three pairs of N-of-1 trials (71.43%). (1) Seen from the individual level, no statistical difference between the experimental decoction and the control (P > 0.05) was observed. However, 5 patients found better decoctions according to the clinical criteria. (2) As revealed by the group data of all the N-of-1 trials, the control was better than the individualized decoction in terms of symptom scores on the Likert scale (1.94 ± 0.69 versus 2.08 ± 0.68, P = 0.04, mean difference, and 95% CI: 0.19 (0.01, 0.37)) and on CAT scores (13.66 ± 6.57 versus 13.95 ± 6.97, P = 0.04, mean difference, and 95% CI: 0.86 (0.042, 1.67)), but such differences were not clinically significant. The other outcomes, such as Likert scale score of respiratory symptoms and 24-hour sputum volume, showed no statistical difference.ConclusionThe experimental design of this study can make the TCM individualized treatment fully play its role and can detect the individualized tendencies according to the severity of phlegm and heat in some subjects. With the intermittent use or reduced use of heat-clearing drugs, most of the subjects, at the group level, enrolled in the series of N-of-1 trials may improve the symptoms and quality of life while saving the cost of TCM and reducing the potential side effects of heat-clearing TCM. This trial is registered with clinicaltrials.goc (NCT03147443).

Project description:BackgroundAction ("gongxiao" in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties ("yaoxing" in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory.MethodsOne hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction.ResultsAs results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered.ConclusionsThe performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Project description:BackgroundIn this study, the association between human papillomavirus (HPV) infection and related cervical intraepithelial neoplasia (CIN) or cervical cancer and vaginal microbiome was evaluated in Chinese cohorts.MethodsThe vaginal bacterial composition of five groups, HPV-infected women without CINs (HPV, n = 78), women with low-grade squamous intraepithelial lesions (LSIL, n = 51), women with high-grade squamous intraepithelial lesions (HSIL, n = 23), women with invasive cervical cancer (Cancer, n = 9) and healthy women without HPV infection (Normal, n = 68), was characterized by deep sequencing of barcoded 16S rRNA gene fragments (V3-4) using Illumina MiSeq.ResultsHPV infection increased vaginal bacterial richness and diversity regardless of the status of CINs. The vaginal bacterial richness and diversity were further augmented in women with cervical cancer. Lactobacillus was the most abundant genus in all groups. HPV infection had a negative influence on the abundances of Lactobacillus, Gardnerella and Atopobium. Accordingly, HPV infection increased the relative abundance of Prevotella, Bacillus, Anaerococcus, Sneathia, Megasphaera, Streptococcus and Anaerococcus. The increased proportions of Bacillus, Anaerococcus and the reduced abundance of Gradnerella vaginalis were probably related with the progression of CINs severity. HPV infection without CINs or cancerous lesions was strongly associated with Megasphaera. The most abundant bacterium in the LSIL group was Prevotella amnii. However, Prevotella timonensis, Shuttleworthia and Streptococcaceae at the family level were three taxa related to HSIL. Furthermore, more taxa were associated with the Cancer group including Bacillus, Sneathia, Acidovorax, Oceanobacillus profundus, Fusobacterium, Veillonellaceae at the family level, Anaerococcus and Porphyromonas uenonis. Samples in the Normal group were mostly assigned to CST III. HPV infection converted the vaginal bacterial community structure from CST III to CST IV. Furthermore, the proportions of CST IV were gradually augmented with the progression of the severity of CINs.ConclusionsThis work interpreted the differential vaginal bacteria under HPV infection and various precancerous or cancerous lesions in a Chinese cohort. We distinguished the specific microbes and the vaginal bacterial structure that were related with the progression of CINs severity in Chinese women.

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Effect of heat-clearing and dampness-eliminating Chinese medicine for high-risk cervical cancer papillomavirus infection: a systematic review and meta-analysis of randomized controlled trials

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