Premedical intervention before diseases is one of the principal practices in traditional Chinese medicine (TCM) for tackling health problems and diseases. This paper explains the preventive treatment principle in TCM regarding the subhealth condition following stress-induced pathophysiological situations, mainly oxidative and glycation stresses, in the framework of metabonomics and cell biology. Scientific interpretations for various preventive treatments in TCM present the idea that disease prevention should surpass medical intervention in humans. Antistress biology is proposed for the first time as a preclinical practice or strategy for antiaging.

Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions. A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model. There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models. Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.

Background: More than 70% of patients with depression who see their doctors experience insomnia. Insomnia treatment is a very important link for depression treatment. Furthermore, antidepression treatment is also important for depression insomnia. In acupuncture, LU-7 (Lie Que) and KID-6 (Zhao Hai), which are two of the eight confluence points in meridian theory, are used as main points. An embedded needle technique is used, alternately, at two groups of points to consolidate the treatment effect. These two groups of points are BL-15 (Xin Shu) with BL-23 (Shen Shu) and BL-19 (Dan Shu) with N-HN-54 (An Mian). The effectiveness of these optimized acupuncture formulas is well proven in the practice by our senior acupuncturists in Guangdong Provincial Hospital of TCM. This study has been designed to examine whether this set of optimized clinical formulas is able to increase the clinical efficacy of depression insomnia treatment. Methods/design: In this randomized controlled multicenter trial, all the eligible participants are diagnosed with depression insomnia. All participants are randomly assigned to one of two groups in a ratio of 1:1 and receive either conventional acupuncture treatment or optimized acupuncture treatment. Patients are evaluated using the Pittsburgh Sleep Quality Index(PSQI)and the Hamilton rating scale(HAMD) for depression. The use of antidepression and hypnotics drugs is also considered. Results are obtained at the start of treatment, 1 and 2 months after treatment has begun, and at the end of treatment. The entire duration of the study will be approximately 36 months. Discussion: A high quality of trial methodologies is utilized in the study, and the results may provide better evidence for the effectiveness of acupuncture as a treatment for depression insomnia. The optimized acupuncture formula has potential benefits in increasing the efficacy of treating depression insomnia. Trial registration: The trial was registered in Chinese Clinical Trial Register (ChiCR-TRC-00000481) on 12 August 2009.