In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion-based Regularized Least Squares for MiRNA-Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA-disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave-one-out cross-validation (LOOCV) and 5-fold cross-validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5-fold cross-validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

Domain-based Comparative Analysis of Bacterial Proteomes: Uniqueness, Interactions, and the Dark Matter.

BACKGROUND: Proteins may have none, single, double, or multiple domains, while a single domain may appear in multiple proteins. Their distribution patterns may have impacts on bacterial physi-ology and lifestyle.Objective: This study aims to understand how domains are distributed and duplicated in bacterial prote-omes, in order to better understand bacterial physiology and lifestyles. METHODS: In this study, we used 16712 Hidden Markov Models to screen 944 bacterial reference prote-omes versus a threshold E-value<0.001. The number of non-redundant domains and duplication rates of redundant domains for each species were calculated. The unique domains, if any, were also identified for each species. In addition, the properties of no-domain proteins were investigated in terms of physico-chemical properties. RESULTS: The increasing number of non-redundant domains for a bacterial proteome follows the trend of an asymptotic function. The domain duplication rate is positively correlated with proteome size and in-creases more rapidly. The high percentage of single-domain proteins is more associated with small pro-teome size. For each proteome, unique domains were also obtained. Moreover, no-domain proteins show differences with the other three groups for several physicochemical properties analysed in this study. CONCLUSION: The study confirmed that a low domain duplication rate and a high percentage of single-domain proteins are more likely to be associated with bacterial host-dependent or restricted niche-adapted lifestyle. In addition, the unique lifestyle and physiology were revealed based on the analysis of species-specific domains and core domain interactions or co-occurrences.

Genetic signatures of plant resistance genes with known function within and between species.

Plant disease resistance (R) genes have undergone significant evolutionary divergence to cope with rapid changes in pathogens. These highly variable evolutionary patterns may have contributed to diversity in R gene protein families or structures. Here, the evolutionary patterns of 76 identified R genes and their homologs were investigated within and between plant species. Results demonstrated that nucleotide binding sites and leucine-rich-repeat genes located in loci with complex evolutionary histories tended to evolve rapidly, have high variation in copy numbers, exhibit high levels of nucleotide variation and frequent gene conversion events, and also exhibit high non-synonymous to synonymous substitution ratios in LRR regions. However, non-NBS-LRR R genes are relatively well conserved with constrained variation and are more likely to participate in the basic defense system of hosts. In addition, both conserved and highly divergent evolutionary patterns were observed for the same R genes and were consistent with inter- and intra-specific distributions of some R genes. These results thus indicate either continuous or altered evolutionary patterns between and within species. The present investigation is the first attempt to investigate evolutionary patterns among all clearly functional R genes. The results reported here thus provide a foundation for future plant disease studies.

Chlorogenic Acid Alleviates Chronic Stress-Induced Intestinal Damage by Inhibiting the P38MAPK/NF-κB Pathway.

Chronic stress can cause intestinal barrier damage. MAPK and NF-κB are closely related to it. Chlorogenic acid (CGA), a dietary polyphenol, has been shown to have intestinal protective effects, but whether by regulating MAPK and NF-κB is not known. Therefore, in this experiment, 24 Wistar rats were randomly divided into 4 groups (C group, CS group, CS + SB203580, and CS + CGA group). Rats in the CS group were restrained stress for 6 h per day for 21 days. Rats in the CS + SB203580 group were given SB203582 (0.5 mg/kg, intraperitoneal injection) 1 h before restraint stress every other day. Rats in the CS + CGA group were given CGA (100 mg/kg, gavage) 1 h before restraint stress. In chronic stress, intestinal barrier damage was evident, while being restored after CGA treatment. After chronic stress, the levels of p-P38 were increased (P < 0.01), while the levels of p-JNK and p-ERK were not changed. The levels of p-p38 were elevated after CGA treatment (P < 0.01). These results suggested that p38MAPK played an important role in chronic stress-induced intestinal injury, and CGA could inhibit p38MAPK activity. Therefore, we chose SB203582 (P38MAPK inhibitor) to elucidate the role of p38. After chronic stress, intestinal tight junction key proteins Occludin, ZO-1, and Claudin3 protein and gene expression were reduced (P < 0.01), while being elevated after CGA or SB203582 intervention (P < 0.05). After CGA treatment, the levels of p-IκB, p-p65, p-p38, and TNF-α were reduced (P < 0.01). SB203582 intervention reduced p-p65 and TNF-α levels significantly (P < 0.01). These results suggested that CGA could inhibit the NF-κB pathway by suppressing p38MAPK, thereby alleviating chronic stress-induced intestinal damage.

Application of mesenchymal stem cells as a vehicle to deliver replication-competent adenovirus for treating malignant glioma.

Although gene therapy was regarded as a promising approach for glioma treatment, its therapeutic efficacy was often disappointing because of the lack of efficient drug delivery systems. Mesenchymal stem cells(MSCs) have been reported to have a tropism for brain tumors and thus could be used as delivery vehicles for glioma therapy. Therefore, in this study, we attempted to treat glioma by using MSCs as a vehicle for delivering replication-competent adenovirus. We firstly compared the infectivity of type 3, type 5, and type 35 fiber-modified adenoviruses in MSCs. We also determined suitable adenovirus titer in vitro and then used this titer to analyze the ability of MSCs to deliver replication-competent adenovirus into glioma in vivo. Our results indicated that type 35 fiber-modified adenovirus showed higher infectivity than did naked type 3 or type 5 fiber-modified adenovirus. MSCs carrying replication-competent adenovirus significantly inhibited tumor growth in vivo compared with other control groups. In conclusion, MSCs are an effective vehicle that can successfully transport replication-competent adenovirus into glioma, making it a potential therapeutic strategy for treating malignant glioma.

Distinctive gene expression patterns in pregnancy-associated breast cancer.

Pregnancy-associated breast cancer (PABC) is diagnosed during pregnancy or within 1 year postpartum, but the unique aspects of its etiology and pathogenesis have not been fully elucidated. This study aimed to ascertain the molecular mechanisms of PABC to facilitate diagnosis and therapeutic development. The Limma package was used to characterize the differentially expressed genes in PABC as compared to non-pregnancy-associated breast cancer (NPABC) and normal breast tissue. A total of 871 dysregulated genes were identified in the PABC versus NPABC groups and 917 in the PABC versus normal groups, with notable differences in the expression of MAGE and CXCL family genes. The dysregulated genes between the PABC and normal groups were mainly associated with signal transduction and immune response, while Kyoto Encyclopedia of Genes and Genomes analysis revealed that the dysregulated genes were enriched in immune-related pathways, including the major histocompatibility complex (MHC) class II protein complex, the type I interferon signaling pathway, regulation of α-ß T-cell proliferation, and the T-cell apoptotic process. Through protein-protein interaction network construction, CD44 and BRCA1 were identified as prominent hub genes with differential expression in PABC versus NPABC. Furthermore, a cluster with eleven hub genes was identified in PABC versus normal adjacent tissues, of which the expression of EGFR, IGF1, PTGS2, FGF1, CAV1, and PLCB1 were verified to be differentially expressed in an independent cohort of PABC patients. Notably, IGF1, PTGS2, and FGF1 were demonstrated to be significantly related to patient prognosis. Our study reveals a distinctive gene expression pattern in PABC and suggests that IGF1, PTGS2, and FGF1 might serve as biomarkers for diagnosis and prognosis of PABC.

Combining Aspartate Aminotransferase-to-Platelet Ratio Index with Future Liver Remnant to Assess Preoperative Hepatic Functional Reserve in Patients with Hepatocellular Carcinoma.

BACKGROUND: Accurate preoperative assessment of hepatic functional reserve is essential for conducting a safe hepatectomy. In recent years, aspartate aminotransferase-to-platelet ratio index (APRI) has been used as a noninvasive model for assessing fibrosis stage, hepatic functional reserve, and prognosis after hepatectomy with a high level of accuracy. The purpose of this research was to evaluate the clinical value of combining APRI with standardized future liver remnant (sFLR) for predicting severe post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). METHODS: Six hundred thirty-seven HCC patients who had undergone hepatectomy were enrolled in this study. The performance of the Child-Pugh (CP) grade, model for end-stage liver disease (MELD), APRI, sFLR, and APRI-sFLR in predicting severe PHLF was assessed using the area under the ROC curve (AUC). RESULTS: Severe PHLF was found to have developed in 101 (15.9%) patients. Multivariate logistic analyses identified that prealbumin, cirrhosis, APRI score, sFLR, and major resection were significantly associated with severe PHLF. The AUC values of the CP, MELD, APRI, and sFLR were 0.626, 0.604, 0.725, and 0.787, respectively, indicating that the APRI and sFLR showed significantly greater discriminatory abilities than CP and MELD (P < 0.05 for all). After APRI was combined with sFLR, the AUC value of APRI-sFLR for severe PHLF was 0.816, which greatly improved the prediction accuracy, compared with APRI or sFLR alone (P < 0.05 for all). Stratified analysis using the status of cirrhosis and extent of resection yielded similar results. Moreover, the incidence and grade of PHLF were significantly different among the three risk groups. CONCLUSION: The combination of APRI and sFLR can be considered to be a predictive factor with increased accuracy for severe PHLF in HCC patients, compared with CP grade, MELD, APRI, or sFLR alone.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review.

In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.

As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model's average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma.

BACKGROUND: Testing for the presence of liver cirrhosis (LC) is one of the most critical diagnostic and prognostic assessments for patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). More non-invasive tools are needed to diagnose LC but the predictive abilities of current models are still inconclusive. This study aimed to develop and validate a novel and non-invasive artificial neural network (ANN) model for diagnosing LC in patients with HBV-related HCC using routine laboratory serological indicators. METHODS: A total of 1152 HBV-related HCC patients who underwent hepatectomy were included and randomly divided into the training set (n = 864, 75%) and validation set (n = 288, 25%). The ANN model was constructed from the training set using multivariate Logistic regression analysis and then verified in the validation set. RESULTS: The morbidity of LC in the training and validation sets was 41.2% and 46.8%, respectively. Multivariate analysis showed that age, platelet count, prothrombin time and total bilirubin were independent risk factors for LC (P < 0.05). The area under the ROC curve (AUC) analyses revealed that the ANN model had higher predictive accuracy than the Logistic model (ANN: 0.757 vs Logistic: 0.721; P < 0.001), and other scoring systems (ANN: 0.757 vs CP: 0.532, MELD: 0.594, ALBI: 0.575, APRI: 0.621, FIB-4: 0.644, AAR: 0.491, and GPR: 0.604; P < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set. CONCLUSION: The ANN model has better diagnostic capabilities than other commonly used models and scoring systems in assessing LC risk in patients with HBV-related HCC.

Identification of claudin­1, ­3, ­7 and ­8 as prognostic markers in human laryngeal carcinoma.

Various genomic and epigenetic modifications that occur during the development of cancer act as potential biomarkers for early diagnosis and treatment. Previous studies have demonstrated abnormal expression of the claudin (CLDN) tight junction (TJ) proteins in numerous types of human cancer. Reverse transcription­quantitative polymerase chain reaction and western blotting were employed to investigate variations in the expression of the CLDN TJ proteins in laryngeal non­neoplastic tissues and laryngeal squamous carcinoma tissues. It was revealed that CLDN2, CLDN4, CLDN5, CLDN6, CLDN9, CLDN11 and CLDN12 were undetectable in laryngeal squamous carcinoma tissues and laryngeal non­neoplastic tissues. Additionally, CLDN10 was expressed in laryngeal squamous carcinoma tissues and laryngeal non­neoplastic tissues; however, no significant difference was reported. Conversely, the expression levels of CLDN1 and CLDN7 mRNA and protein were downregulated in laryngeal squamous carcinoma tissues compared with in adjacent non­neoplastic tissues, whereas those of CLDN3 and CLDN8 were upregulated. A total of 80 samples of laryngeal squamous carcinoma and non­neoplastic tissues were analyzed for the expression of CLDN1, ­3, ­7 and ­8 via streptavidin­peroxidase immunohistochemical staining. It was revealed that the expression levels of CLDN1 and CLDN7 were downregulated in laryngeal squamous carcinoma tissues compared with in non­neoplastic mucosal tissues, whereas those of CLDN3 and CLDN8 were upregulated. Furthermore, the associations between CLDN expression and the clinicopathological factors of patients were analyzed. The expression levels of CLDN3 and CLDN7 were reported to be associated with distant metastasis and serve as potential predictors of poor prognosis. In conclusion, the findings of the present study demonstrated that the expression levels of CLDN1, ­3, ­7 and ­8 varied between laryngeal squamous carcinoma tissues and non­neoplastic tissues. The expression levels of these CLDNs may be useful molecular markers for the diagnosis of laryngeal carcinoma, and determining the metastasis and prognosis of this disease.

Systematic Analysis of Metabolic Pathway Distributions of Bacterial Energy Reserves.

Previous bioinformatics studies have linked gain or loss of energy reserves with host-pathogen interactions and bacterial virulence based on a comparatively small number of bacterial genomes or proteomes. Thus, understanding the theoretical distribution patterns of energy reserves across bacterial species could provide a shortcut route to look into bacterial lifestyle and physiology. So far, five major energy reserves have been identified in bacteria due to their capacity to support bacterial persistence under nutrient deprivation conditions. These include polyphosphate (polyP), glycogen, wax ester (WE), triacylglycerol (TAG), and polyhydroxyalkanoates (PHAs). Although the enzymes related with metabolism of energy reserves are well understood, there is a lack of systematic investigations into the distribution of bacterial energy reserves from an evolutionary point of view. In this study, we sourced 8282 manually reviewed bacterial reference proteomes and combined a set of hidden Markov sequence models (HMMs) to search homologs of key enzymes related with the metabolism of energy reserves. Our results revealed that specific pathways like trehalose-related glycogen metabolism and enzymes such as wax ester synthase/acyl-CoA:diacylglycerol acyltransferase (WS/DGAT) are mainly restricted within specific types of bacterial groups, which provides evolutionary insights into the understanding of their origins and functions. In addition, the study also confirms that loss of energy reserves like polyP metabolism absence in Mollicutes is correlated with bacterial genome reduction. Through this analysis, a clearer picture about the metabolism of energy reserves in bacteria is presented, which could serve as a guide for further theoretical and experimental analyses of bacterial energy metabolism.

Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.

Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the "omic" diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.

Comparative Transcriptomics and Proteomics of Atractylodes lancea in Response to Endophytic Fungus Gilmaniella sp. AL12 Reveals Regulation in Plant Metabolism.

The fungal endophyte Gilmaniella sp. AL12 can establish a beneficial association with the medicinal herb Atractylodes lancea, and improve plant growth and sesquiterpenoids accumulation, which is termed "double promotion." Our previous studies have uncovered the underling primary mechanism based on some physiological evidences. However, a global understanding of gene or protein expression regulation in primary and secondary metabolism and related regulatory processes is still lacking. In this study, we employed transcriptomics and proteomics of Gilmaniella sp. AL12-inoculated and Gilmaniella sp. AL12-free plants to study the impact of endophyte inoculation at the transcriptional and translational levels. The results showed that plant genes involved in plant immunity and signaling were suppressed, similar to the plant response caused by some endophytic fungi and biotroph pathogen. The downregulated plant immunity may contribute to plant-endophyte beneficial interaction. Additionally, genes and proteins related to primary metabolism (carbon fixation, carbohydrate metabolism, and energy metabolism) tended to be upregulated after Gilmaniella sp. AL12 inoculation, which was consistent with our previous physiological evidences. And, Gilmaniella sp. AL12 upregulated genes involved in terpene skeleton biosynthesis, and upregulated genes annotated as ß-farnesene synthase and ß-caryophyllene synthase. Based on the above results, we proposed that endophyte-plant associations may improve production (biomass and sesquiterpenoids accumulation) by increasing the source (photosynthesis), expanding the sink (glycolysis and tricarboxylic acid cycle), and enhancing the metabolic flux (sesquiterpenoids biosynthesis pathway) in A. lancea. And, this study will help to further clarify plant-endophyte interactions.

Natural history of luminal A breast invasive ductal carcinoma in an elderly.

An elderly woman, with no clinically palpable breast lump, presented with an initially benign-appearing lesion on ultrasound. She was followed up to reveal the natural history of luminal A breast cancer.

[Distribution patterns of PAHs in soils from coking plant and the particle-size cut points of soil washing].

Soil particle size distribution and contaminants distribution patterns in different soil size fractions are the basis of soil treatability using soil washing method. Soil particle-size cut points are important parameters of soil washing process. According to ex situ soil washing technology, soil samples were collected in a former coking plant. The soil particle size distribution and the concentrations of 16 polycyclic aromatic hydrocarbons (PAHs) in USEPA priority list were analyzed. Tween 80 and Triton X-100 solutions were used to clean the polluted soil with different particle size. Results showed that the total concentrations of 16 PAHs ranged from 6.27 to 40.18 mg/kg dry weight in the six soil size fractions and present a bimodal distribution. The maximum individual PAH concentration mostly occurred in the 250-500 microm size fraction. The lowest individual PAH concentration was in the 50-75 microm size fraction. The removal efficiencies of PAHs in different soil size fractions depended on their initial concentrations and the characteristics of soil. The PAHs removal efficiencies in coarser size fractions were lower than that in the finer size fractions owing to their higher organic carbon content. Based on the removal efficiency of PAHs in each soil size fractions by surfactant solution and the requirements of waste volume reduction, 50 microm was determined as the particle-size cut point. Then, 82.95% volume reduction can be achieved.

[Effect of acupuncture on blood oxygen saturation in patients of obstructive sleep apnea-hypopnea syndrome].

OBJECTIVE: To investigate the effect of acupuncture on blood oxygen saturation in the patient of obstructive sleep apnea-hypopnea syndrome (OSAHS) in sleeping and to evaluate the therapeutic effect of acupuncture on this disease. METHODS: Thirty cases with OSAHS were treated with acupuncture at Shanglianquan (Extra), Fengfu (GV 16), Yamen (GV 15), Fengchi (GB 20), etc. 3-5 sessions each week. After treatment of 30 sessions, apneahypopnea index (AHI), mean blood oxygen saturation (MSaO2), the lowest blood oxygen saturation (LSaO2), oxygen desaturation > or = 4% index (ODI4), the mean blood oxygen saturation of oxygen desaturation when SaO2 < 90%, the longest time of oxygen saturation > or = 4% were observed before and after treatment. RESULTS: The effective rate of acupuncture was 23.3% for OSAHS. After acupuncture, AHI and ODI4 significantly reduced (P < 0.01); LSaO2 significantly increased (P < 0.01); MSaO2 and the mean blood oxygen saturation of oxygen desaturation when SaO2 < 90% significantly enhanced (P < 0.05); the longest time of oxygen saturation > or = 4% did not significantly change. CONCLUSION: The acupuncture treatment has intervenient effect on OSAHS and alleviates anoxia, so acupuncture is one of therapies improving anoxia in patients of OSAHS.