Synthesis of Nitrones and Nitroalkanes via Chiral Cyclic Imines.

A series of structurally chiral cyclic imines efficiently yields chiral nitrones and nitroalkanes. This is the first report of the synthesis of nitro groups by C═N bond cleavage of imines through a nitrone intermediate.

Active Intra-Abdominal Drainage Following Abdominal Digestive System Surgery: A Meta-Analysis and Systematic Review.

BACKGROUND: Our objective is to compare the early outcomes associated with passive (gravity) drainage (PG) and active drainage (AD) after surgery. METHODS: Studies published until April 28, 2022 were retrieved from the PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, Web of Science databases. RESULTS: Nine studies with 14,169 patients were identified. Two groups had the same intra-abdominal infection rate (RR: 0.55; P = 0.13); In subgroup analysis of pancreaticoduodenectomy, active drainage had no significant effect on postoperative pancreatic fistula (POPF) rate (RR: 1.21; P = 0.26) and clinically relevant POPF (CR-POPF) (RR: 1.05; P = 0.72); Active drainage was not associated with lower percutaneous drainage rate (RR: 1.00; P = 0.96), incidence of sepsis (RR: 1.00; P = 0.99) and overall morbidity (RR: 1.02; P = 0.73). Both groups had the same POPF rate (RR: 1.20; P = 0.18) and CR-POPF rate (RR: 1.20; P = 0.18) after distal pancreatectomy. There was no difference between two groups on the day of drain removal after pancreaticoduodenectomy (Mean difference: -0.16; P = 0.81) and liver surgery (Mean difference: 0.03; P = 0.99). CONCLUSIONS: Active drainage is not superior to passive drainage and both drainage methods can be considered.

Identifying Liver Metastasis-Related Genes Through a Coexpression Network to Construct a 5-Gene Model for Predicting Pancreatic Ductal Adenocarcinoma Patient Prognosis.

OBJECTIVES: This study aimed to develop a liver metastasis-related gene prognostic index (LMPI) for pancreatic ductal adenocarcinoma prognosis and therapy. METHODS: The Cancer Genome Atlas data set was used to identify liver metastasis-related hub genes via weighted gene coexpression network analysis. The core genes were identified to construct an LMPI by using the Cox regression method. An immune cell abundance identifier was applied to determine the immune cell abundance. RESULTS: A total of 78 hub liver metastasis-related genes in the black module were significantly enriched in complement and coagulation cascades, fat digestion and absorption, and the PPAR signaling pathway. Then, an LMPI was constructed on the basis of the 5 prognostic genes (MOGAT3, ASGR1, TRPM8, SGSM1, and LOC101927851). Patients with higher LMPI scores had poor overall survival, more co-occurring or mutually exclusive pairs of driver gene mutations, and less benefit from immunotherapy than patients with lower LMPI scores. In addition, a high correlation was also found between LMPI scores and immune infiltration, such as CD4 naive, CD8 T, cytotoxic T, T helper 2, follicular helper T, and natural killer cells. CONCLUSIONS: The core genes of the LMPI developed may be independent factors for predicting prognosis, immune characteristics, and immunotherapy efficacy in pancreatic ductal adenocarcinoma.

Degradation of xylan by human gut Bacteroides xylanisolvens XB1A.

Although many polysaccharides utilization loci (PULs) have been investigated by genomics and transcriptomics, the detailed functional characterization lags severely behind. We hypothesize that PULs on the genome of Bacteroides xylanisolvens XB1A (BX) dictate the degradation of complex xylan. To address, xylan S32 isolated from Dendrobium officinale was employed as a sample polysaccharide. We firstly showed that xylan S32 promoted the growth of BX which might degrade xylan S32 into monosaccharides and oligosaccharides. We further showed that this degradation was performed mainly via two discrete PULs in the genome of BX. Briefly, a new surface glycan binding protein (SGBP) BX_29290SGBP was identified, and shown to be essential for the growth of BX on xylan S32. Two cell surface endo-xylanases Xyn10A and Xyn10B cooperated to deconstruct the xylan S32. Intriguingly, genes encoding Xyn10A and Xyn10B were mainly distributed in the genome of Bacteroides spp. In addition, BX metabolized xylan S32 to produce short chain fatty acids (SCFAs) and folate. Taken together, these findings provide new evidence to understand the food source of BX and the BX-directed intervention strategy by xylan.

Effects of Nemacystus decipiens polysaccharide on mice with antibiotic associated diarrhea and colon inflammation.

Antibiotic associated diarrhea (AAD) is a common side effect of antibiotic therapy in which gut microbiota plays an important role in the disease. However, the function of gut microbiota in this disease is still not entirely clear. Polysaccharides have shown strong activity in shaping gut microbiota. Whether the polysaccharide can intervene with the microbiota to improve ADD has not been determined. In this study, we extract crude polysaccharides from Nemacystus decipiens (N. decipiens), a traditional Chinese medicine (TCM), named NDH0. The crude polysaccharide NDH0 might significantly relieve the symptom of mice with AAD, including a reduction in body weight, shortening of cecum index and the infiltration of inflammatory cells into the colon. NDH0-treated mice exhibited more abundant gut microbial diversity; significantly increased the abundance of Muribaculum, Lactobacillus, and Bifidobacterium and decreased the abundance of Enterobacter and Clostridioides at genus level. NDH0 treatment down-regulated the level of pro-inflammatory cytokines, including IL-1ß and IL-6 in colon tissue. NDH0 protected the integrity of colon tissues and partially inactivated the related inflammation pathway by maintaining occludin and SH2-containing Inositol 5'-Phosphatase (SHIP). NDH0 could alleviate symptoms of diarrhea by modulating gut microbiota composition, improving intestinal integrity and reducing inflammation. The underlying protective mechanism was to reduce the abundance of opportunistic pathogens and maintain SHIP protein expression. Collectively, our results demonstrated the role of NDH0 as a potential intestinal protective agent in gut dysbiosis.

A highly efficient needle-free-injection delivery system for mRNA-LNP vaccination against SARS-CoV-2.

Despite the various vaccines that have been developed to combat the coronavirus disease 2019 (COVID-19) pandemic, the persistent and unpredictable mutations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) require innovative and unremitting solutions to cope with the resultant immune evasion and establish a sustainable immune barrier. Here we introduce a vaccine-delivery system with a combination of a needle-free injection (NFI) device and a SARS-CoV-2-Spike-specific mRNA-Lipid Nanoparticle (LNP) vaccine. The benefits are duller pain and a significant increase of immunogenicity compared to the canonical needle injection (NI). From physicochemical and bioactivity analyses, the structure of the mRNA-LNP maintains stability upon NFI, contradictory to the belief that LNPs are inclined towards destruction under the high-pressure conditions of NFI. Moreover, mRNA-LNP vaccine delivered by NFI induces significantly more binding and neutralizing antibodies against SARS-CoV-2 variants than the same vaccine delivered by NI. Heterogeneous vaccination of BA.5-LNP vaccine with NFI enhanced the generation of neutralizing antibodies against Omicron BA.5 variants in rabbits previously vaccinated with non-BA.5-specific mRNA-LNP or other COVID-19 vaccines. NFI parameters can be adjusted to deliver mRNA-LNP subcutaneously or intramuscularly. Taken together, our results suggest that NFI-based mRNA-LNP vaccination is an effective substitute for the traditional NI-based mRNA-LNP vaccination.

Development of a Gene Risk Signature for Patients of Pancreatic Cancer.

BACKGROUND: Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment. METHODS: Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan-Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses. RESULT: A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival. CONCLUSION: This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.

Comparison of early postoperative outcomes between omega-like duct-to-mucosa pancreatojejunostomy and conventional duct-to-mucosa pancreatojejunostomy after pancreaticoduodenectomy.

BACKGROUND: Pancreatic fistula is a life-threatening complication of pancreaticoduodenectomy. Omega-like duct-to-mucosa pancreatojejunostomy is a novel technique which helps reduce the risk of fistulation. This study aimed to compare early postoperative outcomes of omega-like and conventional pancreatojejunostomy. METHODS: A retrospective single-centre cohort study comparing outcomes of adult patients who underwent open pancreatoduodenectomy with conventional (CDMP) or omega-like duct-to-mucosa pancreatojejunostomy (ODMP) between 1 January 2015 and 31 December 2019. The primary outcome measure was the pancreatic fistula rate. RESULTS: 440 patients were included in this study of whom 233 underwent CDMP and 207 ODMP. The rate of clinically relevant pancreatic fistula (grade B/C) was significantly higher after CDMP than ODMP (18.5% vs. 10.6%, P = 0.021). 153 patients in CDMP group and 99 patients in ODMP group developed one or more complications (65.7% vs. 47.8%, P = 0.004). The average hospitalization expenses were numerically decreased in ODMP group, although this was not statistically significant (120,000 ± 42,000 [Chinese Yuan] vs. 100,000 ± 40,000 [Chinese Yuan] or 18,581 ± 6503 [United States Dollar] vs. 15,484 ± 6194 [United States Dollar], P = 0.402). CONCLUSION: ODMP may reduce the incidence of pancreatic fistula and other early postoperative complications after pancreatoduodenectomy.

Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning.

BACKGROUND: Due to the complexity and high heterogeneity of the acute exacerbation of chronic obstructive pulmonary disease (AECOPD), the guidelines (global initiative for chronic obstructive, GOLD) is unable to fully guide the treatment of AECOPD. OBJECTIVES: To provide a rapid treatment in line with the development of the AECOPD after admission. In this paper, we propose a multi-stage feature fusion (MSFF) framework combining machine learning to track the diseases deterioration risk of the AECOPD. METHODS: First, we identify 408 AECOPD patients as the study population. Then, feature segment and fusion methods are applied to generate the phased data set. Finally, human studies are designed to evaluate the performance of the MSFF framework. RESULTS: The experimental results show that the proposed framework is potential to obtain the full-process tracking of deterioration risk for the AECOPD patients. The proposed MSFF framework achieves a higher overall accuracy average and F1 scores than the four physician groups i.e., IM, Surgery, Emergency, and ICU. CONCLUSIONS: The proposed MSFF model may serve as a useful disease tracking tool to estimate the deterioration risk at each stage, and finally achieve the disease monitoring and management for AECOPD patients.

Identification of novel hub genes and lncRNAs related to the prognosis and progression of pancreatic cancer by microarray and integrated bioinformatics analysis.

BACKGROUND: Pancreatic cancer (PC) is one of the most invasive and metastatic neoplasms among the fatal malignancies of the digestive system. Abnormal expression of genes and long non-coding RNAs (lncRNAs) are reportedly linked to multiple cancers. However, the lncRNA-mRNA expression profiles and their molecular mechanisms in PC progression are poorly known. This study aimed to map the hub genes and lncRNAs which might play core roles in the development of PC. METHODS: This study used microarray expression analysis to screen for both differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between PC and matched adjacent non-tumor (AN) tissues. In order to clarify the functional classification of DEGs, we conducted GO and KEGG pathway enrichment analyses via the Enrichr database. LncRNA-mRNA co-expressed networks were also constructed to explore the probable core regulating DEGs and DElncRNAs. Subsequently, the hub genes and lncRNAs were validated via the ONCOMINE and GEPIA databases and the co-expressed networks. RESULTS: By analyzing an mRNA-lncRNA microarray, we identified 943 mRNAs and 1,138 lncRNAs differentially expressed in PC tumors compared with the matched AN tissues. GO analysis confirmed that both up-regulated and down-regulated DEGs were enriched in multiple terms. The KEGG pathways enrichment analyses revealed that DEGs were mostly enriched in the focal adhesion and glutathione metabolism pathways, amongst others. Co-expressed networks were established to reveal the differential interactions between DEGs and DElncRNAs, and to indicate the core regulatory factors located at the core nodes of the co-expressed networks. The expression levels of potential core-regulating DEGs were validated by the GEPIA and ONCOMINE databases, and the relationship between overall survival and tumor stage and the potential core-regulating DEGs was analyzed using the GEPIA database. As a result, five genes and sixteen lncRNAs were finally considered as the hub transcripts in PC. CONCLUSIONS: This study identified DEGs and DElncRNAs between PC tumors and matched AN tissues, and these transcripts were connected with malignant phenotypes in PC through different BPs and signaling pathways. Furthermore, five hub genes and sixteen lncRNAs were identified, which are expected to represent candidate diagnostic biomarkers or potential therapeutic targets for PC.

An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients.

In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD.

Identification of exacerbation risk in patients with liver dysfunction using machine learning algorithms.

The prediction of the liver failure (LF) and its proper diagnosis would lead to a reduction in the complications of the disease and prevents the progress of the disease. To improve the treatment of LF patients and reduce the cost of treatment, we build a machine learning model to forecast whether a patient would deteriorate after admission to the hospital. First, a total of 348 LF patients were included from May 2011 to March 2018 retrospectively in this study. Then, 15 key clinical indicators are selected as the input of the machine learning algorithm. Finally, machine learning and the Model for End-Stage Liver Disease (MELD) are used to forecast the LF deterioration. The area under the receiver operating characteristic (AUC) of MELD, GLMs, CART, SVM and NNET with 10 fold-cross validation was 0.670, 0.554, 0.794, 0.853 and 0.912 respectively. Additionally, the accuracy of MELD, GLMs, CART, SVM and NNET was 0.669, 0.456, 0.794, 0.853 and 0.912. The predictive performance of the developed machine model execept the GLMs exceeds the classic MELD model. The machine learning method could support the physicians to trigger the initiation of timely treatment for the LD patients.

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study.

BACKGROUND: The overcrowding of hospital outpatient and emergency departments (OEDs) due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in the degradation in quality of medical care, and even limits its availability. OBJECTIVE: To help OED managers to schedule medical resource allocation during times of excessive health care demands after short-term fluctuations in air pollution and weather, we employed machine learning (ML) methods to predict the peak OED arrivals of patients with chronic respiratory diseases. METHODS: In this paper, we first identified 13,218 visits from patients with chronic respiratory diseases to OEDs in hospitals from January 1, 2016, to December 31, 2017. Then, we divided the data into three datasets: weather-based visits, air quality-based visits, and weather air quality-based visits. Finally, we developed ML methods to predict the peak event (peak demand days) of patients with chronic respiratory diseases (eg, asthma, respiratory infection, and chronic obstructive pulmonary disease) visiting OEDs on the three weather data and environmental pollution datasets in Guangzhou, China. RESULTS: The adaptive boosting-based neural networks, tree bag, and random forest achieved the biggest receiver operating characteristic area under the curve, 0.698, 0.714, and 0.809, on the air quality dataset, the weather dataset, and weather air quality dataset, respectively. Overall, random forests reached the best classification prediction performance. CONCLUSIONS: The proposed ML methods may act as a useful tool to adapt medical services in advance by predicting the peak of OED arrivals. Further, the developed ML methods are generic enough to cope with similar medical scenarios, provided that the data is available.

A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and prediction methods for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients during hospitalization. To enhance the monitoring and treatment of AECOPD patients, we develop a novel C5.0 decision tree classifier to predict the prognosis of AECOPD hospitalized patients with objective clinical indicators. The medical records of 410 hospitalized AECOPD patients are collected and 28 features including vital signs, medical history, comorbidities and various inflammatory indicators are selected. The overall accuracy of the proposed C5.0 decision tree classifier is 80.3% (65 out of 81 participants) with 95% Confidence Interval (CI):(0.6991, 0.8827) and Kappa 0.6054. In addition, the performance of the model constructed by C5.0 exceeds the C4.5, classification and regression tree (CART) model and the iterative dichotomiser 3 (ID3) model. The C5.0 decision tree classifier helps respiratory physicians to assess the severity of the patient early, thereby guiding the treatment strategy and improving the prognosis of patients.

Fast Prediction of Deterioration and Death Risk in Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease Using Vital Signs and Admission History: Retrospective Cohort Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has 2 courses with different options for medical treatment: the acute exacerbation phase and the stable phase. Stable patients can use the Global Initiative for Chronic Obstructive Lung Disease (GOLD) to guide treatment strategies. However, GOLD could not classify and guide the treatment of acute exacerbation as acute exacerbation of COPD (AECOPD) is a complex process. OBJECTIVE: This paper aimed to propose a fast severity assessment and risk prediction approach in order to strengthen monitoring and medical interventions in advance. METHODS: The proposed method uses a classification and regression tree (CART) and had been validated using the AECOPD inpatient's medical history and first measured vital signs at admission that can be collected within minutes. We identified 552 inpatients with AECOPD from February 2011 to June 2018 retrospectively and used the classifier to predict the outcome and prognosis of this hospitalization. RESULTS: The overall accuracy of the proposed CART classifier was 76.2% (83/109 participants) with 95% CI 0.67-0.84. The precision, recall, and F-measure for the mild AECOPD were 76% (50/65 participants), 82% (50/61 participants), and 0.79, respectively, and those with severe AECOPD were 75% (33/44 participants), 68% (33/48 participants), and 0.72, respectively. CONCLUSIONS: This fast prediction CART classifier for early exacerbation detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patients' health.

lncRNA H19 binds VGF and promotes pNEN progression via PI3K/AKT/CREB signaling.

Pancreatic neuroendocrine neoplasms (pNENs) are endocrine tumors arising in pancreas and is the most common neuroendocrine tumors. Mounting evidence indicates lncRNA H19 could be a determinant of tumor progression. However, the expression and mechanism of H19 and the relevant genes mediated by H19 in pNENs remain undefined. Microarray analysis was conducted to identify the differentially expressed lncRNAs in pNENs. H19 expression was analyzed in 39 paired pNEN tissues by qPCR. The biological role of H19 was determined by functional experiments. RNA pulldown, mass spectroscopy and RNA immunoprecipitation were performed to confirm the interaction between H19 and VGF. RNA-seq assays were performed after knockdown H19 or VGF. H19 was significantly upregulated in pNEN tissues with malignant behaviors, and the upregulation predicted poor prognosis in pNENs. In vitro and in vivo data showed that H19 overexpression promoted tumor growth and metastasis, whereas H19 knockdown led to the opposite phenotypes. H19 interacted with VGF, which was significantly upregulated in pNENs, and higher VGF expression was markedly related to poor differentiation and advanced stage. Furthermore, VGF was downregulated when H19 was knocked down, and VGF promoted cell proliferation, migration and invasion. Mechanistic investigations revealed that H19 activated PI3K/AKT/CREB signaling and promoted pNEN progression by interacting with VGF. These findings indicate that H19 is a promising prognostic factor in pNENs with malignant behaviors and functions as an oncogene via the VGF-mediated PI3K/AKT/CREB pathway. In addition, our study implies that VGF may also serve as a candidate prognostic biomarker and therapeutic target in pNENs.

Effects of Anthropometric Variables and Seat Height on Automobile Drivers' Preferred Posture With the Presence of the Clutch.

Objective The effects of seat height and anthropometric dimensions on drivers' preferred postures were investigated using a multiadjustable vehicle mock-up with a large number of adjustments and extended ranges. Background Many studies have been conducted on preferred driving posture under different test conditions showing mixed and even contradictory findings. No studies thus far have considered the clutch and compared Chinese and European drivers. Method Four seat height conditions were tested: free and three imposed heights (250, 300, and 350 mm). Sixty-one subjects (40 French-born and 21 Chinese-born) participated in the experiment, covering a large range of stature and sitting height-to-stature ratio. The RAMSIS kinematic model was used to reconstruct postures, and main intersegmental angles were extracted for characterizing posture. Results Under the free seat height condition, no significant differences in preferred intersegmental angles were observed between different participant groups. Seat height mainly affected trunk-thigh angle, whereas it had almost no effect on trunk orientation and other intersegmental angles. Chinese participants sat more forward in the seat, leading to a more opened trunk-thigh angle and a more reclined trunk. Conclusions Results suggest that intersegmental angles of preferred posture are not dependent on anthropometric dimensions, although shorter drivers prefer a slightly less reclined trunk. Self-selected driving posture results from a compromise between maintaining the intersegmental angles in one's preferred range and a preferred trunk orientation in space. Applications The findings contribute to a better understanding of preferred driving postures and would be helpful for improving vehicle interior design.

Ranges of the least uncomfortable joint angles for assessing automotive driving posture.

Few investigations have been performed on how the ranges of preferred angles should be used for vehicle interior discomfort evaluation. This study investigated the ranges of the least uncomfortable joint angles considering both inter-individual and intra-individual variability. The driving postures of sixty-one subjects were collected using two multi-adjustable vehicle mock-ups under four test conditions by gradually adding the number of control parameters (constraints), from the "least-constrained" driving condition to the configurations close to currently existing vehicles. With help of subjective discomfort evaluation, the intra-and inter-individual variation ranges of least uncomfortable postural angles were quantified. Results show that intra-individual variation ranges of postural angles were much smaller than those of inter-individual variation as expected. An individual may not feel comfortable throughout the whole range of comfortable angles from all participants. Possible relationships between perceived discomfort and ranges of inter and inter individual variations in least uncomfortable angles were explored, suggesting that the inter ranges could be used to detect potential problems of postural discomfort and the intra ranges could be considered as optimum ranges. A three color model, based on the intra-and inter-individual variability ranges of comfortable driving postures, was proposed for ergonomics assessment of a vehicle configuration.

Clinical research of the five needles combined with rehabilitation training treatment dysphagia after stroke.

To compare therapeutic effects of Five Needles of the Nape and routine acupuncture on treatment of pseudo bulbar paralysis dysphagia after Brain stroke. 60 patients were randomly divided into five key groups and routine acupuncture, and 30 cases in each group. The group of Five Needles of the Nape set points to take dumb door, Tianzhu, cure choke point with acupuncture treatment which cooperate with swallowing training . The group of routine acupuncture set points to take Lian Quan, Tong Li, Zhao Hai points with acupuncture treatment. Both groups were acupunctured once a day, 6 times a week and 2 weeks is a period of treatment, evaluating curative effect after two courses of treatment. The total effective rate of Five Needles of the Nape group was 93.3%, when the total effective rate of routine acupuncture group was 80.0%.as a consequence Five Needles of the Nape group is superior to routine acupuncture group (P<0.05). Five Needles of the Nape have better clinical efficacy than routine acupuncture on treatment of pseudo bulbar paralysis dysphagia after Brain stroke.