Optimization of Escherichia coli shake flask culture medium by artificial neural network / 中国生物制品学杂志

@#Objective To optimize a shake flask culture medium for Escherichia coli(E.coli)with high biomass and viability using artificial neural networks(ANN). Methods Using the proportion of glucose(Glu),yeast extract(YE),yeast peptone(YP),soy peptone(SP)and yeast nitrogen base(YNB)as the mixture component,and the A_(600)(A1)value of cell suspension,wet bacterial weight(G,g/L)of culture and cell viability(A2,A_(460))as the response values,the mixture design was used to screen the mixture components that had a significant effect on the response value. The ANN model was constructed with the test results of mixture design as training and verification data samples. The input variables were mixture components and restricted the upper and lower limits of the mixture components,and the output variables were mixture design response values. The optimized medium formula and reference values were obtained by the constructed ANN. The medium formula was further adjusted by Monte Carlo simulation to obtain the shake flask medium formula of E.coli,which was then verified for 10 times. Results The shake flask culture medium of E.coli was composed of Glu 26 g/L,SP 26 g/L,YNB13 g/L with the total concentration of 65 g/L. The verification results showed that the probability of A1 ≥ 14 was 60%,the probability of G ≥ 77 g/L was 50% and the probability of A2 ≥ 11 was 40%. The mean values of the incubation result data were equivalent to the reference values. Conclusion The shake flask culture medium of E.coli optimized in this study can obtain E.coli with high biomass and bacterial activity.

An Artificial Neural Network Model Combined with Dietary Retinol Intake from Different Sources to Predict the Risk of Nonalcoholic Fatty Liver Disease / 生物医学与环境科学（英文）

OBJECTIVE@#This study aimed to develop an artificial neural network (ANN) model combined with dietary retinol intake from different sources to predict the risk of non-alcoholic fatty liver disease (NAFLD) in American adults.@*METHODS@#Data from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) 2007-2014 were analyzed. Eligible subjects ( n = 6,613) were randomly divided into a training set ( n 1 = 4,609) and a validation set ( n 2 = 2,004) at a ratio of 7:3. The training set was used to identify predictors of NAFLD risk using logistic regression analysis. An ANN was established to predict the NAFLD risk using a training set. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of the model using the training and validation sets.@*RESULTS@#Our study found that the odds ratios ( ORs) and 95% confidence intervals ( CIs) of NAFLD for the highest quartile of plant-derived dietary retinol intake (i.e., provitamin A carotenoids, such as β-carotene) ( OR = 0.75, 95% CI: 0.57 to 0.99) were inversely associated with NAFLD risk, compared to the lowest quartile of intake, after adjusting for potential confounders. The areas under the ROC curves were 0.874 and 0.883 for the training and validation sets, respectively. NAFLD occurs when its incidence probability is greater than 0.388.@*CONCLUSION@#The ANN model combined with plant-derived dietary retinol intake showed a significant effect on NAFLD. This could be applied to predict NAFLD risk in the American adult population when government departments formulate future health plans.

Comparison Of Artificial Neural Network and Decision Tree Methods for Predicting the Maternal Outcome in A Tertiary Care Hospital in Odisha, India

Background: This study used an artificial neural network (ANN) and a decision tree to predict maternal outcomes and their major determinants. An artificial neural network (ANN) and a decision tree were used in this study to determine maternal outcomes and their significant determinants. Methods: Data was gathered from 955 pregnant women at a tertiary care hospital in Bhubaneswar, Od-isha. A popular machine learning algorithm, artificial neural networks (ANN), was used to predict mater-nal outcomes and their determinants. Results: In the bivariate analysis, we found gestational age is significantly associated with maternal out-come (p=<0.001). The accuracy of the ANN model and decision tree was 0.882 and 0.823, respectively. Based on the variable importance of ANN, the significant determinants of maternal outcome were birth weight, systolic blood pressure, haemoglobin, gestational age, age of mother, diastolic blood pressure etc. Conclusion: This model can be utilized in future for Proper precautions and medical check-ups required during the maternal period to avoid a negative maternal outcome.

Clinical staging and prognostic risk factors for ocular adnexal lymphoma / 中华实验眼科杂志

Objective:To evaluate the influence of the clinical staging and different risk factors for the prognosis of ocular adnexal lymphoma.Methods:An ambispective cohort study was conducted.Seventy-four patients diagnosed with primary ocular adnexal lymphoma by pathology at Tianjin Medical University Eye Hospital from November 2010 to December 2018 were enrolled.TNM staging was performed according to local tumor extent, lymph node or systemic involvement.Ann Arbor staging was carried out according to lymph node involvement and extranodal extension.The pathological subtype was classified according to World Health Organization classification of lymphoma.The outcome of disease progression or death was analyzed.Kaplan-Meier method was used for univariate survival analysis.Cox proportional hazard model was employed for multivariate survival analysis to predict the risk factors affecting prognosis, hazard ratio ( HR) and 95% confidence interval ( CI) were estimated.This study adhered to the Declaration of Helsinki.The study protocol was approved by an Ethics Committee of Tianjin Medical University Eye Hospital (No.2021KY[L]-32). Written informed consent was obtained from all patients before entering the cohort. Results:For TNM staging, there were 68 cases in stage <T4, accounting for 91.9%, 6 cases in T4, accounting for 8.1%, 71 cases in N0, accounting for 95.9%, 3 cases in ≥N1, accounting for 4.1%, and no case was in stage M. For Ann Arbor staging, there were 72 cases in stage ⅠE, accounting for 97.3%, and 2 cases in stage ⅡE, accounting for 2.7%.As for pathological classification, 64 cases had mucosa-associated lymphoid tissue (MALT) lymphoma, accounting for 86.5% and 10 cases had non-MALT lymphoma, accounting for 13.5%.The follow-up of the patients was 3 to 117 months, with a median follow-up of 53 months.There were 6 cases dying of disease and 19 cases progressed.The 3-year and 5-year overall survival rates were 96.6% and 86.6%, respectively.The 3-year and 5-year progression-free survival rates were 75.6% and 65.9%, respectively.According to single-factor analysis, T4 stage, non-MALT type and Ki67 positive rate ≥10% were related to declined overall survival rate ( P<0.05). T4 stage, ≥N1 stage, ≥Ann Arbor Ⅱ stage, non-MALT type and Ki67 positive rate ≥10% were related to declined progression-free survival rate ( P<0.05). According to multiple-factor analysis, pathological type ( HR=33.193, 95% CI: 3.388-325.156, P=0.003) was the independent risk factor for overall survival rate.N stage ( HR=11.683, 95% CI: 2.720-50.173, P=0.001) and pathological type ( HR=11.337, 95% CI: 3.841-33.464, P<0.001) were independent risk factors for progression-free survival rate. Conclusions:TNM staging and pathological type are important clinical prognostic indicators for ocular adnexal lymphoma.Patients with high TNM stage or non-MALT lymphoma should be monitored closely.

Estimating Hydroxymethyfurfural (HMF) Concentration Via Modified Seliwanoff Test Using Artificial Neural Network (ANN)

Abstract Hydroxymethylfurfural (HMF) is a quality indicator, especially in foods where changes in protein-carbohydrate interactions are observed during the applied process. In this study absorbance and L*, a*, b* values of red color emerged due to the relationship between hydroxymethylfurfural (HMF) and resorcinol during the modified Seliwanoff test were used as input data artificial neural network (ANN) to determine the HMF concentration for the first time. A linear relationship, between HMF concentration and absorbance of red color, can be represented by equation absorbance = 0.0020 + 0.0012* concentration of HMF (mg L-1) with R2 = 99.6%, Fisher ratio: 0.18, p value of lack of fit: 0.975, correlation coefficient: 0.9960. Intra-day and inter-day precision expressed as relative standard deviation (RSD) %, were 2.35 - 3.65% and 3.16 - 4.73%, respectively. Recovery rates and RSDs were in the range of 99.34 - 100.47% and 1.58 - 3.68%. It showed high correlation compared to HPLC method used as reference method (0.998). The R2 values of ANN for estimation of HMF concentration were found 0.90 for training, 0.96 for validation, and 0.99 for testing and AARD was found 8.85%. Evaluation of the absorbance and L*, a*, b* values of the red color with artificial intelligence is a reliable way to determine the HMF concentration.

Milk fraud by the addition of whey using an artificial neural network / Identificação de fraude de leite por adição de soro de leite usando redes neurais artificiais

ABSTRACT: The adulteration of milk by the addition of whey is a problem that concerns national and international authorities. The objective of this research was to quantify the whey content in adulterated milk samples using artificial neural networks, employing routine analyses of dairy milk samples. The analyses were performed with different concentrations of whey (0, 5, 10, and 20%), and samples were analyzed for fat, non-fat solids, density, protein, lactose, minerals, and freezing point, totaling 164 assays, of which 60% were used for network training, 20% for network validation, and 20% for neural network testing. The Garson method was used to determine the importance of the variables. The neural network technique for the determination of milk fraud by the addition of whey proved to be efficient. Among the variables of highest relevance were fat content and density.

RESUMO: A adulteração do leite pela adição de soro de leite é um problema que diz respeito às autoridades nacionais e internacionais. O objetivo deste trabalho foi quantificar o teor de soro em amostras de leite adulterado por meio de redes neurais artificiais, usando como variáveis de entrada os resultados de análises rotineiras em amostras de leite. As análises foram realizadas com diferentes concentrações em relação à adição de soro de leite (0, 5, 10 e 20%), e as amostras foram analisadas quanto à gordura, sólidos não gordurosos, densidade, proteína, lactose, minerais e ponto de congelamento, totalizando 164 ensaios, dos quais 60% foram utilizados para treinamento em rede, 20% para validação de rede e 20% para teste de rede neural. O método de Garson foi utilizado para determinar a importância das variáveis. A técnica de redes neurais para a determinação da fraude ao leite por adição de soro provou ser eficiente. Entre as variáveis de maior relevância estavam o teor de gordura e a densidade.

Contribution of HBV and HCV infection in mortality of B cell non-Hodgkin’s lymphoma subjects / Монголын Анагаах Ухаан

Background@#The correlation between hepatitis B, C viruses (HBV, HCV) and B cell non-Hodgkin’s Lymphoma (B-NHL) and reducing mortality have been studied extensively worldwide@*Objective@#In this study, we aimed to determine the prevalence of HBsAg and anti-HCV positive cases among B-NHL patients and its influence on the survival rate of these patients (on ≤12 months).@*Materials and Methods@#We have done a retrospective analysis on patients who aged over 20 years and newly diagnosed at the Hematology Center of the First State Hospital between 2015-2018. The patients’ information was collected according the study ethics. We divided the patients into 2 groups, survival rate less than 12 months (≤12 months) and survival rate more than 13 months (≥13 months), and compared them regarding age, gender, seroprevalence, and Ann-Arbor stage. @*Results@#Overall, 226 patients (107 males and 119 females with average 54.4) were enrolled in the study. There were 15% HBsAg positive and 41,6% anti-HCV positive cases, while Baatarkhuu et al. (2005) reported (11.8%, 15.6%; p=0.160, p<0.00001) and Bekhbold et al. (2013) reported (11.1%, 10.6%; p=0.055, p<0.00001) in apparently healthy population. Moreover, anti-HCV positive cases among B-NHL patients were higher (p<0.00001) than those (27%) among hepatocellular carcinoma (HCC) patients and same (p=0.404) with those (39%) among liver cirrhosis patients in Mongolia (Bolormaa et al., 2009). Furthermore, 72.0% of all subjects in III-IV stages was accounted for HBsAg, anti-HCV positive group which had ≤12 months, while 52.1% of them was accounted for HBsAg, anti-HCV positive group which had ≥13 months and was statistical significantly lower (p=0.02).@*Conclusion@#Anti-HCV and HBsAg positive cases might contribute to survival rate with the B-NHL patients diagnosed at the III-IV stages. HCV prevalence among B-NHL subjects was significantly higher than that among the general population prevalence and was same with anti-HCV positive prevalence among the HCC.

Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy—on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.

The effects of unsupported arm exercise in patients with chronic obstructive pulmonary disease:a meta-analysis of randomized controlled trails / 中华护理杂志

Objective To evaluate the effects of unsupported arm exercise in patients with chronic obstructive pulmonary disease.Methods Databases such as Cochrane Library,PubMed,EMbase,CNKI and WanFang were searched to recruit eligible randomized controlled trials.The effects of unsupported arm exercise was determined by meta-analysis with RevMan 5.3 after data extraction and quality appraisal.Results Totally 11 RCTs were finally recruited.The results of meta-analysis supported the effectiveness of unsupported arm exercise on improving dyspnea in patients with chronic obstructive pulmonary disease[WMD=-0.76,95％CI(-l.33,-0.20),P=0.008],as well as the effectiveness on enhancing upper arm activity [WMD=25.75,95％CI (10.45,41.05),P=0.0010] and exercise tolerance [WMD=33.98,95％CI (10.20,57.76),P=-0.005].While it failed to support the effectiveness on improving lung function and quality of life(P＞0.05).Conclusion Unsupported arm exercise is effective to improve dyspnea,enhance upper arm activity and exercise tolerance,and is worth introducing in rehabilitation exercise in patients with chronic obstructive pulmonary disease.However,the effects on lung function,quality of life are still inconclusive.Strict designed,multi-centered RCTs with large sample size are needed in the future to gain reliable effectiveness of unsupported arm exercise.

The effects of unsupported arm exercise in patients with chronic obstructive pulmonary disease:a meta-analysis of randomized controlled trails / 中华护理杂志

Objective To evaluate the effects of unsupported arm exercise in patients with chronic obstructive pulmonary disease.Methods Databases such as Cochrane Library,PubMed,EMbase,CNKI and WanFang were searched to recruit eligible randomized controlled trials.The effects of unsupported arm exercise was determined by meta-analysis with RevMan 5.3 after data extraction and quality appraisal.Results Totally 11 RCTs were finally recruited.The results of meta-analysis supported the effectiveness of unsupported arm exercise on improving dyspnea in patients with chronic obstructive pulmonary disease[WMD=-0.76,95％CI(-l.33,-0.20),P=0.008],as well as the effectiveness on enhancing upper arm activity [WMD=25.75,95％CI (10.45,41.05),P=0.0010] and exercise tolerance [WMD=33.98,95％CI (10.20,57.76),P=-0.005].While it failed to support the effectiveness on improving lung function and quality of life(P＞0.05).Conclusion Unsupported arm exercise is effective to improve dyspnea,enhance upper arm activity and exercise tolerance,and is worth introducing in rehabilitation exercise in patients with chronic obstructive pulmonary disease.However,the effects on lung function,quality of life are still inconclusive.Strict designed,multi-centered RCTs with large sample size are needed in the future to gain reliable effectiveness of unsupported arm exercise.

Estudo da caracterização da borra de petróleo e processo de extração do óleo / Study of oil sludge characteristics and oil extraction process

RESUMO: Nesse trabalho, objetivou-se recuperar o óleo presente na borra oleosa por processo de extração, a fim de reutilizá-lo como combustível. Foram aplicados dois planejamentos experimentais: fatorial fracionado e Doehlert. Através da caracterização da borra oleosa (análises físico-químicos, elementar CHN e S, orgânicas e inorgânicas), constatou-se que a borra oleosa utilizada é constituída de 36,2% de óleo, 16,8% de cinzas, 40% de água e 7% de compostos voláteis. A eficiência média do processo de extração foi 70%. Entretanto, a análise estatística mostrou que o modelo quadrático não se ajustou bem ao processo, devido à complexidade do material estudado. Por outro lado, aplicando-se a modelagem de RNA, o coeficiente de determinação foi de 87,5%, mostrando-se bastante satisfatório.

ABSTRACT: This work aimed to recover the oil present in oily sludge by extraction process in order to reuse it as fuel. Two experimental designs were applied: fractional factorial and Doehlert. Through characterization of the oily sludge (physico-chemical analysis, CHN and S elemental, inorganic and organic), it was found that the oily sludge used consists of 36.2% oil, 16.8% ash, 40% water and 7% volatile compounds. The efficiency obtained in the oil extraction process was 70%, in average. However, statistical analysis showed that the quadratic model did not satisfactorily the process due to the complexity of the studied material. By the other hand, applying ANN the coefficient of determination became 87.5% that is quite satisfactory.

Optimisaton techniques: a futuristic approach for formulating and processing of pharmaceuticals.

Designing and formulating an ideal pharmaceutical product is a very tedious job for a formulator as it comprises of multiple objectives. The traditional method followed for years is not only expensive and time consuming but it also needs lot of effort to be put in and in spite of that at times it proves to be unfavourable and unpredictable too.The recent approach to optimise i.e. to achieve the best combination of product and process characteristics under the given conditions is by using Design of Experiment (DoE).Design of Experiment (DoE) is an organised approach to determine the relationship between the factors (Xs) affecting a process and the output of that process (Ys).The recent optimisation methodologies include various approaches that come under the 2 main categories namely, simultaneous optimisation and sequential optimisation. Nowadays there are various software available to carry out the optimisation of pharmaceutical products.

Monitoramento remoto pervasivo da saúde utilizando dispositivo móvel / Pervasive remote monitoring of human health via mobile devices

Este trabalho aplica o método Sliding Window (SW) associado a uma Rede Neural Artifical (RNA) para consolidação de dados advindos de um acelerômetro para o monitoramento de movimentos humanos. A partir desses dados foi extraído um vetor de entrada, para o classificador, com quatro características. Foram feitas várias combinações entre os parâmetros da SW, otimizando a média de acertos, atingindo então 92,63%. Conclui-se que a Sliding Window associada a RNA é apropriada para detecção dos seis movimentos aqui estudados. Esta técnica pode ser amplamente utilizada no monitoramento remoto de pacientes de forma menos invasiva, onde uma central remota realiza o processamento offline dos dados recebidos através do dispositivo móvel.

This paper applies the method Sliding Window (SW) in association with Artificial Neural Network (ANN) for consolidation of data that is generated by an accelerometer, which monitors human movements. From the extracted data was created an input vector with four features to the classifier. Several combinations were made regarding the SWparameters, optimizing the mean hit, which reached 92.63%. It is concluded that a sliding window associated with ANNis appropriate to the detection of the six studied movements. This technique can be widely used in remote monitoring of patients in a less invasive way, while a remote central carries out an offline processing of the received data from a mobile device.

Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using » fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1 percent, respectively. Regression coefficient (R²) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM) obtained were utilized as the objective functions to be maximized with the help of genetic algorithm. The optimal conditions for the maximal biomass yield were 37.4 °C, pH 7.09, inoculum volume 1.97 ml, inoculum age 58.58 h, carbon content 41.74 percent (w/v), and nitrogen content 46.23 percent (w/v). The work reported is a novel concept of combining the statistical modeling and evolutionary optimization for an improved yield of cell mass of B. bifidum 255.

Evaluation of innovation on in vivo - In vitro correlation of gastric retenting drug delivery system for Chinese materia medica / 中草药

The recent study on gastric retenting drug delivery system (GRDDS) used in Chinese medicinal formule has been summaried and overviewed in this paper. The key problems about the development of GRDDS have been explored, the influence about absence of the system for in vivo-in vitro correlation (IVIVC) analyzed, especially the evaluation on IVIVC model discussed, and the possible approach that artificial neural networks (ANN) are applied in GRDDS for Chinese medicinal formule has been put forward. The processing technology, evaluation of IVIVC, and the advantage, characteristic, and problems of the application of ANN have been studied in order to provide the reference for innovating research of Chinese medicinal formule.

Estimación de velocidad del movimiento de mano usando redes neuronales artificiales y mediciones electromiográfíca / Hand movements speed estimation by means of artificial neural networks and electromiographical measurements

En este artículo se presenta el desarrollo de un algoritmo para la estimación de la velocidad de los movimientos básicos de la mano usando redes neuronales artificiales a partir del sensado de la actividad electromiográfica del antebrazo. Parala implementación de dicho algoritmo fue necesario adaptar un modelo funcional de laboratorio para la medición de la velocidad, usando procesado digital de imágenes, presentando un error bajo en la medición de velocidad. Asimismo, para la estimación de velocidad a partir del análisis de la sEMG (señal electromiográfica superficial) se escogió una red NARX (nonlinear autoregressive network with exogenous inputs) como resultado de la comparación de diversas topologías de redes neuronales dinámicas. Losresultados mostrados evidencian una aproximación adecuada en la estimación de velocidad, que sirve como punto de comparación al usarse metodologías diferentes para obtener los perfiles de velocidad.

In this paper an algorithm for estimating the speed of the basic hand movements using artificial neural networksbased on recorded electromyographic activity at the forearm is presented. To implement this algorithm it was necessary to adapt amodel for measuring the speed, using digital image processing, which presented a low error rate measurement. Likewise, for speedestimation, a NARX network (network nonlinear autoregressive with exogenous inputs) was chosen after comparing differentdynamic neural network topologies. The results shown demonstrated a suitable approach to the estimation of speed, which servesas a comparison to the different methodologies used to obtain the velocity profiles.

Relationship between acceptance of disability and posttraumatic stress response in patients with brachial plexus injury / 中华护理杂志

Objective To determine the relationship between acceptance of disability and posttraumatic stress response in patients with brachial plexus injury. Methods Total 160 patients with brachial plexus injury were recruited and investigated with the Impact of Event Scale-Revised (IES-R) and the Acceptance of Disability Scale (ADS). Results The total score of ADS was (79.07±11.99) which showed medium level of acceptance of disability. The total score of IES-R was 4-66 (33.51±14.41), which showed that most of the patients suffered from posttranmatic stress response. Significantly negative correlation was found between acceptance of disability and posttraumatie stress response(r=-0.480, P<0.001). Conclusions Nurses should pay more attention to the acceptance of disability and posttranmatic stress response in patients with brachial plexus injury, and provide appropriate health education and effective psychological intervention to improve the patients' mental health and quality of llfe.

Expression of smad2/3 protein and the effects of flunarizine on its expression in brain tissue following transient cerebral ischemic reperfusion in gerbils / 中国医师杂志

Objective To study the expression of smad2/3 protein and the effects of flunarizine on the its expression in braln tissue following transient cerebral ischemie reperfusion in gerbils. Methods A cerebral ischemia-reperfusion model in gerbils was established by clamping both common carotids. Thirty-five gerbils were randomly divided into three groups, sham operation group, cerebral ischemia-reperfusion group and flunarizine treatment group. The expression of smad2/3 protein in brain tissue was detected by immunohistochemistry technique. Results Experimental results revealed that smad2/3 protein was expressed in neuroeyte in 35 gerbil brain. Compared with sham operation group, the expression of smad2/3 protein in neurecytes of cerebral isehemia-reperfusion group was evidently increased at the lst day, 3rd day and 7th day (P <0. 01). Compared with cerebral ischemia-reperfusion group, the expression of smad2/3 protein in neurecytes of gerbils in flunarizine treatment group was evidently decreased at these time point (P < 0.05). Condusions Smad2/3 protein was expressed in nettrcvytes of gerbils. Expression of smad2/3 protein in neuroeytes of gerbils was evidently increased following cerebral ischemic reperfusion, and its expression in flunarizine treatment group was evidently decreased.