Treatment Strategies and Prognostic Factors in Patients with Stage T3 and T4 Laryngeal Carcinoma / 肿瘤防治研究

Objective To investigate the selection of treatment strategies and prognostic factors for patients with stage T3 and T4 laryngeal carcinoma. Methods A total of 132 patients with stage T3 and T4 laryngeal cancer admitted to our hospital from March 2010 to March 2019 were retrospectively selected as research objects. According to the different treatment strategies, the patients were divided into simple surgery group (group A, 57 cases), simple chemoradiotherapy group (group B, 32 cases), and surgery combined with chemoradiotherapy group (group C, 43 cases). The general data and clinicopathological features of the three groups were compared, and a survival curve was drawn by the Kaplan–Meier method. The 3-year survival rates of the three groups were compared. Then, the same 132 patients were divided into survival and death groups. The clinical data of the two groups were compared, and the prognostic factors were analyzed by multivariate logistic regression. A back propagation (BP) neural network model was constructed, and its differentiation and accuracy were evaluated. Results The proportions and 3 year survival rates of patients with poor differentiation, lymphatic vascular invasion, and involvement of lymph nodes outside the capsule in group C were significantly higher than those in groups A and B (P < 0.05). The 3 year survival rate of 132 patients was 68.94%(41/132). Poor differentiation, N2-N3 stage, lymphatic vascular invasion, and involvement of lymph nodes outside the capsule were risk factors for death (P < 0.05), whereas surgery combined with radiotherapy and chemotherapy were protective factors (P < 0.05). The BP neural network model exhibited good discrimination and high accuracy. Conclusion Surgery combined with radiotherapy and chemotherapy can significantly improve survival rate in patients with poor differentiation, lymphatic vascular invasion, and involvement of lymph nodes outside the capsule. Close attention should be paid to patients with stage N2-N3 in the formulation of reasonable treatment strategies.

Diagnostic Study of Multi-spectral Intelligent Analyzer in Diagnosis of the Infiltration Degree of Lung Adenocarcinoma / 中国肺癌杂志

BACKGROUND@#Lung cancer is one of the most common malignant tumors in the world. The accuracy of intraoperative frozen section (FS) in the diagnosis of lung adenocarcinoma infiltration cannot fully meet the clinical needs. The aim of this study is to explore the possibility of improving the diagnostic efficiency of FS in lung adenocarcinoma by using the original multi-spectral intelligent analyzer.@*METHODS@#Patients with pulmonary nodules who underwent surgery in the Department of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University from January 2021 to December 2022 were included in the study. The multispectral information of pulmonary nodule tissues and surrounding normal tissues were collected. A neural network model was established and the accuracy of the neural network diagnostic model was verified clinically.@*RESULTS@#A total of 223 samples were collected in this study, 156 samples of primary lung adenocarcinoma were finally included, and a total of 1,560 sets of multispectral data were collected. The area under the curve (AUC) of spectral diagnosis in the test set (10% of the first 116 cases) of the neural network model was 0.955 (95%CI: 0.909-1.000, P<0.05), and the diagnostic accuracy was 95.69%. In the clinical validation group (the last 40 cases), the accuracy of spectral diagnosis and FS diagnosis were both 67.50% (27/40), and the AUC of the combination of the two was 0.949 (95%CI: 0.878-1.000, P<0.05), and the accuracy was 95.00% (38/40).@*CONCLUSIONS@#The accuracy of the original multi-spectral intelligent analyzer in the diagnosis of lung invasive adenocarcinoma and non-invasive adenocarcinoma is equivalent to that of FS. The application of the original multi-spectral intelligent analyzer in the diagnosis of FS can improve the diagnostic accuracy and reduce the complexity of intraoperative lung cancer surgery plan.
.

Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning / 中国血吸虫病防治杂志

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Influencing factors of fall injury risks in manufacturing enterprises based on Bayesian networks / 环境与职业医学

Background Falls are one of the most important types of occupational injuries. The incidence of falls is high in manufacturing workers. However, most of the studies on falls in China focus on primary and secondary school students and the elderly, and there are few studies on falls in the occupational population. Objective To evaluate efficiency of Bayesian network model in predicting fall injury risks in manufacturing enterprise staff, and impacts from work content, work environment, enterprise status, and health management on falls and their mutual relationships, and provide a scientific basis for enterprises to carry out fall-associated injury intervention. Methods Data from the European Survey of Enterprises on New and Emerging Risks (ESENER) were used. The survey provided data on work content, working environment, enterprise status, and health management of enterprises in European countries. The outcome indicator, was fall injury risks reported in enterprises. A total of 23 potential impact factors covering work content, working environment, enterprise status, and health management were screened by least absolute shrinkage and selection operator (LASSO) regression, followed by Bayesian network model for structure learning and parameter learning and area under the curve (AUC) for model fitness evaluation, using R and Netica 5.18. Diagnostic inference analysis was also conducted to identify key influencing factors and key influencing chains of fall injury risks based on the change rate of fall injury risks. Results In 5997 enterprises surveyed, 2573 (42.9%) enterprises reported fall injury risks. Ordered by their coefficient estimates from high to low, the 14 variables (mean-squared error=0.20) selected by LASSO regression were: manual handling, repetitive arm movement, poor posture, using desktop computers, and using robots in the category of work content; abnormal temperature and noise in the category of working environment; company size and employee quality in the category of enterprise status; mental health training, regular risk assessment, availability of psychologists, health and safety procedures, and provision of psychological counseling in the category of health management. The fitting result of Bayesian network model for fall injury risks was good (AUC=0.779). The Bayesian network diagnostic inference identified five key influencing factors, including abnormal temperature (change rate=35.9%), poor posture (change rate=27.3%), noise (change rate=23.4%), manual handling (change rate=18.2%), and repetitive arm movement (change rate=5.1%). The key influencing chain was "manual handling - poor posture - repetitive arm movement - fall injury risks" (combined change rate=16.9%). Conclusion The Bayesian network model has a good predictive performance in predicting the risk of falls in manufacturing enterprises. Manufacturing enterprises need to focus on jobs involving manual handling and repetitive arm movement, identify and improve workers' poor posture and mental health problems, and avoid workers working in harsh temperature or noise environment.

Effectiveness of early warning management of nosocomial infection based on neural network model and decision tree model / 公共卫生与预防医学

Objective To predict the effectiveness of nosocomial infection management and effectively control the risk of nosocomial infection. Methods In this study, with the population of ICU patients in a Grade A hospital , 345 ICU patients seen from June 2020 to June 2021 were included in the analysis to collect the infection data in the hospital. Based on the use of the decision tree model to analyze the influencing factors of nosocomial infection, the neural network model was also used to predict the risk of developing nosocomial infection. Results The decision tree model showed that advanced age (age> 80 years) influenced the root node. Type 2 diabetes, gender by male, and BMI level were child nodes, which had different synergistic effects on the occurrence of nosocomial infection. At the same time, random forest (RF), support vector machine (SVM), logical regression (LR) and K nearest neighbor (KNN) algorithms were used to construct a neural network prediction model of nosocomial infection risk, suggesting that the condition, sex and body size of basic diseases are related to the occurrence of nosocomial infection. The combined use of the above model in parallel can effectively increase the specificity and reduce the missed diagnosis. Conclusion The neural network model joint decision tree model in parallel and joint early warning of nosocomial infection risk have excellent effect, and can effectively provide information support for the prevention, management and disposal of nosocomial infection.

A graph-theory-based method for processing of currency metabolites in metabolic networks / 生物工程学报

Graph-theory-based pathway analysis is a commonly used method for pathway searching in genome-scale metabolic networks. However, such searching often results in many pathways biologically infeasible due to the presence of currency metabolites (e.g. H+, H2O, CO2, ATP etc.). Several methods have been proposed to address the problem but up to now there is no well-recognized methods for processing the currency metabolites. In this study, we proposed a new method based on the function of currency metabolites for transferring of functional groups such as phosphate. We processed most currency metabolites as pairs rather than individual metabolites, and ranked the pairs based on their importance in transferring functional groups, in order to make sure at least one main metabolite link exists for any reaction. The whole process can be done automatically by programming. Comparison with existing approaches indicates that more biologically infeasible pathways were removed by our method and the calculated pathways were more reliable, which may facilitate the graph-theory-based pathway design and visualization.

Construction and significance of prediction model for chronic obstructive pulmonary disease assessment test based on fusion deep network fused with air data / 中华健康管理学杂志

Objective:To construct a chronic obstructive pulmonary disease (COPD) assessment test (CAT) score prediction model based on a deep network fused with air data, and to explore its significance.Methods:From February 2015 to December 2017, the outdoor environmental monitoring air data near the residential area of the patients with COPD from the Respiratory Outpatient Clinics of Peking University Third Hospital, Peking University People′s Hospital and Beijing Jishuitan Hospital were collected and the daily air pollution exposure of patients was calculated. The daily CAT scores were recorded continuously. The CAT score of the patients in the next week was predicted by fusing the time series algorithm and neural network to establish a model, and the prediction accuracy of the model was compared with that of the long short-term memory model (LSTM), the LSTM-attention model and the autoregressive integrated moving average model (ARIMA).Results:A total of 47 patients with COPD were enrolled and followed up for an average of 381.60 days. The LSTM-convolutional neural networks (CNN)-autoregression (AR) model was constructed by using the collected air data and CAT score, and the root mean square error of the model was 0.85, and the mean absolute error was 0.71. Compared with LSTM, LSTM-attention and ARIMA, the average prediction accuracy was improved by 21.69%.Conclusion:Based on the air data in the environment of COPD patients, the fusion deep network model can predict the CAT score of COPD patients more accurately.

Analysis of hospitalization cost and influencing factors of diabetic patients in rural areas of Hunan Province-- Take Wugang as an example / 公共卫生与预防医学

Objective To analyze the composition and influencing factors of hospitalization expenses for diabetic patients，and to provide reference for effective control of medical expenses. Methods The hospitalization cost data of diabetes patients in rural areas of Wugang from 2013 to 2017 were collected. Structural change analysis，non-parametric test and BP (Back Propagation)neural network model were used to analyze the hospitalization expenses and influencing factors. Results The top three components of hospitalization expenses were drug cost (50.02%), examination cost (15.35%) and laboratory cost (12.06%). The contribution rates of structural change of hospitalization expenses were the examination fee (41.00%), drug fee (34.92%) and treatment fee (13.41%), respectively. Factors affecting the total hospitalization cost of diabetic patients included length of stay, operation or not, hospital level, age, discharge year, complication or not and gender (P<0.05), among which length of stay had the greatest impact (sensitivity value was 0.669). Conclusion The hospitalization expenses of patients with diabetes is affected by a variety of factors. It is suggested to optimize the composition of hospitalization expenses by improving the price mechanism of medical services, and to control and reasonably reduce hospitalization expenses by implementing standardized management of clinical pathways, implementing two-way referral and strengthening tertiary prevention.

Advances in the development of constraint-based genome-scale metabolic network models / 生物工程学报

Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.

Current status and future perspectives of metabolic network models of industrial microorganisms / 生物工程学报

Genome-scale metabolic network model (GSMM) is an extremely important guiding tool in the targeted modification of industrial microbial strains, which helps researchers to quickly obtain industrial microbes with specific traits and has attracted increasing attention. Here we reviewe the development history of GSMM and summarized the construction method of GSMM. Furthermore, the development and application of GSMM in industrial microorganisms are elaborated by using four typical industrial microorganisms (Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae) as examples. In addition, prospects in the development trend of GSMM are proposed.

Progress and application of metabolic network model based on enzyme constraints / 生物工程学报

Genome-scale metabolic network models have been successfully applied to guide metabolic engineering. However, the conventional flux balance analysis only considers stoichiometry and reaction direction constraints, and the simulation results cannot accurately describe certain phenomena such as overflow metabolism and diauxie growth on two substrates. Recently, researchers proposed new constraint-based methods to simulate the cellular behavior under different conditions more precisely by introducing new constraints such as limited enzyme content and thermodynamics feasibility. Here we review several enzyme-constrained models, giving a comprehensive introduction on the biological basis and mathematical representation for the enzyme constraint, the optimization function, the impact on the calculated flux distribution and their application in identification of metabolic engineering targets. The main problems in these existing methods and the perspectives on this emerging research field are also discussed. By introducing new constraints, metabolic network models can simulate and predict cellular behavior under various environmental and genetic perturbations more accurately, and thus can provide more reliable guidance to strain engineering.

Fear Network Model in Panic Disorder: The Past and the Future

The core concept for pathophysiology in panic disorder (PD) is the fear network model (FNM). The alterations in FNM might be linked with disturbances in the autonomic nervous system (ANS), which is a common phenomenon in PD. The traditional FNM included the frontal and limbic regions, which were dysregulated in the feedback mechanism for cognitive control of frontal lobe over the primitive response of limbic system. The exaggerated responses of limbic system are also associated with dysregulation in the neurotransmitter system. The neuroimaging studies also corresponded to FNM concept. However, more extended areas of FNM have been discovered in recent imaging studies, such as sensory regions of occipital, parietal cortex and temporal cortex and insula. The insula might integrate the filtered sensory information via thalamus from the visuospatial and other sensory modalities related to occipital, parietal and temporal lobes. In this review article, the traditional and advanced FNM would be discussed. I would also focus on the current evidences of insula, temporal, parietal and occipital lobes in the pathophysiology. In addition, the white matter and functional connectome studies would be reviewed to support the concept of advanced FNM. An emerging dysregulation model of fronto-limbic-insula and temporooccipito-parietal areas might be revealed according to the combined results of recent neuroimaging studies. The future delineation of advanced FNM model can be beneficial from more extensive and advanced studies focusing on the additional sensory regions of occipital, parietal and temporal cortex to confirm the role of advanced FNM in the pathophysiology of PD.

Classification of radiographic lung pattern based on texture analysis and machine learning

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.

A feasibility study of building up deep learning classification model based on breast digital breast tomosynthesis image texture feature extraction of the simple mass lesions / 中华放射学杂志

Objective To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) breast X-ray photography image texture characteristics based deep learning classification model on differentiating malignant masses. Methods Retrospectively collected 132 cases with simplex breast lesions (89 benign lesions and 43 malignant lesions) which were confirmed by pathology and DBT during January 2016 to December 2016 in Nanfang Hospital. DBT was performed before biopsy and surgery. Image of cranio-caudal view (CC) and medio-lateral oblique (MLO) were captured. The lesion area was segmented to acquire ROI by ITK-SNAP software. Then the processed images were input into MATLAB R2015b to establish a feature model for extracting texture features. The characteristics with high correlation was analyzed from Fisher score and one sample t test. We built up support vector machine (SVM) classification model based on extracted texture and added neural network model (CNN) for deep learning classification model. We randomly assigned collected cases into training group and validation group. The diagnosis of benign and malignant lesions were served as the reference. The efficiency was evaluated by ROC classification model. Result We extracted 82 texture characteristics from 132 images of leisure (132 images of CC and 132 images of MLO) by establishing deep learning classification model of breast lesions. We randomly chose and combined characteristics from 15 texture characteristics with statistical significance, then differentiated benign and malignant by SVM classification model. After 50 iterations on each combination of characteristics, the average diagnostic efficacy was compared to obtained the one with higher efficacy. Nine of CC and 8 of MLO was selected. The result showed that the sensitivity, specificity, accuracy and area under curve (AUC) of the model to differentiate simplex breast lesions for CC were 0.68, 0.77, 0.74 and 0.74, for MLO were 0.71, 0.71, 0.71 and 0.76. Conclusions MLO has better diagnostic performance for the diagnosis than CC. The deep learning classification model on breast lesions which was built upon DBT image texture characteristics on MLO could differentiate malignant masses effectively.

Neuroprotective effects of Ginkgo biloba extract and Ginkgolide B against oxygen-glucose deprivation/reoxygenation and glucose injury in a new in vitro multicellular network model / 医学前沿

Acute ischemic stroke (AIS), as the third leading cause of death worldwide, is characterized by its high incidence, mortality rate, high incurred disability rate, and frequent reoccurrence. The neuroprotective effects of Ginkgo biloba extract (GBE) against several cerebral diseases have been reported in previous studies, but the underlying mechanisms of action are still unclear. Using a novel in vitro rat cortical capillary endothelial cell-astrocyte-neuron network model, we investigated the neuroprotective effects of GBE and one of its important constituents, Ginkgolide B (GB), against oxygen-glucose deprivation/reoxygenation and glucose (OGD/R) injury. In this model, rat cortical capillary endothelial cells, astrocytes, and neurons were cocultured so that they could be synchronously observed in the same system. Pretreatment with GBE or GB increased the neuron cell viability, ameliorated cell injury, and inhibited the cell apoptotic rate through Bax and Bcl-2 expression regulation after OGD/R injury. Furthermore, GBE or GB pretreatment enhanced the transendothelial electrical resistance of capillary endothelial monolayers, reduced the endothelial permeability coefficients for sodium fluorescein (Na-F), and increased the expression levels of tight junction proteins, namely, ZO-1 and occludin, in endothelial cells. Results demonstrated the preventive effects of GBE on neuronal cell death and enhancement of the function of brain capillary endothelial monolayers after OGD/R injury in vitro; thus, GBE could be used as an effective neuroprotective agent for AIS/reperfusion, with GB as one of its significant constituents.

Knowledge Network and Its Evaluation Application Literature Analysis / 医学信息学杂志

Based on previous research on the knowledge network,the paper summarizes the relevant research on the concept evolution,structure,model,evaluation index and practical application of the knowledge network based on the literature carrier,and provides reference for knowledge management and scientific innovation management.

Incidence difference of cutaneous complications in different targeted drugs for treating malignant melanoma:a network Meta-analysis / 重庆医学

Objective To adopt the network meta analysis method to compare the incidence difference of cutaneous squamous-cell carcinoma(SCC)and rash in 5 kinds of targeted drugs regimen for treating malignant melanoma.Methods PubMed and Cochrane Library databases were retrieved by computer.The retrieval range was from their establishment to November 2015.The network meta analysis pooled the evidences of direct and indirect comparison for evaluating the pooled odds ratio(OR)and cumulative probability of cutaneous complications occurrence difference in 5 kinds of targeted drugs regimen for treating malignant melanoma.Results Six randomized controlled trials(RCTs)conforming to the inclusion criteria were included.The meta analysis results revealed that compared with Dabrafenib+Trametinib,the cutaneous SCC occurrence rate of Vemurafenib was higher(OR=9.20,95％CI=1.26-52.53),while the rash occurrence rate of Vemurafenib+Cobimetinib was higher(OR=6.81,95％CI=1.01-41.87).The surface under the cumulative ranking curves(SUCRA)value showed that adopting Trametinib had the lowest occurrence rate for SCC,and adopting Dabrafenib+Trametinib had the lowest occurrence rate of rash.Conclusion Dabrafenib+Trametinibis generate the lowest complication incidence rate of malignant melanoma.

Comparative study on ARIMA model and GRNN model for predicting the incidence of tuberculosis / 第二军医大学学报

Objective To compare the performance of ARIMA model and GRNN model for predicting the incidence of tuberculosis. Methods ARIMA model was set up by Eviews 7.0.0.1 and GRNN model was set up by neural network toolbox of Matlab 7.1 based on the monthly tuberculosis incidence data from January 2004 to December 2012 in China. Monthly tuberculosis incidence data in 2013 were subjected to the two models for testing, and the results were compared between the two groups. Results The Theil unequal coefficients (TIC) were 0.034 and 0.059 for ARIMA model and GRNN model, respectively, indicating that ARIMA model was better than GRNN model to fit with the monthly incidence of tuberculosis in 2013. The absolute value of the relative error for ARIMA model was only 57.19% of GRNN model. Conclusion ARIMA prediction model is more suitable for predicting the incidence of tuberculosis in China, and it is suggested a combination of models should be used to predict the incidence of tuberculosis.

Progress in omics research of Aspergillus niger / 生物工程学报

Aspergillus niger, as an important industrial fermentation strain, is widely applied in the production of organic acids and industrial enzymes. With the development of diverse omics technologies, the data of genome, transcriptome, proteome and metabolome of A. niger are increasing continuously, which declared the coming era of big data for the research in fermentation process of A. niger. The data analysis from single omics and the comparison of multi-omics, to the integrations of multi-omics based on the genome-scale metabolic network model largely extends the intensive and systematic understanding of the efficient production mechanism of A. niger. It also provides possibilities for the reasonable global optimization of strain performance by genetic modification and process regulation. We reviewed and summarized progress in omics research of A. niger, and proposed the development direction of omics research on this cell factory.

Lamina specific loss of inhibition may lead to distinct neuropathic manifestations: a computational modeling approach

Introduction It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.