Deep decarbonization potential and implementation path under provincial differences in China's fleet electrification.

Fleet electrification is considered to be an important measure for reducing carbon emissions in the road transport industry. Considering the heterogeneity of the NEV market penetration and the vehicle types in different provinces, how to design targeted and time-sequenced road transport decarbonisation reduction strategies has become a key issue that needs to be discussed urgently. In this study, the NEVs ownership in China's 31 provinces is used as an intermediate variable. Considering the process of energy transition and changes in vehicle structure, a two-layer scenario framework that combines Shared Socioeconomic Pathways scenarios and model structure was developed to predict carbon emissions. This study firstly analyzes the electrification process and carbon emission reduction potential of provincial road transport industry by region, vehicle type and stage. The potential for reducing carbon emissions was determined under benchmark, transition, and electrification scenarios. The results indicate that the Pearson Correlation Coefficient-Discrete Wavelet Transform-Bidirectional Long Short-term Memory prediction model has an mean absolute percentage error of 8.583 and an R-squared of 0.975. China's road transportation industry total carbon emissions will reach its peak as early as 2027, due to the rapid implementation of renewable energy and fleet electrification. Shanghai, Jiangsu, Shandong, Henan, and Guangdong have set carbon peak targets that can be achieved faster with the transition plan for new energy vehicles to replace fossil fuel vehicles. This paper proposes a timing-responsive deep decarbonization path and policy recommendations for China's road transport industry in sub provincial and time-series settings.

The Application Value of Combined Detection of Serum IL-6, LDH, S100, NSE, and GFAP in the Early Diagnosis of Brain Damage Caused by Neonatal Asphyxia.

Background: We aimed to investigate the correlation and clinical significance between a group of serum biomarkers and brain damage caused by neonatal asphyxia, and to provide sensitive and effective detection methods for early diagnosis and prognosis improvement. Methods: We enrolled neonates hospitalized in the neonatal department of The Affiliated Hospital of Inner Mongolia Medical University of China from June 2020 to June 2021 as the study subjects. The levels of inter-leukin-6 (IL-6), lactate dehydrogenase (LDH), S100 protein, neuron-specific enolase (NSE), and glial fibrillary acidic protein (GFAP) in serum samples were measured using electrochemiluminescence (ECL), enzyme-linked immunosorbent assay (ELISA) or rate method and the correlations between these serum biomarkers and the degree of neonatal asphyxia and brain damage were statistically analyzed using Spearman test. Results: The levels of serum IL-6, LDH, S100, NSE, and GFAP in the neonatal asphyxia with brain damage group within 12 hours after birth were significantly higher than those in the neonatal asphyxia without brain damage group (all P<0.05). Additionally, these levels were positively correlated with the degree of asphyxia. The Area Under the Curve (AUC) of receiver operating characteristic (ROC) curves of IL-6 (0.8819), LDH (0.8108), S100 (0.8719), NSE (0.8719), and GFAP (0.8073) were revealed. Conclusion: The combined detection of serum marker levels can simultaneously reflect neuronal injury, glial cell injury, and inflammatory injury, improve the accuracy of diagnosis of neonatal asphyxia with brain damage, and enable the formulation of treatment strategies as early as possible to reduce the incidence of complications of brain damage.

Carbon emission potential of new energy vehicles under different electricity structures.

New energy vehicles have a significant impact on reducing green house gas (GHG) emissions in the transportation sector, but the ability of new energy vehicles to reduce emissions under various development scenarios and electricity energy mix needs to be studied in depth. In this research, a GRA-BiLSTM model is constructed to predict the ownership of new energy vehicles by three scenario settings. Then, the structure of the future energy generation is forecast using a regression model. Finally, the carbon emissions under different energy structures are quantified and compared based on the prediction results, focusing on their carbon emission effects. The results show that in 2035, under three different development scenarios, the new energy vehicle ownership will reach 5711, 18122.76, and 218.93 million units, and the carbon emissions will be 60.897 billion kg, 193.246 billion kg, and 233.451 billion kg, respectively, based on the future energy development structure, accounting for 86% of the carbon emissions from the existing power generation structure. The carbon emission potential of new energy vehicles depends to a large extent on the future scenario of the power generation mix as well as the market for new energy vehicle ownership.

Dynamic changes of DNA methylation induced by benzo(a)pyrene in cancer.

Benzo(a)pyrene (BaP), the earliest and most significant carcinogen among polycyclic aromatic hydrocarbons (PAHs), has been found in foods, tobacco smoke, and automobiles exhaust, etc. Exposure to BaP induced DNA damage directly, or oxidative stress-related damage, resulting in cell apoptosis and carcinogenesis in human respiratory system, digestive system, reproductive system, etc. Moreover, BaP triggered genome-wide epigenetic alterations by methylation, which might cause disturbances in regulation of gene expression, and thereby induced cancer. It has been proved that BaP reduced genome-wide DNA methylation, and activated proto-oncogene by hypomethylation in the promoter region, but silenced tumor suppressor genes by promoter hypermethylation, resulting in cancer initiation and progression. Here we summarized the changes in DNA methylation in BaP exposure, and revealed the methylation of DNA plays a role in cancer development.

Clinical Analysis of 80 Patients of Multiple Primary Lung Cancers Undergoing Simultaneous Video-Assisted Thoracoscopic Surgery.

Objective: To explore the diagnosis, treatment and prognosis of multiple primary lung cancers (MPLCs) through summarizing and analyzing the clinical data of 80 patients with MPLCs. Methods: The clinical and pathological data of 80 patients who were diagnosed with MPLCs according to the Martini-Melamed criteria and who underwent simultaneous video-assisted thoracoscopic surgery in our hospital from January 2017 to June 2018 were retrospectively analyzed. The Kaplan-Meier method was used for survival analysis. Log-rank test was used for univariate analysis and Cox proportional hazards regression model for multivariate analysis to evaluate the independent risk factors affecting the prognosis of MPLCs. Results: Among the 80 patients, there were 22 cases with MPLCs and 58 cases with double primary lung cancers. The surgical approach was mainly pulmonary lobectomy and pulmonary segmental or wedge resection (41.25%, 33/80), and lesions occurred predominantly in the upper lobe of the right lung (39.8%, 82/206). The pathology of lung cancers was mainly adenocarcinoma (89.8%, 185/206), with invasive adenocarcinoma as a dominant pathological type (68.6%, 127/185), in which acinar subtype was found to be predominant (79.5%, 101/127). The proportion of MPLCs with the same histopathological type (96.3%, 77/80) was higher than that with different histopathological types (3.7%, 3/80). Postoperative pathological staging showed stage I in most patients (86.25%, 69/80). Univariate analysis revealed that the maximum tumor diameter, highest pathological stage and lymph node metastasis were correlated with disease-free survival (P < .05). The overall median survival time of patients was 50 months. Cox multivariate regression analysis indicated that lymph node metastasis was an independent risk factor affecting the prognosis of MPLC patients (P < .05). Conclusion: MPLCs occur principally in the upper lobe of the right lung and pulmonary adenocarcinoma is the most dominant pathological type, with acinar type as the predominant pathological subtype. Lymph node metastasis is an independent risk factor affecting the prognosis of MPLC patients. A favorable prognosis can be achieved through early diagnosis and active surgical treatment for individuals who are highly suspected of MPLCs indicated by imaging examination.

Prediction of wastewater discharge based on GRA-LSTM: a case study of Beijing.

Water resources, as one of the indispensable resources for urban development, have become an important factor limiting the sustainable development of cities. In order to promote sustainable urban development, Beijing has set the work task of reaching 99% of urban and rural wastewater treatment rate from 2020 to 2035. Accurate prediction of future wastewater discharge is essential to achieve the target. For this reason, this study takes Beijing as the research object and constructs a combined prediction model based on gray relational analysis and long- and short-term memory (GRA-LSTM). Firstly, gray relational analysis (GRA) is used to analyze the correlation of the experimental data indicators affecting the amount of wastewater discharged in order to obtain experimental data indicators with stronger correlation. Secondly, the long short-term memory (LSTM) model was used to learn the characteristics of the key impact indicators and obtain the optimal model parameters. The results showed that the mean absolute percentage error (MAPE) value of the combined GRA-LSTM model constructed in this study was 5.62%, and the prediction accuracy was higher than that of the other seven prediction models. Then, three scenarios with low, medium, and high dimensions were set to predict the wastewater discharge in Beijing from 2020 to 2035, and the prediction result that the wastewater discharge in Beijing will still continue to grow was obtained. Finally, in order to improve the water utilization rate and promote the sustainable development of the city, this study proposes relevant policy recommendations in terms of the unbalanced urban-rural development of Beijing's wastewater treatment capacity and the increase of recycled water usage.

3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images.

Background: Different pathological subtypes of lung adenocarcinoma lead to different treatment decisions and prognoses, and it is clinically important to distinguish invasive lung adenocarcinoma from preinvasive adenocarcinoma (adenocarcinoma in situ and minimally invasive adenocarcinoma). This study aims to investigate the performance of the deep learning approach based on high-resolution computed tomography (HRCT) images in the classification of tumor invasiveness and compare it with the performances of currently available approaches. Methods: In this study, we used a deep learning approach based on 3D conventional networks to automatically predict the invasiveness of pulmonary nodules. A total of 901 early-stage non-small cell lung cancer patients who underwent surgical treatment at Shanghai Chest Hospital between November 2015 and March 2017 were retrospectively included and randomly assigned to a training set (n=814) or testing set 1 (n=87). We subsequently included 116 patients who underwent surgical treatment and intraoperative frozen section between April 2019 and January 2020 to form testing set 2. We compared the performance of our deep learning approach in predicting tumor invasiveness with that of intraoperative frozen section analysis and human experts (radiologists and surgeons). Results: The deep learning approach yielded an area under the receiver operating characteristic curve (AUC) of 0.946 for distinguishing preinvasive adenocarcinoma from invasive lung adenocarcinoma in the testing set 1, which is significantly higher than the AUCs of human experts (P<0.05). In testing set 2, the deep learning approach distinguished invasive adenocarcinoma from preinvasive adenocarcinoma with an AUC of 0.862, which is higher than that of frozen section analysis (0.755, P=0.043), senior thoracic surgeons (0.720, P=0.006), radiologists (0.766, P>0.05) and junior thoracic surgeons (0.768, P>0.05). Conclusions: We developed a deep learning model that achieved comparable performance to intraoperative frozen section analysis in determining tumor invasiveness. The proposed method may contribute to clinical decisions related to the extent of surgical resection.

Regional Collaborative Forecast of Cargo Throughput in China's Circum-Bohai-Sea Region Based on LSTM Model.

Any developed port plays a dominant role both in domestic and international trade reflecting economic prosperity of the port and nearby regions in terms of its cargo throughput and port construction. An attempt is made in this study to use long-and short-term memory (LSTM) artificial neural network method to construct the port cargo throughput prediction model. Three ports namely, Tianjin Port, Dalian Port, and Tangshan Port from China's Bohai Rim region are selected as research objects. The historical cargo throughput of each port for nearly ten years was used as the input index data for joint prediction. The cargo throughput of Bohai Port provides another way to improve the accuracy of port cargo throughput prediction. The prediction results show that the LSTM model can effectively predict the port cargo throughput; the cargo throughput forecasts between the three Bohai Rim ports have both an interactive relationship and differences.

Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model.

The accurate prediction of Municipal Solid Waste (MSW) electricity generation is very important for the fine management of a city. This paper selects Shanghai as the research object, through the construction of a Bidirectional Long Short-Term Memory (BiLSTM) model, and chooses six influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then, this study obtains the MSW electricity generation capacity in Shanghai by using the aforementioned prediction results and the calculation formula of theMSW electricity generation. The experimental results show that, firstly, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of the BiLSTM model are 42.31, 7.390, and 63.32. Second, it is estimated that by 2025, the maximum and minimum production of MSW in Shanghai will be 17.35 million tons and 8.82 million tons under the three scenarios. Third, it is predicted that in 2025, the maximum and minimum electricity generation of Shanghai MSW under the three scenarios will be 512.752 GWh/y and 260.668 GWh/y. Finally, this paper can be used as a scientific information source for environmental sustainability decision-making for domestic MSW electricity generation technology.

Biodegradable magnesium alloy WE43 porous scaffolds fabricated by laser powder bed fusion for orthopedic applications: Process optimization, in vitro and in vivo investigation.

Laser powder bed fusion (L-PBF) of magnesium (Mg) alloy porous scaffolds is expected to solve the dual challenges from customized structures and biodegradable functions required for repairing bone defects. However, one of the key technical difficulties lies in the poor L-PBF process performance of Mg, contributed by the high susceptibility to oxidation, vaporization, thermal expansion, and powder attachment etc. This work investigated the influence of L-PBF energy input and scanning strategy on the formation quality of porous scaffolds by using WE43 powder, and characterized the microstructure, mechanical properties, biocompatibility, biodegradation and osteogenic effect of the as-built WE43 porous scaffolds. With the customized energy input and scanning strategy, the relative density of struts reached over 99.5%, and the geometrical error between the designed and the fabricated porosity declined to below 10%. Massive secondary phases including intermetallic precipitates and oxides were observed. The compressive strength (4.37-23.49 MPa) and elastic modulus (154.40-873.02 MPa) were comparable to those of cancellous bone. Good biocompatibility was observed by in vitro cell viability and in vivo implantation. The biodegradation of as-built porous scaffolds promoted the osteogenic effect, but the structural integrity devastated after 12 h by the immersion tests in Hank's solution and after 4 weeks by the implantation in rabbits' femur, indicating an excessively rapid degradation rate.

Initial experience of video-assisted thoracic surgery lobectomy after neoadjuvant chemotherapy plus toripalimab in a patient with locally advanced non-small cell lung cancer: a case report.

BACKGROUND: Immune checkpoint inhibitors were used for patients with advanced non-small cell lung cancer (NSCLC) more and more frequently and the effects were thrilling. Toripalimab as a new immune checkpoint inhibitor has been shown to be effective in patients with advanced NSCLC. However, data regarding the safety and feasibility of surgical resection after treatment with toripalimab for NSCLC remain scarce. Here, we present a case with locally advanced NSCLC that received video-assisted thoracic surgery (VATS) lobectomy after treatment with toripalimab in combination with chemotherapy. CASE PRESENTATION: A 62-year-old male patient with a history of coronary artery stenting operation for two times was found a 3.4 × 3.2 cm cavity mass in the upper lobe of the left lung and enlarged left hilar and mediastinal lymph nodes. Pathological results identified squamous cell carcinoma. The patient was diagnosed with a locally advanced NSCLC and received VATS left upper lobectomy and lymph node dissection after neoadjuvant chemotherapy plus toripalimab for 3 cycles. The postoperative pathological results showed complete tumor remission. Short-term follow-up results were excellent, and long-term results remain to be revealed. CONCLUSIONS: Our preliminary results showed that the use of neoadjuvant toripalimab and chemotherapy for the locally advanced NSCLC before surgical resection is safe and feasible.

Forecasting of China's solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model.

With the acceleration of China's energy transformation process and the rapid increase of renewable energy market demand, the photovoltaic (PV) industry has created more jobs and effectively alleviated the employment pressure of the labor market under the normalization of the epidemic situation. First, to accurately predict China's solar PV installed capacity, this paper proposes a multi-factor installed capacity prediction model based on bidirectional long short-term memory-grey relation analysis. The results show that, the MAPE value of the GRA-LSTM combined model established in this paper is 5.995, compared with the prediction results of other models, the prediction accuracy of the GRA-BiLSTM model is higher. Second, the BiLSTM model is used to forecast China's installed solar PV capacity from 2020 to 2035. The forecast results show that China's newly installed solar PV capacity will continue to grow and reach 2833GW in 2035. Third, the employment number in China's solar PV industry during 2020-2035 is predicted by the employment factors (EF) method. The results show that the energy transition in China during 2020-2035 will have a positive impact on the future stability and growth of the labor market in the solar PV industry. Overall, an accurate forecast of solar PV installed capacity can provide effective decision support for planning electric power development strategy and formulating employment policy of solar PV industry.

Demand gap analysis of municipal solid waste landfill in Beijing: Based on the municipal solid waste generation.

Achieving accurate prediction of the Municipal Solid Waste (MSW) generation is essential for the sustainable development of the city. This paper selects Beijing as the research object, building a neural network model based on Grey Relational Analysis and Long and Short-Term Memory (GRA-LSTM), and choosing 14 influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then this study obtains the landfill area in Beijing by using the aforementioned prediction results and the calculation formula of the landfill. Firstly, the GRA method is used to sort the influencing factors of the MSW generation for obtain the key influencing indexes. Secondly, the LSTM model is used to learn features of the key influencing indexes. Finally, the area of Beijing landfill is estimated by the calculation formula of landfill area. The results show that, first of all, the MAPE value of the GRA-LSTM combined model established in this paper is 7.3, and the prediction performance of this model is better than the other seven structural methods. Secondly, the area demand for landfills in Beijing shows an upward trend. At last, this paper put forward relevant suggestions to achieve sustainable urban development and deal with the increase in the MSW generation and the demand for landfills.

Global Transcriptional Analyses of the Wnt-Induced Development of Neural Stem Cells from Human Pluripotent Stem Cells.

The differentiation of human pluripotent stem cells (hPSCs) to neural stem cells (NSCs) is the key initial event in neurogenesis and is thought to be dependent on the family of Wnt growth factors, their receptors and signaling proteins. The delineation of the transcriptional pathways that mediate Wnt-induced hPSCs to NSCs differentiation is vital for understanding the global genomic mechanisms of the development of NSCs and, potentially, the creation of new protocols in regenerative medicine. To understand the genomic mechanism of Wnt signaling during NSCs development, we treated hPSCs with Wnt activator (CHIR-99021) and leukemia inhibitory factor (LIF) in a chemically defined medium (N2B27) to induce NSCs, referred to as CLNSCs. The CLNSCs were subcultured for more than 40 passages in vitro; were positive for AP staining; expressed neural progenitor markers such as NESTIN, PAX6, SOX2, and SOX1; and were able to differentiate into three neural lineage cells: neurons, astrocytes, and oligodendrocytes in vitro. Our transcriptome analyses revealed that the Wnt and Hedgehog signaling pathways regulate hPSCs cell fate decisions for neural lineages and maintain the self-renewal of CLNSCs. One interesting network could be the deregulation of the Wnt/ß-catenin signaling pathway in CLNSCs via the downregulation of c-MYC, which may promote exit from pluripotency and neural differentiation. The Wnt-induced spinal markers HOXA1-4, HOXA7, HOXB1-4, and HOXC4 were increased, however, the brain markers FOXG1 and OTX2, were absent in the CLNSCs, indicating that CLNSCs have partial spinal cord properties. Finally, a CLNSC simple culture condition, when applied to hPSCs, supports the generation of NSCs, and provides a new and efficient cell model with which to untangle the mechanisms during neurogenesis.

A Novel Method for Regional NO2 Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network.

Achieving accurate predictions of urban NO2 concentration is essential for effectively control of air pollution. This paper selected the concentration of NO2 in Tianjin as the research object, concentrating predicting model based on Discrete Wavelet Transform and Long- and Short-Term Memory network (DWT-LSTM) for predicting daily average NO2 concentration. Five major atmospheric pollutants, key meteorological data, and historical data were selected as the input indexes, realizing the effective prediction of NO2 concentration in the next day. Firstly, the input data were decomposed by Discrete Wavelet Transform to increase the data dimension. Furthermore, the LSTM network model was used to learn the features of the decomposed data. Ultimately, Support Vector Regression (SVR), Gated Regression Unit (GRU), and single LSTM model were selected as comparison models, and each performance was evaluated by the Mean Absolute Percentage Error (MAPE). The results show that the DWT-LSTM model constructed in this paper can improve the accuracy and generalization ability of data mining by decomposing the input data into multiple components. Compared with the other three methods, the model structure is more suitable for predicting NO2 concentration in Tianjin.

Hypoxia Induces Overexpression of CCL28 to Recruit Treg Cells to Enhance Angiogenesis in Lung Adenocarcinoma.

Lung cancer is the world-leading causative factor of disease-related death. CD4+CD25+ regulatory T cells (CD4+CD25+ Treg), which are involved in immune escape of tumor cells, are highly related to tumor development and metastasis. Hypoxia induces the overexpression of chemokine (C-C motif) ligand 28 (CCL28), thus enhancing the angiogenesis and metastasis of lung adenocarcinoma. Our study revealed that most clinical lung adenocarcinoma samples showed positive expressions of HIF-lα, VEGF, FoxP3, and CCL28. More CD4+CD25+ Treg cells were detected in the cancerous samples. In addition, hypoxia increased the expression of HIF-1α and upregulated CCL28 to recruit CD4+CD25+ Treg cells; knockdown of HIF-1α could reverse this process. Treg cells also promoted invasion, migration, and angiogenesis in two human lung adenocarcinoma cell lines A549 and H1975. Our study suggested a novel potential molecular mechanism involved in the progression of lung adenocarcinoma could be a potential therapeutic target for the treatment of lung cancer.

Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing.

Air pollutant concentration forecasting is an effective way which protects health of the public by the warning of the harmful air contaminants. In this study, a hybrid prediction model has been established by using information gain, wavelet decomposition transform technique, and LSTM neural network, and applied to the daily concentration prediction of atmospheric pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Beijing. First, the collected raw data are selected by feature selection by information gain, and a set of factors having a strong correlation with the prediction is obtained. Then, the historical time series of the daily air pollutant concentration is decomposed into different frequencies by using a wavelet decomposition transform and recombined into a high-dimensional training data set. Finally, the LSTM prediction model is trained with high-dimensional data sets, and the parameters are adjusted by repeated tests to obtain the optimal prediction model. The data used in this study were derived from six air pollution concentration data in Beijing from 1/1/2014 to 31/12/2016, and the atmospheric pollutant concentration data of Beijing between 1/1/2017 and 31/12/2017 were used to test the predictive ability of the data set test model. The results show that the evaluation index MAPE of the model prediction is 7.45%. Therefore, the hybrid prediction model has a higher value of application for atmospheric pollutant concentration prediction, because this model has higher prediction accuracy and stability for future air pollutant concentration prediction.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Immunity Enhancement in Immunocompromised Gastrointestinal Cancer Patients with Allogeneic Umbilical Cord Blood Mononuclear Cell Transfusion.

Objectives. In order to enhance the immunity of cancer patients to prevent relapse or to prolong survival time, umbilical cord blood mononuclear cells (UCMCs) were transplanted to cancer patients. Patients and Methods. UCMCs were transfused to 63 immunocompromised gastrointestinal cancer patients with nonmyeloablative (NMA) conditioning regimen. Results. The clinical study showed that the number of both T and B cells increased much more rapidly after transfusion of UCMCs than that of the control group without transplantation (p < 0.01). Proinflammation cytokines IFNγ and TNFα in serum increased to or above the normal range in 80.9% of patients at 12 weeks after UCMC transfusion. However, they recovered to the normal range in 21.7% of patients at the same time point in the control group only. In addition, the clinical investigation also showed that the transfusion of UCMC increased stable disease (SD) and reduced progressive disease (PD) significantly (p < 0.01); however, it did not have significant effects on complete response (CR), partial response (PR), or mortality rates compared with the control group (p > 0.05). Conclusions. UCMCs have powerful repairing effects on damaged cells and tissues and may reconstruct the impaired immunity. Transfusion of UCMCs could reconstruct the immunity of cancer patients with immunosuppression.

Deep sequencing-based transcriptional analysis of bovine mammary epithelial cells gene expression in response to in vitro infection with Staphylococcus aureus stains.

Staphylococcus aureus (S. aureus) is an important etiological organism in chronic and subclinical mastitis in lactating cows. Given the fundamental role the primary bovine mammary epithelial cells (pBMECs) play as a major first line of defense against invading pathogens, their interactions with S. aureus was hypothesized to be crucial to the establishment of the latter's infection process. This hypothesis was tested by investigating the global transcriptional responses of pBMECs to three S. aureus strains (S56,S178 and S36) with different virulent factors, using a tag-based high-throughput transcriptome sequencing technique. Approximately 4.9 million total sequence tags were obtained from each of the three S. aureus-infected libraries and the control library. Referenced to the control, 1720, 219, and 427 differentially expressed unique genes were identified in the pBMECs infected with S56, S178 and S36 S. aureus strains respectively. Gene ontology (GO) and pathway analysis of the S56-infected pBMECs referenced to those of the control revealed that the differentially expressed genes in S56-infected pBMECs were significantly involved in inflammatory response, cell signalling pathways and apoptosis. In the same vein, the clustered GO terms of the differentially expressed genes of the S178-infected pBMECs were found to comprise immune responses, metabolism transformation, and apoptosis, while those of the S36-infected pBMECs were primarily involved in cell cycle progression and immune responses. Furthermore, fundamental differences were observed in the levels of expression of immune-related genes in response to treatments with the three S. aureus strains. These differences were especially noted for the expression of important pro-inflammatory molecules, including IL-1α, TNF, EFNB1, IL-8, and EGR1. The transcriptional changes associated with cellular signaling and the inflammatory response in this study may reflect different immunomodulatory mechanisms that underlie the interaction between pBMECs and S. aureus strains during infection by the latter.