RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.

Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.

Observation of therapeutic effect of lamp irradiation combined with purple gromwell oil gauze on alleviating intestinal colic in patients.

BACKGROUND: Intestinal colic is a common complication in patients who have undergone radical surgery for colorectal cancer. Traditional Chinese medicine has advantages, including safety and stability, for the treatment of intestinal colic. Lamp irradiation for abdominal ironing has been applied in the treatment of many gastrointestinal diseases. Purple gromwell oil has the effects of clearing heat, cooling blood, reducing swelling, and relieving pain. AIM: To investigate the impact of lamp irradiation combined with purple gromwell oil gauze on ameliorating intestinal colic in patients after radical surgery for colorectal cancer. METHODS: A total of 120 patients who experienced postoperative intestinal colic complications after radical surgery for colorectal cancer and who were admitted to Foshan Traditional Chinese Medicine Hospital between June 2019 and March 2023 were enrolled as study subjects. The patients were divided into a control group (60 patients) and an observation group (60 patients) based on treatment method. The control group was treated with lamp irradiation, while the observation group was treated with lamp irradiation and external application of purple gromwell oil gauze. The clinical efficacy, Numeric Rating Scale (NRS) score, duration of symptoms, and rate of adverse reaction occurrence were further compared between the two groups. RESULTS: The general effective rate in the observation group was 95.00%, which was significantly higher than that in the control group (86.67%, P < 0.05). Before treatment, there was no significant difference in the duration of symptoms between the groups (P > 0.05). After 1, 2, 3, and 4 d of treatment, the duration of symptoms in both groups were decreased, and the duration in the observation group was significantly lower than that in the control group (96.54 ± 9.57 vs 110.45 ± 11.23, 87.26 ± 12.07 vs 104.44 ± 11.68, 80.45 ± 16.21 vs 99.44 ± 14.95, 73.18 ± 15.58 vs 92.17 ± 14.20; P < 0.05). After 1, 3, 5, and 7 d of treatment, the NRS scores in both groups were decreased, and the NRS scores in the observation group were significantly lower than those in the control group (3.56 ± 0.41 vs 4.04 ± 0.58, 3.07 ± 0.67 vs 3.74 ± 1.02, 2.52 ± 0.76 vs 3.43 ± 0.85, 2.03 ± 0.58 vs 3.03 ± 0.82; P < 0.05). There was no significant difference in the rate of adverse reaction occurrence between the groups (P > 0.05). CONCLUSION: The use of lamp irradiation combined with purple gromwell oil gauze in patients with intestinal colic after radical surgery for colorectal cancer can reduce symptom duration, alleviate intestinal colic, and improve treatment efficacy, and this approach is safe. It is worth promoting the use of this treatment in clinical practice.

CD20highCD138low tumor-infiltrating lymphocytes predominantly related to cytokine‒cytokine receptor interactions are associated with favorable outcomes in neuroblastoma patients.

Recent advances have revealed that the role of the immune system is prominent in the antitumor response. In the present study, it is aimed to provide an expression profile of tumor-infiltrating lymphocytes (TILs), including mature B cells, plasma cells, and their clinical relevance in neuroblastoma. The expression of CD20 and CD138 was analyzed in the Cangelosi786 dataset (n = 769) as a training dataset and in our cohort (n = 120) as a validation cohort. CD20 high expression was positively associated with favorable overall survival (OS) and event-free survival (EFS) (OS: P < 0.001; EFS: P < 0.001) in the training dataset, whereas CD138 high expression was associated with poor OS and EFS (OS: P < 0.001; EFS: P < 0.001) in both the training and validation datasets. Accordingly, a combined pattern of CD20 and CD138 expression was developed, whereby neuroblastoma patients with CD20highCD138low expression had a consistently favorable OS and EFS compared with those with CD20lowCD138high expression in both the training and validation cohorts (P < 0.0001 and P < 0.01, respectively). Examination of potential molecular functions revealed that signaling pathways, including cytokine‒cytokine receptor interactions, chemokine, and the NF-kappa B signaling pathways, were involved. Differentially expressed genes, such as BMP7, IL7R, BIRC3, CCR7, CXCR5, CCL21, and CCL19, predominantly play important roles in predicting the survival of neuroblastoma patients. Our study proposes that a new combination of CD20 and CD138 signatures is associated with neuroblastoma patient survival. The related signaling pathways reflect the close associations among the number of TILs, cytokine abundance and patient outcomes and provide therapeutic insights into neuroblastoma.

Thiostrepton suppresses triple-negative breast cancer through downregulating c-FLIP/SMAD2/3 signaling pathway.

Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis of breast cancer. Thiostrepton exerts anti-tumor activities against several cancers including TNBC. Herein we discussed the new molecular mechanisms of thiostrepton in TNBC. Thiostrepton inhibited MDA-MB-231 cell viability, accompanied by a decrease of c-FLIP and p-SMAD2/3. c-FLIP overexpression reduced the sensitivity of MDA-MB-231 cells to thiostrepton, while SMAD2/3 knockdown increased the sensitivity of MDA-MB-231 cells to thiostrepton. Moreover, c-FLIP overexpression significantly increased the expression and phosphorylation of SMAD2/3 proteins and vice versa. In conclusion, our study reveals c-FLIP/SMAD2/3 signaling pathway as a novel mechanism of antitumor activity of thiostrepton.

Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.

BACKGROUND: Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC. METHODS: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC). RESULTS: In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients. CONCLUSIONS: This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

ITRAQ-based proteomics analysis of human ectopic endometrial stromal cells treated by Maqian essential oil.

BACKGROUND: Endometriosis is a common and complex syndrome characterized by the presence of endometrial-like tissue outside the uterus. Chinese medicine has been recently found to show good efficacy in treating endometriosis. Our previous results revealed that Maqian fruit essential oil (MQEO) could inhibit the proliferation and induce apoptosis of ectopic endometrial stromal cells (EESCs), but the mechanisms remain unclear. In this study, we aim to explore the molecular mechanism of MQEO's specific effects in EESCs. METHODS: We conducted a quantitative proteomics analysis by iTRAQ on EESCs treated with MQEO or DMSO. Then deep analysis was performed based on differentially expressed proteins, including Gene Ontology enrichment analysis, pathway enrichment analysis and protein interaction analysis. Candidate protein targets were subsequently verified by western blotting. RESULTS: Among 6575 identified proteins, 435 proteins exhibited altered expression levels in MQEO-treated EESCs. Of these proteins, most were distributed in signal transduction as well as immune system and the most significantly altered pathway was complement and coagulation cascades. Moreover, two differentially expressed proteins (Heme oxygenase 1 and Acyl-CoA 6-desaturase) were verified and they can be potential biomarkers for endometriosis treatment. CONCLUSIONS: Our proteomic analysis revealed distinct protein expression patterns induced by MQEO treatment in EESCs, highlighting the potential of MQEO for endometriosis treatment and biomarker discovery.

Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach.

BACKGROUND: Herbal medicine Sanqi (SQ), the dried root or stem of Panax notoginseng (PNS), has been reported to have anti-diabetic and anti-obesity effects and is usually administered as a decoction for Chinese medicine. Alternative to utilizing PNS pure compound for treatment, we are motivated to propose an unconventional scheme to investigate the functions of PNS mixture. However, studies providing a detailed overview of the transcriptomics-based signaling network in response to PNS are seldom available. METHODS: To explore the reasoning of PNS in treating metabolic disorders such as insulin resistance, we implemented a systems biology-based approach with RNA sequencing (RNA-seq) and miRNA sequencing data to elucidate key pathways, genes and miRNAs involved. RESULTS: Functional enrichment analysis revealed PNS up-regulating oxidative stress-related pathways and down-regulating insulin and fatty acid metabolism. Superoxide dismutase 1 (SOD1), peroxiredoxin 1 (PRDX1), heme oxygenase-1 (Hmox1) and glutamate cysteine ligase (GCLc) mRNA and protein levels, as well as related miRNA levels, were measured in PNS treated rat pancreatic ß cells (INS-1). PNS treatment up-regulated Hmox1, SOD1 and GCLc expression while down-regulating miR-24-3p and miR-139-5p to suppress oxidative stress. Furthermore, we verified the novel interactions between miR-139-5p and miR-24-3p with GCLc and SOD1. CONCLUSION: This work has demonstrated the mechanism of how PNS regulates cellular molecules in metabolic disorders. Therefore, combining omics data with a systems biology strategy could be a practical means to explore the potential function and molecular mechanisms of Chinese herbal medicine in the treatment of metabolic disorders.

The impact of hospital-family integrated continuation nursing based on information technology on patients unhealthy mood, family function and sexual function after cervical cancer surgery.

The objective of this study was to explore the impact on hospital-family integrated continuation care based on information technology on the negative emotions, family function, and sexual function of patients after cervical cancer surgery. The clinical case data onto 114 postoperative cervical cancer patients who were nursing in our hospital from July 2019 to July 2021 were selected and were divided into a control group and an observation group. The control group used routine nursing care, and on this basis, the observation group used information technology as the basis for hospital-family integrated continuation care, and we observed and compared the differences in the 2 groups of patients bad mood, family function, and sexual function. The score of anxiety (P = .017), depression (P = .009), fatigue rating (P = .012), and anger (P < .001) in the observation group after care were significantly lower than those in the control group. Problem solving, role, emotional response, emotional involvement, and family function total score in the observation group after care was significantly lower than those in the control group (P < .05). Sexual desire score, sexual arousal score, vaginal lubrication score, orgasm score, sexual satisfaction score, dyspareunia score, and Female Sexual Function Inventory total scores in the observation and control groups after care were significantly higher than those before care (P < .05). The sexual function scores in the observation group after care was significantly higher than those in the control group (P < .05). The hospital-family integrated continuation care based on information technology is more effective than conventional nursing care for patients after cervical cancer surgery.

Stem Cell Therapy for Diabetic Foot Ulcers: Theory and Practice.

Diabetic foot ulcers are associated with increases in limb amputation, morbidity, and mortality. Recently, a stem cell application is emerging as promising adjuvant therapy. We presented available remedies by conducting a literature review on the application, safety, and efficacy of stem cell therapy. Relevant literature, including randomized control trials and article journals, was obtained from reputable search engines (PubMed, Scopus, and Web of Science). We analyzed five credible cohorts, with variable sources of stem cells, in a total of 216 participants, 151 males and 65 females, age (mean ± SD) of 64.5 ± 9.6 years. With an average success of 86.41% in all Wagner-II lesions, mesenchymal SCA (stem cell application) is safe and effective, hence can significantly prevent limb amputation.

In Vitro Inhibitory Effects of Maqian Essential Oil against Ectopic Endometrial Stromal Cells and LPS-Induced Endometrial Epithelial Cells.

Previous studies revealed that MQEO (Maqian fruits essential oil), which is extracted from the fruit of Maqian (Zanthoxylum myriacanthum var. Pubescens), had a good anti-inflammatory effect, but the effect on endometriosis in vitro remains unknown. In the present study, the inhibitory effects of MQEO against the EESCs (ectopic endometrial stromal cells) were investigated. Cells were treated with a concentration gradient (from 0.025 % to 0.15 %) of MQEO for 24 h and cell viability was detected by CCK-8. In addition, apoptotic rates were investigated using flow cytometry. The effect of MQEO on cell migration was determined by wound-healing and transwell assay. The expression of apoptosis-associated and cell adhesion-related proteins was assessed by western blotting. The transcriptional levels of IL-1, IL-6 and TNF-α were determined by Real-time qPCR. RNA-seq was used to identify the DEGs (differentially expressed genes) in MQEO-pretreated EESCs. We found that the MQEO condition dosage-dependently reduced the cell viability of EESCs. Based on flow cytometry results, the number of apoptotic cells increased significantly with dosage. The wound-healing and transwell results showed that MQEO group exhibited a significantly decreased cell motility and migration ability in comparison with the normal group. Western blotting results showed that MQEO down-regulated the expression of Bcl-2, ICAM-1 (intercellular adhesion molecule 1) and CD44, but up-regulated the cleaved caspase-3 expression in EESCs. What's more, MQEO also inhibited the LPS-induced inflammation in human EECs (endometrial epithelial cells). RNA-seq revealed that 221 DEGs were up-regulated genes and 284 DEGs were down-regulated in MQEO-pretreated EESCs. Our data uncovered the beneficial effects of MQEO in endometriosis and provided new insights into the mechanism of the effect of MQEO on EESCs, suggesting MQEO could be a promising new therapeutic agent for endometriosis.

SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes.

Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA-miRNA interactions but also to predict circRNA-cancer and circRNA-gene associations. The AUCs of circRNA-miRNA, circRNA-disease, and circRNA-gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

Global, Regional, and National Burden of Cancer in Children Younger Than 5 Years, 1990-2019: Analysis of the Global Burden of Disease Study 2019.

Objectives: To quantify the burden and variation trends of cancers in children under 5 years at the global, regional, and national levels from 1990 to 2019. Methods: Epidemiological data for children under 5 years who were diagnosed with any one childhood cancer were obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) from 1990 to 2019. The outcomes were the absolute numbers and rates of incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) for different types of cancer. Results: In 2019, 8,774,979.1 incident cases (95% uncertainty interval [UI]: 6,243,599.2 to11,737,568.5) and 8,956,583.8 (6,446,323.9 to 12,364,520.8) prevalent cases of cancer in children under 5 years were identified worldwide; these cancers resulted in 44,451.6 (36,198.7 to 53,905.9) deaths and 3,918,014.8 (3,196,454.9 to 4,751,304.2) DALYs. From 1990 to 2019, although the numbers of incident and prevalent cases only decreased by -4.6% (-7.0 to -2.2) and -8.3% (-12.6 to -3.4), respectively, the numbers of deaths and DALYs clearly declined by -47.8% (-60.7 to -26.4) and -47.7% (-60.7 to -26.2), respectively. In 2019, the middle sociodemographic index (SDI) regions had the highest incidence and prevalence, whereas the low SDI regions had the most mortality and DALYs. Although all of the SDI regions displayed a steady drop in deaths and DALYs between 1990 and 2019, the low-middle and low SDI regions showed increasing trends of incidence and prevalence. Leukemia remained the most common cancer globally in 2019. From 1990 to 2019, the burdens of leukemia, liver cancer, and Hodgkin's lymphoma declined, whereas the incidence and prevalence of other cancers grew, particularly testicular cancer. Conclusions: The global childhood cancer burden in young children has been steadily decreasing over the past three decades. However, the burdens and other characteristics have varied across different regions and types of cancers. This highlights the need to reorient current treatment strategies and establish effective prevention methods to reduce the global burden of childhood cancer.

BioChemDDI: Predicting Drug-Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism.

During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other's mechanisms of action, correctly identifying potential drug-drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.

[Pollution Characterization, Source Identification, and Health Risks of Atmospheric Particle-Bound Heavy Metals in PM2.5 in Zhengzhou City: Based on High-resolution Data].

In order to study the pollution characteristics and sources of heavy metals in urban atmospheric PM2.5, 21 elements in atmospheric PM2.5 in Zhengzhou City were detected using an online metal analyzer during July and October 2017 and January and April 2018, and the changes in heavy metal concentrations were analyzed. Heavy metals were traced by enrichment factors, principal component analysis, and potential source function. The US EPA risk assessment model was used to assess their health risks. The results showed that:the concentrations of K, Zn, Mn, Pb, Cu, As, Cr, and Se increased with the increase in pollution level. The results of enrichment factors and principal component analysis showed that the main sources of heavy metals were crust, mixed combustion, industry, and motor vehicles. The characteristic radar charts showed that the pollution dominated by crustal sources mainly occurred in spring and winter, whereas the pollution dominated by mixed combustion sources mainly occurred in winter. Pb, As, and Ni were greatly affected by the transport of a fen nutrient-laden plain, Beijing-Tianjin-Hebei, and southern Henan, whereas Cd was greatly affected by the northwest region of the sampling site. As presented a significant carcinogenic risk in both adults and children, whereas Pb and Sb presented a significant non-carcinogenic risk in children.

Analysis of Granulocyte-Macrophage Colony-Stimulating Factor-Producing T Helper Cells in a Mouse Model of Contact Hypersensitivity.

Parallel to traditional Th1/Th2/Th17/Treg lineages, granulocyte-macrophage colony-stimulating factor-producing T helper (Th-GM) cells have been identified as a distinct subset of T helper cells (GM-CSF+ IFN-γ- IL-17A- IL-22- effector CD4+ T cells) in human and mice. Contact hypersensitivity (CHS) is considered an excellent animal model for allergic contact dermatitis (ACD) in human, manifesting an intact T cell-mediated immune response. To provide a standardized and comprehensive assay to analyze the Th-GM cell subset in the T cell-dependent immune response in vivo, a murine CHS model was induced by sensitization/challenge with a reactive, low-molecular-weight, organic hapten, 2,4-dinitrofluorobenzene (DNFB). The Th-GM subset in effector CD4+ T cells generated upon immunization with the hapten was analyzed by flow cytometry. We found that Th-GM was mainly expanded in lesions and draining lymph nodes in the DNFB-induced CHS mouse model. This method can be applied to further study the biology of Th-GM cells and pharmacological research of therapeutic strategies centered on GM-CSF in various conditions, such as ACD.

Anesthesia Management at Fuwai Hospital:Practice, Evidence and Outcomes.

Fuwai Hospital was established in 1956 and the Anesthesia Department of Fuwai Hospital was one of the earliest anesthesia departments then in China. Under the leadership of several department directors and with the concerted efforts of all generations of colleagues, the Anesthesia Department of Fuwai Hospital has dramatically transformed, upgraded and modernized. For more than six decades, the Anesthesia Department has been providing high-quality peri-operative anesthesia care for cardiovascular surgeries, conducting innovative experimental and clinical researches, and offering comprehensive training on cardiovascular anesthesiology for professionals across China. Currently, Fuwai Hospital is the National Center for Cardiovascular Diseases of China and one of the largest cardiovascular centers in the world. The present review introduces the Anesthesia Department of Fuwai Hospital, summarizes its current practice of anesthesia management, the outcomes of cardiovascular surgeries at Fuwai Hospital, accumulates relevant evidence, and provides prospects for future development of cardiovascular anesthesiology.