Association between glucose-to-lymphocyte ratio and in-hospital mortality in intensive care patients with sepsis: A retrospective observational study based on Medical Information Mart for Intensive Care IV.

Background: This study aimed to evaluate the association between the glucose-to-lymphocyte ratio (GLR) and in-hospital mortality in intensive care unit (ICUs) patients with sepsis. Methods: This is a retrospective cohort study. Patients with sepsis from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database had their baseline data and in-hospital prognosis retrieved. Multivariable Cox regression analyses were applied to calculate adjusted hazard ratios (HR) with 95% confidence intervals (CI). Survival curves were plotted, and subgroup analyses were stratified by relevant covariates. To address the non-linearity relationship, curve fitting and a threshold effect analysis were performed. Results: Of the 23,901 patients, 10,118 patients with sepsis were included. The overall in-hospital mortality rate was 17.1% (1,726/10,118). Adjusted for confounding factors in the multivariable Cox regression analysis models, when GLR was used as a categorical variable, patients in the highest GLR quartile had increased in-hospital mortality compared to patients in the lowest GLR quartile (HR = 1.26, 95% CI: 1.15-1.38). When GLR was used as a continuous variable, each unit increase in GLR was associated with a 2% increase in the prevalence of in-hospital mortality (adjusted HR = 1.02, 95% CI: 1.01-1.03, p = 0.001). Stratified analyses indicated that the correlation between the GLR and in-hospital mortality was stable. The non-linear relationship between GLR and in-hospital mortality was explored in a dose-dependent manner. In-hospital mortality increased by 67% (aHR = 1.67, 95% CI: 1.45-1.92) for every unit GLR increase. When GLR was beyond 1.68, in-hospital mortality did not significantly change (aHR: 1.04, 95% CI: 0.92-1.18). Conclusion: There is a non-linear relationship between GLR and in-hospital mortality in intensive care patients with sepsis. A higher GLR in ICU patients is associated with in-hospital mortality in the United States. However, further research is needed to confirm the findings.

LITHOPHONE: Improving lncRNA Methylation Site Prediction Using an Ensemble Predictor.

N 6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications, which plays an important role in many biological processes, such as splicing, RNA localization, and degradation. Studies have shown that m6A on lncRNA has important functions, including regulating the expression and functions of lncRNA, regulating the synthesis of pre-mRNA, promoting the proliferation of cancer cells, and affecting cell differentiation and many others. Although a number of methods have been proposed to predict m6A RNA methylation sites, most of these methods aimed at general m6A sites prediction without noticing the uniqueness of the lncRNA methylation prediction problem. Since many lncRNAs do not have a polyA tail and cannot be captured in the polyA selection step of the most widely adopted RNA-seq library preparation protocol, lncRNA methylation sites cannot be effectively captured and are thus likely to be significantly underrepresented in existing experimental data affecting the accuracy of existing predictors. In this paper, we propose a new computational framework, LITHOPHONE, which stands for long noncoding RNA methylation sites prediction from sequence characteristics and genomic information with an ensemble predictor. We show that the methylation sites of lncRNA and mRNA have different patterns exhibited in the extracted features and should be differently handled when making predictions. Due to the used experiment protocols, the number of known lncRNA m6A sites is limited, and insufficient to train a reliable predictor; thus, the performance can be improved by combining both lncRNA and mRNA data using an ensemble predictor. We show that the newly developed LITHOPHONE approach achieved a reasonably good performance when tested on independent datasets (AUC: 0.966 and 0.835 under full transcript and mature mRNA modes, respectively), marking a substantial improvement compared with existing methods. Additionally, LITHOPHONE was applied to scan the entire human lncRNAome for all possible lncRNA m6A sites, and the results are freely accessible at: http://180.208.58.19/lith/.

GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion.

Circular RNA (circRNA) is a closed-loop structural non-coding RNA molecule which plays a significant role during the gene regulation processes. There are many previous studies shown that circRNAs can be regarded as the sponges of miRNAs. Thus, circRNA is also a key point for disease diagnosing, treating and inferring. However, traditional experimental approaches to verify the associations between the circRNA and disease are time-consuming and money-consuming. There are few computational models to predict potential circRNA-disease associations, which become our motivation to propose a new computational model. In this study, we propose a machine learning based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). The known circRNA-disease associations' data are downloaded from cricR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). The feature vector of each circRNA-disease association pair is composed of four parts, which are the statistics information of different biological networks, the graph theory information of different biological networks, circRNA-disease associations' network information and circRNA nucleotide sequence information, respectively. Therefore, we use those feature vectors to train the gradient boosting decision tree regression model. Then, the leave one out cross validation (LOOCV) is adopted to evaluate the performance of our computational model. As for predicting some common diseases related circRNAs, our method GBDTCDA also obtains the better results. The Area under the ROC Curve (AUC) values of Basal cell carcinoma, Non-small cell lung cancer and cervical cancer are 95.8%, 88.3% and 93.5%, respectively. For further illustrating the performance of GBDTCDA, a case study of breast cancer is also supplemented in this study. Thus, our proposed method GBDTCDA is a powerful tool to predict potential circRNA-disease associations based on experimental results and analyses.

Predicting circRNA-Disease Associations Based on Improved Collaboration Filtering Recommendation System With Multiple Data.

With the development of high-throughput techniques, various biological molecules are discovered, which includes the circular RNAs (circRNAs). Circular RNA is a novel endogenous noncoding RNA that plays significant roles in regulating gene expression, moderating the microRNAs transcription as sponges, diagnosing diseases, and so on. Based on the circRNA particular molecular structures that are closed-loop structures with neither 5'-3' polarities nor polyadenylated tails, circRNAs are more stable and conservative than the normal linear coding or noncoding RNAs, which makes circRNAs a biomarker of various diseases. Although some conventional experiments are used to identify the associations between circRNAs and diseases, almost the techniques and experiments are time-consuming and expensive. In this study, we propose a collaboration filtering recommendation system-based computational method, which handles the "cold start" problem to predict the potential circRNA-disease associations, which is named ICFCDA. All the known circRNA-disease associations data are downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Based on these data, multiple data are extracted from different databases to calculate the circRNA similarity networks and the disease similarity networks. The collaboration filtering recommendation system algorithm is first employed to predict circRNA-disease associations. Then, the leave-one-out cross validation mechanism is adopted to measure the performance of our proposed computational method. ICFCDA achieves the areas under the curve of 0.946, which is better than other existing methods. In order to further illustrate the performance of ICFCDA, case studies of some common diseases are made, and the results are confirmed by other databases. The experimental results show that ICFCDA is competent in predicting the circRNA-disease associations.

PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases.

Database URL: http://bioinfo.snnu.edu.cn/CircR2Disease/.

[Health-based risk assessment in the excavating process of VOCs contaminated site].

The dissipative concentration test of VOCs in the remediation process of a typical contaminated site was operated, and three routes of exposure were set up for health risk assessment in the repair process. Analysis showed that carbon tetrachloride was the single pollutant with highest multi-route cumulative non-carcinogenic index, which was as high as 8.86E + 01, and its contribution rate to the integrated non-carcinogenic effects was 74.45%. Respiratory exposure was the exposure route with highest multi-pollutant hazard index, which was 1.01E + 02, accounting for 84. 87% of the comprehensive risk index, and the index of integrated non-carcinogenic damage was 1.19E + 02. 1,2-dichloroethane was the single pollutant with highest multi-route cumulative carcinogenic index, which was as high as 3.08E-02, and its contribution rate to the integrated carcinogenic effects was 69.53%. Respiratory exposure was the exposure route with highest multi-pollutant hazard index, which was 3.96E -02, accounting for 89.39% of the comprehensive risk index, and the index of integrated carcinogenic damage was 4.43E-02.

[Characteristics of gaseous pollutants distribution during remedial excavation at a volatile organic compound contaminated site].

Volatile and semi-volatile organic compounds (VOCs/SVOCs) are commonly identified contaminants in industrial contaminated sites in China. VOCs migrate easily in the environment due to their relatively high volatilities. When disturbed during excavation, for example, VOCs in the soil release to the air in high concentrations within relatively short period of time, joepodizing the health of the sorrounding population, if not appropriately protected. In this study, distribution of gas phase VOCs was monitored during excavation of a site remediation project, using a combined method of field testing instrument and gas phase sampling tubes. Monitoring results indicated that gas phase concentration decreased with distance, exhibiting an alternating peak-and-valley pattern in the down-wind direction. The monitoring results could be stimulated using Gaussian Puff Model. Remediation site health and safety zoning method was developed combining appropriate workplace health and safety air limits and site monitoring results. Personal protection measures deemed appropriated for each safety zone were proposed.