LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk.

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

Chinese Herbal Medicine for Idiopathic Pulmonary Fibrosis: An Overview of Systematic Review.

Objective: To summarize the use of Chinese Herbal Medicines (CHMs) for Idiopathic Pulmonary Fibrosis (IPF) and provide high-level evidence for clinical decisions. Methods: We analyzed systematic reviews (SRs). Two English-language and three Chinese-language electronic databases were searched from inception to July 1, 2019. Published SRs and meta-analyses evaluating CHM use in IPF and reporting clinically-relevant outcomes such as lung function, PO2, and quality of life were eligible for inclusion in this overview. The methodological qualities of the included SRs were assessed by AMSTAR and ROBIS tools. Results: All reviews were published from 2008 to 2019. 15SRs were published in Chinese-language while 2 were in English. A total of 15550 participants were included. All intervention arms received CHM with or without conventional treatment and were compared with control arms with conventional treatment alone, or hormone therapy. Twelve SRs were assessed with low risk of bias while five were assessed high risk by ROBIS. The quality of evidence was assessed to be "moderate" or "low" or "very low" using GRADE. Conclusions: CHM has potential benefits for patients with IPF especially in improving lung function (forced vital capacity (FVC), total lung capacity (TLC), and diffusing capacity of the lungs for carbon monoxide (DLCO)), PO2 level, and the quality of life of patients. Due to the low methodological quality of reviews, our findings should be interpreted with caution.

Association of genetic polymorphisms with mercapturic acids in the urine of young healthy subjects before and after exposure to outdoor air pollution.

We aimed to identify the relationship between variations in metabolic genes and human urinary changes in mercapturic acids (MAs), including CEMA, HMPMA, SPMA, HPMA and HEMA, before and after air pollution exposure. Genotype detection for 47 relevant single nucleotide polymorphisms (SNPs) collected by literature research was performed. Five MAs expression levels in the urinary samples of 50 young healthy individuals with short-term exposure to clean, polluted and purified air at five time points were detected by targeted online solid-phase extraction liquid chromatography tandem mass spectrometry (SPE-LC-MS/MS), followed with associations of SNPs with MAs changes. Difference in MAs between polluted and clean/purified air was significantly associated with 21 SNPs mapped into 9 genes. Five SNPs in GSTP1 showed the most prominent association with the changes in SPMA expression, indicating that those SNPs in GSTP1 and SPMA might serve as biomarkers for susceptibility and the prognosis of lung cancer.

Potential urinary biomarkers in young adults with short-term exposure to particulate matter and bioaerosols identified using an unbiased metabolomic approach.

Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.