Prioritization of control factors for heavy metals in groundwater based on a source-oriented health risk assessment model.

Heavy metals (HMs) in groundwater seriously threaten ecological safety and human health. To facilitate the effective management of groundwater contamination, priority control factors of HMs in groundwater need to be categorized. A total of 86 groundwater samples were collected from the Huangpi district of Wuhan city, China, during the dry and wet seasons. To determine priority control factors, a source-oriented health risk assessment model was applied to compare the pollution sources and health risks of seven HMs (Cu, Pb, Zn, Cr, Ni, As, and Fe). The results showed that the groundwater had higher As and Fe contents. The sources of HM pollution during the wet period were mainly industrial and agricultural activities and natural sources. During the dry period, origins were more complex due to the addition of domestic discharges, such as sewage wastewater. Industrial activities (74.10% during the wet period), agricultural activities (53.84% during the dry period), and As were identified as the priority control factors for groundwater HMs. The results provide valuable insights for policymakers to coordinate targeted management of HM pollution in groundwater and reduce the cost of HM pollution mitigation.

Assessment of microbiota in the gut and upper respiratory tract associated with SARS-CoV-2 infection.

BACKGROUND: The human microbiome plays an important role in modulating the host metabolism and immune system. Connections and interactions have been found between the microbiome of the gut and oral pharynx in the context of SARS-CoV-2 and other viral infections; hence, to broaden our understanding of host-viral responses in general and to deepen our knowledge of COVID-19, we performed a large-scale, systematic evaluation of the effect of SARS-CoV-2 infection on human microbiota in patients with varying disease severity. RESULTS: We processed 521 samples from 203 COVID-19 patients with varying disease severity and 94 samples from 31 healthy donors, consisting of 213 pharyngeal swabs, 250 sputa, and 152 fecal samples, and obtained meta-transcriptomes as well as SARS-CoV-2 sequences from each sample. Detailed assessment of these samples revealed altered microbial composition and function in the upper respiratory tract (URT) and gut of COVID-19 patients, and these changes are significantly associated with disease severity. Moreover, URT and gut microbiota show different patterns of alteration, where gut microbiome seems to be more variable and in direct correlation with viral load; and microbial community in the upper respiratory tract renders a high risk of antibiotic resistance. Longitudinally, the microbial composition remains relatively stable during the study period. CONCLUSIONS: Our study has revealed different trends and the relative sensitivity of microbiome in different body sites to SARS-CoV-2 infection. Furthermore, while the use of antibiotics is often essential for the prevention and treatment of secondary infections, our results indicate a need to evaluate potential antibiotic resistance in the management of COVID-19 patients in the ongoing pandemic. Moreover, a longitudinal follow-up to monitor the restoration of the microbiome could enhance our understanding of the long-term effects of COVID-19. Video Abstract.

A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with Phospho-STAT3 and IL-31 as Immune Infiltration Relevant Genes.

INTRODUCTION: Ovarian carcinoma is a malignant tumor with a high mortality rate and a lack of effective treatment options for patients at advanced stages. For improving outcomes and helping patients with poor prognosis, choose a suitable therapy and an excellent risk assessment model and new treatment options are needed. MATERIALS AND METHODS: Ovarian cancer gene expression profile of GSE32062 was downloaded from the NCBI GEO database for screening differentially expressed genes (DEGs) between well and poor prognosis groups using limma package in R (version 3.4.1). Prognosis-related genes and clinical prognostic factors were obtained from univariate and multivariate Cox regression analyses, and a comprehensive risk assessment model was constructed using a Pathway Dysregulation Score (PDS) matrix, Cox-Proportional Hazards (Cox-PH) regression, as well as L1-least absolute shrinkage and selection operator (L1-LASSO) penalization. Then, significant DEGs were converted to pathways and optimal prognosis-related pathways were screened. Finally, risk prediction models based on pathways, genes involved in pathways, and comprehensive clinical risk factors with pathways were built. Their prognostic functions were assessed in verification sets. Besides, genes involved in immune-pathways were checked for immune infiltration using immunohistochemistry. RESULTS: A superior risk assessment model involving 9 optimal combinations of pathways and one clinical factor was constructed. The pathway-based model was found to be superior to the gene-based model. Phospho-STAT3 (from JAK-STAT signaling pathway) and IL-31 (from DEGs) were found to be related to immune infiltration. CONCLUSION: We have generated a comprehensive risk assessment model consisting of a clinical risk factor and pathways that showed a possible bright foreground. The set of significant pathways might play as a better prognosis model which is more accurate and applicable than the DEG set. Besides, p-STAT3 and IL-31 showing correlation to immune infiltration of ovarian cancer tissues may be potential therapeutic targets for treating ovarian cancers.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC+ and LSC- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC+ and LSC- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

Susceptibility breakpoint for Danofloxacin against swine Escherichia coli.

BACKGROUND: Improper use of antimicrobials results in poor treatment and severe bacterial resistance. Breakpoints are routinely used in the clinical laboratory setting to guide clinical decision making. Therefore, the objective of this study was to establish antimicrobial susceptibility breakpoints for danofloxacin against Escherichia coli (E.coli), which is an important pathogen of digestive tract infections. RESULTS: The minimum inhibitory concentrations (MICs) of 1233 E. coli isolates were determined by the microdilution broth method in accordance with the guidelines in Clinical and Laboratory Standards Institute (CLSI) document M07-A9. The wild type (WT) distribution or epidemiologic cutoff value (ECV) was set at 8 µg/mL with statistical analysis. Plasma drug concentration data were used to establish pharmacokinetic (PK) model in swine. The in vitro time kill test in our study demonstrated that danofloxacin have concentration dependent activity against E.coli. The PK data indicated that danofloxacin concentration in plasma was rapidly increased to peak levels at 0.97 h and remained detectable until 48 h after drug administration. The pharmacodynamic cutoff (COPD) was determined as 0.03 µg/mL using Monte Carlo simulation. To the best of our knowledge, this is the first study to establish the ECV and COPD of danofloxacin against E.coli with statistical method. CONCLUSIONS: Compared to the COPD of danofloxacin against E.coli (0.03 µg/mL), the ECV for E.coli seemed reasonable to be used as the final breakpoint of danofloxacin against E.coli in pigs. Therefore, the ECV (MIC ≤8 µg/mL) was finally selected as the optimum danofloxacin susceptibility breakpoint for swine E.coli. In summary, this study provides a criterion for susceptibility testing and improves prudent use of danofloxacin for protecting public health.

Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method.

Adult neural stem cells (NSCs) are a group of multi-potent, self-renewing progenitor cells that contribute to the generation of new neurons and oligodendrocytes. Three subtypes of NSCs can be isolated based on the stages of the NSC lineage, including quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs). Although it is widely accepted that these three groups of NSCs play different roles in the development of the nervous system, their molecular signatures are poorly understood. In this study, we applied the Monte-Carlo Feature Selection (MCFS) method to identify the gene expression signatures, which can yield a Matthews correlation coefficient (MCC) value of 0.918 with a support vector machine evaluated by ten-fold cross-validation. In addition, some classification rules yielded by the MCFS program for distinguishing above three subtypes were reported. Our results not only demonstrate a high classification capacity and subtype-specific gene expression patterns but also quantitatively reflect the pattern of the gene expression levels across the NSC lineage, providing insight into deciphering the molecular basis of NSC differentiation.