Peritoneal dialysis (PD)-related peritonitis is a major cause of injury and technique failure in patients undergoing PD. Aeromonas hydrophila is ubiquitous in the environment, and is a Gram-negative rod associated with infections in fish and amphibians in most cases; however, it can also cause opportunistic infections in immunocompromised patients. We report a case of A. hydrophila peritonitis in a 56-year-old male on automated PD. Peritonitis may have been caused by contamination of the Set Plus, a component of the automated peritoneal dialysis device. Although Set Plus is disposable, the patient reused the product by cleansing with tap water. He was successfully treated with intraperitoneally-administered ceftazidime and has been well without recurrence for more than 2 years.

Background: Many blood markers have been shown to predict the recurrence and survival of various malignancies, but the effects of surgery on the body's inflammatory levels may cause changes in these inflammatory markers. Therefore, in this study, we assessed the relationship between changes in platelet to lymphocyte ratio (PLR) and survival and recurrence in patients with T3-T4 laryngeal squamous cell carcinoma (LSCC). Methods: Data of patients with T3-T4 HSCC were reviewed. Continuous variables were expressed as mean ± SD and were compared using t test or Mann-Whitney U test. The covariate distributions were compared by Chi-square test. Survival curve was estimated by Kaplan-Meier analysis, and Log-Rank test were performed to estimate the survival curve and significance of the difference in survival distribution between groups, respectively. The prognostic value was uncovered by univariate and multivariate Cox hazards analysis. Results: The 413 consecutive patients with LSCC were reviewed. Of these, 362 patients who met the criteria were selected, multi-factor analysis found that pathological T classification(hazard ratio [HR] = 1.878; 95% confidence interval [CI] = 1.342–3.023; P<0.001), pathological N classification (HR = 1.212; 95% CI = 0.867–2.125; P< 0.001) and change of PLR (HR = 2.158; 95% CI = 1.332–2.889; P = 0.004) associated with postoperative recurrence of T3-T4 LSCC. In addition, the pathological T classification (HR = 1.901; 95% CI = 1.255–2.999; P<0.001), pathological N classification (HR = 1.244; 95% CI = 0.810–2.212; P<0.001) and change of PLR (HR = 2.011; 95% CI = 1.354–2.753; P = 0.001) associated with postoperative survival in patients with T3-T4 LSCC. Conclusions: Results demonstrate that change in PLR may serve as a useful prognostic predictor for patients with T3-T4 LSCC.

Osteosarcoma (OSA) represents the most common primary bone tumor in humans and pet dogs. Little progress has been made with regard to viable treatment options in the past three decades and patients presenting with metastatic disease continue to have a poor prognosis. Recent mouse studies have suggested that microRNA-34a (miR-34a) may have anti-tumor activities in human OSA models. Due to the conservation of microRNA across species, we hypothesized that a bioengineered miR-34a prodrug (tRNA/miR-34a) would have similar effects in canine OSA, providing a valuable preclinical model for development of this therapeutic modality. Using a panel of canine OSA cell lines, we found that tRNA/miR-34a reduced viability, clonogenic growth, and migration and invasion while increasing tumor cell apoptosis. Furthermore, canine OSA cells successfully process the tRNA/miR-34a into mature miR-34a which reduces expression of target proteins such as platelet derived growth factor receptor alpha (PDGFRα), Notch1 and vascular endothelial growth factor (VEGF). Additionally, our subcutaneous OSA xenograft model demonstrated in vivo tumor growth delay, increased necrosis and apoptosis by tRNA/miR-34a, and decreased cellular proliferation ability. Taken together, these data support that this novel microRNA-based therapy may possess clinical utility in a spontaneously-occurring large animal model of OSA, which can then serve to inform the clinical development of this therapy for human OSA patients.

Environmental contaminants are one of the important causal factors for development of various human diseases. In particular, the perinatal period is highly vulnerable to environmental toxicants and resultant dysregulation of fetal development can cause detrimental health outcomes potentially affecting life-long health. Perfluoroalkyl compounds (PFCs), emerging environmental pollutants, are man-made organic molecules, which are widely used in diverse industries and consumer products. PFCs are non-degradable and bioaccumulate in the environment. Importantly, PFCs can be found in cord blood and breast milk as well as in the general population. Due to their physicochemical properties and potential toxicity, many studies have evaluated the health effects of PFCs. This review summarizes the epidemiological and experimental studies addressing the association of PFCs with neurotoxicity and immunotoxicity. While the relationships between PFC levels and changes in neural and immune health are not yet conclusive, accumulative studies provide evidence for positive associations between PFC levels and the incidence of attention deficit hyperactivity disorder and reduced immune response to vaccination both in children and adults. In conclusion, PFCs have the potential to affect human health linked with neurological disorders and immunosuppressive responses. However, our understanding of the molecular mechanism of the effects of PFCs on human health is still in its infancy. Therefore, along with efforts to develop methods to reduce exposure to PFCs, studies on the mode of action of these chemicals are required in the near future.

Background Micro-RNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA in a data driven fashion. On one hand, sequence-based methods for predicting miRNA targets tend to make too many false positive calls. On the other hand, analyzing expression correlation between miRNAs and mRNAs cannot establish whether relationship between a pair of correlated miRNA and mRNA is causal. Methods In this study, we designed a deep learning model, referred to as miRNA causal deep net (mCADET), which aims to explicitly represent two types of statistical relationships between miRNAs and mRNAs: correlation resulting from confounded co-regulation and correlation as a result of causal regulation. The model utilizes a deep neural network to simulate transcription mechanism that leads to co-expression of miRNA and mRNA, and, in addition, it also contains directed edges from miRNAs to mRNAs to capture causal relationships among them. Results We trained the mCADET model using pan-cancer miRNA and mRNA data from The Cancer Genome Atlas (TCGA) project to investigate mechanism of co-expression and causal interactions between miRNAs and mRNAs. Quantitative analyses of the results indicate that the mCADET significantly outperforms conventional deep learning models when modeling combined miRNA and mRNA expression data, indicating its superior capability of capturing the high-order statistical structures in the data. Qualitative analysis of predicted targets of miRNAs indicate that predictions by mCADET agree well with existing knowledge. Finally, the predictions by mCADET have a significantly lower false discovery rate and better overall accuracy in comparison to sequence-based and correlation-based methods when comparing to experimental results. Conclusion The mCADET model can simultaneously infer the states of cellular signaling system regulating co-expression of miRNAs and mRNAs, while capturing their causal relationships in a data-driven fashion.

Eight yeast strains of the genera Saccharomyces and Kluyveromyces were screened to ferment high lactose-load cheese whey permeate (CWP) (>130 g/L lactose) without nutrient supplementation. The fermentation conditions (temperature, pH and time) were optimized to maximize the fermentation performance (ethanol titer, ethanol yield and lactose consumption) for the two preselected strains, K. marxianus DSM 5422 and S. cerevisiae Ethanol Red, using a response surface methodology (RSM). Under optimized conditions, K. marxianus DSM 5422 attained ethanol titers of 6% (v/v) in only 44 h. Moreover, the feasibility of immobilizing this strain on four different inorganic supports (plastic, glass and Tygon silicone Raschig rings and alumina beads) was assessed. Glass Raschig rings and alumina beads showed a more stable performance over time, yielding ethanol titers of 60 g/L during 1,000 hours, which remarkably reduces yeast cultivation costs. Results demonstrate the feasibility of using CWP for successful ethanol production in a simple and economical process, which represents an attractive alternative for waste treatment in dairy industries.

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

The International Antibody Validation meetings offer a welcome British forum for discussing this important topic, which is existentially crucial for the biological sciences community. Now in its 6th year, the biennial meeting is organized by Andrew Chalmers (University of Bath; CiteAb), this year with Carly Dix (Astra Zeneca).  The organizers gathered some 100 members of industry and academia, producers and users, for a day and a half to describe their efforts to ensure that their antibodies have the desired specificity and selectively for well-defined molecular targets. The meeting is largely available as WebCasts ( http://www.antibodyvalidation.co.uk/past-events/2018).

A green, mussel-inspired bioadhesive based on oligomerization of hydrocaffeic acid was synthesized in water by an ultrafast one-step reaction in the presence of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide as an activating agent. The resulting oligomers exhibited strong wet adhesion when applied to different substrates including glass, stainless steel, and aluminum. Compared to most commercial adhesives, this bioinspired adhesive is produced via a sustainable and green process, i.e., aqueous-based synthesis, one-step reaction, and in the absence of any purification step to obtain the final functional adhesive.

Previous studies have suggested that exposure to ionizing radiation increases the risk of ischemic heart disease (IHD). The data from the Mayak nuclear worker cohort have indicated enhanced risk for IHD incidence. The goal of this study was to elucidate molecular mechanisms of radiation-induced IHD by integrating proteomics data with a transcriptomics study on post mortem cardiac left ventricle samples from Mayak workers categorized in four radiation dose groups (0 Gy, < 100 mGy, 100–500 mGy, > 500 mGy). The proteomics data that were newly analysed here, originated from a label-free analysis of cardiac samples. The transcriptomics analysis was performed on a subset of these samples. Stepwise linear regression analyses were used to correct the age-dependent changes in protein expression, enabling the separation of proteins, the expression of which was dependent only on the radiation dose, age or both of these factors. Importantly, the majority of the proteins showed only dose-dependent expression changes. Hierarchical clustering of the proteome and transcriptome profiles confirmed the separation of control and high-dose samples. Restrictive (separate p-values) and integrative (combined p-value) approaches were used to investigate the enrichment of biological pathways. The integrative method proved superior in the validation of the key biological pathways found in the proteomics analysis, namely PPAR signalling, TCA cycle and glycolysis/gluconeogenesis. This study presents a novel, improved, and comprehensive statistical approach of analysing biological effects on a limited number of samples.