Occurrence, Seasonal Variation and Risk Assessment of Antibiotics in the Surface Water of North China.

In this study, the occurrence, seasonal, and spatial variations of four classes antibiotics were investigated in the surface water of North China. Water samples were taken from 24 sampling sites along rivers in May and August and antibiotics in water samples were detected by SPE-UPLC-MS/MS. The occurrence of all antibiotics except for FLO in May were higher than in August. The mean concentrations of four classes antibiotics detected in May and August were in the following order respectively: quinolones (421.23 ng/L) > tetracyclines (28.37 ng/L) > amphenicols (20.38 ng/L) > sulfonamides (5.79 ng/L) and amphenicols (284.36 ng/L) > quinolones (15.74 ng/L) > tetracyclines (3.05 ng/L) > sulfonamides (0.20 ng/L). The results showed that quinolones and amphenicols were dominant antibiotics among four classes antibiotics. To explore the source of antibiotics from the fish ponds nearby, antibiotic concentration data, which was investigated in the sediment, fish feed and fish revealed a direct relationship between the main antibiotics and fish farms along the rivers. Risk assessment data indicated enrofloxacin and florfenicol could cause higher safety risks to aquatic organisms compared to other antibiotics.

A novel feature extraction scheme with ensemble coding for protein-protein interaction prediction.

Protein-protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp.

HGFK1 Enhances the Anti-Tumor Effects of Angiogenesis Inhibitors via Inhibition of CD90+ CSCs in Hepatocellular Carcinoma.

The combination of anti-angiogenesis agents with immune-checkpoint inhibitors is a promising treatment for patients with advanced hepatocellular carcinoma (HCC); however, therapeutic resistance caused by cancer stem cells present in tumor microenvironments remains to be overcome. In this study, we report for the first time that the Kringle 1 domain of human hepatocyte growth-factor α chain (HGFK1), a previously described anti-angiogenesis peptide, repressed the sub-population of CD90+ cancer stem cells (CSCs) and promoted their differentiation and chemotherapy sensitivity mainly through downregulation of pre-Met protein expression and inhibition of Wnt/ß-catenin and Notch pathways. Furthermore, we showed that the i.p. injection of PH1 (a tumor-targeted and biodegradable co-polymer), medicated plasmids encoding Endostatin (pEndo), HGFK1 genes (pEndo), and a combination of 50% pEndo + 50% pHGFK1 all significantly suppressed tumor growth and prolonged the survival of the HCC-bearing mice. Importantly, the combined treatment produced a potent synergistic effect, with 25% of the mice showing the complete clearance of the tumor via a reduction in the microvessel density (MVD) and the number of CD90+ CSCs in the tumor tissues. These results suggest for the first time that HGFK1 inhibits the CSCs of HCC. Furthermore, the combination of two broad-spectrum anti-angiogenic factors, Endo and HGFK1, is the optimal strategy for the development of effective anti-HCC drugs.

Architecture and active motif engineering of N-CoS2@C yolk-shell nanoreactor for boosted tetracycline removal via peroxymonosulfate activation: Performance, mechanism and destruction pathways.

Rational construction of yolk-shell architecture with regulated binding configuration is crucially important but challengeable for antibiotic degradation via peroxymonosulfate (PMS) activation. In this study, we report the utilization of yolk-shell hollow architecture consisted of nitrogen-doped cobalt pyrite integrated carbon spheres (N-CoS2@C) as PMS activator to boost tetracycline hydrochloride (TCH) degradation. The creation of yolk-shell hollow structure and nitrogen-regulated active site engineering of CoS2 endow the resulted N-CoS2@C nanoreactor with high activity for PMS activating toward TCH degradation. Intriguingly, the N-CoS2@C nanoreactor exhibits an optimal degradation performance with a rate constant of 0.194 min-1 toward TCH via PMS activation. The 1O2 and SO4•- species are demonstrated as the dominant active substances for TCH degradation through quenching experiments and electron spin resonance characterization. The possible degradation mechanism, intermediates and degradation pathways for TCH removal over the N-CoS2@C/PMS nanoreactor are unveiled. Graphitic N, sp2-hybrid carbon, oxygenated group (C-OH) and Co species are verified as the possible catalytic sites of N-CoS2@C for PMS activation toward TCH removal. This study offers a unique strategy to engineer sulfides as highly efficient and promising PMS activators for antibiotic degradation.

Structural repurposing of SGLT2 inhibitor empagliflozin for strengthening anti-heart failure activity with lower glycosuria.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have been reapproved for heart failure (HF) therapy in patients with and without diabetes. However, the initial glucose-lowering indication of SGLT2i has impeded their uses in cardiovascular clinical practice. A challenge of SGLT2i then becomes how to separate their anti-HF activity from glucose-lowering side-effect. To address this issue, we conducted structural repurposing of EMPA, a representative SGLT2 inhibitor, to strengthen anti-HF activity and reduce the SGLT2-inhibitory activity according to structural basis of inhibition of SGLT2. Compared to EMPA, the optimal derivative JX01, which was produced by methylation of C2-OH of the glucose ring, exhibited weaker SGLT2-inhibitory activity (IC50 > 100 nmol/L), and lower glycosuria and glucose-lowering side-effect, better NHE1-inhibitory activity and cardioprotective effect in HF mice. Furthermore, JX01 showed good safety profiles in respect of single-dose/repeat-dose toxicity and hERG activity, and good pharmacokinetic properties in both mouse and rat species. Collectively, the present study provided a paradigm of drug repurposing to discover novel anti-HF drugs, and indirectly demonstrated that SGLT2-independent molecular mechanisms play an important role in cardioprotective effects of SGLT2 inhibitors.

Prediction of interacting protein pairs from sequence using a Bayesian method.

With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein-protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.

Identifying protein-protein interaction sites using covering algorithm.

Identification of protein-protein interface residues is crucial for structural biology. This paper proposes a covering algorithm for predicting protein-protein interface residues with features including protein sequence profile and residue accessible area. This method adequately utilizes the characters of a covering algorithm which have simple, lower complexity and high accuracy for high dimension data. The covering algorithm can achieve a comparable performance (69.62%, Complete dataset; 60.86%, Trim dataset with overall accuracy) to a support vector machine and maximum entropy on our dataset, a correlation coefficient (CC) of 0.2893, 58.83% specificity, 56.12% sensitivity on the Complete dataset and 0.2144 (CC), 53.34% (specificity), 65.59% (sensitivity) on the Trim dataset in identifying interface residues by 5-fold cross-validation on 61 protein chains. This result indicates that the covering algorithm is a powerful and robust protein-protein interaction site prediction method that can guide biologists to make specific experiments on proteins. Examination of the predictions in the context of the 3-dimensional structures of proteins demonstrates the effectiveness of this method.

Fabric-phase sorptive extraction coupled with ion mobility spectrometry for on-site rapid detection of PAHs in aquatic environment.

The contamination of water is a high risk to human health, so there is an urgent need to rapidly detect water pollution in the field. Ion mobility spectrometry (IMS) is suitable for on-site analysis with the merit of rapid analysis and compact size. In this study, we developed a new method which coupled fabric phase sorptive extraction (FPSE) with IMS for rapid detection of polycyclic aromatic hydrocarbons (PAHs) in water present in the field. Polydimethylsiloxane (PDMS) was coated on the glass fiber cloth through a sol-gel reaction. After extracting the PAHs in water, the fabric coated PDMS could be directly put into the inlet of IMS instrument for thermal desorption. The PAHs were analyzed by the IMS instrument operated in the positive ion mode with a corona discharge (CD) ionization source. The primary parameters affecting extraction efficiency such as extraction time, extraction temperature, and ionic strength were investigated and optimized by using phenanthrene (Phe), benzo[a]anthracene (BaA) and benzo[a]pyrene (BaP) as model compounds. Under the optimal conditions, the FPSE-IMS detection limits were 5 ng ml-1,8 ng ml-1 and 10 ng ml-1 respectively. Satisfactory recoveries were obtained in the range from 80.5% to 100.5% by testing the spiked real water samples and validated by the standard method（HJ487-2009）. Based on the results, the method of FPSE-IMS could be feasibly applied for monitoring the water quality on-site and providing early warning in the field.

Divergent topological networks in Alzheimer's disease: a diffusion kurtosis imaging analysis.

BACKGROUND: Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer's disease (AD). METHODS: Magnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches. RESULTS: We found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks. CONCLUSIONS: Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.

Cognitive dysfunction in adult patients with neuromyelitis optica: a systematic review and meta-analysis.

The objective of this study was to investigate cognitive dysfunction in 24-60-year-old neuromyelitis optica (NMO) patients, demographically matched healthy subjects, and MS patients. We conducted a comprehensive literature review of the PubMed, Medline, EMBASE, CNKI, Wan Fang Date, Web of Science, and Cochrane Library databases from inception to May 2016 for case-control studies that reported cognitive test scores in NMO patients, healthy subjects, and MS patients. Outcome measures were cognitive function evaluations, including performance on attention, language, memory, information processing speed, and executive function tests. The meta-analysis included eight studies. NMO patients performed significantly worse on attention (P < 0.00001), language (P = 0.00008), memory (P = 0.00004), information processing speed (P < 0.00001), and executive function tests (P = 0.00009) than healthy subjects. There were no significant differences in performance between NMO patients and MS patients on these tests. This meta-analysis indicates that NMO patients aged 24-60 years have significantly worse cognitive performance than demographically matched healthy subjects. However, this was comparable to the performance of demographically matched MS patients. There is a need for further rigorous randomized controlled trials with focus on elucidating the underlying mechanism of cognitive dysfunction in NMO patients.

Identification and analysis of driver missense mutations using rotation forest with feature selection.

Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes. Many approaches are proposed which use supervised machine learning techniques for prediction with features obtained by some databases. However, often we do not know which features are important for driver mutations prediction. In this study, we propose a novel feature selection method (called DX) from 126 candidate features' set. In order to obtain the best performance, rotation forest algorithm was adopted to perform the experiment. On the train dataset which was collected from COSMIC and Swiss-Prot databases, we are able to obtain high prediction performance with 88.03% accuracy, 93.9% precision, and 81.35% recall when the 11 top-ranked features were used. Comparison with other various techniques in the TP53, EGFR, and Cosmic2plus datasets shows the generality of our method.

Improved prediction of protein binding sites from sequences using genetic algorithm.

We undertook this project in response to the rapidly increasing number of protein structures with unknown functions in the Protein Data Bank. Here, we combined a genetic algorithm with a support vector machine to predict protein-protein binding sites. In an experiment on a testing dataset, we predicted the binding sites for 66% of our datasets, made up of 50 testing hetero-complexes. This classifier achieved greater sensitivity (60.17%), specificity (58.17%), accuracy (64.08%), and F-measure (54.79%), and a higher correlation coefficient (0.2502) than those of the support vector machine. This result can be used to guide biologists in designing specific experiments for protein analysis.