Quantitative risk assessments of Salmonella spp. in domestic pork in China.

Pork is one of the most commonly consumed meats, and its safety has always been a concern. Recently, safety incidents caused by chemical or biological contamination such as drug residues, heavy metals, and pathogenic microorganisms in pork have been reported, and the safety of pork is a cause for concern. Salmonella spp. is one of the important foodborne pathogens that threaten human health. Pork is a high-risk vector food for Salmonella spp. infection. The assessment of the safety risk of Salmonella spp. in pork is conducive to the prevention of related foodborne diseases. In this paper, risk assessment models for Salmonella spp. in meat were developed. The quantitative risk assessment model for Salmonella spp. based on the pork supply chain showed that the annual number of cases of salmonellosis due to pork consumption in China is approximately 27 per 10,000 males and 24 per 10,000 females. Sensitivity analysis showed that the main factors affecting the risk of Salmonella spp. in pork were the display temperature, display time, and Salmonella spp. contamination concentration in pork at the sale.

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment.

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

Risk assessment of norovirus and hepatitis A virus in strawberries imported into China.

Norovirus (NoV) and hepatitis A virus (HAV) pose a considerable health risk worldwide. In recent years, many cases of virus infection caused by virus-contaminated strawberries have occurred worldwide. This study applied a critical control point system to analyze the main hazards during the production and marketing of strawberries imported into China and explore the key control points in the whole process. To further evaluate the risks in the supply chain, the established quantitative microbial risk assessment (QMRA) was used to determine the probability that residents would be infected with viruses after consuming imported strawberries. It was found that the risk of virus contamination from imported strawberries was low, and the virus contamination mainly results from water resources and personnel. This research helps the regulatory authorities formulate strategies to ensure the long-term microbial safety of imported strawberries. In addition, the methods may prove useful in evaluating the risks of other agricultural produce.

Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches.

Calcium-activated chloride channels (CaCCs) are chloride channels that are regulated according to intracellular calcium ion concentrations. The channel protein ANO1 is widely present in cells and is involved in physiological activities including cellular secretion, signaling, cell proliferation and vasoconstriction and diastole. In this study, the ANO1 inhibitors were investigated with machine learning and molecular simulation. Two-dimensional structure-activity relationship (2D-SAR) and three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for the qualitative and quantitative prediction of ANO1 inhibitors. The results showed that the prediction accuracies of the model were 85.9% and 87.8% for the training and test sets, respectively, and 85.9% and 87.8% for the rotating forest (RF) in the 2D-SAR model. The CoMFA and CoMSIA methods were then used for 3D QSAR modeling of ANO1 inhibitors, respectively. The q2 coefficients for model cross-validation were all greater than 0.5, implying that we were able to obtain a stable model for drug activity prediction. Molecular docking was further used to simulate the interactions between the five most promising compounds predicted by the model and the ANO1 protein. The total score for the docking results between all five compounds and the target protein was greater than 6, indicating that they interacted strongly in the form of hydrogen bonds. Finally, simulations of amino acid mutations around the docking cavity of the target proteins showed that each molecule had two or more sites of reduced affinity following a single mutation, indicating outstanding specificity of the screened drug molecules and their protein ligands.

An ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry identification and characterization of the active constituents from Abrus mollis Hance.

Abrus mollis Hance is a traditional Chinese medicine that is widely used to treat acute and chronic hepatitis, steatosis, and fibrosis. Its therapeutic qualities of it have long been acknowledged, although the active ingredients responsible for its efficacy and the mechanisms of its action are unknown. In this study, the chemical constituents absorbed into the blood from Abrus mollis Hance were assessed by using liquid chromatography-quadrupole-time-of-flight mass spectrometry and the data was analyzed with the UNIFI screening platform. The results obtained were compared to existing chromatographic-mass spectrometry information, including retention times and molecular weights as well as known reference compounds. 41 chemical constituents were found in Abrus mollis Hance, and these included 16 flavonoids, 13 triterpenoids, five organic acids, and two alkaloids. Experimentally it was found that Abrus mollis Hance had a therapeutic benefit when treating α-naphthalene isothiocyanate-induced acute liver injury in rats. In addition, 11 blood prototypical constituents, including six flavonoids, three triterpenoids, and two alkaloids, were found in serum samples following intragastric administration of Abrus mollis Hance extracts to rats. This novel study can be used for the quality control and pharmacodynamic assessment of Abrus mollis Hance in order to assess its efficacy in the therapeutic treatment of patients.

Anti-inflammatory effect of miR-125a-5p on experimental optic neuritis by promoting the differentiation of Treg cells.

Methylprednisolone pulse treatment is currently used for optic neuritis. It can speed visual recovery, but does not improve the ultimate visual outcomes. Recent studies have reported that miR-125a-5p has immunomodulatory effects on autoimmune diseases. However, it remains unclear whether miR-125a-5p has effects on optic neuritis. In this study, we used adeno-associated virus to overexpress or silence miR-125a-5p in mice. We found that silencing miR-125a-5p increased the latency of visual evoked potential and aggravated inflammation of the optic nerve. Overexpression of miR-125a-5p suppressed inflammation of the optic nerve, protected retinal ganglion cells, and increased the percentage of Treg cells. Our findings show that miR-125a-5p exhibits anti-inflammatory effects through promoting the differentiation of Treg cells.

Epidemiologic and import risk analysis of Peste des petits ruminants between 2010 and 2018 in India.

BACKGROUND: Peste des petits ruminants (PPR) is a serious disease that affects goats, sheep and other small ruminants. As one of the earliest and most serious countries, PPR has seriously threatened India's animal husbandry economy. RESULTS: In this study, the spatiotemporal characteristics of the PPR in India outbreaks were analyzed. Between 2010 and 2018, the epidemic in India broke out all over the country in a cluster distribution. Epidemic clusters in northern and southern India are at higher risk, and the outbreak time of PPR has significant seasonality. The results of the analysis of the development and transmission of PPR under the natural infection conditions showed that the PPR outbreak in India reached a peak within 15 days. Finally, the quantitative risk analysis results based on scenario tree show showed that the average probability of infecting PPRV in live sheep exported from India was 1.45 × 10-4. CONCLUSIONS: This study analyzed the prevalence of PPR in India. The analysis of transmission dynamics on the development of the epidemic provides a reference for the prevention and control of the epidemic. At the same time, it provides risk analysis and suggestions on trade measures for the trading countries of India.

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches.

Alzheimer's disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with　K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q2 = 0.612 (cross-validated correlation coefficient) and r2 = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry.

Risk assessment and early warning of the presence of heavy metal pollution in strawberries.

Heavy metal pollution is a major threat to agricultural produce and it can pose potential ecological risks which subsequently impacts on human health. Strawberries are an economically important produce of China. The intrinsic link of heavy metal pollution risk in the soil-strawberry ecosystem is of concern. In this study, the pollution index of heavy metal pollutants in farmlands of different provinces were evaluated, and the results showed significantly high levels of cadmium. In addition, Nemerow integrated pollution index analysis showed that low-pollution farmlands only accounted for 14.07% of the total arable land area. Then, the transfer factors were used to calculate the migration of heavy metals from the soil into strawberries. The results showed that cadmium and nickel were relatively high in strawberries from the Guangxi province. Similar results were found for mercury in Jiangxi Province. The pollution index of single food pollution also showed that mercury in strawberries from Jiangxi Province was at a moderate pollution level. The comprehensive pollution index indicated that heavy metal pollution in strawberries in Central China may be severe. In addition, spatial clustering analysis showed that cadmium, chromium, lead, arsenic and zinc in strawberries had significant hotspot clustering in central, south and southwest China. Finally, our studies also suggested that the risk of carcinogenic and non-carcinogenic diseases was higher in the (2, 4] years age group than in other age groups. People in Yunnan Province were also found to have a higher non-carcinogenic risk than those in other provinces and cities in China. This study provides a comprehensive view of the potential risks of heavy metal contamination in strawberries, which could provide assistance in the design of regulatory and risk management programs for chemical pollutants in strawberries, thus ensuring the safety of consumption of these edible fruits.

Risk Assessment of Veterinary Drug Residues in Pork on the Market in the People's Republic of China.

ABSTRACT: Veterinary drugs, including antibiotics, antiparasitics, and growth promoters, are widely used in animal husbandry. Veterinary drug residues are key issues of food safety because they arouse public concern and can seriously endanger the health of consumers. To assess the risk of veterinary drug residues in pork sold in the People's Republic of China, the potential veterinary drug residue risks in imported and domestic pork were analyzed based on regulatory differences and veterinary drug residue safety incidents. For imported pork, a risk assessment model was established based on the differences in veterinary drug residue limits for the People's Republic of China, Brazil, the United States, Australia, Thailand, and Russia combined with comprehensive evaluation methods. The potential risk of veterinary drug residues in U.S. pork was the highest, and that in Brazilian pork was the lowest. For domestic pork, the distribution and aggregation of veterinary drug residue safety incidents in the People's Republic of China was analyzed from 2015 to 2019 with a geographic information system. This study provides new insights into the safety of pork on the Chinese market and a scientific basis for formulating targeted supervision and early warning strategies.

Application of Machine Learning Methods for the Development of Antidiabetic Drugs.

Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development and opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Recent Advances in Neuromyelitis Optica Spectrum Disorder: Pathogenesis, Mechanisms and Potential Treatments.

Neuromyelitis optica spectrum disorder (NMOSD) is an acute or subacute demyelinating disease that affects mainly the optic nerve and spinal cord. A major proportion of NMOSD cases have a relationship with autoimmunity to aquaporin 4 (AQP4) found on the central nervous system. NMOSD can occur repeatedly, causing symptoms such as decreased vision and weakness of limbs. The main goal of the current therapy is to relieve acute symptoms and prevent recurrence of the disease. Without timely and appropriate treatment, the recurrence and disability rates are high. In the present work, we review recent advances in the diagnosis and treatment of patients with NMOSD, as well as the pathogenesis and mechanisms of AQP4-IgG-seropositive NMOSD.

Advances in Anoctamin 1: A Potential New Drug Target in Medicinal Chemistry.

ANO1, anoctamin 1(also known as TMEM16A), is the molecular basis of calcium-activated chloride channels with ten transmembrane segments which are widely expressed in mammalian cells, including epithelial cells, vascular smooth muscle tissues, electro-excitatory cells, and some tumor cells. To date, multiple studies have shown that many natural and synthetic compounds have regulatory effects on ANO1. Therefore, ANO1 could be a potential new drug target for the treatment of cancer, pain, diarrhea, hypertension, and asthma. In this study, we review the structure of ANO1 and its involvement in cancer, pain, diarrhea, hypertension, and asthma.

Rubus chingii var. suavissimus alleviates high-fat diet-induced lipid metabolism disorder via modulation of the PPARs/SREBP pathway in Syrian golden hamsters.

While the underlying mechanism remains unknown, Rubus chingii var. suavissimus (S. K. Lee) L. T. Lu or Rubus suavissimus S. Lee (RS), a sweet plant distributed in southwest of China, has been used as beverage and folk medicine. Pharmacological studies indicated the potential of RS improving the obesity phenotype and hyperlipidemia. The mechanism is still not yet to be put forward. To verify the substantial effects of RS on lipid metabolism, a Syrian golden hamster model was adopted. The physiological and pathological evaluation of experimental animals demonstrated that RS can relieve the lipid metabolism disorder induced by high-fat diet and alleviated liver injury. RS upregulation the expressions of peroxisome proliferator-activated receptor α (PPARα), PPARγ and CCAAT/enhancer binding protein α (C/EBPα), as well as adipocyte-specific genes, glucose transporter 4 (Glut4), lipoprotein lipase (LPL) and fatty acid binding protein 4 (aP2). On the other side, RS suppressed the sterol regulatory element binding protein 1 (SREBP1) and downstream acetyl-CoA carboxylase 1 (ACC1), stearoyl-CoA desaturase-1 (SCD1) and fatty acid synthase (FAS). In conclusion, RS alleviated lipid metabolism disorder symptoms caused by high-fat diet accompanied with 8 weeks of treatment, involving enhanced ß-oxidation, increased adipogenesis and decreased the metabolism of fatty acids, via modulation of the PPARs/SREBP pathway in Syrian golden hamsters.

Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends.

Since the first two novel coronavirus cases appeared in January of 2020, the outbreak of the COVID-19 epidemic seriously threatens the public health of Italy. In this article, the distribution characteristics and spreading of COVID-19 in various regions of Italy were analysed by heat maps. Meanwhile, spatial autocorrelation, spatiotemporal clustering analysis and kernel density method were also applied to analyse the spatial clustering of COVID-19. The results showed that the Italian epidemic has a temporal trend and spatial aggregation. The epidemic was concentrated in northern Italy and gradually spread to other regions. Finally, the Google Trends index of the COVID-19 epidemic was further employed to build a prediction model combined with machine learning algorithms. By using Adaboost algorithm for single-factor modelling,the results show that the AUC of these six features (mask, pneumonia, thermometer, ISS, disinfection and disposable gloves) are all >0.9, indicating that these features have a large contribution to the prediction model. It is also implied that the public's attention to the epidemic is increasing as well as the awareness of the need for protective measures. This increased awareness of the epidemic will prompt the public to pay more attention to protective measures, thereby reducing the risk of coronavirus infection.

1H-NMR Metabolomics Analysis of the Effect of Rubusoside on Serum Metabolites of Golden Hamsters on a High-Fat Diet.

Rubusoside is a natural sweetener and the active component of Rubus suavissimus. The preventive and therapeutic effect of rubusoside on high-fat diet-induced (HFD) serum metabolite changes in golden hamsters was analyzed by 1H-NMR metabolomics to explore the underlying mechanism of lipid metabolism regulation. 1H-NMR serum metabolomics analyses revealed a disturbed amino acid-, sugar-, fat-, and energy metabolism in HFD animals. Animals supplemented with rubusoside can partly reverse the metabolism disorders induced by high-fat diet and exerted good anti-hypertriglyceridemia effect by intervening in some major metabolic pathways, involving amino acid metabolism, synthesis of ketone bodies, as well as choline and 4-hydroxyphenylacetate metabolism. This study indicates that rubusoside can interfere with and normalize high-fat diet-induced metabolic changes in serum and could provide a theoretical basis to establish rubusoside as a potentially therapeutic tool able to revert or prevent lipid metabolism disorders.

Metabolomics of the Protective Effect of Ampelopsis grossedentata and Its Major Active Compound Dihydromyricetin on the Liver of High-Fat Diet Hamster.

The flavonoid dihydromyricetin (DMY) is the main component of Ampelopsis grossedentata (Hand-Mazz) W. T. Wang (AG), a daily beverage and folk medicine used in Southern China to treat jaundice hepatitis, cold fever, and sore throat. Recently, DMY and AG were shown to have a beneficial effect on lipid metabolism disorder. However, the mechanisms of how DMY and AG protect the liver during lipid metabolism disorder remain unclear. In this study, we first analyzed the chemical compounds of AG by HPLC-DAD-ESI-IT-TOF-MS n . Of the 31 compounds detected, 29 were identified based on previous results. Then, the effects of DMY and AG on high-fat diet hamster livers were studied and the metabolite levels and metabolic pathway activity of the liver were explored by 1H NMR metabolomics. Compared to the high-fat diet group, supplementation of AG and DMY attenuated the high-fat-induced increase in body weight, liver lipid deposition, serum triglycerides and total cholesterol levels, and normalized endogenous metabolite concentrations. PCA and PLS-DA score plots demonstrated that while the metabolic profiles of hamsters fed a high-fat diet supplemented with DMY or AG were both far from those of hamsters fed a normal diet or a high-fat diet alone, they were similar to each other. Our data suggest that the underlying mechanism of the protective effect of DMY and AG might be related to an attenuation of the deleterious effect of high-fat diet-induced hyperlipidemia on multiple metabolic pathways including amino acid metabolism, ketone body metabolism, energy metabolism, tricarboxylic acid cycle, and enhanced fatty acid oxidation.

Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data.

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data.

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

Advances in the Detection, Mechanism and Therapy of Chronic Kidney Disease.

Chronic Kidney Disease (CKD) is characterized by the gradual loss of renal mass and functions. It has become a global health problem, with hundreds of millions of people being affected. Both its incidence and prevalence are increasing over time. More than $20,000 are spent on each patient per year. The economic burden on the patients, as well as the society, is heavy and their life quality worsen over time. However, there are still limited effective therapeutic strategies for CKD. Patients mainly rely on dialysis and renal transplantation, which cannot prevent all the complications of CKD. Great efforts are needed in understanding the nature of CKD progression as well as developing effective therapeutic methods, including pharmacological agents. This paper reviews three aspects in the research of CKD that may show great interests to those who devote to bioanalysis, biomedicine and drug development, including important endogenous biomarkers quantification, mechanisms underlying CKD progression and current status of CKD therapy.