Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods

Persistent organic pollutants (POPs) present in foods have been a major concern for food safety due to their persistence and toxic effects. To ensure food safety and protect human health from POPs, it is critical to achieve a better understanding of POP pathways into food and develop strategies to reduce human exposure. POPs could present in food in the raw stages, transferred from the environment or artificially introduced during food preparation steps. Exposure to these pollutants may cause various health problems such as endocrine disruption, cardiovascular diseases, cancers, diabetes, birth defects, and dysfunctional immune and reproductive systems. This review describes potential sources of POP food contamination, analytical approaches to measure POP levels in food and efforts to control food contamination with POPs.

Outcomes of Systemic Lupus Erythematosus in Patients who Discontinue Hydroxychloroquine

Background

Hydroxychloroquine (HCQ) is an antimalarial drug that is recommended as a safe, daily prophylactic intervention for individuals with systemic lupus erythematosus (SLE) based on previous studies that showed an association of HCQ use with reductions in flares compared with placebo. Our study aims to determine whether the discontinuation of HCQ leads to relapse of disease and whether the duration of HCQ use impacts the success of its eventual discontinuation.

Methods

A retrospective chart review was performed on the medical records of patients diagnosed with SLE between July 1, 2006, and June 30, 2016. The data gathered included demographic factors, diagnostic symptoms, laboratory values, and SLE medications. Additionally, HCQ usage and discontinuation rates were collected as well as the timing and prevalence of flares during and after HCQ usage. Patients who were diagnosed with SLE but never used HCQ were excluded from the study. The occurrence of flares, clinical characteristics, and duration of treatment with HCQ were compared between the group that continued HCQ and the group that discontinued HCQ.

Results

Of the 509 patients who met inclusion criteria, 66.2% (n = 337) continued HCQ throughout the duration of their treatment (median duration of HCQ treatment was 8.0 years), whereas 33.8% (n = 172) did not (median duration of HCQ treatment was 1.9 years). Patients who received HCQ for less than 1 year before discontinuation (median duration of HCQ treatment was 2.5 months) were more likely to experience SLE flares compared with those who continued HCQ for more than 1 year (13.1% vs 5.7%, P = 0.019). Patients who experienced a flare while on HCQ were more likely to have arthritis, oral ulcers, leukopenia, and thrombocytopenia.

Conclusion

With over 500 patient charts reviewed, this is the largest study comparing outcomes for patients on HCQ with those who discontinued it. Patients who discontinue HCQ after being on it for less than 1 year are at greater risk for flares compared with those who take HCQ for longer than 1 year. These findings should be used to guide treatment, educate patients on the role of continued treatment with HCQ, and ultimately reduce morbidity and mortality.

P4347Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert

Background

Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by standard calculation of model-based end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could be developed, which circumvents border detection and instead estimates the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images.

Purpose

This study was designed to test the feasibility and accuracy of this approach.

Methods

Machine learning algorithm was developed and trained on a database of >50,000 echocardiographic studies, including multiple apical 2- and 4-chamber views, to automatically estimate LVEF (AutoEF, BayLabs). Testing was performed on an independent group of 99 unselected patients, whose automated EF values were compared to reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis of bias and limits of agreement (LOA). Consistency was assessed by mean absolute deviation (MAD) among automated estimates based on different combinations of apical views. Finally, sensitivity and specificity of detecting of EF≤35% was calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers.

Results

Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (MAD=2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, LOA=±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, LOA=±13.4%,sensitivity 0.93, specificity 0.87.

Conclusions

Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared to reference values provided by an expert panel.

Acknowledgement/Funding

Bay Labs, Inc.

Evaluation of miLINC to shorten time to treatment for rifampicin-resistant Mycobacterium tuberculosis

BACKGROUND: Achieving the 90-90-90 targets for tuberculosis (TB) will require interventions that enhance diagnosis, linkage, treatment and adherence to care. As a first step in the process, our team designed a suite of smartphone applications known as miLINC to improve time from diagnosis to treatment initiation in drug-resistant TB patients.SETTING: Three clinical locations in a large, peri-urban district in KwaZulu-Natal, South Africa.OBJECTIVE: To assess the acceptability, feasibility and impact of the miLINC mobile health applications as a solution to reducing the time from presentation to treatment initiation of rifampicin-resistant (RR) TB patients.METHODS: We used a prospective, observational quality improvement evaluation of miLINC's impact among newly diagnosed patients with RR-TB.RESULTS: A convenience sample comprising details of 6341 patients with presumptive TB were entered into miLINC. Of the 631 TB-positive sputum specimens, 41 (6.5%) were found to be RR-TB. The mean time from clinical presentation to RR-TB treatment initiation was 3 days, 21 h, 17 min.CONCLUSION: This is the first study to suggest that the time from presentation to diagnosis and to treatment initiation for patients with RR-TB can be significantly improved using an integrated approach combining technology with appropriate human resources.

The Association between FLAIR Vascular Hyperintensity and Stroke Outcome Varies with Time from Onset

BACKGROUND AND PURPOSE

FLAIR vascular hyperintensity has been recognized as a marker of collaterals in ischemic stroke, but the impact on outcome is still controversial. We hypothesized that the association between FLAIR vascular hyperintensity and outcome varies with time.

MATERIALS AND METHODS

We included 459 consecutive patients with middle cerebral artery stroke and divided them into 3 groups by symptom-to-MR imaging time (group 1, ≤7 days; group 2, 8-14 days; group 3, ≥15 days). The FLAIR vascular hyperintensity score, ranging from 0 to 3 points, was based on territory distributions of different MCA segments. The associations between FLAIR vascular hyperintensity and outcome with time were analyzed qualitatively and quantitatively.

RESULTS

No patients underwent MR imaging within 6 hours of onset. The proportion of FLAIR vascular hyperintensity (+) and severe stenosis or occlusion of MCA was not significantly dependent on time. In groups 1 and 2, FLAIR vascular hyperintensity (+) was significantly associated with larger lesions, the prevalence of flow injury, and unfavorable outcome (mRS ≥ 2). There were no such associations in group 3. Multiple logistic regressions demonstrated that FLAIR vascular hyperintensity (+) was an independent risk factor for unfavorable outcome in group 2. Infarction volume tended to increase with the increase of the distal FLAIR vascular hyperintensity score in groups 1 and 2, while declining in group 3.

CONCLUSIONS

FLAIR vascular hyperintensity is associated with unfavorable outcome within 6 hours to 14 days of onset, while the wider distribution of distal FLAIR vascular hyperintensity may be favorable beyond 14 days of onset in MCA infarction. Symptom-to-MR imaging time should be considered when assessing the prognostic value of FLAIR vascular hyperintensity.

Increased risk of aminoglycoside-induced hearing loss in MDR-TB patients with HIV coinfection

SETTING

A high proportion of individuals with multidrug-resistant tuberculosis (MDR-TB) develop permanent hearing loss due to ototoxicity caused by injectable aminoglycosides (AGs). The prevalence of AG-induced hearing loss is greatest in tuberculosis (TB) and human immunodeficiency virus (HIV) endemic countries in sub-Saharan Africa. However, whether HIV coinfection is associated with a higher incidence of AG-induced hearing loss during MDR-TB treatment is controversial.

OBJECTIVE

To evaluate the impact of HIV coinfection on AG-induced hearing loss among individuals with MDR-TB in sub-Saharan Africa.

DESIGN

This was a meta-analysis of articles published in PubMed, Embase, Scopus, Cumulative Index to Nursing and Allied Health Literature, Web of Science, Cochrane Review, and reference lists using search terms 'hearing loss', 'aminoglycoside', and 'sub-Saharan Africa'.

RESULTS

Eight studies conducted in South Africa, Botswana and Namibia and published between 2012 and 2016 were included. As the included studies were homogeneous (χ2 = 8.84, df = 7), a fixed-effects model was used. Individuals with MDR-TB and HIV coinfection had a 22% higher risk of developing AG-induced hearing loss than non-HIV-infected individuals (pooled relative risk 1.22, 95%CI 1.10-1.36) during MDR-TB treatment.

CONCLUSION

This finding is critical for TB programs with regard to the expansion of injectable-sparing regimens. Our findings lend credibility to using injectable-sparing regimens and more frequent hearing monitoring, particularly in resource-limited settings for HIV-coinfected individuals.

Sequencing XMET genes to promote genotype-guided risk assessment and precision medicine

High-throughput next generation sequencing (NGS) is a shotgun approach applied in a parallel fashion by which the genome is fragmented and sequenced through small pieces and then analyzed either by aligning to a known reference genome or by de novo assembly without reference genome. This technology has led researchers to conduct an explosion of sequencing related projects in multidisciplinary fields of science. However, due to the limitations of sequencing-based chemistry, length of sequencing reads and the complexity of genes, it is difficult to determine the sequences of some portions of the human genome, leaving gaps in genomic data that frustrate further analysis. Particularly, some complex genes are difficult to be accurately sequenced or mapped because they contain high GC-content and/or low complexity regions, and complicated pseudogenes, such as the genes encoding xenobiotic metabolizing enzymes and transporters (XMETs). The genetic variants in XMET genes are critical to predicate inter-individual variability in drug efficacy, drug safety and susceptibility to environmental toxicity. We summarized and discussed challenges, wet-lab methods, and bioinformatics algorithms in sequencing "complex" XMET genes, which may provide insightful information in the application of NGS technology for implementation in toxicogenomics and pharmacogenomics.

Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38

After publication of this supplement article.

In silico identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of Kochia scoparia

Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis.

Similarities and differences between variants called with human reference genome HG19 or HG38

Background

Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed.

Methods

We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs.

Results

The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected).

Conclusion

A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2620-0) contains supplementary material, which is available to authorized users.

Deep learning for predicting toxicity of chemicals: a mini review

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

A review on machine learning methods for in silico toxicity prediction

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

Comparative toxicogenomics of three insensitive munitions constituents 2,4-dinitroanisole, nitroguanidine and nitrotriazolone in the soil nematode Caenorhabditis elegans

BACKGROUND

Ecotoxicological studies on the insensitive munitions formulation IMX-101 and its components 2,4-dinitroanisole (DNAN), nitroguanidine (NQ) and nitrotriazolone (NTO) in various organisms showed that DNAN was the main contributor to the overall toxicity of IMX-101 and suggested that the three compounds acted independently. These results motivated this toxicogenomics study to discern toxicological mechanisms for these compounds at the molecular level.

METHODS

Here we used the soil nematode Caenorhabditis elegans, a well-characterized genomics model, as the test organism and a species-specific, transcriptome-wide 44 K-oligo probe microarray for gene expression analysis. In addition to the control treatment, C. elegans were exposed for 24 h to 6 concentrations of DNAN (1.95-62.5 ppm) or NQ (83-2667 ppm) or 5 concentrations of NTO (187-3000 ppm) with ten replicates per treatment. The nematodes were transferred to a clean environment after exposure. Reproduction endpoints (egg and larvae counts) were measured at three time points (i.e., 24-, 48- and 72-h). Gene expression profiling was performed immediately after 24-h exposure to each chemical at the lowest, medium and highest concentrations plus the control with four replicates per treatment.

RESULTS

Statistical analyses indicated that chemical treatment did not significantly affect nematode reproduction but did induce 2175, 378, and 118 differentially expressed genes (DEGs) in NQ-, DNAN-, and NTO-treated nematodes, respectively. Bioinformatic analysis indicated that the three compounds shared both DEGs and DEG-mapped Reactome pathways. Gene set enrichment analysis further demonstrated that DNAN and NTO significantly altered 12 and 6 KEGG pathways, separately, with three pathways in common. NTO mainly affected carbohydrate, amino acid and xenobiotics metabolism while DNAN disrupted protein processing, ABC transporters and several signal transduction pathways. NQ-induced DEGs were mapped to a wide variety of metabolism, cell cycle, immune system and extracellular matrix organization pathways.

CONCLUSION

Despite the absence of significant effects on apical reproduction endpoints, DNAN, NTO and NQ caused significant alterations in gene expression and pathways at 1.95 ppm, 187 ppm and 83 ppm, respectively. This study provided supporting evidence that the three chemicals may exert independent toxicity by acting on distinct molecular targets and pathways.

Quantum chemical simulations revealed the toxicokinetic mechanisms of organic phosphorus flame retardants catalyzed by P450 enzymes

The metabolic fate and toxicokinetics of organic phosphorus flame retardants catalyzed by cytochrome P450 enzymes (CYPs) are here investigated by in silico simulations, leveraging an active center model to mimic the CYPs, triphenyl phosphate (TPHP), tris(2-butoxyethyl) phosphate and tris(1,3-dichloro-2-propyl) phosphate as substrates. Our calculations elucidated key main pathways and predicted products, which were corroborated by current in vitro data. Results showed that alkyl OPFRs are eliminated faster than aryl and halogenated alkyl-substituted OPFRs. In addition, we discovered a proton shuttle pathway for aryl hydroxylation of TPHP and P = O bond-assisted H-transfer mechanisms (rather than nonenzymatic hydrolysis) that lead to O-dealkylation/dearylation of phosphotriesters.

Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology

As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.

Application of genome analysis strategies in the clinical testing for pediatric diseases

Next‐generation sequencing (NGS) is being used in clinical testing. Government authorities in both China and the United States are overseeing the clinical application of NGS instruments and reagents. In addition, the US Association for Molecular Pathology and the College of American Pathologists have jointly released a guidance to standardize the analysis and interpretation of NGS data involved in clinical testing. At present, the analysis strategies and pipelines for NGS data related to the clinical detection of pediatric disease are similar to those used for adult diseases. However, for rare pediatric diseases without linkage to known genetic variants, it is currently difficult to detect the relevant pathogenic genes using NGS technology. Additionally, it is challenging to identify novel pathogenic genes of familial pediatric tumors. Therefore, characterization of the pathogenic genes associated with above diseases is important for the diagnosis and treatment of rare diseases in children. This article introduces the general pipelines for NGS data analyses of diseases and elucidates data analysis strategies for the pathogenic genes of rare pediatric diseases and familial pediatric tumors.

In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.