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Humans feel visceral disgust when faced with potential contaminants like bodily effluvia. The emotion serves to reject potentially contaminated food and is paired with proto-nausea: alterations in gastric rhythm in response to disgust. Here, we offer a narrative synthesis of the existing literature on the effects of disgust on the stomach as measured through electrogastrography, a non-invasive technique that measures stomach activity with electrodes placed on the abdominal skin surface. After identifying and assessing 368 studies for eligibility and inclusion based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses process, we reviewed a final sample of only 10 articles that employed electrogastrography to assess gastric responses to unpleasant stimuli, including disgust elicitors. Reviewed findings illustrate that changes in gastric rhythm are associated with negatively valenced emotions, and most reliably with visceral disgust elicitors. This rhymes with recent evidence for a causal role of gastric state in reductions in visceral disgust avoidance. Because limitations in the reviewed body of work come from the low number of studies and relatively small sample sizes, we strongly encourage studies of proto-nausea in designs with higher statistical power, ideally paired with experimental manipulations of gastric state., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© The Author(s) 2024.)

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles., (Published by Oxford University Press on behalf of the Society of Toxicology 2024.)

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10 -3  μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R 2  = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals., Competing Interests: Declaration of competing interest The authors declare no conflict of interest. This manuscript has been reviewed by the Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use., (Published by Elsevier Inc.)

How do cancer cells grow, divide, proliferate, and die? How do drugs infuence these processes? These are diffcult questions that we can attempt to answer with a combination of time-series microscopy experiments, classifcation algorithms, and data visualization. However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative. State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory. To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

Disgust is a vital emotion in the avoidance of illness. Human adults across cultures show disgust towards sources of potential contamination or pathogens, and elect to avoid their ingestion or even to look at them. Stomach rhythms appear to play an important role: disgust reduces normogastric power, and the pharmacological normalisation of gastric state reduces disgust avoidance. Human children are remarkably slow to develop disgust as measured by self-report and facial expressions. Here, we investigate whether disgust-induced avoidance (measured using eye tracking) and changes in gastric rhythm (measured using electrogastrography) exist in children aged 5 to 13 years ( N  = 45). We found that children in this bracket showed oculomotor avoidance of disgusting stimuli in a preferential-looking task, similar to adult samples in previous research. However, in contrast to adult samples in previous research, children did not show an attenuation in normogastric power. These findings could suggest that avoidance behaviour precedes gastric involvement during disgust. This would support the idea that children initially respond to parental modelling: parents set (and enforce) the social norm of disgust avoidance, and children initially conform and only later do they internalise disgust as an interoceptive signal. Alternatively, the employed stimuli could have been potent enough to induce oculomotor avoidance, but not a gastric response. Research is slim in this area, and future work should focus on elucidating the role of the stomach in disgust, and on longitudinal studies of disgust development from childhood to adolescence., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Chemicals assessment and management frameworks rely on regulatory toxicity values, which are based on points of departure (POD) identified following rigorous dose-response assessments. Yet, regulatory PODs and toxicity values for inhalation exposure ( i.e. , reference concentrations [RfCs]) are available for only ∼200 chemicals. To address this gap, we applied a workflow to determine surrogate inhalation route PODs and corresponding toxicity values, where regulatory assessments are lacking. We curated and selected inhalation in vivo data from the U.S. EPA's ToxValDB and adjusted reported effect values to chronic human equivalent benchmark concentrations (BMC h ) following the WHO/IPCS framework. Using ToxValDB chemicals with existing PODs associated with regulatory toxicity values, we found that the 25th %-ile of a chemical's BMC h distribution ( POD p 25 BMC h ) could serve as a suitable surrogate for regulatory PODs (Q 2 ≥ 0.76, RSE ≤ 0.82 log 10 units). We applied this approach to derive POD p 25 BMC h for 2,095 substances with general non-cancer toxicity effects and 638 substances with reproductive/developmental toxicity effects, yielding a total coverage of 2,160 substances. From these POD p 25 BMC h , we derived probabilistic RfCs and human population effect concentrations. With this work, we have expanded the number of chemicals with toxicity values available, thereby enabling a much broader coverage for inhalation risk and impact assessment.

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Per- and polyfluoroalkyl substances (PFAS) encompass a class of chemically and structurally diverse compounds that are extensively used in industry and detected in the environment. The US Environmental Protection Agency (US EPA) 2021 PFAS Strategic Roadmap describes national research plans to address the challenge of PFAS., Objectives: Systematic Evidence Map (SEM) methods were used to survey and summarize available epidemiological and mammalian bioassay evidence that could inform human health hazard identification for a set of 345 PFAS that were identified by the US EPA's Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing and through interagency discussions on PFAS of interest. This work builds from the 2022 evidence map that collated evidence on a separate set of ∼ 150 PFAS. Like our previous work, this SEM does not include PFAS that are the subject of ongoing or completed assessments at the US EPA., Methods: SEM methods were used to search, screen, and inventory mammalian bioassay and epidemiological literature from peer-reviewed and gray literature sources using manual review and machine-learning software. For each included study, study design details and health end points examined were summarized in interactive web-based literature inventories. Some included studies also underwent study evaluation and detailed extraction of health end point data. All underlying data is publicly available online as interactive visuals with downloadable metadata., Results: More than 13,000 studies were identified from scientific databases. Screening processes identified 121 mammalian bioassay and 111 epidemiological studies that met screening criteria. Epidemiological evidence (available for 12 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Mammalian bioassay evidence (available for 30 PFAS) commonly assessed effects in the reproductive, whole-body, nervous, and hepatic systems. Overall, 41 PFAS had evidence across mammalian bioassay and epidemiology data streams (roughly 11% of searched chemicals)., Discussion: No epidemiological and/or mammalian bioassay evidence were identified for most of the PFAS included in our search. Results from this SEM, our 2022 SEM on ∼ 150 PFAS, and other PFAS assessment products from the US EPA are compiled into a comprehensive PFAS dashboard that provides researchers and regulators an overview of the current PFAS human health landscape including data gaps and can serve as a scoping tool to facilitate prioritization of PFAS-related research and/or risk assessment activities. https://doi.org/10.1289/EHP13423.

The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage in vitro assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells. Wastewater and surface water extracts induced the largest changes in expression among cell proliferation-related genes and neurological, estrogenic, and antibiotic pathways, whereas drinking and reclaimed water extracts that underwent advanced treatment showed substantially reduced bioactivity on both gene and pathway levels. Importantly, reclaimed water extracts induced fewer changes in gene expression than laboratory blanks, which reinforces previous conclusions based on targeted assays and improves confidence in bioassay-based monitoring of water quality.

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Reardon, Farmahin, Williams, Meier, Addicks, Yauk, Matteo, Atlas, Harrill, Everett, Shah, Judson, Ramaiahgari, Ferguson and Barton-Maclaren.)

Background: Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals., Objectives: Using in vivo experimental animal data from the U.S. Environmental Protection Agency's Toxicity Value Database, we developed a semiautomated approach to determine surrogate oral route PODs, and corresponding toxicity values where regulatory assessments are unavailable., Methods: We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses ( BMD h ). We hypothesized that a quantile of the BMD h distribution could serve as a surrogate POD and determined the appropriate quantile by calibration to regulatory PODs. Finally, we characterized uncertainties around the surrogate PODs from intra- and interstudy variability and derived probabilistic toxicity values using a standardized workflow., Results: The BMD h distribution for each chemical was adequately fit by a lognormal distribution, and the 25th percentile best predicted the available regulatory PODs [ R 2 ≥ 0.78 , residual standard error   ( RSE ) ≤ 0.53 log 10 units]. We derived surrogate PODs for 10,145 chemicals from the curated data set, differentiating between general noncancer and reproductive/developmental effects, with typical uncertainties (at 95% confidence) of a factor of 10 and 12, respectively. From these PODs, probabilistic reference doses (1% incidence at 95% confidence), as well as human population effect doses (10% incidence), were derived., Discussion: In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.

The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy., (Copyright © 2022. Published by Elsevier Inc.)

Background: Per- and polyfluoroalkyl substances (PFAS) are a large class of synthetic (man-made) chemicals widely used in consumer products and industrial processes. Thousands of distinct PFAS exist in commerce. The 2019 U.S. Environmental Protection Agency (U.S. EPA) Per- and Polyfluoroalkyl Substances (PFAS) Action Plan outlines a multiprogram national research plan to address the challenge of PFAS. One component of this strategy involves the use of systematic evidence map (SEM) approaches to characterize the evidence base for hundreds of PFAS., Objective: SEM methods were used to summarize available epidemiological and animal bioassay evidence for a set of ∼ 150 PFAS that were prioritized in 2019 by the U.S. EPA's Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing., Methods: Systematic review methods were used to identify and screen literature using manual review and machine-learning software. The Populations, Exposures, Comparators, and Outcomes (PECO) criteria were kept broad to identify mammalian animal bioassay and epidemiological studies that could inform human hazard identification. A variety of supplemental content was also tracked, including information on in vitro model systems; exposure measurement-only studies in humans; and absorption, distribution, metabolism, and excretion (ADME). Animal bioassay and epidemiology studies meeting PECO criteria were summarized with respect to study design, and health system(s) were assessed. Because animal bioassay studies with ≥ 21 -d exposure duration (or reproductive/developmental study design) were most useful to CCTE analyses, these studies underwent study evaluation and detailed data extraction. All data extraction is publicly available online as interactive visuals with downloadable metadata., Results: More than 40,000 studies were identified from scientific databases. Screening processes identified 44 animal and 148 epidemiology studies from the peer-reviewed literature and 95 animal and 50 epidemiology studies from gray literature that met PECO criteria. Epidemiological evidence (available for 15 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Animal evidence (available for 40 PFAS) commonly assessed effects in the reproductive, developmental, urinary, immunological, and hepatic systems. Overall, 45 PFAS had evidence across animal and epidemiology data streams., Discussion: Many of the ∼ 150 PFAS were data poor. Epidemiological and animal evidence were lacking for most of the PFAS included in our search. By disseminating this information, we hope to facilitate additional assessment work by providing the initial scoping literature survey and identifying key research needs. Future research on data-poor PFAS will help support a more complete understanding of the potential health effects from PFAS exposures. https://doi.org/10.1289/EHP10343.

Background: The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line., Results: We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events., Conclusions: This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance., (© 2022. The Author(s).)

Summary: Many applications of chemical screening are performed in concentration or dose-response mode, and it is necessary to extract appropriate parameters, including whether the chemical/assay pair is active and if so, what are concentrations where activity is seen. Typically, multiple mathematical models or curve shapes are tested against the data to assess the best fit. There are several commercial programs used for this purpose as well as open-source libraries. A widely used system for managing high-throughput screening (HTS) concentration-response data is tcpl (ToxCast Pipeline). The current implementation of tcpl has the concentration-response modeling code tightly integrated with the data management and databasing aspects of HTS data processing. Tcplfit2 is a stand-alone version of the curve-fitting and hitcalling core of tcpl that has been extended to include a large number of standard curve classes and to use benchmark dose modeling. This package will be useful for HTS concentration-response data such as high-throughput whole genome transcriptomics., Availability and Implementation: tcplfit2 is written in R and is available from CRAN., (Published by Oxford University Press 2021. This work is written by US Government employees and is in the public domain in the US.)

Which drug is most promising for a cancer patient? A new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer this question in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, adjusting filters, removing noise, and analyzing the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both derived data such as growth rates and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach for choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.

Objective: To examine the validity of the Comprehensive and Brief International Classification of Functioning, Disability and Health (ICF) Core Sets for Traumatic Brain Injury for patients with traumatic brain injury living in the community in Australia., Design: Qualitative methodology using focus groups and individual interviews., Patients: Community-dwelling adult persons with traumatic brain injury., Methods: Patients sustaining traumatic brain injury with post-traumatic amnesia between September 2009 and August 2013, selected from the Royal Melbourne Hospital Trauma Registry, were invited to participate in the study. Participants were asked structured questions based on the ICF framework. Digital recordings of the discussions were transcribed in full for linking to the ICF categories., Results: Saturation of data was reached after 5 groups involving 21 participants. Participants identified as relevant 77.7% (n = 108/139) and 100% (n = 23/23) of the Comprehensive and Brief ICF Core Sets for traumatic brain injury, respectively. Additional ICF categories identified in 2 or more groups were: b180 "experience of self and time functions"; b250 "taste function"; b265 "touch function"; b530 "weight maintenance function"; b780 "sensation related to muscles and movement"; and d650 "caring for household objects"., Conclusion: The study found additional ICF categories to consider and supports the use of the ICF Core Sets for traumatic brain injury in Australian adults in the community.

Regulatory agencies world-wide face the challenge of performing risk-based prioritization of thousands of substances in commerce. In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk +/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The 'best' consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity.

Purpose: Reducing chemical pressure on human and environmental health is an integral part of the global sustainability agenda. Guidelines for deriving globally applicable, life cycle based indicators are required to consistently quantify toxicity impacts from chemical emissions as well as from chemicals in consumer products. In response, we elaborate the methodological framework and present recommendations for advancing near-field/far-field exposure and toxicity characterization, and for implementing these recommendations in the scientific consensus model USEtox., Methods: An expert taskforce was convened by the Life Cycle Initiative hosted by UN Environment to expand existing guidance for evaluating human toxicity impacts from exposure to chemical substances. This taskforce evaluated advances since the original release of USEtox. Based on these advances, the taskforce identified two major aspects that required refinement, namely integrating near-field and far-field exposure and improving human dose-response modeling. Dedicated efforts have led to a set of recommendations to address these aspects in an update of USEtox, while ensuring consistency with the boundary conditions for characterizing life cycle toxicity impacts and being aligned with recommendations from agencies that regulate chemical exposure. The proposed framework was finally tested in an illustrative rice production and consumption case study., Results and Discussion: On the exposure side, a matrix system is proposed and recommended to integrate far-field exposure from environmental emissions with near-field exposure from chemicals in various consumer product types. Consumer exposure is addressed via submodels for each product type to account for product characteristics and exposure settings. Case study results illustrate that product-use related exposure dominates overall life cycle exposure. On the effect side, a probabilistic dose-response approach combined with a decision tree for identifying reliable points of departure is proposed for non-cancer effects, following recent guidance from the World Health Organization. This approach allows for explicitly considering both uncertainty and human variability in effect factors. Factors reflecting disease severity are proposed to distinguish cancer from non-cancer effects, and within the latter discriminate reproductive/developmental and other non-cancer effects. All proposed aspects have been consistently implemented into the original USEtox framework., Conclusions: The recommended methodological advancements address several key limitations in earlier approaches. Next steps are to test the new characterization framework in additional case studies and to close remaining research gaps. Our framework is applicable for evaluating chemical emissions and product-related exposure in life cycle assessment, chemical alternatives assessment and chemical substitution, consumer exposure and risk screening, and high-throughput chemical prioritization.

Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modeling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. Agonist batteries of as few as six assays and antagonist batteries of as few as five assays can yield balanced accuracies of 95% or better relative to the full model. Balanced accuracy for predicting reference chemicals is 100%. An approach is outlined for researchers to develop their own subset batteries to accurately detect AR activity using assays that map to the pathway of key molecular and cellular events involved in chemical-mediated AR activation and transcriptional activity. This work indicates in vitro bioactivity and in silico predictions that map to the AR pathway could be used in an integrated approach to testing and assessment for identifying chemicals that interact directly with the mammalian AR., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models based on chemical structure information, can predict hazard in the absence of experimental data. Risk assessment requires identification of a quantitative point-of-departure (POD) value, the point on the dose-response curve that marks the beginning of a low-dose extrapolation. This study presents two sets of QSAR models to predict POD values (POD QSAR ) for repeat dose toxicity. For training and validation, a publicly available in vivo toxicity dataset for 3592 chemicals was compiled using the U.S. Environmental Protection Agency's Toxicity Value database (ToxValDB). The first set of QSAR models predict point-estimates of POD values (POD QSAR ) using structural and physicochemical descriptors for repeat dose study types and species combinations. A random forest QSAR model using study type and species as descriptors showed the best performance, with an external test set root mean square error (RMSE) of 0.71 log 10 -mg/kg/day and coefficient of determination (R 2 ) of 0.53. The second set of QSAR models predict the 95% confidence intervals for POD QSAR using a constructed POD distribution with a mean equal to the median POD value and a standard deviation of 0.5 log 10 -mg/kg/day, based on previously published typical study-to-study variability that may lead to uncertainty in model predictions. Bootstrap resampling of the pre-generated POD distribution was used to derive point-estimates and 95% confidence intervals for each POD prediction. Enrichment analysis to evaluate the accuracy of POD QSAR showed that 80% of the 5% most potent chemicals were found in the top 20% of the most potent chemical predictions, suggesting that the repeat dose POD QSAR models presented here may help inform screening level human health risk assessments in the absence of other data.

The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Cl int (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (C ss ); and subsequent in vitro-in vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Cl int . The model with the highest observed performance for fub had an external test set RMSE/σ=0.62 and R 2 =0.61, for Cl int classification had an external test set accuracy = 65.9%, and for intrinsic clearance regression had an external test set RMSE/σ=0.90 and R 2 =0.20. This relatively low performance is in part due to the large uncertainty in the underlying Cl int data. We show that C ss is relatively insensitive to uncertainty in Cl int . The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.

Objective: To determine the frequency of finger thoracostomy performed by intensive care flight paramedics after the introduction of a training programme in this procedure and complications of the procedure that were diagnosed after hospital arrival., Methods: This was a retrospective cohort study of adult and paediatric trauma patients undergoing finger thoracostomy performed by paramedics on a helicopter emergency medical service between June 2015 and May 2018. Hospital data were obtained through a manual search of the medical records at each of the three receiving major trauma services. Additional data were sourced from the Victorian State Trauma Registry., Results: The final analysis included 103 cases, of which 73.8% underwent bilateral procedures with a total of 179 finger thoracostomies performed. The mean age of patients was 42.8 (standard deviation 21.4) years and 73.8% were male. Motor vehicle collision was the most common mechanism of injury accounting for 54.4% of cases. The median Injury Severity Score was 41 (interquartile range 29-54). There were 30 patients who died pre-hospital, with most (n = 25) having finger thoracostomy performed in the setting of a traumatic cardiac arrest. A supine chest X-ray was performed prior to intercostal catheter insertion in 38 of 73 patients arriving at hospital; of these, none demonstrated a tension pneumothorax. There were three cases of potential complications related to the finger thoracostomy., Conclusion: Finger thoracostomy was frequently performed by intensive care flight paramedics. It was associated with a low rate of major complications and given the deficiencies of needle thoracostomy, should be the preferred approach for chest decompression., (© 2020 Australasian College for Emergency Medicine.)

New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log 10 -mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20-0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log 10 -mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.

Background: Functional recovery is an important outcome for those who survive critical illness. The present study aimed to assess nutrition provision and nutrition-related outcomes in a multi-trauma cohort following intensive care unit (ICU) discharge., Methods: The present study investigated a prospective cohort of patients discharged from an ICU, who had been admitted because of major trauma and required mechanical ventilation for at least 48 h. Nutrition-related outcomes, including body weight, quadriceps muscle layer thickness (QMLT), handgrip strength and subjective global assessment, were recorded on ICU discharge, days 5-7 post-ICU discharge and then weekly until hospital discharge. Nutrition intake was recorded for 5 days post-ICU discharge. Unless otherwise stated, data are presented as the mean (SD)., Results: Twenty-eight patients [75% males, 55 (22.5) years] were included. Intake met 64% (28%) of estimated energy and 72% (32%) of protein requirements over the 5 days post-ICU discharge, which was similar to over the ICU admission. From ICU admission to hospital discharge, the mean reduction in weight was 4.2 kg (95% confidence interval = 2.2-6.3, P < 0.001) and after ICU discharge, the mean reduction in weight and QMLT was 2.6 kg (95% confidence interval = 1.0-4.2, P = 0.004) and 0.23 cm (95% confidence interval = 0.06-0.4, P = 0.01), respectively., Conclusions: Patients received less energy and protein than estimated requirements after ICU discharge. Weight loss and reduction in QMLT also occurred during this period., (© 2019 The British Dietetic Association Ltd.)

HIV-1 vaccine development aims to elicit broadly neutralizing antibodies (bnAbs) against diverse viral strains. In some HIV-1-infected individuals, bnAbs evolved from precursor antibodies through affinity maturation. To induce bnAbs, a vaccine must mediate a similar antibody maturation process. One way to test a vaccine is to immunize mouse models that express human bnAb precursors and assess whether the vaccine can convert precursor antibodies into bnAbs. A major problem with such mouse models is that bnAb expression often hinders B cell development. Such developmental blocks may be attributed to the unusual properties of bnAb variable regions, such as poly-reactivity and long antigen-binding loops, which are usually under negative selection during primary B cell development. To address this problem, we devised a method to circumvent such B cell developmental blocks by expressing bnAbs conditionally in mature B cells. We validated this method by expressing the unmutated common ancestor (UCA) of the human VRC26 bnAb in transgenic mice. Constitutive expression of the VRC26UCA led to developmental arrest of B cell progenitors in bone marrow; poly-reactivity of the VRC26UCA and poor pairing of the VRC26UCA heavy chain with the mouse surrogate light chain may contribute to this phenotype. The conditional expression strategy bypassed the impediment to VRC26UCA B cell development, enabling the expression of VRC26UCA in mature B cells. This approach should be generally applicable for expressing other bnAbs that are under negative selection during B cell development., Competing Interests: Competing interest statement: F.W.A. is a cofounder of a startup biotech company, Otoro Biopharmaceuticals, which, when operational, aims to develop therapeutic human antibodies with Ig-humanized mouse models. M.T., H-L.C., and F.W.A. hold a US patent on the conditional expression strategy, as described in this study.

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: The incidence of older adult traumatic brain injury (TBI) is increasing in both high and middle to low-income countries. It is unknown whether older adults with isolated, serious TBI can be safely managed outside of major trauma centres. This registry based cohort study aimed to compare mortality and functional outcomes of older adults with isolated, serious TBI who were managed at specialised Major Trauma Services (MTS) and Metropolitan Neurosurgical Services (MNS)., Method: Older adults (65 years and over) who sustained an isolated, serious TBI following a low fall (from standing or ≤ 1 m) were extracted from the Victorian State Trauma Registry from 2007 to 2016. Multivariable models were fitted to assess the association between hospital designation (MTS vs. MNS) and the two outcomes of interest: in-hospital mortality and functional outcome, adjusting for potential confounders. Functional outcomes were measured using the Glasgow Outcome Scale Extended at six months post-injury., Results: From 2007-2016, there were 1904 older adults who sustained an isolated, serious TBI from a low fall who received definitive care at an MTS (n = 1124) or an MNS (n = 780). After adjusting for confounders, there was no mortality benefit for patients managed at an MTS over an MNS (OR = 0.84; 95% CI: 0.65, 1.08; P = 0.17) or improvement in functional outcome six months post-injury (OR = 1.13; 95% CI: 0.94, 1.36; P = 0.21)., Conclusion: For older adults with isolated, serious TBI following a low fall, there was no difference in mortality or functional outcome based on definitive management at an MTS or an MNS. This confirms that MNS without the added designation of a major trauma centre are a suitable destination for the management of isolated, serious TBI in older adults., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Despite the reliance on administrative data in epidemiological studies, there is little information on the completeness of co-morbidities in administrative data coded from medical records., Objective: The aim of this study was to quantify the agreement between the International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) administrative coding of mental health, drug and alcohol co-morbidities and medical records in a severely injured patient population., Method: A random sample of patients ( n = 500) captured by the Victorian State Trauma Registry and definitively managed at the state's adult major trauma services was selected for the study. Retrospective medical record review was conducted to collect data about documented co-morbidities. The agreement between ICD-10-AM data generated from routine hospital coding and medical record-based co-morbidities was determined using Cohen's κ and prevalence-adjusted bias-adjusted kappa (PABAK) statistics., Results: The percentage of agreement between the medical record and ICD-10-AM coding for mental health, drug and alcohol co-morbidities was 72.8%, and the PABAK showed moderate agreement (PABAK = 0.46; 95% confidence interval (CI): 0.37, 0.54). There was no difference in agreement between unintentional injury patients (PABAK = 0.52; 95% CI: 0.42, 0.62) compared with intentional injury patients (PABAK = 0.36, 95% CI: 0.23, 0.49), and no change in agreement for patients admitted before (PABAK = 0.40; 95% CI: 0.30, 0.50) and after the introduction of mandatory co-morbidity coding (PABAK = 0.46; 95% CI: 0.37, 0.54)., Conclusion: Despite documentation in the medical record, a large proportion of mental health, drug and alcohol conditions were not coded in ICD-10-AM. Acknowledgement of these limitations is needed when using ICD-10-AM coded co-morbidities in research studies and health policy development., Implications: This work has implications for researchers of drug and alcohol abuse; mental health; accidents and injuries; workers' compensation; health workforce; health services; and policy decisions for healthcare, emergency services, insurance industry, national productivity and welfare costings reliant on those research outcomes.

Introduction: An increasing proportion of the major trauma population are older persons. The pattern of injury is different in this age group and serious chest injuries represent a significant subgroup, with implications for trauma system design. The aim of this study was to examine trends in thoracic injuries among major trauma patients in an inclusive trauma system., Methods: This was a retrospective review of all adult cases of major trauma with thoracic injuries of Abbreviated Injury Scale score of 3 or more, using data from the Victorian State Trauma Registry from 2007 to 2016. Prevalence and pattern of thoracic injury was compared between patients with multitrauma and patients with isolated thoracic injury. Poisson regression was used to determine whether population-based incidence had changed over the study period., Results: There were 8805 cases of hospitalised major trauma with serious thoracic injuries. Over a 10-year period, the population-adjusted incidence of thoracic injury increased by 8% per year (incidence rate ratio [IRR] 1.08, 95% CI 1.07 to 1.09). This trend was observed across all age groups and mechanisms of injury. The greatest increase in incidence of thoracic injuries, 14% per year, was observed in people aged 85 years and older (IRR 1.14, 95% CI 1.09 to 1.18)., Conclusions: Admissions for thoracic injuries in the major trauma population are increasing. Older patients are contributing to an increase in major thoracic trauma. This is likely to have important implications for trauma system design, as well as morbidity, mortality and use of healthcare resources., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Environmental triggers is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, novel technologies, precision medicine and pragmatic clinical research. The Challenges in IBD research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the environmental triggers section is focused on the main research gaps in elucidating causality of environmental factors in IBD. Research gaps were identified in: 1) epidemiology of exposures; 2) identification of signatures of biological response to exposures; and 3) mechanisms of how environmental exposures drive IBD. To address these gaps, the implementation of longitudinal prospective studies to determine disease evolution and identify sub-clinical changes in response to exposures is proposed. This can help define critical windows of vulnerability and risk prediction. In addition, systems biology analysis and in silico modeling were proposed as approaches to integrate the IBD exposome for the identification of biological signatures of response to exposures, and to develop prediction models of the effects of environmental factors in driving disease activity and response to therapy. This research could lead to identification of biomarkers of exposures and new modalities for therapeutic intervention. Finally, hypothesis-driven mechanistic studies to understand gene-environment interactions and to validate causality of priority factors should be performed to determine how environment influences clinical outcomes., (© 2019 Crohn’s & Colitis Foundation. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Reviewing prehospital trauma deaths provides an opportunity to identify system improvements that may reduce trauma mortality. The objective of this study was to identify the number and rate of potentially preventable trauma deaths through expert panel reviews of prehospital and early in-hospital trauma deaths., Methods: We conducted a retrospective review of prehospital and early in-hospital (<24 h) trauma deaths following a traumatic out-of-hospital cardiac arrest that were attended by Ambulance Victoria (AV) in the state of Victoria, Australia, between 2008 and 2014. Expert panels were used to review cases that had resuscitation attempted by paramedics and underwent a full autopsy. Patients with a mechanism of hanging, drowning or those with anatomical injuries deemed to be unsurvivable were excluded., Results: Of the 1183 cases that underwent full autopsies, resuscitation was attempted by paramedics in 336 (28%) cases. Of these, 113 cases (34%) were deemed to have potentially survivable injuries and underwent expert panel review. There were 90 (80%) deaths that were not preventable, 19 (17%) potentially preventable deaths and 4 (3%) preventable deaths. Potentially preventable or preventable deaths represented 20% of those cases that underwent review and 7% of cases that had attempted resuscitation., Conclusions: The number of potentially preventable or preventable trauma deaths in the pre-hospital and early in-hospital resuscitation phase was low. Specific circumstances were identified in which the trauma system could be further improved., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The application of toxic equivalency factors (TEFs) or toxic units to estimate toxic potencies for mixtures of chemicals which contribute to a biological effect through a common mechanism is one approach for filling data gaps. Toxic Equivalents (TEQ) have been used to express the toxicity of dioxin-like compounds (i.e., dioxins, furans, and dioxin-like polychlorinated biphenyls (PCBs)) in terms of the most toxic form of dioxin: 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD). This study sought to integrate two data gap filling techniques, quantitative structure-activity relationships (QSARs) and TEFs, to predict neurotoxicity TEQs for PCBs. Simon et al. (2007) previously derived neurotoxic equivalent (NEQ) values for a dataset of 87 PCB congeners, of which 83 congeners had experimental data. These data were taken from a set of four different studies measuring different effects related to neurotoxicity, each of which tested overlapping subsets of the 83 PCB congeners. The goals of the current study were to: (i) evaluate an alternative neurotoxic equivalent factor (NEF) derivations from an expanded dataset, relative to those derived by Simon et al. and (ii) develop QSAR models to provide NEF estimates for the large number of untested PCB congeners. The models used multiple linear regression, support vector regression, k-nearest neighbor and random forest algorithms within a 5-fold cross validation scheme and position-specific chlorine substitution patterns on the biphenyl scaffold as descriptors. Alternative NEF values were derived but the resulting QSAR models had relatively low predictivity (RMSE ∼0.24). This was mostly driven by the large uncertainties in the underlying data and NEF values. The derived NEFs and the QSAR predicted NEFs to fill data gaps should be applied with caution., (Published by Elsevier Inc.)

This review aim to provide an overview of recommendations and quality of existing clinical practice guidelines (CPGs) for the management of traumatic brain injury (TBI) from the rehabilitation perspective. Comprehensive literature search, including health databases, CPG clearinghouse/developer websites, and grey literature using Internet search engines up to September 2017. All TBI CPGs published in the last decade were selected if their scope included management of TBI, systematic methods for evidence search, clear defined recommendations, and supporting evidence for rehabilitation interventions. Three authors independently critically appraised the quality of included CPGs using the Appraisal of Guidelines, Research, and Evaluation II (AGREE II) Instrument. Four of 13 potential CPGs met the inclusion criteria. Despite variation in scope, target population, size, and guideline development processes, all four CPGs assessed were good quality (AGREE score of 5-7/7). Key rehabilitation recommendations included education, physical rehabilitation, integrated computer-based management, repetitive task-specific practice in daily living activities, safe equipment usage, cognitive/behavioral feedback, compensatory memory/visual strategies, swallowing/communication, and psychological input for TBI survivors. In conclusion, although rehabilitation is an integral component in TBI management, many published CPGs do not include rehabilitation. These CPGs, however, recommend comprehensive, flexible coordinated multidisciplinary care and appropriate follow-up, education, and support for patients with TBI (and carers).

Objective : To evaluate published traumatic brain injury (TBI) clinical practice guidelines (CPGs) and assess rehabilitation intervention recommendations for applicability in disaster settings. Methods : Recommendations for rehabilitation interventions were synthesized from currently published TBI CPGs, developed by the Department of Labor and Employment (DLE); Scottish Intercollegiate Guidelines Network (SIGN); Department of Veterans Affairs/Department of Defence (DVA/DOD); and American Occupational Therapy Association (AOTA). Three authors independently extracted, compared, and categorized evidence-based rehabilitation intervention recommendations from these CPGs for applicability in disaster settings. Results : The key recommendations from a rehabilitation perspective for TBI survivors in disaster settings included patient/carer education, general physical therapy, practice in daily living activities and safe equipment use, direct cognitive/behavioral feedback, basic compensatory memory/visual strategies, basic swallowing/communication, and psychological input. More advanced interventions are generally not applicable following disasters due to limited access to services, trained staff/resources, equipment, funding, and operational issues. Conclusions : Many recommendations for TBI care are challenging to implement in disaster settings due to complexities related to the environment, resources, service provision, workforce, and other reasons. Further research is needed to identify and address barriers for implementation.

Recently, numerous organizations, including governmental regulatory agencies in the U.S. and abroad, have proposed using data from New Approach Methodologies (NAMs) for augmenting and increasing the pace of chemical assessments. NAMs are broadly defined as any technology, methodology, approach or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. High-throughput transcriptomics (HTTr) is a type of NAM that uses gene expression profiling as an endpoint for rapidly evaluating the effects of large numbers of chemicals on in vitro cell culture systems. As compared to targeted high-throughput screening (HTS) approaches that measure the effect of chemical X on target Y , HTTr is a non-targeted approach that allows researchers to more broadly characterize the integrated response of an intact biological system to chemicals that may affect a specific biological target or many biological targets under a defined set of treatment conditions (time, concentration, etc.). HTTr screening performed in concentration-response mode can provide potency estimates for the concentrations of chemicals that produce perturbations in cellular response pathways. Here, we discuss study design considerations for HTTr concentration-response screening and present a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach. The framework involves concentration-response modeling of HTTr data, mapping gene level responses to biological pathways, determination of BPACs, in vitro -to- in vivo extrapolation (IVIVE) and comparison to human exposure predictions.

Background: The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA)., Objectives: The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose–response modeling., Discussion: The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose–response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose–response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential., Conclusions: Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks. https://doi.org/10.1289/EHP3871.