Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.

Introduction: The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening. Methods: Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical. Results and Discussion: Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1. Conclusion: Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Optimizing androgen receptor prioritization using high-throughput assay-based activity models.

Introduction: Computational models using data from high-throughput screening assays have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared with the androgen receptor (AR) pathway model, and to incorporate chemical cluster analysis into this evaluation. Although several of the assays in the AR pathway model are no longer available through the original vendor, this approach could be used for future evaluations of minimal assay models for prioritization and screening. Methods: We compared two previously published models and found that an expanded 14-assay model had higher sensitivity for antagonists, whereas the original 11-assay model had slightly higher sensitivity for agonists. We then investigated subsets of assays in the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results and Discussion: Evaluation of the critical assays across subset models derived from the 14-assay model identified three critical assays for predicting antagonism and two critical assays for predicting agonism. A minimum of nine assays is required for predicting agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist, according to its structure. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusion: This work illustrates a data-driven approach that incorporates chemical clustering and simultaneous consideration of antagonism and agonism mechanisms to more efficiently screen chemicals. This case study provides a proof of concept for prioritization and screening strategies that can be utilized in future analyses to minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP.

Why adverse outcome pathways need to be FAIR.

Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.

New approach methodologies (NAMs) can detect biological phenomena that occur before they add up to serious problems like cancer, infertility, death, and others. NAMs detect key events (KE) along well-proven and agreed adverse outcome pathways (AOP). If a substance tests positive in a NAM for an upstream KE, this signals an early warning that actual adversity might follow. However, what if the knowledge about these AOPs is a well-kept secret? And what if decision-makers find AOPs too exotic to apply in risk assessment? This is where FAIR comes in! FAIR stands for making information findable, accessible, interoperable and re-useable. It aims to increase availability, usefulness, and trustworthiness of data. Here, we show that by interpreting the FAIR principles beyond a purely technical level, AOPs can ring in a new era of 3Rs applicability ‒ by increasing their visibility and making their creation process more transparent and reproducible.

Complement Component 5 (C5) Deficiency Improves Cognitive Outcome After Traumatic Brain Injury and Enhances Treatment Effects of Complement Inhibitors C1-Inh and CR2-Crry in a Mouse Model.

A potent effector of innate immunity, the complement system contributes significantly to the pathophysiology of traumatic brain injury (TBI). This study investigated the role of the complement cascade in neurobehavioral outcomes and neuropathology after TBI. Agents acting at different levels of the complement system, including 1) C1 esterase inhibitor (C1-Inh), 2) CR2-Crry, an inhibitor of all pathways acting at C3, and 3) the selective C5aR1 antagonist, PMX205, were administered at 1 h post-TBI. Their effects were evaluated on motor function using the rotarod apparatus, cognitive function using the active place avoidance (APA) task, and brain lesion size at a chronic stage after controlled cortical impact injury in C5-sufficient (C5+/+) and C5-deficient (C5-/-) CD1 mice. In post-TBI C5+/+ mice, rotarod performance was improved by CR2-Crry, APA performance was improved by CR2-Crry and PMX205, and brain lesion size was reduced by PMX205. After TBI, C5-/- mice performed better in the APA task compared with C5+/+ mice. C5 deficiency enhanced the effect of C1-Inh on motor function and brain damage and the effect of CR2-Crry on brain damage after TBI. Our findings support critical roles for C3 in motor deficits, the C3/C5/C5aR1 axis in cognitive deficits, and C5aR1 signaling in brain damage after TBI. Findings suggest the combination of C5 inhibition with C1-Inh and CR2-Crry as potential therapeutic strategies in TBI.

Effects of C1-INH Treatment on Neurobehavioral Sequelae and Late Seizures After Traumatic Brain Injury in a Mouse Model of Controlled Cortical Impact.

C1 human-derived C1 esterase inhibitor (C1-INH) is a U.S. Food and Drig Administration-approved drug with anti-inflammatory actions. In the present study, we investigated the therapeutic effects of C1-INH on acute and chronic neurobehavioral outcomes and on seizures in the chronic stage in a mouse traumatic brain injury (TBI) model. Adult male CD1 mice were subjected to controlled cortical impact and randomly allocated to receive C1-INH or vehicle solution 1 h post-TBI. Effects of C1-INH treatment on inflammatory responses and brain damage after TBI were examined using the Cytometric Bead Array, C5a enzyme-linked immunosorbent assay, Fluoro-Jade C staining, and Nissl staining. Neurobehavioral outcomes after TBI were assessed with modified neurological severity scores, the rotarod and open field tests, and the active place avoidance task. Video-electroencephalographic monitoring was performed in the 15th and 16th weeks after TBI to document epileptic seizures. We found that C1-INH treatment reduced TNFα expression and alleviated brain damage. Treatment with C1-INH improved neurological functions, increased locomotor activity, alleviated anxiety-like behavior, and exhibited an effect on seizures in the chronic stage after TBI. These findings suggest that C1-INH has beneficial effects on the treatment of TBI.

Retaining natural vegetation to safeguard biodiversity and humanity.

Global efforts to deliver internationally agreed goals to reduce carbon emissions, halt biodiversity loss, and retain essential ecosystem services have been poorly integrated. These goals rely in part on preserving natural (e.g., native, largely unmodified) and seminatural (e.g., low intensity or sustainable human use) forests, woodlands, and grasslands. To show how to unify these goals, we empirically derived spatially explicit, quantitative, area-based targets for the retention of natural and seminatural (e.g., native) terrestrial vegetation worldwide. We used a 250-m-resolution map of natural and seminatural vegetation cover and, from this, selected areas identified under different international agreements as being important for achieving global biodiversity, carbon, soil, and water targets. At least 67 million km2 of Earth's terrestrial vegetation (∼79% of the area of vegetation remaining) required retention to contribute to biodiversity, climate, soil, and freshwater conservation objectives under 4 United Nations' resolutions. This equates to retaining natural and seminatural vegetation across at least 50% of the total terrestrial (excluding Antarctica) surface of Earth. Retention efforts could contribute to multiple goals simultaneously, especially where natural and seminatural vegetation can be managed to achieve cobenefits for biodiversity, carbon storage, and ecosystem service provision. Such management can and should co-occur and be driven by people who live in and rely on places where natural and sustainably managed vegetation remains in situ and must be complemented by restoration and appropriate management of more human-modified environments if global goals are to be realized.

Retención de la vegetación natural para salvaguardar la biodiversidad y la humanidad Resumen Hoy en día hay muy poca integración de los esfuerzos mundiales para alcanzar los objetivos internacionales de reducción de las emisiones de carbono, impedimento de la pérdida de biodiversidad y conservación de los servicios ambientales esenciales. Estos objetivos dependen parcialmente de la conservación de los bosques, selvas y praderas naturales (por ejemplo, nativos y en su mayoría sin alteraciones) y seminaturales (por ejemplo, de uso humano sostenible o de baja intensidad). Obtuvimos de manera empírica objetivos espacialmente explícitos, cuantitativos y basados en áreas para la conservación de la vegetación terrestre natural y seminatural (por ejemplo, nativa) en todo el mundo para mostrar cómo unificar los objetivos internacionales. Usamos un mapa de 250 m de resolución de la cubierta vegetal natural y seminatural y, a partir de él, seleccionamos las áreas identificadas como importantes en diferentes acuerdos internacionales para alcanzar los objetivos globales de biodiversidad, carbono, suelo y agua. Al menos 67 millones de km2 de la vegetación terrestre de la Tierra (∼79% de la superficie de vegetación restante) requieren ser conservados para contribuir a los objetivos de conservación de la biodiversidad, el clima, el suelo y el agua dulce en virtud de cuatro de las resoluciones de las Naciones Unidas. Esto equivale a conservar la vegetación natural y seminatural en al menos el 50% de la superficie terrestre total de la Tierra (sin contar a la Antártida). Los esfuerzos de retención podrían contribuir a alcanzar múltiples objetivos simultáneamente, especialmente en donde la vegetación natural y seminatural puede gestionarse para lograr beneficios colaterales para la biodiversidad, el almacenamiento de carbono y la provisión de servicios ambientales. Esta gestión puede y debe ser impulsada y llevada a cabo por las personas que viven en y dependen de los lugares donde la vegetación natural y gestionada de forma sostenible permanece in situ y debe complementarse con la restauración y la gestión adecuada de entornos modificados por el hombre si se quieren alcanzar los objetivos globales.

Linking complex disease and exposure data-insights from an environmental and occupational health study.

The disparate measurement protocols used to collect study data are an intrinsic barrier to combining information from environmental health studies. Using standardized measurement protocols and data standards for environmental exposures addresses this gap by improving data collection quality and consistency. To assess the prevalence of environmental exposures in National Institutes of Health (NIH) public data repositories and resources and to assess the commonality of the data elements, we analyzed clinical measures and exposure assays by comparing the Caribbean Consortium for Research in Environmental and Occupational Health study with selected NIH environmental health resources and studies. Our assessment revealed that (1) environmental assessments are widely collected in these resources, (2) biological assessments are less prevalent, and (3) NIH resources can help identify common data for meta-analysis. We highlight resources to help link environmental exposure data across studies to support data sharing. Including NIH data standards in environmental health research facilitates comparing and combining study data, and the use of NIH resources and adoption of standard measures will allow integration of multiple studies and increase the scientific impact of individual studies.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Epidemiology and variability in management of acute mastoiditis in children.

BACKGROUND: To evaluate the epidemiology, management and outcome of acute mastoiditis (AM) in children and to improve strategies for antimicrobial stewardship. METHODS: We conducted a retrospective observational study of children aged >6 months to ≤18 years of age admitted to a tertiary care hospital with AM over an 8-year period (2011-2019). Electronic medical records were reviewed to collect data. RESULTS: A total of 129 patients met inclusion criteria for AM during this time period. Eighty-one (63 %) were males with 110 (81 %) White and 67 (52 %) non-Hispanic. The median age at presentation was 6.4 years (3-10.1 years). Ear protrusion was associated with reduced odds of having AM with intracranial extension (ICE) (OR 0.307, 95 % CI = 0.107-0.883) whereas presence of headaches and/or neck pain increased the odds of having AM with ICE (OR = 3.96, 95%CI 1.29-12.1). The most common etiologies were Streptococcus pyogenes (n = 23, 19.2 %), Pseudomonas aeruginosa (n = 20, 17 %), and Streptococcus pneumoniae (n = 15, 12.5 %). Empiric antibiotic selection and duration of therapy was highly variable. The most common empiric antibiotic used was intravenous vancomycin with a third generation cephalosporin (n = 45, 34.8 %). Majority completed course (n = 92; 73 %) with an oral antibiotic. Shorter (≤10 and ≤14 days) versus longer courses (>10 and >14 days) did not affect readmission rates for AM without ICE. CONCLUSION: There is high variability of treatment of AM in children. Broad spectrum antibiotics, especially vancomycin were used most frequently despite low rates of Methicillin Resistant Staphylococcus aureus. The use of antibiotic stewardship is essential for judicious antibiotic use.

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements.

Regulatory agencies around the world have committed to reducing or eliminating animal testing for establishing chemical safety. Adverse outcome pathways can facilitate replacement by providing a mechanistic framework for identifying the appropriate non-animal methods and connecting them to apical adverse outcomes. This study separated 11,992 chemicals with curated rat oral acute toxicity information into clusters of structurally similar compounds. Each cluster was then assigned one or more ToxCast/Tox21 assays by looking for the minimum number of assays required to record at least one positive hit call below cytotoxicity for all acutely toxic chemicals in the cluster. When structural information is used to select assays for testing, none of the chemicals required more than four assays and 98% required two assays or less. Both the structure-based clusters and activity from the associated assays were significantly associated with the GHS toxicity classification of the chemicals, which suggests that a combination of bioactivity and structural information could be as reproducible as traditional in vivo studies. Predictivity is improved when the in vitro assay directly corresponds to the mechanism of toxicity, but many indirect assays showed promise as well. Given the lower cost of in vitro testing, a small assay battery including both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to improve performance further. This approach illustrates the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy that can reduce or eliminate animal testing for acute oral toxicity.

Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks.

The workshop titled "Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks" was co-organized by the Evidence-based Toxicology Collaboration and the European Food Safety Authority (EFSA) and hosted by EFSA at its headquarters in Parma, Italy on October 2 and 3, 2019. The goal was to explore integration of systematic review with mechanistic evidence evaluation. Participants were invited to work on concrete products to advance the exploration of how evidence-based approaches can support the development and application of adverse outcome pathways (AOP) in chemical risk assessment. The workshop discussions were centered around three related themes: 1) assessing certainty in AOPs, 2) literature-based AOP development, and 3) integrating certainty in AOPs and non-animal evidence into decision frameworks. Several challenges, mostly related to methodology, were identified and largely determined the workshop recommendations. The workshop recommendations included the comparison and potential alignment of processes used to develop AOP and systematic review methodology, including the translation of vocabulary of evidence-based methods to AOP and vice versa, the development and improvement of evidence mapping and text mining methods and tools, as well as a call for a fundamental change in chemical risk and uncertainty assessment methodology if to be conducted based on AOPs and new approach methodologies (NAM). The usefulness of evidence-based approaches for mechanism-based chemical risk assessments was stressed, particularly the potential contribution of the rigor and transparency inherent to such approaches in building stakeholders' trust for implementation of NAM evidence and AOPs into chemical risk assessment.

HeartMath as an Integrative, Personal, Social, and Global Healthcare System.

COVID-19 is a recent major event, adding to planet Earth's contexts of chaos, crime, injustice, illness, and violence. The HeartMath system has produced research evidence for scientific interventions that alter contexts characterized by chaos and stress, promoting health, coherence, and interconnectedness. This study provides an updated overview of HeartMath as an interdisciplinary, scientific, coherent, integral heart-based healthcare system, operated locally through various initiatives and globally through the Global Coherence Initiative. The HeartMath approach integrates ancient and contemporary, indigenous and mainstream, popular and folk, Eastern, Western, and African forms of healing. The HeartMath interdisciplinary, personal, social, and global vision and mission have considerable theoretical and practical potential for promoting planetary health, education, and development.

Defining clinical outcome pathways.

Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) â†’ intermediate event(s) â†’ clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.

Predicting nonlinear relationships between external and internal concentrations with physiologically based pharmacokinetic modeling.

Although external concentrations are more readily quantified and often used as the metric for regulating and mitigating exposures to environmental chemicals, the toxicological response to an environmental chemical is more directly related to its internal concentrations than the external concentration. The processes of absorption, distribution, metabolism, and excretion (ADME) determine the quantitative relationship between the external and internal concentrations, and these processes are often susceptible to saturation at high concentrations, which can lead to nonlinear changes in internal concentrations that deviate from proportionality. Using generic physiologically-based pharmacokinetic (PBPK) models, we explored how saturable absorption or clearance influence the shape of the internal to external concentration (IEC) relationship. We used the models for hypothetical chemicals to show how differences in kinetic parameters can impact the shape of an IEC relationship; and models for styrene and caffeine to explore how exposure route, frequency, and duration impact the IEC relationships in rat and human exposures. We also analyzed available plasma concentration data for 2,4-dichlorophenoxyacetic acid to demonstrate how a PBPK modeling approach can be an alternative to common statistical methods for analyzing dose proportionality. A PBPK modeling approach can be a valuable tool used in the early stages of a chemical safety assessment program to optimize the design of longer-term animal toxicity studies or to interpret study results.

HeartMath Coherence Model Throws New Light on Arka Dhyana Intuitive Meditation.

In an experimental evaluation of an introductory Arka Dhyana (Intuitive Meditation) course, HeartMath (HM) Inner Balance or emWave2 electronic technology showed highly significant increases in both coherence and achievement in six participants who learned how to change their level of consciousness as proposed by the Theory of the Six Main Levels of Consciousness. During the course, which was offered to an international audience via Zoom technology, participants intended to connect with their deeper self, being, or essence, by bringing their I-ego-awareness from the thinking mind, often associated with the frontal part of the brain, to 19 energetic stations in the body including the heart centre. Considering the results from the HeartMath Coherence Model viewpoint, it seems this intended shift leads to cardiovascular phase synchronicity and interconnection of various bodily subsystems, which is also comprehensible based on our bodily development during embryogenesis. Qualitative statements involving feeling also point to increases in well-being, indicating changes in levels and individual transformative experiences as predicted by the Theory of the Six Main Levels of Consciousness. Increased focus, stilling of the mind, and calming of emotions also seem to be health benefit by-products of Intuitive Meditation (IM), but further research on more advanced practitioners is needed. This preliminary study needs to be repeated using a bigger sample size and further research on more advanced IM practitioners is required.

Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework.

Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.ciao-covid.net/), to streamline collaboration between scientists across the world toward development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

The Impact of a Novel Mimicry Task for Increasing Emotion Recognition in Adults with Autism Spectrum Disorder and Alexithymia: Protocol for a Randomized Controlled Trial.

BACKGROUND: Impaired facial emotion expression recognition (FEER) has typically been considered a correlate of autism spectrum disorder (ASD). Now, the alexithymia hypothesis is suggesting that this emotion processing problem is instead related to alexithymia, which frequently co-occurs with ASD. By combining predictive coding theories of ASD and simulation theories of emotion recognition, it is suggested that facial mimicry may improve the training of FEER in ASD and alexithymia. OBJECTIVE: This study aims to evaluate a novel mimicry task to improve FEER in adults with and without ASD and alexithymia. Additionally, this study will aim to determine the contributions of alexithymia and ASD to FEER ability and assess which of these 2 populations benefit from this training task. METHODS: Recruitment will primarily take place through an ASD community group with emphasis put on snowball recruiting. Included will be 64 consenting adults equally divided between participants without an ASD and participants with an ASD. Participants will be screened online using the Kessler Psychological Distress Scale (K-10; cut-off score of 22), Autism Spectrum Quotient (AQ-10), and Toronto Alexithymia Scale (TAS-20) followed by a clinical interview with a provisional psychologist at the Federation University psychology clinic. The clinical interview will include assessment of ability, anxiety, and depression as well as discussion of past ASD diagnosis and confirmatory administration of the Autism Mental Status Exam (AMSE). Following the clinical interview, the participant will complete the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and then undertake a baseline assessment of FEER. Consenting participants will then be assigned using a permuted blocked randomization method into either the control task condition or the mimicry task condition. A brief measure of satisfaction of the task and a debriefing session will conclude the study. RESULTS: The study has Federation University Human Research Ethics Committee approval and is registered with the Australian New Zealand Clinical Trials. Participant recruitment is predicted to begin in the third quarter of 2021. CONCLUSIONS: This study will be the first to evaluate the use of a novel facial mimicry task condition to increase FEER in adults with ASD and alexithymia. If efficacious, this task could prove useful as a cost-effective adjunct intervention that could be used at home and thus remove barriers to entry. This study will also explore the unique effectiveness of this task in people without an ASD, with an ASD, and with alexithymia. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12619000705189p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377455. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/24543.

Applying evidence-based methods to the development and use of adverse outcome pathways

The workshop "Application of evidence-based methods to construct mechanistic frameworks for the development and use of non-animal toxicity tests" was organized by the Evidence-based Toxicology Collaboration and hosted by the Grading of Recommendations Assessment, Development and Evaluation Working Group on June 12, 2019. The purpose of the workshop was to bring together international regulatory bodies, risk assessors, academic scientists, and industry to explore how systematic review methods and the adverse outcome pathway framework could be combined to develop and use mechanistic test methods for predicting the toxicity of chemical substances in an evidence-based manner. The meeting covered the history of biological frameworks, the way adverse outcome pathways are currently developed, the basic principles of systematic methodology, including systematic reviews and evidence maps, and assessment of cer­tainty in models, and adverse outcome pathways in particular. Specific topics were discussed via case studies in small break-out groups. The group concluded that adverse outcome pathways provide an important framework to support mechanism-based assessment in environmental health. The process of their development has a few challenges that could be addressed with systematic methods and automation tools. Addressing these challenges will increase the transparency of the evidence behind adverse outcome pathways and the consistency with which they are defined; this in turn will increase their value for supporting public health decisions. It was suggested to explore the details of applying systematic methods to adverse outcome pathway development in a series of case studies and workshops.

Practitioners' Experiences of the Influence of Bonsai Art on Health.

Bonsai art refers to the cultivation of a miniature tree. This study was motivated by the hypothesis that bonsai art may also be an ecopsychological, therapeutic practice that can have meaningful healing qualities. An international online survey elicited the meaning of bonsai art for 255 skilled bonsai practitioners. Questionnaires and interviews were used to elicit the experiences of participants. The findings supported the hypothesis that, for skilled practitioners, bonsai art was associated with meaningful healing experiences. In particular, the evidence suggests that bonsai art facilitates improved ecological, spiritual and emotional awareness, as well as various healing dimensions, including aesthetic creativity, resilience, adaptability, and social, physical, and personal health. It is viewed as an intervention technique that requires few resources, is easy to apply, and has a minimal impact on any environmental setting. The conclusions drawn point to the ethically sound health promotion value of bonsai art in various settings, such as psychiatric hospitals, retirement homes, rehabilitation centres and prisons.