For almost fifteen years, the availability and regulatory acceptance of new approach methodologies (NAMs) to assess the absorption, distribution, metabolism and excretion (ADME/biokinetics) in chemical risk evaluations are a bottleneck. To enhance the field, a team of 24 experts from science, industry, and regulatory bodies, including new generation toxicologists, met at the Lorentz Centre in Leiden, The Netherlands. A range of possibilities for the use of NAMs for biokinetics in risk evaluations were formulated (for example to define species differences and human variation or to perform quantitative in vitro-in vivo extrapolations). To increase the regulatory use and acceptance of NAMs for biokinetics for these ADME considerations within risk evaluations, the development of test guidelines (protocols) and of overarching guidance documents is considered a critical step. To this end, a need for an expert group on biokinetics within the Organisation of Economic Cooperation and Development (OECD) to supervise this process was formulated. The workshop discussions revealed that method development is still required, particularly to adequately capture transporter mediated processes as well as to obtain cell models that reflect the physiology and kinetic characteristics of relevant organs. Developments in the fields of stem cells, organoids and organ-on-a-chip models provide promising tools to meet these research needs in the future.
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Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N=101) in comparison to students with other health conditions (N=2465) and students with no health conditions (N=25,077). We applied propensity score weighting to balance outcomes. The research showed that autistic students’ academic success was predictable, and these predictions were more accurate than predictions of their peers’ success. For first-year success, study choice issues were the most important predictors (parallel program and application timing). Issues with participation in pre-education (missingness of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors for the second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance (average grades) was the strongest predictor for degree completion in 3 years. These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.
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Summary: Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.
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Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration–response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose–response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.
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Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil).
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BACKGROUND: Ambulatory children with Spina Bifida (SB) often show a decline in physical activity leading to deconditioning and functional decline. Therefore, assessment and promotion of physical activity is important. Because energy expenditure during activities is higher in these children, the use of existing pediatric equations to predict physical activity energy expenditure (PAEE) may not be valid. AIMS: (1) To evaluate criterion validity of existing predictions converting accelerocounts into PAEE in ambulatory children with SB and (2) to establish new disease-specific equations for PAEE. METHODS: Simultaneous measurements using the Actical, the Actiheart, and indirect calorimetry took place to determine PAEE in 26 ambulatory children with SB. DATA ANALYSIS: Paired T-tests, Intra-class correlations limits of agreement (LoA), and explained variance (R2) were used to analyze validity of the prediction equations using true PAEE as criterion. New equations were derived using regression techniques. RESULTS: While T-tests showed no significant differences for some models, the predictions developed in healthy children showed moderate ICC’s and large LoA with true PAEE. The best regression models to predict PAEE were: PAEE = 174.049 + 3.861 × HRAR – 60.285 × ambulatory status (R2 = 0.720) and PAEE = 220.484 + 0.67 × Actical counts – 60.717 × ambulatory status (R2 = 0.681). CONCLUSIONS: Existing equations to predict PAEE are not valid for use in children with SB for the individual evaluation of PAEE. The best regression model was based on HRAR in combination with ambulatory status, followed by a new model for the Actical monitor. A benefit of HRAR is that it does not require the use of expensive accelerometry equipment. Further cross-validation of these models is still needed.
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Whitepaper: The use of AI is on the rise in the financial sector. Utilizing machine learning algorithms to make decisions and predictions based on the available data can be highly valuable. AI offers benefits to both financial service providers and its customers by improving service and reducing costs. Examples of AI use cases in the financial sector are: identity verification in client onboarding, transaction data analysis, fraud detection in claims management, anti-money laundering monitoring, price differentiation in car insurance, automated analysis of legal documents, and the processing of loan applications.
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Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
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Objectives: To cross-validate the existing peak rate of oxygen consumption (VO2peak) prediction equations in Dutch law enforcement officers and to determine whether these prediction equations can be used to predict VO2peak for groups and in a single individual. A further objective was to report normative absolute and relative VO2peak values of a sample of law enforcement officers in the Netherlands. Material and Methods: The peak rate of oxygen consumption (ml×kg–1×min–1) was measured using a maximal incremental bicycle test in 1530 subjects, including 1068 male and 461 female police officers. Validity of the prediction equations for groups was assessed by comparing predicted VO2peak with measured VO2peak using paired t-tests. For individual differences limits of agreement (LoA) were calculated. Equations were considered valid for individuals when the difference between measured and predicted VO2peak did not exceed ±1 metabolic equivalent (MET) in 95% of individuals. Results: None of the equations met the validity criterion of 95% of individuals having ±1 MET difference or less than the measured value. Limits of agreement (LoAs) were large in all predictions. At the individual level, none of the equations were valid predictors of VO2peak (ml×kg–1×min–1). Normative values for Dutch law enforcement officers were presented. Conclusions: Substantial differences between measured and predicted VO2peak (ml×kg–1×min–1) were found. Most tested equations were invalid predictors of VO2peak at group level and all were invalid at individual levels.
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This research focuses on exit choices within SMEs. In this study, “exit choice” refers to the decision to opt for either liquidation or sale of the firm. The predictions focus on human-capital and firm-resource variables. The hypotheses are tested on a set of 158 owners of small firms, the majority of which are micro-firms with 0–9 employees. The results of a series of binominal logistic regression analyses show that firm-resource characteristics (previous sales turnover, the firm’s independence from its owner, and firm size), together with one aspect of the owner’s specific human capital (the owner’s acquisition experience), predict exit choice. The conclusions have been made with caution, as the dataset is relatively small and the number of predictors is limited.
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