Linear low-dose extrapolation for noncancer health effects is the exception, not the rule

The nature of the exposure-response relationship has a profound influence on risk analyses. Several arguments have been proffered as to why all exposure-response relationships for both cancer and noncarcinogenic end-points should be assumed to be linear at low doses. We focused on three arguments that have been put forth for noncarcinogens. First, the general “additivity-to-background” argument proposes that if an agent enhances an already existing disease-causing process, then even small exposures increase disease incidence in a linear manner. This only holds if it is related to a specific mode of action that has nonuniversal properties—properties that would not be expected for most noncancer effects. Second, the “heterogeneity in the population” argument states that variations in sensitivity among members ofthe target population tend to “flatten out and linearize” the exposure-response curve, but this actually only tends to broaden, not linearize, the dose-response relationship. Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints. It is more likely that this is attributable to exposure measurement error rather than a true non-threshold association. Assuming that every chemical is toxic at high exposures and linear at low exposures does not comport to modern-day scientific knowledge of biology. There is no compelling evidence-based justification for a general low-exposure linearity; rather, case-specific mechanistic arguments are needed

Review of the literature and suggestions for the design of rodent survival studies for the identification of compounds that increase health and life span

Much of the literature describing the search for agents that increase the life span of rodents was found to suffer from confounds. One-hundred-six studies, absent 20 contradictory melatonin studies, of compounds or combinations of compounds were reviewed. Only six studies reported both life span extension and food consumption data, thereby excluding the potential effects of caloric restriction. Six other studies reported life span extension without a change in body weight. However, weight can be an unreliable surrogate measure of caloric consumption. Twenty studies reported that food consumption or weight was unchanged, but it was unclear whether these data were anecdotal or systematic. Twenty-nine reported extended life span likely due to induced caloric restriction. Thirty-six studies reported no effect on life span, and three a decrease. The remaining studies suffer from more serious confounds. Though still widely cited, studies showing life span extension using short-lived or “enfeebled” rodents have not been shown to predict longevity effects in long-lived animals. We suggest improvements in experimental design that will enhance the reliability of the rodent life span literature. First, animals should receive measured quantities of food and its consumption monitored, preferably daily, and reported. Weights should be measured regularly and reported. Second, a genetically heterogeneous, long-lived rodent should be utilized. Third, chemically defined diets should be used. Fourth, a positive control (e.g., a calorically restricted group) is highly desirable. Fifth, drug dosages should be chosen based on surrogate endpoints or accepted cross-species scaling factors. These procedures should improve the reliability of the scientific literature and accelerate the identification of longevity and health span-enhancing agents

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Non-Chemical Stressors and Cumulative Risk Assessment: An Overview of Current Initiatives and Potential Air Pollutant Interactions

Regulatory agencies are under increased pressure to consider broader public health concerns that extend to multiple pollutant exposures, multiple exposure pathways, and vulnerable populations. Specifically, cumulative risk assessment initiatives have stressed the importance of considering both chemical and non-chemical stressors, such as socioeconomic status (SES) and related psychosocial stress, in evaluating health risks. The integration of non-chemical stressors into a cumulative risk assessment framework has been largely driven by evidence of health disparities across different segments of society that may also bear a disproportionate risk from chemical exposures. This review will discuss current efforts to advance the field of cumulative risk assessment, highlighting some of the major challenges, discussed within the construct of the traditional risk assessment paradigm. Additionally, we present a summary of studies of potential interactions between social stressors and air pollutants on health as an example of current research that supports the incorporation of non-chemical stressors into risk assessment. The results from these studies, while suggestive of possible interactions, are mixed and hindered by inconsistent application of social stress indicators. Overall, while there have been significant advances, further developments across all of the risk assessment stages (i.e., hazard identification, exposure assessment, dose-response, and risk characterization) are necessary to provide a scientific basis for regulatory actions and effective community interventions, particularly when considering non-chemical stressors. A better understanding of the biological underpinnings of social stress on disease and implications for chemical-based dose-response relationships is needed. Furthermore, when considering non-chemical stressors, an appropriate metric, or series of metrics, for risk characterization is also needed. Cumulative risk assessment research will benefit from coordination of information from several different scientific disciplines, including, for example, toxicology, epidemiology, nutrition, neurotoxicology, and the social sciences

Numerical and physical assessment of control measures to mitigate fugitive dust emissions from harbor activities

In recent years, the industrial demand for petcoke—a solid residue derived from the refinement of crude oil—has been growing due to its low cost. The use of petcoke is causing environmental concern associated with its high level of contaminants and air pollutant emissions, mainly particulate matter (PM). Given the impact of petcoke on the environment and human health, increased attention has been given to its production, storage, transportation, and application processes. The main goal of this work was to assess the effectiveness of placing a barrier to reduce PM emissions from petcoke in a harbor area. The Port of Aveiro, Portugal, was used as case study. Firstly, wind tunnel experiments were performed for different types of barrier to (i) assess the effect on PM emissions of different types of barriers, namely solid, porous, and raised porous barriers; (ii) determine the optimal size and location of the barrier to achieve maximum reduction of PM emissions; and (iii) estimate the impact of placing such barrier in the attenuation of petcoke emissions over the harbor area. Secondly, the numerical model VADIS (pollutant DISpersion in the atmosphere under VAriable wind conditions) was run to evaluate the effect of implementing the barrier on the local air quality. Results showed that the best solution would be the implementation of two solid barriers: a main barrier of 109 m length plus a second barrier of 30 m length. This measure produced the best results in terms of reduction of the dispersion of particulate matter from the petcoke stockpile and minimization of the PM concentrations in the harbor surrounding area.publishe