Machine Learning Demonstrates Dominance of Physical Characteristics over Particle Composition in Coal Dust Toxicity.

Mine dust has been linked to the development of pneumoconiotic diseases such as silicosis and coal workers' pneumoconiosis. Currently, it is understood that the physicochemical and mineralogical characteristics drive the toxic nature of dust particles; however, it remains unclear which parameter(s) account for the differential toxicity of coal dust. This study aims to address this issue by demonstrating the use of the partial least squares regression (PLSR) machine learning approach to compare the influence of D50 sub 10 µm coal particle characteristics against markers of cellular damage. The resulting analysis of 72 particle characteristics against cytotoxicity and lipid peroxidation reflects the power of PLSR as a tool to elucidate complex particle-cell relationships. By comparing the relative influence of each characteristic within the model, the results reflect that physical characteristics such as shape and particle roughness may have a greater impact on cytotoxicity and lipid peroxidation than composition-based parameters. These results present the first multivariate assessment of a broad-spectrum data set of coal dust characteristics using latent structures to assess the relative influence of particle characteristics on cellular damage.

The impact of coal mine dust characteristics on pathways to respiratory harm: investigating the pneumoconiotic potency of coals.

Exposure to dust from the mining environment has historically resulted in epidemic levels of mortality and morbidity from pneumoconiotic diseases such as silicosis, coal workers' pneumoconiosis (CWP), and asbestosis. Studies have shown that CWP remains a critical issue at collieries across the globe, with some countries facing resurgent patterns of the disease and additional pathologies from long-term exposure. Compliance measures to reduce dust exposure rely primarily on the assumption that all "fine" particles are equally toxic irrespective of source or chemical composition. For several ore types, but more specifically coal, such an assumption is not practical due to the complex and highly variable nature of the material. Additionally, several studies have identified possible mechanisms of pathogenesis from the minerals and deleterious metals in coal. The purpose of this review was to provide a reassessment of the perspectives and strategies used to evaluate the pneumoconiotic potency of coal mine dust. Emphasis is on the physicochemical characteristics of coal mine dust such as mineralogy/mineral chemistry, particle shape, size, specific surface area, and free surface area-all of which have been highlighted as contributing factors to the expression of pro-inflammatory responses in the lung. The review also highlights the potential opportunity for more holistic risk characterisation strategies for coal mine dust, which consider the mineralogical and physicochemical aspects of the dust as variables relevant to the current proposed mechanisms for CWP pathogenesis.

Sulfur and oxygen isotope constraints on sulfate sources and neutral rock drainage-related processes at a South African colliery.

Understanding the fundamental controls that govern the generation of mine drainage is essential for waste management strategies. Combining the isotopic composition of water (H and O) and dissolved sulfate (S and O) with hydrogeochemical measurements of surface and groundwater, microbial analysis, composition of sediments and precipitates, and geochemical modeling results in this study we discussed the processes that control mine water chemistry and identified the potential source(s) and possible mechanisms governing sulfate formation and transformation around a South African colliery. Compared to various South African water standards, water samples collected from the surroundings of a coal waste disposal facility had elevated Fe2+ (0.9 to 56.9 mg L-1), Ca (33.0 to 527.0 mg L-1), Mg (6.2 to 457.0 mg L-1), Mn (0.1 to 8.6 mg L-1) and SO4 (19.7 to 3440.8 mg L-1) and circumneutral pH. The pH conditions are mainly controlled by the release of H+ from pyrite oxidation and the subsequent dissolution of carbonates and aluminosilicate minerals. The phases predicted to precipitate by equilibrium calculation were green rusts, ferrihydrite, gypsum, ±epsomite. Low concentrations of deleterious metals in solution are due to their low abundance in the local host rocks, and their attenuation through adsorption onto secondary Fe precipitates and co-precipitation at the elevated pH values. The δ34S values of sulfate are enriched (-6.5 ‰ to +5.6 ‰) compared to that of pyrite sampled from the mine (mean -22.5 ‰) and overlap with that of the organic sulfur of coal from the region (-2.5 to +4.9 ‰). The presence of both sulfur reducing and oxidizing bacteria were detected in the collected sediment samples. Combined, the data are consistent with the dissolved sulfate in the sampled waters from the colliery being derived primarily from pyrite probably with the subordinate contribution of organic sulfur, followed by its partial removal through precipitation and microbially-induced reduction.

Bifactor model of cognition in schizophrenia: Evidence for general and specific abilities.

BACKGROUND: Despite extensive study of cognition in schizophrenia, it remains unclear as to whether cognitive deficits and their latent structure are best characterized as reflecting a generalized deficit, specific deficits, or some combination of general and specific constructs. METHOD: To clarify latent structure of cognitive abilities, confirmatory factor analysis was used to examine the latent structure of cognitive data collected for the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for Schizophrenia study. Baseline assessment data (n = 813) were randomly divided into calibration (n = 413) and cross-validation samples (n = 400). To examine whether generalized or specific deficit models provided better explanation of the data, we estimated first-order, hierarchical, and bifactor models. RESULTS: A bifactor model with seven specific factors and one general factor provided the best fit to the data for both the calibration and cross-validation samples. CONCLUSIONS: These findings lend support for a replicable bifactor model of cognition in schizophrenia, characterized by both a general cognitive factor and specific domains. This suggests that cognitive deficits in schizophrenia might be best understood by separate general and specific contributions.

Social Cognition in Children With ADHD.

Objective: Despite evidence of social skill deficits in children with ADHD, there is no consensus regarding a social cognitive profile and whether these skills predict behavior. Therefore, a comprehensive battery was used to investigate the relationship between social cognition and behavioral functioning. Method: Children ages 7 to 13 with ADHD (n = 25) and controls (n = 25) completed tests assessing social cognitive domains (affect recognition and theory of mind [ToM]). Parents completed measures of social cognition (pragmatic language ability and empathy), behavioral symptoms, and adaptive functioning. Results: Children with ADHD performed significantly worse on measures of cognitive ToM and affect recognition and received lower ratings of pragmatic language and cognitive empathy than typically developing peers. These domains, particularly pragmatic language, predicted parent ratings of problematic and adaptive behaviors. Conclusion: Results establish a relationship between specific social cognitive abilities and daily functioning, which has implications for treatment.

Outcomes in CME/CPD - Special Collection Standardising Outcomes Assessment: Demonstrating the Power of Comparative Outcomes Data.

One challenge in medical education is the inability to compare and aggregate outcomes data across continuing educational activities due to variations in evaluation tools, data collection approaches and reporting. To address this challenge, Gilead collaborated with CE Outcomes to develop, pilot, and implement a standardized outcomes evaluation across Gilead directed medical education activities around the world. Development of the standardized tool occurred during late 2018, with Gilead stakeholders invited to provide input on the questions and structure of the evaluation form. Once input was captured, a draft evaluation tool was developed and circulated for feedback. Questions were created to collect 1) participant demographic characteristics 2)data on planned changes to practice, key learnings and anticipated barriers, and 3) learner satisfaction with content and perceived achievement of learning objectives. The evaluation tool was piloted in H1 2019 across 7 medical education activities. Revisions based on pilot feedback were incorporated. The evaluation tool was broadly released during H2 2019 and data were collected from over 30 educational activities. By the end of 2019, it was possible to compare outcomes results from individual activities and aggregate data to demonstrate overall educational reach and impact. Continuing education activities provide valuable up-to-date information to clinicians with the goal of improving patient care. While often challenging to highlight the impact of education due to variations in outcomes, this standardized approach establishes a method to collect meaningful outcomes data that demonstrates the collective impact of continuing education and allows for comparison across individual activities.

Executive Function Profiles in Pediatric Traumatic Brain Injury.

Traumatic brain injury (TBI) results in heterogeneous patterns of neuropsychological impairment. This study investigated heterogeneity in executive function (EF) using the Comprehensive Trail Making Test (CTMT) to evaluate 121 children and adolescents with TBI and 121 matched normal controls. The TBI group performed approximately two standard deviations below controls. Cluster analyses indicated that a three-cluster solution best classified the TBI group and a four-cluster solution best classified controls. Greater impairment in EF was associated with lower intellectual, achievement, and neuropsychological test performance in the TBI group. Results suggest that EF deficits reflected in CTMT performance may be useful for classifying severity of TBI.