Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Coal workers' pneumoconiosis (CWP) is a preventable, but not fully curable occupational lung disease. More and more coal miners are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control.</p> <p>Methods</p> <p>The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. Sensitivity and ROC analyses were adapted to explain the importance of input variables and the performance of the neural network. The occupational characteristics and the probability values predicted were used to categorize coal miners for their levels of risk for CWP.</p> <p>Results</p> <p>The sensitivity analysis showed that influence of the duration of dust exposure and occupational category on CWP was 65% and 67%, respectively. The area under the ROC in 3 sets was 0.981, 0.969, and 0.992. There were 7959 coal miners with a probability value < 0.001. The average duration of dust exposure was 15.35 years. The average duration of ex-dust exposure was 0.69 years. Of the coal miners, 79.27% worked in helping and mining. Most of the coal miners were born after 1950 and were first exposed to dust after 1970. One hundred forty-four coal miners had a probability value ≥0.1. The average durations of dust exposure and ex-dust exposure were 25.70 and 16.30 years, respectively. Most of the coal miners were born before 1950 and began to be exposed to dust before 1980. Of the coal miners, 90.28% worked in tunneling.</p> <p>Conclusion</p> <p>The duration of dust exposure and occupational category were the two most important factors for CWP. Coal miners at different levels of risk for CWP could be classified by the three-layer neural network analysis based on occupational history.</p

Metal-organic framework templated electrodeposition of functional gold nanostructures

Utilizing a pair of quick, scalable electrochemical processes, the permanently porous MOF HKUST-1 was electrochemically grown on a copper electrode and this HKUST-1-coated electrode was used to template electrodeposition of a gold nanostructure within the pore network of the MOF. Transmission electron microscopy demonstrates that a proportion of the gold nanostructures exhibit structural features replicating the pore space of this ∼1.4 nm maximum pore diameter MOF, as well as regions that are larger in size. Scanning electron microscopy shows that the electrodeposited gold nanostructure, produced under certain conditions of synthesis and template removal, is sufficiently inter-grown and mechanically robust to retain the octahedral morphology of the HKUST-1 template crystals. The functionality of the gold nanostructure within the crystalline HKUST-1 was demonstrated through the surface enhanced Raman spectroscopic (SERS) detection of 4-fluorothiophenol at concentrations as low as 1 μM. The reported process is confirmed as a viable electrodeposition method for obtaining functional, accessible metal nanostructures encapsulated within MOF crystals

Translational toxicology in setting occupational exposure limits for dusts and hazard classification – a critical evaluation of a recent approach to translate dust overload findings from rats to humans

Background We analyze the scientific basis and methodology used by the German MAK Commission in their recommendations for exposure limits and carcinogen classification of “granular biopersistent particles without known specific toxicity” (GBS). These recommendations are under review at the European Union level. We examine the scientific assumptions in an attempt to reproduce the results. MAK’s human equivalent concentrations (HECs) are based on a particle mass and on a volumetric model in which results from rat inhalation studies are translated to derive occupational exposure limits (OELs) and a carcinogen classification. Methods We followed the methods as proposed by the MAK Commission and Pauluhn 2011. We also examined key assumptions in the metrics, such as surface area of the human lung, deposition fractions of inhaled dusts, human clearance rates; and risk of lung cancer among workers, presumed to have some potential for lung overload, the physiological condition in rats associated with an increase in lung cancer risk. Results The MAK recommendations on exposure limits for GBS have numerous incorrect assumptions that adversely affect the final results. The procedures to derive the respirable occupational exposure limit (OEL) could not be reproduced, a finding raising considerable scientific uncertainty about the reliability of the recommendations. Moreover, the scientific basis of using the rat model is confounded by the fact that rats and humans show different cellular responses to inhaled particles as demonstrated by bronchoalveolar lavage (BAL) studies in both species. Conclusion Classifying all GBS as carcinogenic to humans based on rat inhalation studies in which lung overload leads to chronic inflammation and cancer is inappropriate. Studies of workers, who have been exposed to relevant levels of dust, have not indicated an increase in lung cancer risk. Using the methods proposed by the MAK, we were unable to reproduce the OEL for GBS recommended by the Commission, but identified substantial errors in the models. Considerable shortcomings in the use of lung surface area, clearance rates, deposition fractions; as well as using the mass and volumetric metrics as opposed to the particle surface area metric limit the scientific reliability of the proposed GBS OEL and carcinogen classification.International Carbon Black Associatio

Identifying rail asset maintenance processes: a human-centric and sensemaking approach

Efficient asset maintenance is key for delivering services such as transport. Current rail maintenance processes have been mostly reactive with a recent shift towards exploring proactive modes. The introduction of new ubiquitous technologies and advanced data analytics facilitates the embedding of a ‘predict-and-prevent’ approach to managing assets. Successful, user-centred integration of such technology is still, however, a sparsely understood area. This study reports results from a set of interviews, based on Critical Decision Method, with rail asset maintenance and management experts regarding current procedural aspects of asset management and maintenance. We analyse and present the results from a human-centric sensemaking timeline perspective. We found that within a complex sociotechnical environment such as rail transport, asset maintenance processes apply not just at local levels, but also to broader, strategic levels that involve different stakeholders and necessitate different levels of expertise. This is a particularly interesting aspect within maintenance that has not been discussed as of yet within a process-based and timeline-based models of asset maintenance. We argue that it is important to consider asset maintenance activities within both micro (local) and macro (broader) levels to ensure reliability and stability in transport services. We also propose that the traditionally distinct notions of individual, collaborative and artefact-based sensemaking are in fact all in evidence in this sensemaking context, and argue that a more holistic view of sensemaking is therefore appropriate by placing these results within an amended Recogntion Primed Decsion making model

Bridging the gap from genetic association to functional understanding: the next generation of mouse models of multiple sclerosis.

Multiple sclerosis (MS) is a disabling autoimmune disease of the central nervous system, which affects approximately 0.1% of the population with variable degrees of severity. Disease susceptibility is jointly determined by genetic predisposition and environmental contribution. However, as only a handful of genetic risk factors have been investigated beyond initial genome-wide association studies and environmental factors are largely unidentified, the exact mechanism of how these associations interact remains speculative. Our current understanding of this complex and heterogeneous disease has been advanced by experimental data obtained from animal modeling, with particular focus on the mouse MS model, experimental autoimmune encephalomyelitis. Manipulation of the mouse genome to study genetic risk factors has largely proved informative, but it also has limitations. Integration effects of transgene insertion, gene copy number, and expression variation, as well as differences in regulatory elements between mouse and human, are some of the hurdles faced when using such models to understand human gene variants in mice. Furthermore, as the list of MS disease-associated genetic variants continues to increase, so does the demand to find new approaches to study them. A new generation of humanized mice aims to tighten the gap between mouse and human, such that MS-associated genetic variants can be modeled more physiologically and systematically

The immunology of multiple sclerosis

Our incomplete understanding of the causes and pathways involved in the onset and progression of multiple sclerosis (MS) limits our ability to effectively treat this complex neurological disease. Recent studies explore the role of immune cells at different stages of MS and how they interact with cells of the central nervous system (CNS). The findings presented here begin to question the exclusivity of an antigen-specific cause and highlight how seemingly distinct immune cell types can share common functions that drive disease. Innovative techniques further expose new disease-associated immune cell populations and reinforce how environmental context is critical to their phenotype and subsequent role in disease. Importantly, the differentiation of immune cells into a pathogenic state is potentially reversible through therapeutic manipulation. As such, understanding the mechanisms that provide plasticity to causal cell types is likely key to uncoupling these disease processes and may identify novel therapeutic targets that replace the need for cell ablation

Identifying CNS-colonizing T cells as potential therapeutic targets to prevent progression of multiple sclerosis

Background Multiple sclerosis (MS), an autoimmune disease of the central nervous system (CNS), can be suppressed in its early stages but eventually becomes clinically progressive and unresponsive to therapy. Here, we investigate whether the therapeutic resistance of progressive MS can be attributed to chronic immune cell accumulation behind the blood-brain barrier (BBB). Methods We systematically track CNS-homing immune cells in the peripheral blood of 31 MS patients and 31 matched healthy individuals in an integrated analysis of 497,705 single-cell transcriptomes and 355,433 surface protein profiles from 71 samples. Through spatial RNA sequencing, we localize these cells in post mortem brain tissue of 6 progressive MS patients contrasted against 4 control brains (20 samples, 85,000 spot transcriptomes). Findings We identify a specific pathogenic CD161+/lymphotoxin beta (LTB)+ T cell population that resides in brains of progressive MS patients. Intriguingly, our data suggest that the colonization of the CNS by these T cells may begin earlier in the disease course, as they can be mobilized to the blood by usage of the integrin-blocking antibody natalizumab in relapsing-remitting MS patients. Conclusions As a consequence, we lay the groundwork for a therapeutic strategy to deplete CNS-homing T cells before they can fuel treatment-resistant progression. Funding This study was supported by funding from the University Medical Center Hamburg-Eppendorf, the Stifterverband für die Deutsche Wissenschaft, the OAK Foundation, Medical Research Council UK, and Wellcome

Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria

Lassa fever is a longstanding public health concern in West Africa. Recent molecular studies have confirmed the fundamental role of the rodent host (Mastomys natalensis) in driving human infections, but control and prevention efforts remain hampered by a limited baseline understanding of the disease’s true incidence, geographical distribution and underlying drivers. Here, we show that Lassa fever occurrence and incidence is influenced by climate, poverty, agriculture and urbanisation factors. However, heterogeneous reporting processes and diagnostic laboratory access also appear to be important drivers of the patchy distribution of observed disease incidence. Using spatiotemporal predictive models we show that including climatic variability added retrospective predictive value over a baseline model (11% decrease in out-of-sample predictive error). However, predictions for 2020 show that a climate-driven model performs similarly overall to the baseline model. Overall, with ongoing improvements in surveillance there may be potential for forecasting Lassa fever incidence to inform health planning