The main aim of this study was to investigate exposure to airborne substances that are potentially harmful to health during the production of wood pellets, including wood dust, monoterpenes, and resin acids, and as an indicator of diesel exhaust nitrogen dioxide. In addition, area measurements were taken to assess background exposure levels of these substances, volatile organic compounds (VOCs), and carbon monoxide. Measurements were taken at four wood pellet production plants from May 2004 to April 2005. Forty-four workers participated in the study, and a total of 68 personal measurements were taken to determine personal exposure to wood dust (inhalable and total dust), resin acids, monoterpenes, and nitrogen dioxide. In addition, 42 measurements of nitrogen dioxide and 71 measurements of total dust, resin acids, monoterpenes, VOCs, and carbon monoxide were taken to quantify their indoor area concentrations. Personal exposure levels to wood dust were high, and a third of the measured levels of inhalable dust exceeded the Swedish occupational exposure limit (OEL) of 2 mg/m3. Parallel measurements of inhalable and total dust indicated that the former were, on average, 3.2 times higher than the latter. The data indicate that workers at the plants are exposed to significant amounts of the resin acid 7-oxodehydroabietic acid in the air, an observation that has not been recorded previously at wood processing and handling plants. The study also found evidence of exposure to dehydroabietic acid, and exposure levels for resin acids approached 74% of the British OEL for colophony, set at 50 microg/m3. Personal exposure levels to monoterpenes and nitrogen dioxide were low. Area sampling measurements indicated that aldehydes and terpenes were the most abundant VOCs, suggesting that measuring personal exposure to aldehydes might be of interest. Carbon monoxide levels were under the detection limit in all area measurements. High wood dust exposure levels are likely to have implications for worker health; therefore, it is important to reduce exposure to wood dust in this industry.

Exposure to cobalt in the hard metal industry entails severe adverse health effects, including lung cancer and hard metal fibrosis. The main aim of this study was to determine exposure air concentration levels of cobalt and tungsten for risk assessment and dose–response analysis in our medical investigations in a Swedish hard metal plant. We also present mass-based, particle surface area, and particle number air concentrations from stationary sampling and investigate the possibility of using these data as proxies for exposure measures in our study. Personal exposure full-shift measurements were performed for inhalable and total dust, cobalt, and tungsten, including personal real-time continuous monitoring of dust. Stationary measurements of inhalable and total dust, PM2.5, and PM10 was also performed and cobalt and tungsten levels were determined, as were air concentration of particle number and particle surface area of fine particles. The personal exposure levels of inhalable dust were consistently low (AM 0.15mg m−3, range <0.023–3.0mg m−3) and below the present Swedish occupational exposure limit (OEL) of 10mg m−3. The cobalt levels were low as well (AM 0.0030mg m−3, range 0.000028–0.056mg m−3) and only 6% of the samples exceeded the Swedish OEL of 0.02mg m−3. For continuous personal monitoring of dust exposure, the peaks ranged from 0.001 to 83mg m−3 by work task. Stationary measurements showed lower average levels both for inhalable and total dust and cobalt. The particle number concentration of fine particles (AM 3000 p·cm−3) showed the highest levels at the departments of powder production, pressing and storage, and for the particle surface area concentrations (AM 7.6 µm2·cm−3) similar results were found. Correlating cobalt mass-based exposure measurements to cobalt stationary mass-based, particle area, and particle number concentrations by rank and department showed significant correlations for all measures except for particle number. Linear regression analysis of the same data showed statistically significant regression coefficients only for the mass-based aerosol measures. Similar results were seen for rank correlation in the stationary rig, and linear regression analysis implied significant correlation for mass-based and particle surface area measures. The mass-based air concentration levels of cobalt and tungsten in the hard metal plant in our study were low compared to Swedish OELs. Particle number and particle surface area concentrations were in the same order of magnitude as for other industrial settings. Regression analysis implied the use of stationary determined mass-based and particle surface area aerosol concentration as proxies for various exposure measures in our study.

SummaryBackgroundOccupational exposure to cobalt is well established in hard metal manufacture. Cobalt is known to cause contact allergy, asthma, hard metal lung disease, and lung cancer. The relationship between skin exposure and uptake determined in blood has not been extensively investigated.ObjectiveTo examine whether skin and inhalable air exposure to cobalt contributes to uptake, determined as cobalt in blood, in a hard metal manufacturing factory.MethodsThe amount of cobalt on the skin found with an acid wash technique, the air concentrations of inhalable cobalt and cobalt blood concentrations were determined and correlated in exposed workers.ResultsWe found a significant rank correlation for cobalt concentrations on the skin, in inhalable air, and in blood (0.376–0.498). Multiple linear regression showed significant regression coefficients for cobalt skin exposure and blood (B = 0.01, p < 0.05) and for inhalable cobalt in air and blood (B = 49.1, p < 0.001). According to our model based on data from the regression analyses, a twofold increase in skin exposure levels at different air concentrations caused a 3–14% increase in blood levels.ConclusionsOur data suggest that skin exposure to cobalt in the hard metal industry could affect the total uptake at the same order of magnitude as air exposure.

Background Histological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive techniques based on machine learning. Explicit feature representations in computer tissue models can assist explainability of machine learning predictions. Different approaches to feature representation within digital tissue images have been proposed. Cell-graphs have been demonstrated to provide precise and general constructs that can model both low- and high-level features. The basement membrane is high-level tissue architecture, and interactions across the basement membrane are involved in multiple disease processes. Thus, the basement membrane is an important histological feature to study from a cell-graph and machine learning perspective. Results We present a two stage machine learning pipeline for generating a cell-graph from a digital H &E stained tissue image. Using a combination of convolutional neural networks for visual analysis and graph neural networks exploiting node and edge labels for topological analysis, the pipeline is shown to predict both low- and high-level histological features in oral mucosal tissue with good accuracy. Conclusions Convolutional and graph neural networks are complementary technologies for learning, representing and predicting local and global histological features employing node and edge labels. Their combination is potentially widely applicable in histopathology image analysis and can enhance explainability in CAD tools for disease prediction.

Asthma is a chronic disease affected by environmental factors that may increase symptoms that impact on a persons' well-being. An important issue in occupational therapy is to improve the relationship between a person's functional capacity and the physical environment. The aim of the study was to compare the housing environment of persons with asthma (cases, n = 49) and persons without asthma (controls, n = 48), with regard to building construction and condition, physical, chemical and biological factors, and cleaning routines. A secondary aim was to compare different types of accommodation within cases and controls. A specialist team, including a construction engineer, a biological scientist, and an occupational therapist, conducted the study. Data were collected using protocols, as well as a number of established technical methods from the field of occupational and environmental medicine. The primary results showed no major differences in the housing environment between the two groups. However, in individual homes environmental factors at levels that could increase symptoms were identified. When single-family houses were compared with multi-family houses, significant differences were found indicating that preventive interventions may be needed in some single-family houses. Further studies are needed to clarify the person-environment relationship for persons with asthma, focusing on their ability to perform daily activities.