Confounders in the assessment of the renal effects associated with low-level urinary cadmium: an analysis in industrial workers

<p>Abstract</p> <p>Background</p> <p>Associations of proteinuria with low-level urinary cadmium (Cd) are currently interpreted as the sign of renal dysfunction induced by Cd. Few studies have considered the possibility that these associations might be non causal and arise from confounding by factors influencing the renal excretion of Cd and proteins.</p> <p>Methods</p> <p>We examined 184 healthy male workers (mean age, 39.5 years) from a zinc smelter (n = 132) or a blanket factory (n = 52). We measured the concentrations of Cd in blood (B-Cd) and the urinary excretion of Cd (U-Cd), retinol-binding protein (RBP), protein HC and albumin. Associations between biomarkers of metal exposure and urinary proteins were assessed by simple and multiple regression analyses.</p> <p>Results</p> <p>The medians (interquartile range) of B-Cd (μg/l) and U-Cd (μg/g creatinine) were 0.80 (0.45-1.16) and 0.70 (0.40-1.3) in smelter workers and 0.66 (0.47-0.87) and 0.55 (0.40-0.90) in blanket factory workers, respectively. Occupation had no influence on these values, which varied mainly with smoking habits. In univariate analysis, concentrations of RBP and protein HC in urine were significantly correlated with both U-Cd and B-Cd but these associations were substantially weakened by the adjustment for current smoking and the residual influence of diuresis after correction for urinary creatinine. Albumin in urine did not correlate with B-Cd but was consistently associated with U-Cd through a relationship, which was unaffected by smoking or diuresis. Further analyses showed that RBP and albumin in urine mutually distort their associations with U-Cd and that the relationship between RBP and Cd in urine was almost the replicate of that linking RBP to albumin</p> <p>Conclusions</p> <p>Associations between proteinuria and low-level urinary Cd should be interpreted with caution as they appear to be largely driven by diuresis, current smoking and probably also the co-excretion of Cd with plasma proteins.</p

Automatic sleep stage classification: From classical machine learning methods to deep learning

Background and objectives: The classification of sleep stages is a preliminary exam that contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time-consuming task when conducted manually by experts. Many studies explored ways of automating polysomnogram signals analysis. They are based on two main strategies: conventional machine learning and deep learning methods. The objective of this work is to carry out a comparative study on these two classes of models. Method: A primary comparison of performance of these classifiers is carried out using eight conventional machine learning algorithms and a feed-forward neural networks to assess whether this latter method have definitely supplanted the first. As sleep epochs show inter-epochs correlation, a study of the distinctive influence of this temporal dependence on the classifiers performance is then conducted introducing for this purpose (uni- and bi-directional) long short-term memory networks. In a context of generalization of the use of wearable devices, a comparison of the classification methods examined is also carried out in their accuracy when dealing with a reduced number of channels. Finally, the robustness of the results obtained to the choice of features selection algorithms is discussed. Results and conclusion: Our results show that support vector machine with radial basis function and random forest are just as valid for predicting sleep stages classification as feature-based neural networks with performance closed to the state of the art. This conclusion remains valid even after the introduction of inter-epochs temporal dependence, reduction of the number of channels or change in features selection method

Lung epithelium injury biomarkers in workers exposed to sulphur dioxide in a non-ferrous smelter.

Serum Clara cell protein (CC16) and surfactant-associated protein D (SP-D) were measured in 161 workers exposed to sulphur dioxide (SO(2)) in a non-ferrous smelter. Seventy workers from a blanket manufacture served as referents. Exposure to SO(2) and tobacco smoking were associated with a decrease of CC16 and an increase of SP-D in serum. Tobacco smoking and exposure SO(2) interacted synergistically to decrease serum CC16 but not to increase serum SP-D. While further illustrating the potential of serum CC16 and SP-D, our study confirms that SO(2) can cause airways damage at exposure levels below current occupational exposure limits