Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.
The aim of the present study was to find if workers chronically exposed to lead (Pb) and cadmium (Cd) presented changes in their general health and in the clinical parameters of the population under study. We carried out a cross-sectional survey in a sample of informal workers in Cartagena, Colombia. The population under study was composed of male informal workers (≥18 years of age), with experience in their job, selected from occupational settings with potential exposure to Pb and Cd (i.e., mechanics, battery and garbage recyclers, and welders). The median age was 45 years (interquartile range (IQR), 33–53). The median blood Pb level (BLL) was 2 μg/dL (IQR, 0.76–6.22), and the median of blood Cd level (BCL) was 1.22 μg/L (IQR, 0.33–2.01). The study found that 33% of high exposure jobs with BLL > 5 μg/dL (n = 57), whereas in ‘control’ workers, this was 15.3% (n = 9). The highest BLLs were found in battery recyclers (82.1%; n = 23), followed by mechanics (37.3%, n = 22). In the logistic regression model adjusted by age, time on the job, smoking and elevated BCL and BLL increased 3.2 times (95% CI, 1.1–9.7) in mechanics and 29.6 times (95% CI, 7.2–145.6) in battery recyclers. This study found negative changes in the health of workers with higher chronic exposure to lead in Cartagena, Colombia.
The aim of this study was, first of all, to associate the mercury (Hg) concentrations and respiratory functions of the gold miners in the artisanal small-scale gold mining (ASGM) environment in San Martín de Loba, Colombia. We carried out a cross-sectional study using a survey whereby we collected basic demographic information, occupational medical history, and applied two validated questionnaires (Q16 and SF36). We measured Hg levels in all volunteers using direct thermal decomposition-atomic absorption spectrometry. Univariate and bivariate statistical analyses were carried out for all variables, performing logistic regression to assess the effect of ASGM on health outcomes. Volunteers enrolled (n = 124) were between the ages of 20 and 84 years (84% miners and 79% males). No changes were found in the systolic blood pressure, diastolic blood pressure, and heart rate from the ASGM miners, in crude and adjusted statistical analyses. ASGM miners increased 8.91 (95% confidence interval, 1.55–95.70) times the risk of having these than of having neurotoxic effects. Concentrations of total whole blood mercury (T-Hg) in all participants ranged from 0.6 to 82.5 with a median of 6.0 μg/L. Miners had higher T-Hg concentrations than non-miners (p-value = 0.011). Normal and abnormal respiratory spirometry patterns showed significant differences with the physical role and physical function of quality-of-life scales (the (p-value was 0.012 and 0.004, respectively). The spirometry test was carried out in 87 male miners, with 25% of these miners reporting abnormalities. Out of these, 73% presented a restrictive spirometry pattern, and 27%, an obstructive spirometry pattern. The ASGM population had higher Hg concentrations and worse neurotoxic symptomatology than non-miners of the same community.
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