Pulmonary functional and morphological damage after exposure to tripoli dust

Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning

OBJECTIVE

Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorithms (ML) and develop a model to predict the occurrence of mild cognitive impairment in miners.

METHODS

A total of 1938 miners were included in the study. Univariate analysis and multivariable logistic regression were employed to identify independent risk factors for cognitive impairment among miners. The dataset was randomly divided into a training set and a validation set in an 8:2 ratio of 1550 and 388 individuals, respectively. An additional group of 351 miners was collected as a test set for cognitive impairment assessment. Seven machine learning algorithms, including XGBoost, Logistic Regression, Random Forest, Complement Naive Bayes, Multi-layer Perceptron, Support Vector Machine, and K-Nearest Neighbors, were used to establish a predictive model for mild cognitive impairment in the dust-exposed population, based on baseline characteristics of the workers. The predictive performance of the models was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), and the XGBoost model was further explained using the Shapley Additive exPlanations (SHAP) package. Cognitive function assessments using rank sum tests were conducted to compare differences in cognitive domains between the mild cognitive impairment group and the normal group.

RESULTS

Univariate and multivariable logistic regression analyses revealed that education level, Age, Work years, SSRS (Self-Rating Scale for Stress), and HAMA (Hamilton Anxiety Rating Scale) were independent risk factors for cognitive impairment among dust-exposed workers. Comparative analysis of the performance of the seven machine learning algorithms demonstrated that XGBoost (training set: AUC=0.959, validation set: AUC=0.795) and Logistic Regression (training set: AUC=0.818, validation set: AUC=0.816) models exhibited superior predictive performance. Results from the test set showed that the AUC of the XGBoost model (AUC=0.913) outperformed the Logistic Regression model (AUC=0.778). Miners with mild cognitive impairment exhibited significant impairments (p<0.05) in visual-spatial abilities, attention, abstract thinking, and memory areas.

CONCLUSION

Machine learning algorithms can predict the risk of cognitive impairment in this population, with the XGBoost algorithm showing the best performance. The developed model can guide the implementation of appropriate preventive measures for dust-exposed workers.

Lung inflammation induced by silica particles triggers hippocampal inflammation, synapse damage and memory impairment in mice

BACKGROUND

Considerable evidence indicates that a signaling crosstalk between the brain and periphery plays important roles in neurological disorders, and that both acute and chronic peripheral inflammation can produce brain changes leading to cognitive impairments. Recent clinical and epidemiological studies have revealed an increased risk of cognitive impairment and dementia in individuals with impaired pulmonary function. However, the mechanistic underpinnings of this association remain unknown. Exposure to SiO₂ (silica) particles triggers lung inflammation, including infiltration by peripheral immune cells and upregulation of pro-inflammatory cytokines. We here utilized a mouse model of lung silicosis to investigate the crosstalk between lung inflammation and memory.

METHODS

Silicosis was induced by intratracheal administration of a single dose of 2.5 mg SiO₂/kg in mice_. Molecular and behavioral measurements were conducted 24 h and 15 days after silica administration. Lung and hippocampal inflammation were investigated by histological analysis and by determination of pro-inflammatory cytokines. Hippocampal synapse damage, amyloid-β (Aβ) peptide content and phosphorylation of Akt, a proxy of hippocampal insulin signaling, were investigated by Western blotting and ELISA. Memory was assessed using the open field and novel object recognition tests.

RESULTS

Administration of silica induced alveolar collapse, lung infiltration by polymorphonuclear (PMN) cells, and increased lung pro-inflammatory cytokines. Lung inflammation was followed by upregulation of hippocampal pro-inflammatory cytokines, synapse damage, accumulation of the Aβ peptide, and memory impairment in mice.

CONCLUSION

The current study identified a crosstalk between lung and brain inflammatory responses leading to hippocampal synapse damage and memory impairment after exposure to a single low dose of silica in mice.

Intratracheal instillation of coal and coal fly ash particles in mice induces DNA damage and translocation of metals to extrapulmonary tissues

Continuous exposure to coal mining particles can cause a variety of lung diseases. We aimed to evaluate the outcomes of exposure to detailed characterized coal and coal fly ash (CFA) particles on DNA, lung and extrapulmonary tissues. Coal samples (COAL11 and COAL16) and CFA samples (CFA11 and CFA16) were included in this study. Intending to enhance the combustion process COAL16 was co-fired with a mixture of fuel oil and diesel oil, producing CFA16. Male BALB/c mice were intratracheally instilled with coal and CFA particles. Measurements were done 24h later. Results showed significant rigidity and obstruction of the central airways only for animals acutely exposed to coal particles. The COAL16 group also showed obstruction of the peripheral airways. Mononuclear cells were recruited in all treatment groups and expression of cytokines, particularly TNF-α and IL-1β, was observed. Only animals exposed to COAL16 showed a significant expression of IL-6 and recruitment of polymorphonuclear cells. DNA damage was demonstrated by Comet assay for all groups. Cr, Fe and Ni were detected in liver, spleen and brain, showing the efficient translocation of metals from the bloodstream to extrapulmonary organs. These effects were associated with particle composition (oxides, hydroxides, phosphates, sulfides, sulphates, silciates, organic-metalic compounds, and polycyclic aromatic hidrocarbons) rather than their size. This work provides state of knowledge on the effects of acute exposure to coal and CFA particles on respiratory mechanics, DNA damage, translocation of metals to other organs and related inflammatory processes.

Novel porous silica granules for instant hemostasis

Granulation is one of the most feasible methods to improve hemostatic efficacy by stabilize the capillary structure of silica particles. Its usability was improved significantly through granulation by enhancing flowability and eliminating dust.