Influencing factors for thyroid function among radiation workers in Wuhan City / 预防医学

Objective@# To investigate the thyroid functions and influencing factors among radiation workers in Wuhan City, so as to provide insights into occupational health monitoring among radiation workers.@*Methods @#Radiation workers receiving physical examinations in Wuhan Prevention and Treatment Center for Occupational Diseases from January to October 2022 were enrolled, and participants' gender, age, smoking, alcohol consumption, medical history, medication use, types of occupational radiation and work duration were collected. Triiodothyronine (TT3), thyroxine (TT4), free thyroxine (FT4), free triiodothyronine (FT3) and thyroid stimulating hormone (TSH) were measured using a magnetic microparticle-based chemiluminescence immunoassay. Personnel dose equivalent was monitored using thermoluminescent dosimetry, and annual cumulative radiation dose was estimated. Factors affecting thyroid function were identified using a multivariable linear regression model.@*Results@#Totally 978 radiation workers were recruited, with a median age of 32.00 (interquartile range, 10.00) years, and including 782 men (79.96%) and 196 women (20.04%). There were 246 smokers (25.15%), 257 workers with alcohol consumption (26.28%) and 489 with a history of radiation work (50.00%). The median annual cumulative radiation dose was 0.20 (interquartile range, 0.24) mSv. The percentage of abnormal thyroid function was 14.72%. Multivariable logistic regression analysis showed that women (OR=1.925, 95%CI: 1.061-3.490), history of radiation work (OR=2.810, 95%CI: 1.119-7.057) and involving in medical application (OR=1.915, 95%CI: 1.101-3.332) were associated with abnormal thyroid function.@*Conclusions@#The percentage of abnormal thyroid function was 14.72% among radiation workers in Wuhan City. History of exposure to ionizing radiation, types of occupational radiation and gender were main factors affecting thyroid function.

Relationship between Occupational Metal Exposure and Hypertension Risk Based on Conditional Logistic Regression Analysis.

Occupational exposure is a significant source of metal contact; previous studies have been limited regarding the effect of occupational metal exposure on the development of hypertension. This study was conducted to assess the levels of exposure of certain metals (chromium (Cr), iron (Fe), manganese (Mn), and nickel (Ni)) in hypertensive and non-hypertensive workers and to assess the relationship between the risk of hypertension and metal exposure level. Our study included 138 hypertensive patients as case groups and 138 non-hypertensive participants as controls. The exposure risk level was divided according to the limit value after collecting and testing the metal dust in the workshop. Considering the influence of single- and poly-metal, single factor analysis and conditional logistic regression analysis of poly-metal were carried out. The results of the model indicated that the incidence of hypertension increased with an increase in Cr exposure level, and the risk of hypertension was 1.85 times higher in the highest exposure than in the lowest exposure (95% CI: 1.20−2.86, p < 0.05). Mn has the same effect as Cr. There was no significant correlation between Fe or Ni and hypertension. Our findings suggested that Cr and Mn exposure in the work environment might increase the risk of hypertension, while no effect of Fe and Ni on blood pressure was found. Prospective study designs in larger populations are needed to confirm our findings.

A nomogram for predicting lung-related diseases among construction workers in Wuhan, China.

Objective: To develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers. Methods: Seven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge builders' lung health. The potential predictors subsets of the risk of LRD were screened by the least absolute shrinkage and selection operator regression and univariate analysis, and determined by using multivariate logistic regression analysis, then were used for developing a prediction nomogram for the risk of LRD. C-index, calibration curve, receiver operating characteristic curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to evaluation the identification, calibration, predictive ability and clinical effectiveness of the nomogram. Results: Five hundred and twenty-six construction workers were allocated to training group and 226 to validation group. The predictors included in the nomogram were symptoms, years of dust exposure, work in shifts and labor intensity. Our model showed good discrimination ability, with a bootstrap-corrected C index of 0.931 (95% CI = 0.906-0.956), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were (95% CI = 0.906-0.956) and 0.945 (95% CI = 0.891-0.999) in the training and validation groups, respectively. The results of DCA and CICA indicated that the nomogram may have clinical usefulness. Conclusion: We established and validated a novel nomogram that can provide individual prediction of LRD for construction workers. This practical prediction model may help occupational physicians in decision making and design of occupational health examination.

Risk for lung-related diseases associated with welding fumes in an occupational population: Evidence from a Cox model.

Background: Welding fumes are a risk factor for welder pneumoconiosis. However, there is a lack of population information on the occurrence of welding fume-induced lung cancer, and little is known about the welding fume pathogenesis. Methods: Welding fume and metal ion concentrations were assessed in a vehicle factory in Wuhan. A Cox regression model estimated lung-related disease risk in workers by independent and combined factors. Results: Workers' exposures were divided into four grades; the highest exposure was among the welders in the maintenance workshop, the highest Mn and Fe exposure was 4 grades, and the highest Cr exposure was 3 grades. Subgroup analysis found that the risk of lung-related disease was 2.17 (95% CI: 1.31-3.57, p < 0.05) in welders compared with non-welders, and the risk of pulmonary disease in male welders was 2.24 (95% CI: 1.34-3.73, p < 0.05) compared to non-welders. Smoking welders had a 2.44 (95% CI: 1.32-4.51, p < 0.01) higher incidence of lung-related diseases than non-welders. Total years of work as an independent protective factor for lung-related disease risk was 0.72 (95% CI: 0.66-0.78, p < 0.01). As an independent risk factor, high-high and high-low exposure had a 5.39 (95% CI: 2.52-11.52, p < 0.001) and 2.17 (95% CI: 1.07-4.41, p < 0.05) higher risk for lung-related diseases, respectively. Conclusions: High welding fume exposure is a significant risk factor for lung-related disease in workers.

Deep-Learning-Based Automatic Detection of Photovoltaic Cell Defects in Electroluminescence Images.

Photovoltaic (PV) cell defect detection has become a prominent problem in the development of the PV industry; however, the entire industry lacks effective technical means. In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category weight assignment, which effectively mitigates the impact of the problem of scant data and data imbalance on model performance; (2) to propose a feature fusion method based on ResNet152-Xception. A coordinate attention (CA) mechanism is incorporated into the feature map to enhance the feature extraction capability of the existing model. The proposed model was conducted on two global publicly available PV-defective electroluminescence (EL) image datasets, and using CNN, Vgg16, MobileNetV2, InceptionV3, DenseNet121, ResNet152, Xception and InceptionResNetV2 as comparative benchmarks, it was evaluated that several metrics were significantly improved. In addition, the accuracy reached 96.17% in the binary classification task of identifying the presence or absence of defects and 92.13% in the multiclassification task of identifying different defect types. The numerical experimental results show that the proposed deep-learning-based defect detection method for PV cells can automatically perform efficient and accurate defect detection using EL images.

Association of urinary metal profiles with serum uric acid: a cross-sectional study of traffic policemen in Wuhan, China.

OBJECTIVES: Serum uric acid (SUA) is both a strong antioxidant and one of the key risk factors of cardiovascular diseases (CVDs). We aimed to investigate the associations of urinary metal profile with SUA in traffic policemen in Wuhan, China. DESIGN: A cross-sectional study was carried out in traffic policemen. SETTING: A seriously polluted Chinese city. PARTICIPANTS: A total of 186 traffic policemen were recruited in this study. About 56 of them worked in the logistics department and the other 130 maintained traffic order or dealt with traffic accidents on the roads. All these subjects had worked as a policeman for at least 1 year. MAIN OUTCOME MEASURES: SUA. RESULTS: The significantly negative association of lead with SUA was consistent between single-metal and multiple-metal models (p=0.004 and p=0.020, respectively). Vanadium, chromium and tin were reversely associated with SUA levels in the single-metal models after false discovery rate (FDR) adjustment (all P_FDR < 0.05). One IQR increase in vanadium, chromium, tin and lead was associated with 26.9 µmol/L (95% CI -44.6 to -9.2; p=0.003), 27.4 µmol/L (95% CI -46.1 to -8.8; p=0.004), 11.2 µmol/L (95% CI -18.9 to -3.4; p=0.005) and 16.4 µmol/L (95% CI -27.6 to -5.2; p=0.004) decrease in SUA, respectively. Significant interaction between smoking and vanadium on decreased SUV was found (pfor interaction = 0.007 and p_FDR = 0.028). CONCLUSIONS: Urinary vanadium, chromium, tin and lead were negatively associated with SUA. Vanadium and cigarette smoking jointly affected SUA levels. Further studies are needed to replicate these findings and to investigate the potential mechanisms.

Targeting ROR1 inhibits epithelial to mesenchymal transition in human lung adenocarcinoma via mTOR signaling pathway.

The receptor tyrosine kinase-like orphan receptor 1 (ROR1) is a type I surface transmembrane protein that contributes to progression of tumor-cell growth and metastasis. We and others have shown that the roles of ROR1 include inhibiting apoptosis, potentiating EGFR signaling, and inducing proliferation in lung cancer, but the roles and mechanisms of ROR1 in lung adenocarcinoma metastasis have not been elucidated. Here we chose four lung adenocarcinoma cell lines, PC9 (erlotinib-sensitive), PC9erlo (acquired erlotinib-resistant), NCI-H358 (partial erlotinib-resistant), and NCI-H1975 (erlotinib-resistant) as cell models to simulate the clinical situation. We found that ROR1 prompted epithelial to mesenchymal transition (EMT) by increasing the expression level of a key epithelial gene, E-cadherin, while decreasing the expression level of the key mesenchymal gene vimentin. Silencing ROR1 by siRNA significantly reduced the migration and invasion of lung adenocarcinoma cells in vitro and also significantly inhibited the phosphorylation of Akt (Ser473), mTOR (Ser2448), Raptor (Ser792) and p70S6K (Thr389) in all four cell lines. This strongly supports our proposal that ROR1 may play a central role in tumor progression and metastasis in lung adenocarcinoma through mTOR signaling, regardless of its EGFR-TKI sensitivity status.

Silencing of Receptor Tyrosine Kinase ROR1 Inhibits Tumor-Cell Proliferation via PI3K/AKT/mTOR Signaling Pathway in Lung Adenocarcinoma.

Receptor tyrosine kinase ROR1, an embryonic protein involved in organogenesis, is expressed in certain hematological malignancies and solid tumors, but is generally absent in adult tissues. This makes the protein an ideal drug target for cancer therapy. In order to assess the suitability of ROR1 as a cell surface antigen for targeted therapy of lung adenocarcinoma, we carried out a comprehensive analysis of ROR1 protein expression in human lung adenocarcinoma tissues and cell lines. Our data show that ROR1 protein is selectively expressed on lung adenocarcinoma cells, but do not support the hypothesis that expression levels of ROR1 are associated with aggressive disease. However silencing of ROR1 via siRNA treatment significantly down-regulates the activity of the PI3K/AKT/mTOR signaling pathway. This is associated with significant apoptosis and anti-proliferation of tumor cells. We found ROR1 protein expressed in lung adenocarcinoma but almost absent in tumor-adjacent tissues of the patients. The finding of ROR1-mediated proliferation signals in both tyrosine kinase inhibitor (TKI)-sensitive and -resistant tumor cells provides encouragement to develop ROR1-directed targeted therapy in lung adenocarcinoma, especially those with TKI resistance.