Objective:To analyze the characteristics of patients with pneumoconiosis complicated with chronic obstructive pulmonary disease (COPD), and to explore the comorbidity of pneumoconiosis and COPD and its influencing factors.Methods:From October to December 2022, 255 pneumoconiosis patients admitted to an occupational disease prevention and control hospital from January 2018 to December 2021 were selected as the study subjects. According to whether the pneumoconiosis patients were complicated with COPD or not, they were divided into pneumoconiosis and COPD comorbidity group and pneumoconiosis group. The general condition and dust exposure of the two groups of patients were analyzed, and the relationship between different types and different periods of pneumoconiosis and COPD comorbidity was analyzed by multivariate logistic regression.Results:A total of 255 subjects were collected, including 64 patients with comorbidity of pneumoconiosis and COPD, and the comorbidity rate was 25.1%. There were 186 males (72.9%) and 69 females (27.1%), ranging in age from 35 to 90 (63.79±11.79) years, and working age from 1 to 45 (20.31±10.57) years. The comorbidity of pneumoconiosis and COPD increased with the increase of working age (χ 2trend=8.19, P=0.004), and the comorbidity rate for COPD with working age of more than 30 years was 37.7% (23/61). The comorbidity rate of pneumoconiosis and COPD also increased with the increase of the stage of pneumoconiosis (χ 2trend=13.14, P<0.001), and the comorbidity rate of pneumoconiosis and COPD in the stage Ⅲ was as high as 44.0% (11/25). The cumulative dust exposure was negatively correlated with forced expiratory volume in one second/forced vital capacity (FEV 1/FVC), and the linear regression equation y=-0.04 x+78.4. Multivariate logistic regression analysis showed that the length of services ≥30 years ( OR=3.30, 95% CI: 1.15-9.52) and stageⅡ ( OR=3.05, 95% CI: 1.03-9.04) were the risk factors for comorbidity between pneumoconiosis and COPD ( P<0.05) . Conclusion:The comorbidity rate of pneumoconiosis and COPD is high. Working age, pneumoconiosis stage and cumulative dust exposure are the main influencing factors of pneumoconiosis and COPD comorbidity, so more attention should be paid to the comorbidity of pneumoconiosis and COPD.

A retrospective investigation was conducted to analyze the occupational exposure history, clinical manifestations, laboratory tests, imaging findings, and diagnosis and treatment of two cases of sequential latent occupational acute organotin poisoning. Both patients were successively employed in the same enterprise, engaged in crushing of waste polyvinyl chloride plastics, and thus potentially exposed to organotin hazards. Within several days of employment, both patients developed discomfort symptoms, and central nervous system impairment was observed, including short-term memory loss, slow response, and cognitive dysfunction. Hypokalemia was detected in both cases. Cranial magnetic resonance imaging showed abnormalities (multiple ischemic lesions in the bilateral frontal and parietal lobes), and urinary tin was positive. Symptoms relieved in both patients after treatments with tin-exclusion, potassium supplementation, and neurotrophic treatment. Based on the GBZ 26-2007 Diagnostic Criteria of Occupational Acute Trialkyltin Poisoning, and combined with worksite survey of occupational health and exclusion of cerebrovascular disease, viral encephalitis, and autoimmune encephalitis and other neurological disorders, both patients were diagnosed with mild occupational acute trialkyltin poisoning. Sequential latent occupational acute organotin poisoning is prone to misdiagnosis, with great difficulty in etiological identification. Comprehensive assessment of occupational exposure history and biomarker testing are essential for differential diagnosis. Early recognition and intervention improve prognosis, highlighting the need for strengthened occupational health supervision and protection in high-risk work posts.

Objective To investigate the prevalence and influencing factors of work-related musculoskeletal disorders (WMSDs) among bus drivers. Methods A total of 962 drivers from a bus company in Shenzhen City were selected as the research subjects using the judgment sampling method. The Musculoskeletal Disorders Questionnaire for Bus Drivers was used to investigate the prevalence of WMSDs among the research subjects. Results The prevalence of WMSDs was 37.8% in the bus drivers. The prevalence of WMSDs was higher in the low back/waist, neck, and shoulder compared with other body parts, with prevalence of 24.0%, 20.2%, and 14.8%, respectively. The prevalence of single-site and multi-site WMSDs was 18.5% and 19.3%, respectively. The results of the multivariable logistic regression analysis showed that longer job tenure and higher alcohol consumption frequency were associated with higher WMSDs risks (all P<0.01). Weekly work time >48 hours, insufficient rest, work-related fatigue, uncomfortable auxiliary lenses, non-upright trunk posture, prolonged static trunk posture, prolonged wrist flexion, and habitual staying up late were risk factors of WMSDs in the bus drivers (all P<0.05). Conclusion The prevention and treatment of WMSDs among the bus drivers cannot be ignored. Personal characteristics, work organization, work environment, working posture and sleeping habits are the factors that influence the development of WMSDs.

Objective To investigate the prevalence and risk factors of work-related musculoskeletal disorders (WMSDs) among construction workers. Methods A total of 5 783 workers were selected as participants from 12 construction companies in Guangdong Province, Guangxi Zhuang Autonomous Region and Zhejiang Province using a convenient sampling method. The revised Musculoskeletal Disorders Questionnaire was used to investigate the prevalence and influencing factors of WMSDs. Results The prevalence of WMSDs was 27.4% among the construction workers. The prevalence of WMSDs in shoulder, neck, waist/lower back and hand/wrist was 10.6%, 9.5%, 9.5% and 9.4% respectively, which was higher than that in other body parts. Bianry logistic regression analysis showed that the risk of WMSDs in construction workers with junior high school education and below was higher than that of high school/ college and above (P<0.05). The risk of WMSDs was higher in drinkers than that in non-drinkers (P<0.01). The worse the health status of construction workers, the higher the risk of WMSDs (P<0.01). The risk of WMSDs in those who exercised once or twice a month was lower than that in those who did not exercise (P<0.05). The risk of WMSDs was higher in construction workers with longer working hours in uncomfortable postures and greater back bending amplitude at work (all P<0.01). The risk of WMSDs in construction workers with hands holding above the shoulder was higher than that with hands below the shoulder (P<0.05). Construction workers who repeated the same work daily, involved in high-temperature work, often worked overtime, had insufficient rest time, and had a shortage of department personnel had a relatively high risk of WMSDs (all P<0.01). Conclusion The prevalence of WMSDs among the construction workers was relatively high, and the most common WMSDs occurred in shoulder, neck, waist/lower back and hand/wrist. Individual characteristic, work type, work posture and work organization are the influencing factors of WMSDs. Comprehensive measures, especially ergonomic measures based on personal and occupational characteristics should be taken to reduce the risk of WMSDs among construction workers.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:Analyze the correlation between exposure to occupational hazard factors and clinical characteristics of chronic obstructive pulmonary disease (COPD) to provide a basis for early identification and prevention of occupational-related COPD.Methods:In May 2020, a study was conducted involving 258 chronic obstructive pulmonary disease (COPD) patients with occupational exposure history from three general hospitals in Jinan City. Such as symptoms, signs and the percentage of forced expiratory volnmein one second to predicted valve (FEV 1%pred) collectted exposure to occupational hazard factors such as length of strvice and types of exposure. Clinical characteristics were analyzed through questionnaire surveys and COPD-related data collection. Group comparisons employed t-tests or F-tests, while non-parametric tests were applied to non-normal distribution data. Differences in categorical variables between groups were examined using χ2-tests or Fisher's exact test. Multivariate analysis was performed using generalized linear models, with normal distribution correlation analyses conducted through linear regression. Results:Among 258 patients, 145 were male (56.2%) and 113 were female (43.8%), with 210 being smokers (81.4%). The exposure dust primarily consisted of plant-based organic dust (157 cases, 75.5%) and carbon-containing inorganic dust (24 cases, 11.5%). The majority (94 cases, 36.4%) were diagnosed in the 60+ age group. Regarding pulmonary function severity, 55 patients (21.3%) had mild airflow limitation, 99 (38.4%) moderate, 64 (24.8%) severe, and 40 (15.5%) very severe. For acute exacerbation hospitalizations within the past year, 195 (75.6%) had fewer than 2 hospitalizations, while 63 (24.4%) had more than 2. Comprehensive severity assessment showed most patients (91 cases, 35.3%) were in Group B and 62 (24.0%) in Group D. mMRC scores ranged from 0-1 (58.9%) and 2-4 (106 cases, 41.1%). The average CAT questionnaire score was 17.45±1.68. Respiratory symptoms significantly increased with higher occupational hazard exposure levels ( P<0.05). Moderate-to-high risk exposure showed a correlation with disease severity, with OR values (95% CI) of 1.30 (1.10-1.53) and 1.38 (1.20-1.59). There was a certain correlation between high risk exposure and the number of acute exacerbations in COPD patients in the past year, OR value (95% CI) was 1.410 (1.33-1.50) . Conclusion:Contact level is a major hazard factor affecting COPD respiratory symptoms, disease severity, and number of acute exacerbations. Older age at diagnosis, lower literacy, more smoking and higher exposure level, patients had worse lung function and more severe clinical symptoms.

Objective:Analyze the correlation between exposure to occupational hazard factors and clinical characteristics of chronic obstructive pulmonary disease (COPD) to provide a basis for early identification and prevention of occupational-related COPD.Methods:In May 2020, a study was conducted involving 258 chronic obstructive pulmonary disease (COPD) patients with occupational exposure history from three general hospitals in Jinan City. Such as symptoms, signs and the percentage of forced expiratory volnmein one second to predicted valve (FEV 1%pred) collectted exposure to occupational hazard factors such as length of strvice and types of exposure. Clinical characteristics were analyzed through questionnaire surveys and COPD-related data collection. Group comparisons employed t-tests or F-tests, while non-parametric tests were applied to non-normal distribution data. Differences in categorical variables between groups were examined using χ2-tests or Fisher's exact test. Multivariate analysis was performed using generalized linear models, with normal distribution correlation analyses conducted through linear regression. Results:Among 258 patients, 145 were male (56.2%) and 113 were female (43.8%), with 210 being smokers (81.4%). The exposure dust primarily consisted of plant-based organic dust (157 cases, 75.5%) and carbon-containing inorganic dust (24 cases, 11.5%). The majority (94 cases, 36.4%) were diagnosed in the 60+ age group. Regarding pulmonary function severity, 55 patients (21.3%) had mild airflow limitation, 99 (38.4%) moderate, 64 (24.8%) severe, and 40 (15.5%) very severe. For acute exacerbation hospitalizations within the past year, 195 (75.6%) had fewer than 2 hospitalizations, while 63 (24.4%) had more than 2. Comprehensive severity assessment showed most patients (91 cases, 35.3%) were in Group B and 62 (24.0%) in Group D. mMRC scores ranged from 0-1 (58.9%) and 2-4 (106 cases, 41.1%). The average CAT questionnaire score was 17.45±1.68. Respiratory symptoms significantly increased with higher occupational hazard exposure levels ( P<0.05). Moderate-to-high risk exposure showed a correlation with disease severity, with OR values (95% CI) of 1.30 (1.10-1.53) and 1.38 (1.20-1.59). There was a certain correlation between high risk exposure and the number of acute exacerbations in COPD patients in the past year, OR value (95% CI) was 1.410 (1.33-1.50) . Conclusion:Contact level is a major hazard factor affecting COPD respiratory symptoms, disease severity, and number of acute exacerbations. Older age at diagnosis, lower literacy, more smoking and higher exposure level, patients had worse lung function and more severe clinical symptoms.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To analyze the characteristics of patients with pneumoconiosis complicated with chronic obstructive pulmonary disease (COPD), and to explore the comorbidity of pneumoconiosis and COPD and its influencing factors.Methods:From October to December 2022, 255 pneumoconiosis patients admitted to an occupational disease prevention and control hospital from January 2018 to December 2021 were selected as the study subjects. According to whether the pneumoconiosis patients were complicated with COPD or not, they were divided into pneumoconiosis and COPD comorbidity group and pneumoconiosis group. The general condition and dust exposure of the two groups of patients were analyzed, and the relationship between different types and different periods of pneumoconiosis and COPD comorbidity was analyzed by multivariate logistic regression.Results:A total of 255 subjects were collected, including 64 patients with comorbidity of pneumoconiosis and COPD, and the comorbidity rate was 25.1%. There were 186 males (72.9%) and 69 females (27.1%), ranging in age from 35 to 90 (63.79±11.79) years, and working age from 1 to 45 (20.31±10.57) years. The comorbidity of pneumoconiosis and COPD increased with the increase of working age (χ 2trend=8.19, P=0.004), and the comorbidity rate for COPD with working age of more than 30 years was 37.7% (23/61). The comorbidity rate of pneumoconiosis and COPD also increased with the increase of the stage of pneumoconiosis (χ 2trend=13.14, P<0.001), and the comorbidity rate of pneumoconiosis and COPD in the stage Ⅲ was as high as 44.0% (11/25). The cumulative dust exposure was negatively correlated with forced expiratory volume in one second/forced vital capacity (FEV 1/FVC), and the linear regression equation y=-0.04 x+78.4. Multivariate logistic regression analysis showed that the length of services ≥30 years ( OR=3.30, 95% CI: 1.15-9.52) and stageⅡ ( OR=3.05, 95% CI: 1.03-9.04) were the risk factors for comorbidity between pneumoconiosis and COPD ( P<0.05) . Conclusion:The comorbidity rate of pneumoconiosis and COPD is high. Working age, pneumoconiosis stage and cumulative dust exposure are the main influencing factors of pneumoconiosis and COPD comorbidity, so more attention should be paid to the comorbidity of pneumoconiosis and COPD.

ObjectiveTo explore the influencing factors of occupational hand-arm vibration disease (OHAVD) caused by handheld workpiece polishing. Methods A total of 222 OHAVD patients (case group), 275 hand-transmitted vibration-exposed workers (exposed group) and 243 healthy workers without hand-transmitted vibration exposure (control group) in a sports equipment manufacturing enterprise were selected as the study subjects using the convenience sampling method. Worksite survey of occupational health was conducted on these three groups, and the human vibration measurement equipment was used to measure the vibration exposure level of handheld vibration among the study subjects. The 8-hour energy equivalent frequency-weighted vibrating acceleration ［A(8)］ and cumulative vibration exposure level (CVEL) were calculated. Results The prevalence of coldness, numbness, tingling fingers, and vibration-induced white finger was higher in the exposed group and the case group compared with the control group (all P<0.05). The prevalence of the above-mentioned hand symptoms was higher in the case group compared with the exposed group (all P<0.05). The A(8) and CVEL levels of the study subjects in the case group were higher than those in the exposed group (all P<0.05). Binary logistic analysis result showed that age and CVEL were both influencing factors of OHAVD (all P<0.05). According to the restricted cubic spline models, CVEL of the study subjects in the exposed group had a positive nonlinear dose-response relationship with the risk of OHAVD (overall trend P<0.01, nonlinear P<0.01), indicating an increasing risk of OHAVD with increasing CVEL. Conclusion Hand-transmitted vibration exposure is a risk factor for OHAVD. Early intervention should be carried out for hand-transmitted vibration-exposed individuals to reduce vibration-exposed levels and control vibration exposure time.