Background: The coronavirus pneumonia is still spreading around the world. Much progress has been made in vaccine development, and vaccination will become an inevitable trend in the fight against this pandemic. However, the public acceptance of COVID-19 vaccination still remains uncertain.Methods: An anonymous questionnaire was used in Wen Juan Xing survey platform. All the respondents were divided into healthcare workers and non-healthcare workers. Multinomial logistic regression analyses were performed to identify the key sociodemographic, cognitive, and attitude associations among the samples of healthcare workers and non-healthcare workers.Results: A total of 2,580 respondents completed the questionnaire, including 1,329 healthcare workers and 1,251 non-healthcare workers. This study showed that 76.98% of healthcare workers accepted the COVID-19 vaccine, 18.28% workers were hesitant, and 4.74% workers were resistant. Among the non-healthcare workers, 56.19% workers received the COVID-19 vaccine, 37.57% workers were hesitant, and 6.24% workers were resistant. Among the healthcare workers, compared with vaccine recipients, vaccine-hesitant individuals were more likely to be female (AOR = 1.52, 95% CI: 1.12–2.07); vaccine-resistant individuals were more likely to live in the suburbs (AOR = 2.81, 95% CI: 1.44–3.99) with an income of 10,000 RMB or greater (AOR = 2.00, 95% CI: 1.03–3.90). Among the non-healthcare workers, vaccine-hesitant individuals were more likely to be female (AOR = 1.66, 95% CI: 1.31–2.11); vaccine-resistant individuals were also more likely to be female (AOR = 1.87, 95% CI: 1.16–3.02) and older than 65 years (AOR = 4.96, 95% CI: 1.40–7.62). There are great differences between healthcare workers and non-healthcare workers in their cognition and attitude toward vaccines.Conclusions: Our study shows that healthcare workers are more willing to be vaccinated than non-healthcare workers. Current vaccine safety issues continue to be a major factor affecting public acceptance, and to expand vaccine coverage in response to the COVID-19 pandemic, appropriate vaccination strategies and immunization programs are essential, especially for non-healthcare workers.

Background: Occupational noise has long been recognized as a significant risk factor for hearing loss, particularly among workers. This study aimed to assess the disease burden of hearing loss caused by occupational noise in China from 1990 to 2019, with a focus on differences across sex and age groups, so as to address the existing gaps in the Global Burden of Disease Study (GBD) reports. By analyzing changes in years lived with disability (YLDs) among different sex and age groups over the past 30 years, this study provides valuable insights for the development of occupational noise safety measures in China. Methods: We extracted data on the burden of hearing loss attributable to occupational noise from the 2019 Global Burden of Disease study. R software (version 4.12) was used to calculate the YLDs, age-standardized rates (ASRs), and average annual percent change (AAPC), stratified by age and sex. The Joinpoint regression model was used to analyze the trends in the burden of disease attributable to occupational noise exposure from 1990 to 2019. Results: In China, the YLDs attributable to occupational noise-induced hearing loss in 2019 were 2.3277 million [95% uncertainty interval (UI): 1.5779–3.3478 million, marking a 70.95% increase compared with that in 1990. Throughout the study period, YLD rates exhibited a declining trend, with rates of 127.7 per 100,000 (95% UI: 87.4–181.4) in 1990 and 119.8 per 100,000 (95% UI: 81.2–182.1) in 2019. Using the Joinpoint regression model, the annual percent change (APC) in age-standardized YLD rates for occupational noise-related hearing loss initially increased from 1990 to 2000, followed by a decline, reaching its lowest point in 2015. From a sex perspective, the burden of YLDs in Chinese males exceeded that in females, although the rate of decline was less pronounced in females. With regard to age, the number and rate of YLDs attributable to occupational noise-induced hearing loss generally increased with age, particularly among middle-aged individuals and older adults. However, from 1990 to 2019, the number of YLDs attributable to occupational noise-induced hearing loss decreased among young people aged 15–19 years, accompanied by a significant reduction in YLDs rates. Conclusion: Hearing loss attributable to occupational noise represents a substantial public health concern, especially among middle-aged and older adult workers in China. These findings underscore the importance of implementing effective measures to mitigate occupational noise exposure.

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

Background: Many studies have shown that the pollution of fine particles in the air is related to the incidence of chronic diseases. However, research on air pollution and metabolism-associated fatty liver disease (MAFLD) is limited. Objective: The purpose of this study was to explore the relationship between short-term ambient air pollution and daily outpatient visits for metabolic-related fatty liver. Methods: We used a quasi-Poisson regression generalized additive model to stratify analyses by season, age, and gender. Results: From January 1, 2017, to August 31, 2019, 10,562 confirmed MAFLD outpatient visits were recorded. A 10 μg/m³ increase of fine particular matter (PM10and PM2.5) and NO₂ concentrations corresponding with percent change were 0.82 (95% confidence interval [CI], 0.49– 1.15), 0.57 (95% CI, 0.18– 0.98), and 0.86 (95% CI, 0.59– 1.13) elevation in MAFLD outpatient visits. In terms of season, the impact estimates of NO₂ and PM2.5% change were 3.55 (95% CI, 1.23– 5.87) and 1.12 (95% CI, 0.78– 1.46) in the hot season and transition season, respectively. Compared with the warm season, the impact estimates of PM10were more significant in the cool season: 2.88 (95% CI, 0.66– 5.10). NO₂ has the greatest effect in the transition season, whereas PM10 has the greatest highest effect in the cool and hot seasons. Compared with other pollutants, PM2.5 has the greatest impact in the age stratification, which percent change are 2.69 (95% CI, 0.77– 5.61) and 2.88 (95% CI, 0.37– 6.40) respectively. The impact values of PM2.5 in male and female percent change were 3.60 (95% CI, 0.63– 6.57) and 1.65 (95% CI, 1.05– 2.25), respectively. Conclusion: This study shows that the air pollutants are related to the number of outpatient visits for MAFLD. The effects of different air pollutants on MAFLD outpatient visits were different by season, ages, and gender.

Summary: We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 SwissProt proteotypic proteins across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a landmark effort toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.