Health threat of PM2.5-bound trace elements exposure on asthma hospital admission: A time-stratified case-crossover study

Source Tracing of Kidney Injury via the Multispectral Fingerprint Identified by Machine Learning-Driven Surface-Enhanced Raman Spectroscopic Analysis

Early diagnosis of drug-induced kidney injury (DIKI) is essential for clinical treatment and intervention. However, developing a reliable method to trace kidney injury origins through retrospective studies remains a challenge. In this study, we designed ordered fried-bun-shaped Au nanocone arrays (FBS NCAs) to create microarray chips as a surface-enhanced Raman scattering (SERS) analysis platform. Subsequently, the principal component analysis (PCA)-two-layer nearest neighbor (TLNN) model was constructed to identify and analyze the SERS spectra of exosomes from renal injury induced by cisplatin and gentamycin. The established PCA-TLNN model successfully differentiated the SERS spectra of exosomes from renal injury at different stages and causes, capturing the most significant spectral features for distinguishing these variations. For the SERS spectra of exosomes from renal injury at different induction times, the accuracy of PCA-TLNN reached 97.8% (cisplatin) and 93.3% (gentamicin). For the SERS spectra of exosomes from renal injury caused by different agents, the accuracy of PCA-TLNN reached 100% (7 days) and 96.7% (14 days). This study demonstrates that the combination of label-free exosome SERS and machine learning could serve as an innovative strategy for medical diagnosis and therapeutic intervention.

Characterization and childhood exposure assessment of toxic heavy metals in household dust under true living conditions from 10 China cities

Health hazards caused by metal exposure in household dust are concerning environmental health problems. Exposure to toxic metals in household dust imposes unclear but solid health risks, especially for children. In this multicenter cross-sectional study, a total of 250 household dust samples were collected from ten stratified cities in China (Panjin, Shijiazhuang, Qingdao, Lanzhou, Luoyang, Ningbo, Xi'an, Wuxi, Mianyang, Shenzhen) between April 2018 and March 2019. Questionnaire was conducted to gather information on individuals' living environment and health status in real-life situations. Multivariate logistic regression and principal component analysis were conducted to identify risk factors and determine the sources of metals in household dust. The median concentration of five metals in household dust from 10 cities ranged from 0.03 to 73.18 μg/g. Among the five heavy metals, only chromium in household dust of Mianyang was observed significantly both higher in the cold season and from the downwind households. Mercury, cadmium, and chromium were higher in the third-tier cities, with levels of 0.08, 0.30 and 97.28 μg/g, respectively. There were two sources with a contribution rate of 38.3 % and 25.8 %, respectively. Potential risk factors for increased metal concentration include long residence time, close to the motorway, decoration within five years, and purchase of new furniture within one year. Under both moderate and high exposure scenarios, chromium showed the highest level of exposure with 6.77 × 10-4 and 2.28 × 10-3 mg·kg-1·d-1, and arsenic imposed the highest lifetime carcinogenic risk at 1.67 × 10-4 and 3.17 × 10-4, respectively. The finding highlighted the priority to minimize childhood exposure of arsenic from household dust.

Short-term associations of particulate matter with different aerodynamic diameters with mortality due to mental disorders and dementia in Ningde, China

Limited evidence is available regarding the impact of ambient inhalable particulate matter (PM) on mental disorder (MD) or dementia-related deaths, particularly PM1, PM1-2.5, and coarse particles (PM2.5-10). Moreover, individual confounders have rarely been considered. In addition, evidence from low-pollution areas is needed but is inadequate. Using death records from the Death Registration System during 2015-2021 in Ningde, a coastal city in southeast China, we combined a conditional quasi-Poisson model with a distributed lag nonlinear model to estimate the nonlinear and lagged associations of PM exposure with MD or dementia-related deaths in Ningde, China, comprehensively controlling for individual time-invariant confounders using a time-stratified case-crossover design. The attributable fraction and number were calculated to quantify the burden of MD or dementia-related deaths that were related to PMs. We found J-shaped relationships between MD or dementia-related deaths and PMs, with different thresholds of 13, 9, 19, 33 and 12 μg/m3 for PM1, PM1-2.5, PM2.5, PM10 and PM2.5-10. An inter-quartile range increase for PM1, PM1-2.5, PM2.5, PM10 and PM2.5-10 above the thresholds led to an increase of 31.8% (95% confidence interval, 14.3-51.9%), 53.7% (22.4-93.1%), 32.6% (15.0-53.0%), 35.1% (17.7-55.0%) and 25.9% (13.0-40.3%) in MD-related deaths at lag 0-3 days, respectively. The associations were significant in the cool season rather than in the warm season and were significantly greater among people aged 75-84 years than in others. The fractions of MD-related deaths attributable to PM1, PM1-2.5, PM2.5, PM10 and PM2.5-10 were 5.55%, 6.49%, 7.68%, 10.66%, and 15.11%, respectively; however, only some of them could be protected by the concentrations recommended by the World Health Organisation or China grade I standard. Smaller associations and similar patterns were observed between PMs and dementia-related death. These findings suggest stricter standards, and provide evidence for the development of relevant policies and measures.

High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.