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Abstract There is limited evidence regarding the effects of long‐term exposure to PM2.5 constituents on the risk of arthritis and rheumatoid arthritis, and the interaction between PM2.5 and green space remains unclear. This study examined the relationship between long‐term exposure to PM2.5 constituents and the risk of arthritis and rheumatoid arthritis, with the exposure period extending from recruitment until self‐reported outcomes, death, loss to follow‐up, or end of follow‐up. Additionally, the study assessed whether there was an interactive effect between PM2.5 and green space on these risks. We gathered cohort data on 18,649 individuals aged ≥45 years. We applied generalized linear mixed‐effects models to estimate the effects of PM2.5 constituents, NDVI, and their interaction on arthritis and rheumatoid arthritis. The quantile g‐computation and weighted quantile sum regression model were applied to estimate the combined effect of PM2.5 constituents. Our results showed that exposure to single and mixed PM2.5 constituents adversely affected arthritis and rheumatoid arthritis, and was mainly attributed to the black carbon component. We observed “U” or “J” shaped exposure‐response curves for the effects of PM2.5, OM, NO3− and NH4+ exposure on the development of arthritis/rheumatoid arthritis. Additionally, the odds ratio of arthritis for per interquartile range (IQR) increase in PM2.5 was 1.209 (95% CI:1.198, 1.221), per 0.1‐unit decrease in NDVI was 1.091 (95% CI:1.033, 1.151), and the interaction term was 1.005 (95% CI:1.002, 1.007). These findings flesh out the existing evidence for PM2.5 constituents, NDVI and arthritis, rheumatoid arthritis, but the underlying mechanisms still require further exploration.

Studies on the interaction among genetic susceptibility, blue space exposure, and rheumatoid arthritis (RA) risk have been lacking. Therefore, we examined the association between blue space exposure and RA incidence and assess the modifying effect of genetic susceptibility and air pollutants. Form the UK Biobank, 322,783 participants without RA were enrolled in this study. The association between blue space exposure and RA incidence was estimated using a cox proportional hazards model. The combined effect of blue space and genetic factors on the risk of RA was further evaluated. The polygenic risk score (PRS) for RA was calculated to represent individual genetic risk, and the potential modification effect of air pollution on the relationship between blue space, PRS, and RA were explored. During a median follow-up of 12.4 years, 3659 RA cases were identified. A 10 % increase in blue space300 m was associated with a 22.6 % reduction in RA incidence (HR=0.774, 95 % CI: 0.670, 0.895), exhibiting a consistent downward trend in the exposure-response curve. A high PRS was an independent risk factor for RA (HR=1.393, 95 % CI: 1.347, 1.439). The associations between blue space exposure, PRS, and the risk of RA were dose-dependent, with the lowest risk observed among those with high levels of blue space and lower PRS (HRbluespace300m=0.501, 95 % CI: 0.431, 0.583; HRbluespace1000m=0.476, 95 % CI: 0.408, 0.555). Interaction analysis indicated that increased concentrations of air pollutants strengthened the relationship between PRS and RA. Blue space exposure mitigated the risk of RA development, particularly in individuals with low genetic risk.

Introduction: Air pollution is speculated to increase the risk of Coronavirus disease-2019 (COVID-19). Nevertheless, the results remain inconsistent and inconclusive. This study aimed to explore the association between ambient air pollution (AAP) and COVID-19 risks using a meta-analysis with meta-regression modelling. Methods: The inclusion criteria were: original studies quantifying the association using effect sizes and 95 % confidence intervals (CIs); time-series, cohort, ecological or case-crossover peer-reviewed studies in English. Exclusion criteria encompassed non-original studies, animal studies, and data with common errors. PubMed, Web of Science, Embase and Google Scholar electronic databases were systemically searched for eligible literature, up to 31, March 2023. The risk of bias (ROB) was assessed following the Agency for Healthcare Research and Quality parameters. A random-effects model was used to calculate pooled risk ratios (RRs) and their 95 % CIs. Results: A total of 58 studies, between 2020 and 2023, met the inclusion criteria. The global representation was skewed, with major contributions from the USA (24.1 %) and China (22.4 %). The distribution included studies on short-term (43.1 %) and long-term (56.9 %) air pollution exposure. Ecological studies constituted 51.7 %, time-series-27.6 %, cohorts-17.2 %, and case crossover-3.4 %. ROB assessment showed low (86.2 %) and moderate (13.8 %) risk. The COVID-19 incidences increased with a 10 μg/m3 increase in PM2.5 [RR = 4.9045; 95 % CI (4.1548–5.7895)], PM10 [RR = 2.9427: (2.2290–3.8850)], NO2 [RR = 3.2750: (3.1420–3.4136)], SO2 [RR = 3.3400: (2.7931–3.9940)], CO [RR = 2.6244: (2.5208–2.7322)] and O3 [RR = 2.4008: (2.1859–2.6368)] concentrations. A 10 μg/m3 increase in concentrations of PM2.5 [RR = 3.0418: (2.7344–3.3838)], PM10 [RR = 2.6202: (2.1602–3.1781)], NO2 [RR = 3.2226: (2.1411–4.8504)], CO [RR = 1.8021 (0.8045–4.0370)] and O3 [RR = 2.3270 (1.5906–3.4045)] was significantly associated with COVID-19 mortality. Stratified analysis showed that study design, exposure period, and country influenced exposure-response associations. Meta-regression model indicated significant predictors for air pollution-COVID-19 incidence associations. Conclusion: The study, while robust, lacks causality demonstration and focuses only on the USA and China, limiting its generalizability. Regardless, the study provides a strong evidence base for air pollution-COVID-19-risks associations, offering valuable insights for intervention measures for COVID-19.

Background: The effects of heavy metal exposure on immunological function have sparked widespread concern, but unequivocal evidence on the association between mixed metal exposure and novel systemic inflammatory indexes remains scarce. Objectives: This study aimed to analyze the associations of heavy metals with two novel systemic inflammation indexes and the mediated effects of serum albumin. Methods: Nineteen metals were detected among 4082 U.S. adults based on the NHANES. A linear regression, restricted cubic splines (RCS) regression, weighted quantile sum (WQS), Quantile-based Gcomputation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted to evaluate the associations of single metal and mixed metals with systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) levels, respectively. A series of subgroup analyses were used to identify potentially vulnerable populations. Furthermore, we conducted mediation analyses to investigate the mediated effects of serum albumin on the associations of metals with SII and SIRI. Results: In the single-exposure model, exposure to various metals such as urinary Co, As, and serum Zn, Cu was associated with SII and SIRI (PFDR

A number of studies from the literature have suggested that exposure to air pollutants is associated with a declined bone mineral density (BMD), and increased risks of osteoporosis (OP) and bone fractures. This study was performed to systemically assess the genetically causal associations of air pollutants with site-/age-specific BMD and risk of bone fractures with the implementation of two-sample Mendelian randomization (TSMR) and multivariate Mendelian randomization (MVMR). The TSMR analysis was implemented to infer the causal associations between air pollutants and BMD and the risk of bone fractures, additional MVMR analysis was used to further estimate the direct causal effects between air pollutants and BMD, the occurrence of OP, and bone fractures. The results showed that NOx exposure contributed to lower femoral neck BMD (FN-BMD) (β = −0.71, 95%CI: −1.22, −0.20, p = 0.006) and total body BMD (TB-BMD) (β = −0.55, 95%CI: −0.90, −0.21, p = 0.002). Additionally, exposure to PM10 was found to be associated with a decreased TB-BMD (B β = −0.42, 95%CI: −0.66, −0.18, p = 0.001), further age-specific subgroup analysis demonstrated the causal effect of PM10 exposure on the decreased TB-BMD in a subgroup aged 45 to 60 years (β = −0.70, 95%CI: −1.12, −0.29, p = 0.001). Moreover, the findings of the MVMR analysis implied that there was a direct causal effect between PM10 exposure and the decreased TB-BMD (45 < age < 60), after adjusting for PM2.5 and PM2.5 —10 exposure. Our study provides additional evidence to support the causal associations of higher concentrations of air pollutant exposure with decreased BMD, especially in those populations aged between 45 to 60 years, suggesting that early intervention measures and public policy should be considered to improve public health awareness and promote bone health.

The Airy transform was first introduced for a Gaussian beam, and the output beam is an Airy beam. When the Gaussian beam is extended to the Gaussian beam with optical vortex, what kind of output beam will be achieved by executing the Airy transformation. Therefore, the experimental research on Airy transformation of a Gaussian beam with optical vortex is carried out, including the generation of Gaussian beams with optical vortex, the realization of Airy transform, and the related measurements of the output beams. The phase pattern is indirect measured and is recovered from the intensity pattern which is the interference result of a plane wave and the output beam. The experimental measurement results of the light intensity and the phase patterns of transformed Gaussian beams with the optical vortex are consistent with the corresponding numerical simulation results. Based on the first and the second moments of light intensity, the centroid and the beam size are measured. According to the hyperbolic law of the beam width along the axial propagation distance, the propagation factor of the output beam is measured. The influences of the Airy coefficients and the topological charge on the intensity pattern, the phase pattern, the centroid, the beam size, and the propagation factor of transformed Gaussian beams with optical vortex are experimentally investigated, respectively. The intensity pattern, the phase pattern, the centroid, the beam size, and the propagation factor of a transformed Gaussian beam with optical vortex are also compared with those of the corresponding transformed Gaussian vortex beam. This experiment fully proves the effect of the optical vortex on the Airy transformation of Gaussian beams. Meanwhile, this study offers an optional method to generate Airy-like beams from Gaussian beams with optical vortex, which is beneficial to the applications of Gaussian beams with optical vortex.

By using the first three terms of expansion of the Lorentz function, the approximate description of an incident Lorentz-Gauss beam is analytically obtained. Moreover, this approximation is proved to be effective. Based on this approximate description, analytical outcome of the Airy transformation of a Lorentz-Gauss beam is presented, which is separable in two transversal directions. Analytical formulas of the centroid and the beam half width of a Lorentz-Gauss beam in the output plane of Airy transformation are also derived. Since the optical fields of transformed Lorentz-Gauss beams have the same evolution law in two transversal directions, first only one transversal direction is considered. The effects of the Airy control parameter and the Lorentzian parameter on the intensity, the centroid, the beam half width, the location of the predominant peak, and the intensity of the predominant peak for a transformed Lorentz-Gauss beam are investigated in one transversal direction, respectively. Using the above-mentioned parameters, the Airy transformation of a Lorentz-Gauss and a Gaussian beam is compared in detail. Finally, two-dimensional intensity contours of Lorentz-Gauss beams propagating through different Airy transform optical systems are demonstrated. The characteristics of a Lorentz-Gauss beam in the output plane of Airy transformation are well displayed. Also, this research reveals the difference of the Airy transformation of a Lorentz-Gauss and a Gaussian beam.