Air pollution, genetic factors, and chronic rhinosinusitis: A prospective study in the UK Biobank.

BACKGROUND: Chronic rhinosinusitis (CRS) is a prevalent upper respiratory condition that manifests in two primary subtypes: CRS with nasal polyps (CRSwNP) and CRS without nasal polyps (CRSsNP). While previous studies indicate a correlation between air pollution and CRS, the role of genetic predisposition in this relationship remains largely unexplored. We hypothesized that higher air pollution exposure would lead to the development of CRS, and that genetic susceptibility might modify this association. METHODS: This cohort study involving 367,298 adult participants from the UK Biobank, followed from March 2006 to October 2021. Air pollution metrics were estimated at residential locations using land-use regression models. Cox proportional hazard models were employed to explore the associations between air pollution exposure and CRS, CRSwNP, and CRSsNP. A polygenic risk score (PRS) was constructed to evaluate the joint effect of air pollution and genetic predisposition on the development of CRS. RESULTS: We found that the risk of CRS increased under long-term exposure to PM2.5 [the hazard ratios (HRs) with 95 % CIs: 1.59 (1.26-2.01)], PM10 [1.64 (1.26-2.12)], NO2 [1.11 (1.04-1.17)], and NOx [1.18 (1.12-1.25)], respectively. These effects were more pronounced among participants with CRSwNP, although the differences were not statistically significant. Additionally, we found that the risks for CRS and CRSwNP increased in a graded manner among participants with higher PRS or higher exposure to PM2.5, PM10, or NOx concentrations. However, no multiplicative or additive interactions were observed. CONCLUSIONS: Long-term exposure to air pollution increases the risk of CRS, particularly CRSwNP underscoring the need to prioritize clean air initiatives and environmental regulations.

Association between drinking water quality and mental health and the modifying role of diet: a prospective cohort study.

BACKGROUND: Environmental factors play an important role in developing mental disorders. This study aimed to investigate the associations of metal and nonmetal elements in drinking water with the risk of depression and anxiety and to assess whether diets modulate these associations. METHODS: We conducted a prospective cohort study including 24,285 participants free from depression and anxiety from the Yinzhou Cohort study in the 2016-2021 period. The exposures were measured by multiplying metal and nonmetal element concentrations in local pipeline terminal tap water samples and total daily drinking water intakes. Cox regression models adjusted for multi-level covariates were used to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (95%CIs). RESULTS: During an average follow-up period of 4.72 and 4.68 years, 773 and 1334 cases of depression and anxiety were identified, respectively. A 1 standard deviation (SD) increase in manganese exposure reduced the incidence of depression by 8% (HR 0.92, 95%CI 0.88 to 0.97). In contrast, with a 1 SD increase in copper and cadmium exposure, the incidence of depression increased by 6% (HR 1.06, 95%CI 1.01 to 1.11) and 8% (HR 1.08, 95%CI 1.00 to 1.17), respectively. The incidence of anxiety increased by 39% (HR 1.39, 95%CI 1.20 to 1.62), 33% (HR 1.33, 95%CI 1.03 to 1.71), and 14% (HR 1.14, 95%CI 1.03 to 1.25) respectively for a 1 SD increase in manganese, iron, and selenium exposure. Diets have a moderating effect on the associations of metal and nonmetal elements with the risk of anxiety. Stronger associations were observed in older, low-income groups and low-education groups. CONCLUSIONS: We found significant associations between exposure to metal and nonmetal elements and depression and anxiety. Diets regulated the associations to some extent.

The incidence of asthma attributable to temperature variability: An ecological study based on 1990-2019 GBD data.

BACKGROUND: Asthma, the second leading cause of death from chronic respiratory diseases, is associated with climate change, especially temperature changes. It is currently unclear about the relationship between long-term temperature variability and the incidence of asthma on a global scale. METHODS: We used asthma incidence, demographic and socioeconomic data from the Global Burden of Disease (GBD) Results Database, and environmental and geographical statistics from TerraClimate between 1990 and 2019 to determine the association between maximum temperature variability and asthma incidence. We also predicted the incidence of heat-related asthma in the future (2020-2100) under four shared socioeconomic pathways (SSPs: 126, 245, 370, and 585). RESULTS: Between 1990 and 2019, the global median incidence of asthma was 402.0 per 100,000 with a higher incidence (median: 1380.3 per 100,000) in children under 10 years old. We found that every 1 °C increase in maximum temperature variability increased the risk of asthma globally by 5.0 %, and the effect was robust for individuals living in high-latitude areas or aged from 50 to 70 years. By 2100, the average incidence of asthma is estimated to be reduced by 95.55 %, 79.32 %, and 40.02 % under the SSP126, SSP245, and SSP370 scenarios, respectively, compared to the SSP585 at latitudes >60°. CONCLUSION: Our study provides evidence that maximum temperature variability is associated with asthma incidence. These findings suggest that implementing stricter mitigation and adaptation strategies may be importment in reducing asthma cases caused by climate change.

Factors Affecting the Breastfeeding Duration of Infants and Young Children in China: A Cross-Sectional Study.

OBJECTIVE: To investigate the factors affecting the duration of continuous breastfeeding of infants within 2 years of age, and to explore intervention strategies that may promote breastfeeding duration in China. METHOD: A self-made electronic questionnaire was used to investigate the breastfeeding duration of infants, and the influencing factors were collected from three levels of individual, family, and social support. The Kruskal-Wallis rank sum test and the multivariable ordinal logistic regression model were used for data analysis. Subgroup analysis was carried out according to region and parity. RESULTS: A total of 1001 valid samples from 26 provinces across the country were obtained. Among them, 9.9% breastfed for less than 6 months, 38.6% for 6 to 12 months, 31.8% for 12 to 18 months, 6.7% for 18 to 24 months, and 13.1% for more than 24 months. Barriers to sustained breastfeeding included the mother's age at birth being over 31, education level below junior high, cesarean delivery, and the baby's first nipple sucking at 2 to 24 h after birth. Factors that promote continued breastfeeding included freelancer or full-time mother, high breastfeeding knowledge score, supporting breastfeeding, baby with low birth weight, first bottle feeding at 4 months and later, first supplementary food at over 6 months old, high family income, the mother's family and friends supporting breastfeeding, breastfeeding support conditions after returning to work, etc. Conclusion: The breastfeeding duration in China is generally short, and the proportion of mothers breastfeeding until the age of 2 years and above, recommended by WHO, is very low. Multiple factors at the individual, family, and social support levels influence the duration of breastfeeding. It is suggested to improve the current situation by strengthening health education, improving system security, and enhancing social support.

A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials.

Solid-state nuclear magnetic resonance (ssNMR) provides local environments and dynamic fingerprints of alkali ions in paramagnetic battery materials. Linking the local ionic environments and NMR signals requires expensive first-principles computational tools that have been developed for over a decade. Nevertheless, the assignment of the dynamic NMR spectra of high-rate battery materials is still challenging because the local structures and dynamic information of alkali ions are highly correlated and difficult to acquire. Herein, we develop a novel machine learning (ML) protocol that could not only quickly sample atomic configurations but also predict chemical shifts efficiently, which enables us to calculate dynamic NMR shifts with the accuracy of density functional theory (DFT). Using structurally well-defined P2-type Na2/3(Mg1/3Mn2/3)O2 as an example, we validate the ML protocol and show the significance of dynamic effects on chemical shifts. Moreover, with the protocol, it is demonstrated that the two experimental 23Na shifts (1406 and 1493 ppm) of P2-type Na2/3(Ni1/3Mn2/3)O2 originate from two stacking sequences of transition metal (TM) layers for the first time, which correspond to space groups P63/mcm and P6322, respectively. This ML protocol could help to correlate dynamic ssNMR spectra with the local structures and fast transport of alkali ions and is expected to be applicable to a wide range of fast dynamic systems.

Mitigating the Surface Reconstruction of Ni-Rich Cathode via P2-Type Mn-Rich Oxide Coating for Durable Lithium Ion Batteries.

Ni-rich materials have received widespread attention as one of the mainstream cathodes in high-energy-density lithium-ion batteries for electric vehicles. However, Ni-rich cathodes suffer from severe surface reconstruction in a high delithiation state, constraining their rate capabilities and life span. Herein, a novel P2-type NaxNi0.33Mn0.67O2 (NNMO) is rationally selected as the surficial modification layer for LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode, which undergoes a spontaneous Na+-Li+ exchange reaction to form an O2-type LixNi0.33Mn0.67O2 (LNMO) layer revealed by combining X-ray diffraction and solid-state nuclear magnetic resonance techniques. Owing to the specific oxygen stacking sequence, O2-type LNMO significantly prevents the initial layered structure of NCM811 from transforming to the spinel or rock-salt phases during cycling, thus effectively maintaining the integral surficial structure and the Li+ diffusion channels of NCM811. Eventually, the NNMO@NCM811 electrode yields enhanced thermal stability, outstanding rate performance, and long cycling stability with 80% capacity retention after 294 cycles at 200 mA g-1, and its life span is further extended to 531 cycles while enhancing the mechanical stability of the bulk material.

Constructing a High-Energy and Durable Single-Crystal NCM811 Cathode for All-Solid-State Batteries by a Surface Engineering Strategy.

Single-crystal LiNi0.8Co0.1Mn0.1O2 (S-NCM811) with an electrochemomechanically compliant microstructure has attracted great attention in all-solid-state batteries (ASSBs) for its superior electrochemical performance compared to the polycrystalline counterpart. However, the undesired side reactions on the cathode/solid-state electrolyte (SSE) interface causes inferior capacity and rate capability than lithium-ion batteries, limiting the practical application of S-NCM811 in the ASSB technology. Herein, it shows that S-NCM811 delivers a high capacity (205 mAh g-1, 0.1C) with outstanding rate capability (175 mAh g-1 at 0.3C and 116 mAh g-1 at 1C) in ASSBs by the coating of a nano-lithium niobium oxide (LNO) layer via the atomic layer deposition technique combined with optimized post-annealing treatment. The working mechanism is verified as the nano-LNO layer effectively suppresses the decomposition of sulfide SSE and stabilizes the cathode/SSE interface. The post-annealing of the LNO layer at 400 °C improves the coating uniformity, eliminates the residual lithium salts, and leads to small impedance increasing and less electrochemical polarization during cycling compared with pristine materials. This work highlights the critical role of the post-annealed nano-LNO layer in the applications of a high-nickel cathode and offers some new insights into the designing of high-performance cathode materials for ASSBs.