Seasonal and spatial variations of arsenic and its species in particulate matter in an urban environment of Brno, Czech Republic.

The present paper deals with an analysis of total arsenic concentration using ICP-MS/MS and an analysis of concentration of several arsenic species, arsenite (AsIII), arsenate (AsV), monomethylarsonate (MMA), dimethylarsenite (DMA), and trimethylarsine oxide (TMAO), using HPLC-ICP-MS/MS in the PM10 fraction of airborne urban aerosol. The samples were collected during two campaigns, in the autumn of 2022 and in the winter of 2023, at three locations within the central European city of Brno, with the aim to evaluate the seasonal and spatial variations in the PM10 composition. The results confirmed only the seasonal variability in the content of the methylated arsenic species in PM10 influenced by biomethylation processes. To gain better understanding of the possible arsenic origin, a supplementary analysis of the total arsenic concentrations was performed in samples of different size fractions of particulate matter collected using ELPI + . Local emissions, including industrial activities and heating during the winter season, were suggested as the most likely predominant source contributing to the total As content in PM10.

Features of extreme PM 2.5 pollution and its influencing factors: evidence from China.

Extreme PM 2.5 pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme PM 2.5 pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level PM 2.5 concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that SO 2 concentration, NO 2 concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of SO 2 concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of NO 2 concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of SO 2 and NO 2 concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.

Research on characteristics and influencing factors of road dust emission in a southern city in China.

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban.

SETTING: off fireworks during the Spring Festival (SF) is a traditional practice in China. However, because of its environmental impact, the Chinese government has banned this practice completely. Existing evaluations of the effectiveness of firework prohibition policies (FPPs) lack spatiotemporal perspectives, making it difficult to comprehensively assess their effects on air quality. Consequently, this study used remote sensing technology based on aerosol optical depth and multiple variables, compared nine statistical learning methods, and selected the optimal model, transformer, to estimate daily spatiotemporal continuous PM2.5 concentration datasets for Tianjin from 2016 to 2020. The overall model accuracy reached a root mean square error of 15.30 µg/m³, a mean absolute error of 9.55 µg/m³, a mean absolute percentage error of 21.07%, and an R2 of 0.88. Subsequently, we analysed the variations in PM2.5 concentrations from three time dimensions-the entire year, winter, and SF periods-to exclude the impact of interannual variations on the experimental results. Additionally, we quantitatively estimated firework-specific PM2.5 concentrations based on time-series forecasting. The results showed that during the three years following the implementation of the FPPs, firework-specific PM2.5 concentrations decreased by 52.70%, 49.76%, and 86.90%, respectively, compared to the year before the implementation of the FPPs. Spatially, the central urban area and industrial zones are more affected by FPPs than the suburbs. However, daily variations of PM2.5 concentrations during the SF showed that high concentrations of PM2.5 produced in a short period will return to normal rapidly and will not cause lasting effects. Therefore, the management of fireworks needs to consider both environmental protection and the public's emotional attachment to traditional customs, rather than simply imposing a blanket ban on fireworks. We advocate improving firework policies in four aspects-production, sales, supervision, and control-to promote sustainable development of the ecological environment and human society.

Weekly-specific ambient PM1 before and during pregnancy and the risk of gestational diabetes mellitus.

BACKGROUND: Exposure to fine or respirable particulate matter has been linked to an elevated risk of gestational diabetes mellitus (GDM). However, the association between exposure to particulate matter with an aerodynamic diameter ≤ 1 µm (PM1) and GDM has not been explored. METHODS: We conducted a cohort study involving 60,173 pregnant women from nine hospitals in Beijing, China, from February 2015 to April 2021. Daily concentrations of PM1 and ozone were obtained from a validated spatiotemporal artificial intelligence model. We used a modified Poisson regression combined with distributed lag models to estimate the association between weekly-specific PM1 exposure and the risk of GDM after adjusting for individual-level covariates. RESULTS: Among the 51,299 pregnant women included in the final analysis, 4008 were diagnosed with GDM. Maternal exposure to PM1 during preconception and gestational periods was generally associated with an increased risk of GDM. The most pronounced associations were identified during the 12th week before pregnancy, the 5th-8th weeks of the first trimester, and the 23rd-24th weeks of the second trimester. Each 10 µg/m3 increase in PM1 was associated with a relative risk of GDM of 1.65 (95 % CI: 1.59, 1.72) during the preconception period, 1.67 (95 % CI: 1.61, 1.73) in the first trimester, 1.52 (95 % CI: 1.47, 1.58) in the second trimester, and 2.54 (95 % CI: 2.45, 2.63) when considering the first and second trimester combined. CONCLUSIONS: Exposure to PM1 before and during pregnancy was associated with an increased risk of GDM, particularly during the 12 weeks before pregnancy and gestational weeks 5-8 and 23-24.

PM2.5 Induces Developmental Neurotoxicity in Cortical Organoids.

There is mounting evidence implicating the potential neurotoxic effects of PM2.5 during brain development, as it has been observed to traverse both the placental barrier and the fetal blood-brain barrier. However, the current utilization of 2D cell culture and animal models falls short in providing an accurate representation of human brain development. Consequently, the precise mechanisms underlying PM2.5-induced developmental neurotoxicity in humans remain obscure. To address this research gap, we constructed three-dimensional (3D) cortical organoids that faithfully recapitulate the initial stages of human cerebral cortex development. Our goal is to investigate the mechanisms of PM2.5-induced neurotoxicity using 3D brain organoids that express cortical layer proteins. Our findings demonstrate that exposure to PM2.5 concentrations of 5 µg/mL and 50 µg/mL induces neuronal apoptosis and disrupts normal neural differentiation, thereby suggesting a detrimental impact on neurodevelopment. Furthermore, transcriptomic analysis revealed PM2.5 exposure induced aberrations in mitochondrial complex I functionality, which is reminiscent of Parkinson's syndrome, potentially mediated by misguided axon guidance and compromised synaptic maintenance. This study is a pioneering assessment of the neurotoxicity of PM2.5 pollution on human brain tissues based on 3D cortical organoids, and the results are of great significance in guiding the formulation of the next air pollution prevention and control policies in China to achieve the sustainable improvement of air quality and to formulate pollution abatement strategies that can maximize the benefits to public health.

Nitrogen fate in riparian zones: Insights from experiments and analysis of sediment porosity and surface water-groundwater exchange.

Riparian zones play a vital role in the river ecosystem. Solutes in vertical riparian zones are transported being by alternating hydraulic gradients between river water and groundwater, due to natural or human activities. This study investigates the impacts of porous sediments and alternating rate of surface water-groundwater on nitrogen removal in the riparian zone through experiments based on the field sampled. The experimental results, combined with dimensionless numbers (Péclet and Damköhler) and Partial Least Squares-Path Modeling, analyze the nitrogen fate responding to hydrodynamics changes. The results show that increased sediment porosity contributes to the ammonium removal, particularly when the oxygen content of river water is low, with the removal rate up to 72.57%. High ammonium content and dissolved organic carbon (DOC) in rural rivers lead to a constant low-oxygen condition (4 mg/L) during surface water-groundwater alternation, and promote denitrification. This threatens groundwater with ammonium pollution and causes accumulation at the top of vertical riparian zones during upwelling, potentially causing secondary river pollution. However, increasing the alternating rate hinders the nitrate denitrification and drastically changes in the redox environment of the riparian zone, despite contributing to ammonium removal. Rapid oxygen consumption during aerobic metabolism and nitrification in groundwater-surface water exchange created favorable conditions for denitrification. Floodplains sediment porosity is unfavorable for nitrification. This study improves understanding of coupled hydrologic and solute processes in vertical riparian zones, informing strategies for optimizing nitrogen attenuation and riparian zone construction.

Parkin deficiency exacerbates particulate matter-induced injury by enhancing airway epithelial necroptosis.

Exposure to fine particulate matter (PM) disrupts the function of airway epithelial barriers causing cellular stress and damage. However, the precise mechanisms underlying PM-induced cellular injury and the associated molecular pathways remain incompletely understood. In this study, we used intratracheal instillation of PM in C57BL6 mice and PM treatment of the BEAS-2B cell line as in vivo and in vitro models, respectively, to simulate PM-induced cellular damage and inflammation. We collected lung tissues and bronchoalveolar lavage fluids to assess histopathological changes, necroptosis, and airway inflammation. Our findings reveal that PM exposure induces necroptosis in mouse airway epithelial cells. Importantly, concurrent administration of a receptor interacting protein kinases 3 (RIPK3) inhibitor or the deletion of the necroptosis effector mixed-lineage kinase domain-like protein (MLKL) effectively attenuated PM-induced airway inflammation. PM exposure dose-dependently induces the expression of Parkin, an E3 ligase we recently reported to play a pivotal role in necroptosis through regulating necrosome formation. Significantly, deletion of endogenous Parkin exacerbates inflammation by enhancing epithelial necroptosis. These results indicate that PM-induced Parkin expression plays a crucial role in suppressing epithelial necroptosis, thereby reducing airway inflammation. Overall, these findings offer valuable mechanistic insights into PM-induced airway injury and identify a potential target for clinical intervention.

ZmCRK1 negatively regulates maize's response to drought stress by phosphorylating plasma membrane H+-ATPase ZmMHA2.

Drought severely affects crop growth and yields. Stomatal regulation plays an important role in plant response to drought stress. Light-activated plasma membrane-localized proton ATPase (PM H+-ATPase) mainly promoted the stomatal opening. Abscisic acid (ABA) plays a dominant role in the stomatal closure during drought stress. It is not clear how PM H+-ATPase is involved in the regulation of ABA-induced stomatal closure. We found that a CALCIUM-DEPENDENT PROTEIN KINASE RELATED KINASE 1 (ZmCRK1), and its mutant zmcrk1 exhibited slow water loss in detached leaves, high-survival rate after drought stress, and sensitivity to stomatal closure induced by ABA. The ZmCRK1 overexpression lines are opposite. ZmCRK1 interacted with the maize PM H+-ATPase ZmMHA2. ZmCRK1 phosphorylated ZmMHA2 at the Ser-901 and inhibited its proton pump activity. ZmCRK1 overexpression lines and zmmha2 mutants had low H+-ATPase activity, resulting in impaired ABA-induced H+ efflux. Taken together, our study indicates that ZmCRK1 negatively regulates maize drought stress response by inhibiting the activity of ZmMHA2. Reducing the expression level of ZmCRK1 has the potential to reduce yield losses under water deficiency.

Fine Particulate Matter and Osteoporosis: Evidence, Mechanisms, and Emerging Perspectives.

Air pollution, particularly fine particulate matter with an aerodynamic diameter of ≤ 2.5 µm (PM2.5), has been recognized for its adverse effects on multiple organs beyond the lungs. Among these, the bone began to garner significant attention. This review covers epidemiological, animal and cell studies on PM2.5 exposure and bone health as well as studies on PM2.5-induced diseases with skeletal complications. Emerging evidence from epidemiological studies indicates a positive association between PM2.5 exposure and the incidence of osteoporosis and fractures, along with a negative association with bone mineral density. Experimental studies have demonstrated that PM2.5 can disrupt the metabolic balance between osteoclasts and osteoblasts through inflammatory responses, oxidative stress, and endocrine disruption, thereby triggering bone loss and osteoporosis. Additionally, this review proposes a secondary mechanism by which PM2.5 may impair bone homeostasis via pathological alterations in other organs, offering new perspectives on the complex interactions between environmental pollutants and bone health. In conclusion, this contemporary review underscores the often-overlooked risk factors of PM2.5 in terms of its adverse effects on bone and elucidates the mechanisms of both primary and secondary toxicity. Further attention should be given to exploring the molecular mechanisms of PM2.5-induced bone impairment and developing effective intervention strategies. With global climate change, increasing ozone pollution, emerging pollutants, and multifactorial exposure to environmental factors, these issues are likely to become of greater concern in the near future.

Household air pollution disparities between socioeconomic groups in Chicago.

Purpose: To assess household air pollution levels in urban Chicago households and examine how socioeconomic factors influence these levels. Methods: We deployed wireless air monitoring devices to 244 households in a diverse population in Chicago to continuously record household fine particulate matter (PM2.5) concentration. We calculated hourly average PM2.5 concentration in a 24-hour cycle. Four factors-race, household income, area deprivation, and exposure to smoking-were considered in this study. Results: A total of 93085 h of exposure data were recorded. The average household PM2.5 concentration was 43.8 µg m-3. We observed a significant difference in the average household PM2.5 concentrations between Black/African American and non-Black/African American households (46.3 versus 31.6 µg m-3), between high-income and low-income households (18.2 versus 52.5 µg m-3), and between smoking and non-smoking households (69.7 versus 29.0 µg m-3). However, no significant difference was observed between households in less and more deprived areas (43.7 versus 43.0 µg m-3). Implications: Household air pollution levels in Chicago households are much higher than the recommended level, challenging the hypothesis that household air quality is adequate for populations in high income nations. Our results indicate that it is the personal characteristics of participants, rather than the macro environments, that lead to observed differences in household air pollution.

Association of the weight-adjusted waist index with hypertension in the context of predictive, preventive, and personalized medicine.

Objective: Hypertension (HTN) is a prevalent global health concern. From the standpoint of preventive and personalized medicine (PPPM/3PM), early detection of HTN offers a crucial opportunity for targeted prevention and personalized treatment. This study aimed to evaluate the association between the weight-adjusted waist index (WWI) and HTN risk. Methods: A case-control study using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 was conducted. Logistic regression models assessed the association between WWI and HTN. Subgroup analyses explored differences in age, sex, ethnicity, and diabetes status. Restricted cubic spline (RCS) analyses examined potential nonlinear relationships. Results: A total of 32,116 participants, with an average age of 49.28 ± 17.56 years, were included in the study. A significant positive association between WWI and the risk of HTN was identified (odds ratio [OR], 2.49; 95% CI, 2.39-2.59; P < 0.001). When WWI was categorized into quartiles (Q1-Q4), the highest quartile (Q4) exhibited a stronger association compared to Q1 (OR, 2.94; 95% CI, 2.65-3.27; P < 0.001). Subgroup analyses indicated that WWI was a risk factor for HTN across different populations, although variations in the magnitude of effect were observed. Furthermore, the findings from the RCS elucidated a nonlinear positive correlation between WWI and HTN. Conclusion: WWI is independently associated with HTN risk, highlighting its potential as a risk assessment tool in clinical practice. Incorporating WWI into early detection strategies enhances targeted prevention and personalized management of HTN. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00375-3.

The caregiving role influences Suboptimal Health Status and psychological symptoms in unpaid carers.

Background: Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM). Methods: A cross-sectional study was conducted among 368 participants who were enrolled from Australia, including 203 unpaid carers as cases and 165 individuals from the general population as controls. SHS scores were measured using SHSQ-25 (Suboptimal Health Status Questionnaire-25), whilst psychological symptoms were measured by DASS-21 (Depression, Anxiety and Stress Scale-21). Chi-square was used to measure SHS and psychological symptom prevalence. Spearman correlation analysis was utilised to identify the relationship between SHSQ-25 and DASS-21 scores. Logistic regression analysis was used for multivariate analysis. Results: The prevalence of SHS in carers was 43.0% (98/203), significantly higher than the prevalence 12.7% (21/165) in the general population (p < 0.001). In addition, suboptimal health prevalence was higher in female carers (50.3%; 95/189) than females in the general population (12.4%; 18/145). Logistic regression showed that the caregiving role influenced SHS, with carers 6.4 times more likely to suffer from SHS than their non-caring counterparts (aOR = 6.400, 95% CI = 3.751-10.919). Conclusions: Unpaid carers in Australia have a significantly higher prevalence of SHS than that in the general population and experience poorer health. The SHSQ-25 is a powerful tool that can be utilised to screen at-risk individuals to predict their risk of chronic disease development, an essential pillar for shifting the paradigm change from reactive medicine to that of predictive, preventive and personalised medicine (PPPM). Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00370-8.

Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling.

Background: Ovarian cancer patients' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel. Objectives: Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM. Methods: This study employed "Beyondcell," an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088. Results: This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients' prognosis prediction. Conclusions: This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00374-4.

Eyecare-cloud: an innovative electronic medical record cloud platform for pediatric research and clinical care.

Background and objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians. Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0). Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001). Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care.

Towards a personalized prediction, prevention and therapy of insomnia: gut microbiota profile can discriminate between paradoxical and objective insomnia in post-menopausal women.

Background: Insomnia persists as a prevalent sleep disorder among middle-aged and older adults, significantly impacting quality of life and increasing susceptibility to age-related diseases. It is classified into objective insomnia (O-IN) and paradoxical insomnia (P-IN), where subjective and objective sleep assessments diverge. Current treatment regimens for both patient groups yield unsatisfactory outcomes. Consequently, investigating the neurophysiological distinctions between P-IN and O-IN is imperative for devising novel precision interventions aligned with primary prediction, targeted prevention, and personalized medicine (PPPM) principles.Working hypothesis and methodology.Given the emerging influence of gut microbiota (GM) on sleep physiology via the gut-brain axis, our study focused on characterizing the GM profiles of a well-characterized cohort of 96 Italian postmenopausal women, comprising 54 insomniac patients (18 O-IN and 36 P-IN) and 42 controls, through 16S rRNA amplicon sequencing. Associations were explored with general and clinical history, sleep patterns, stress, hematobiochemical parameters, and nutritional patterns. Results: Distinctive GM profiles were unveiled between O-IN and P-IN patients. O-IN patients exhibited prominence in the Coriobacteriaceae family, including Collinsella and Adlercreutzia, along with Erysipelotrichaceae, Clostridium, and Pediococcus. Conversely, P-IN patients were mainly discriminated by Bacteroides, Staphylococcus, Carnobacterium, Pseudomonas, and respective families, along with Odoribacter. Conclusions: These findings provide valuable insights into the microbiota-mediated mechanism of O-IN versus P-IN onset. GM profiling may thus serve as a tailored stratification criterion, enabling the identification of women at risk for specific insomnia subtypes and facilitating the development of integrated microbiota-based predictive diagnostics, targeted prevention, and personalized therapies, ultimately enhancing clinical effectiveness. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00369-1.

Body fluid multiomics in 3PM-guided ischemic stroke management: health risk assessment, targeted protection against health-to-disease transition, and cost-effective personalized approach are envisaged.

Because of its rapid progression and frequently poor prognosis, stroke is the third major cause of death in Europe and the first one in China. Many independent studies demonstrated sufficient space for prevention interventions in the primary care of ischemic stroke defined as the most cost-effective protection of vulnerable subpopulations against health-to-disease transition. Although several studies identified molecular patterns specific for IS in body fluids, none of these approaches has yet been incorporated into IS treatment guidelines. The advantages and disadvantages of individual body fluids are thoroughly analyzed throughout the paper. For example, multiomics based on a minimally invasive approach utilizing blood and its components is recommended for real-time monitoring, due to the particularly high level of dynamics of the blood as a body system. On the other hand, tear fluid as a more stable system is recommended for a non-invasive and patient-friendly holistic approach appropriate for health risk assessment and innovative screening programs in cost-effective IS management. This article details aspects essential to promote the practical implementation of highlighted achievements in 3PM-guided IS management. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00376-2.

Fission yeast Duc1 links to ER-PM contact sites and influences PM lipid composition and cytokinetic ring anchoring.

Cytokinesis is the final stage of the cell cycle that results in the physical separation of daughter cells. To accomplish cytokinesis, many organisms build an actin- and myosin-based cytokinetic ring (CR) anchored to the plasma membrane (PM). Defects in CR-PM anchoring can arise when the PM lipid, phosphatidylinositol-4,5- bisphosphate [PI(4,5)P2], is depleted. In Schizosaccharomyces pombe, reduced PM PI(4,5)P2 results in a CR that cannot maintain its medial position and slides toward one cell end, resulting in two differently sized daughter cells. S. pombe PM PI(4,5)P2 is synthesized by the PI5-kinase Its3, but what regulates this enzyme to maintain appropriate PM PI(4,5)P2 levels is not known in S. pombe. To identify Its3 regulators, we used proximity-based biotinylation and the uncharacterized protein Duc1 was specifically detected. We discovered that Duc1 decorates the PM except at the cell division site and that its unique localization pattern is dictated by binding to the ER-PM contact site proteins, Scs2 and Scs22. Our evidence suggests Duc1 also binds PI(4,5)P2 and helps enrich Its3 at the lateral PM, thereby promoting PM PI(4,5)P2 synthesis and robust CR-PM anchoring.

Insight of effects of air quality and sustainable aviation fuel blend on energy saving and emission reduction in airport.

Air quality in airport attracts a widespread attention due to the emission of GHGs and pollutants related with aircraft flight. Sustainable aviation fuel (SAF) has confirmed PM2.5 reduction due to free of aromatics and sulphur, and thus air quality improvement in airport is prospected by SAF blend. Two types of SAF were assessed the potential of energy saving and emission reduction by ZF850 jet engine. FT fuel is characterized with only paraffins without aromatics and cycloparaffins while HCHJ fuels is characterized with no aromatics. The descend of air quality and SAF blend were both investigated the effect on the engine performance and emission characteristic. The critical parameters were extracted from fuel compositions and air pollutants. Ambient air with a higher PM2.5 could lead to the rise of engine emission especially in UHC and PM2.5 despite at the low thrust setting and high thrust setting, and even couple with 3.2% rise in energy consumption and 1% reduction in combustion efficiency. CO, NO and NO2 in ambient air show less influence on engine performance and emission characteristic than PM2.5. Both types of SAF blend were observed significant reductions in PM2.5 and UHC. PM2.5 reduction obtained 37.9%-99.8% by FT blend and 0.64%-93.9% by HCHJ blend through the whole trust settings. There are almost 6.67% positive benefit in TSFC through the whole thrust setting by 7% FT blend. The effects of air quality and SAF blend on engine emission present significant changes on PM and UHC but the slight change on CO and NOx. By SAF blend, the energy saving and pollutant reduction obtained could be both benefit for air quality improvement in airport and further reduce engine emission as the feedback of less pollutants in ambient air.

Association between daily circulatory emergency ambulance dispatches and short-term PM2.5 exposure in a heavily polluted area.

Emergency ambulance dispatches (EAD) have been proven to be associated with ambient particulate matter with diameter < 2.5 µm (PM2.5) concentration, but the associations of circulatory EAD remained inconclusive, especially in heavily polluted areas. In this time series conducted in Shenyang City, Northeastern China, we explored the associations between circulatory EAD and ambient PM2.5 and its constituents. Data including 113,508 circulatory EAD records, five types of PM2.5 constituents, and meteorological information spanning from 2014 to 2019 were retrieved. Using generalized additive models (GAMs), we explored the association between circulatory EAD and calculated excess risks induced by a 10 µg/m3 increase (ERR10) in PM2.5 mass and its constituents. ERR by percentage change (ERRpc) to compare among the different constituents were also calculated. Positive associations between circulatory EAD and PM2.5 mass, sulfates, organic matters, and black carbon, were found particularly at lag0 and lag0-5, with the ERR10 of 3.8% (3.2%-4.4%), 6.5% (2.2%-10.8%), 4.2% (1.7%-6.6%), and 30.2% (17.2%-43.4%) at lag0-5, respectively. Similar associations were observed for cardiovascular EAD, while cerebrovascular EAD suggested a positive association with O3 rather than PM2.5 or its constituents. Notably, PM2.5 mass exhibited the largest ERRpc for circulatory and cardiovascular EAD, followed by sulfates and black carbon. Moreover, the risks were enhanced for circulatory and cardiovascular EAD in males compared to females and during warmer seasons compared to colder seasons. Our findings contribute new evidence on PM2.5 exposure and circulatory EAD in relatively polluted areas.