Pollution characteristics, source apportionment, and health risk assessment of PM10 and PM2.5 in rooftop and kerbside environment of Lanzhou, NW China.

To investigate air pollution in the kerbside environment and its associated human health risks, a study was conducted in Lanzhou during December 2018, as well as in April, June, and September 2019. The research aimed to characterize the composition of PM10 and PM2.5, including elements, ions, and carbonaceous components, at both rooftop and kerbside locations. Additionally, source apportionment and health risk assessment were conducted. The results showed that the average mass concentrations of PM10 on the rooftop were 176.01 ± 83.23 µg/m3, and for PM2.5, it was 94.07 ± 64.89 µg/m3. The PM10 and PM2.5 levels at the kerbside are 2.21 times and 1.79 times, respectively, greater than those on the rooftop. Moreover, the concentrations of elements, ions, and carbonaceous components in kerbside PM were higher than those at the rooftop location. Chemical mass closure analysis identified various sources, including organic matter, mineral dust, secondary ions, other ions, elements, and other components. In comparison to rooftop particulate matter (PM), mineral dust makes a more substantial contribution to kerbside PM. Secondary ions show an opposite trend, making a greater contribution to rooftop PM. The contribution of organic components within PM of the same particle size remains relatively consistent. The outcome of the health risk assessment indicates that Co, Cd, and As in PM within the kerbside and rooftop environments do not pose a notable carcinogenic risk. However, Al and Mn do present specific non-carcinogenic risks, particularly in the kerbside environment. Furthermore, children experience elevated non-carcinogenic risk compared to adults. These findings can serve as a scientific foundation for formulating policies within the local health department.

Identification of Dihydrobenzofuran Neolignans as Novel PDE4 Inhibitors and Evaluation of Antiatopic Dermatitis Efficacy in DNCB-Induced Mice Model.

Atopic dermatitis is a chronic relapsing skin disease characterized by recurrent, pruritic, localized eczema, while PDE4 inhibitors have been reported to be effective as antiatopic dermatitis agents. 3',4-O-dimethylcedrusin (DCN) is a natural dihydrobenzofuran neolignan isolated from Magnolia biondii with moderate potency against PDE4 (IC50 = 3.26 ± 0.28 µM) and a binding mode similar to that of apremilast, an approved PDE4 inhibitor for the treatment of psoriasis. The structure-based optimization of DCN led to the identification of 7b-1 that showed high inhibitory potency on PDE4 (IC50 = 0.17 ± 0.02 µM), good anti-TNF-α activity (EC50 = 0.19 ± 0.10 µM), remarkable selectivity profile, and good skin permeability. The topical treatment of 7b-1 resulted in the significant benefits of pharmacological intervention in a DNCB-induced atopic dermatitis-like mice model, demonstrating its potential for the development of novel antiatopic dermatitis agents.

Veronica linariifolia subsp. dilatata ameliorates LPS-induced acute lung injury by attenuating endothelial cell barrier dysfunction via EGFR/Akt/ZO-1 pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The dried aerial parts of Veronica linariifolia subsp. dilatata (Nakai & Kitag.) D.Y.Hong named Shui Man Jing (SMJ) is a traditional Chinese medicine with a long history of clinical use in the treatment of chronic bronchitis and coughing up blood, however, its role on acute lung injury (ALI) has not been revealed yet. AIM OF THE STUDY: To assess the efficiency of SMJ on ALI and to investigate whether it inhibited endothelial barrier dysfunction by regulating the EGFR/Akt/ZO-1 pathway to alleviate ALI in vivo and in vitro based on the result of network pharmacology. MATERIALS AND METHODS: An in vivo model of ALI was established using inhalation of atomized lipopolysaccharide (LPS), and the effects of SMJ on ALI were evaluated through histopathological examination and inflammatory cytokines, lung histology and edema, vascular and alveolar barrier disruption. Network pharmacology was applied to predict the mechanism of SMJ in the treatment of ALI. The crucial targets were validated by RT-PCR, Western Blotting, molecular docking, immunohistochemistry and immunofluorescence methods in vivo and in virto. RESULTS: Administration of SMJ protected mice against LPS-induced ALI, including ameliorating the histological alterations in the lung tissues, and decreasing lung edema, protein content of bronchoalveolar lavage fluid, infiltration of inflammatory cell and secretion of cytokines. SMJ exerted protective effects in ALI by inhibiting endothelial barrier dysfunction in mice and bEnd.3 cell. SMJ relieved endothelial barrier dysfunction induced by LPS through upregulating the EGFR expression. SMJ also increased the phosphorylation of Akt, and ZO-1 expression both in vivo and in vitro. CONCLUSION: SMJ attenuates vascular endothelial barrier dysfunction for LPS-induced ALI via EGFR/Akt/ZO-1 pathway, and is a promising novel therapeutic candidate for ALI.

Development of interpretable machine learning models associated with environmental chemicals to predict all-cause and specific-cause mortality：A longitudinal study based on NHANES.

Limited information is available on potential predictive value of environmental chemicals for mortality. Our study aimed to investigate the associations between 43 of 8 classes representative environmental chemicals in serum/urine and mortality, and further develop the interpretable machine learning models associated with environmental chemicals to predict mortality. A total of 1602 participants were included from the National Health and Nutrition Examination Survey (NHANES). During 154,646 person-months of follow-up, 127 deaths occurred. We found that machine learning showed promise in predicting mortality. CoxPH was selected as the optimal model for predicting all-cause mortality with time-dependent AUROC of 0.953 (95%CI: 0.951-0.955). Coxnet was the best model for predicting cardiovascular disease (CVD) and cancer mortality with time-dependent AUROCs of 0.935 (95%CI: 0.933-0.936) and 0.850 (95%CI: 0.844-0.857). Based on clinical variables, adding environmental chemicals could enhance the predictive ability of cancer mortality (P < 0.05). Some environmental chemicals contributed more to the models than traditional clinical variables. Combined the results of association and prediction models by interpretable machine learning analyses, we found urinary methyl paraben (MP) and urinary 2-napthol (2-NAP) were negatively associated with all-cause mortality, while serum cadmium (Cd) was positively associated with all-cause mortality. Urinary bisphenol A (BPA) was positively associated with CVD mortality.

Ursolic acid derivative UAOS-Na treats experimental autoimmune encephalomyelitis by immunoregulation and protecting myelin.

Introduction: Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS). Ursolic acid (UA) can be used in the MS treatment with anti-inflammatory and neuroprotective activities. However, UA is insoluble in water, which may affect its medication effectiveness. In our previous study, UAOS-Na, a water-soluble derivative of UA was obtained. In this study, we evaluated the pharmacological effects and explored its underlying mechanism of UAOS-Na on experimental autoimmune encephalomyelitis (EAE). Methods: Firstly, the pharmacodynamics of UAOS-Na was investigated in EAE and Cuprizone-induced mice. And then the possible mechanisms were investigated by TMT proteomics and verified by in vitro and in vivo experiments. Results: UAOS-Na (30 mg/kg/d) delayed the onset time of EAE from 11.78 days post immunization (dpi) to 14.33 dpi, reduced the incidence from 90.0% to 42.9%. UAOS-Na (60 mg/kg/d) reduced the serum levels of IFN-γ, IL-17A, TNF-α and IL-6, reduced the mononuclear cell infiltration of spinal cord, and inhibited the overexpression of key transcription factors T-bet and ROR-γt of EAE mouse spinal cord. In addition, UAOS-Na attenuated demyelination and astrogliosis in the CNS of EAE and cuprizone-induced mice. Mechanistically, proteomics showed that 96 differential expression proteins (DEPs) were enriched and 94 were upregulated in EAE mice compared with normal group. After UAOS-Na treatment, 16 DEPs were enriched and 15 were downregulated, and these DEPs were markedly enriched in antigen processing and presentation (APP) signaling pathway. Moreover, UAOS-Na downregulated the protein levels of Tapbp and H2-T23 in MHC-I antigen presentation pathway and reduced the proliferation of splenic CD8 T cells, thereby inhibiting the CNS infiltration of CD8 T cells. Conclusion: Our findings demonstrated that UAOS-Na has both myelin protective and anti-inflammatory effects. And it could reduce the inflammation of MS by downregulating the expression of Tapbp and H2-T23 in the MHC-I antigen presentation pathway.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.

BACKGROUND: Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. METHODS: Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. RESULTS: The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. CONCLUSION: Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China.

Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl-, NO3-, SO42-), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 µg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China.

Magnetism and Grain-Size Distribution of Particles Deposited on the Surface of Urban Trees in Lanzhou City, Northwestern China.

Studies on the variation in the particulate matter (PM) content, Saturation Isothermal Remanent Magnetization (SIRM), and particle grain-size distribution at a high spatial resolution are helpful in evaluating the important role of urban forests in PM removal. In this study, the trees located in dense urban forests (T0) retained more PM than trees located in open spaces (T1-T4); the SIRM and PM weight of T0 were 1.54-2.53 and 1.04-1.47 times more than those of T1-T4, respectively. In addition, the SIRM and PM weight decreased with increasing distance to the road, suggesting that distance from pollution sources plays a key role in reducing the air concentration of PM. The different grain-size components were determined from frequency curve plots using a laser particle-size analyzer. A unimodal spectrum with a major peak of approximately 20 µm and a minor peak between 0.1 and 1 µm was observed, indicating that a large proportion of fine air PM was retained by the needles of the study trees. Additionally, more <2.5 µm size fraction particles were observed at the sampling site near the traffic source but, compared to a tree in a row of trees, the percentage of the >10 µm size fraction for the tree in the dense urban forest was higher, indicating that the particles deposited on the needle surface originating from traffic sources were finer than those from natural atmospheric dust. The exploration of the variation in the PM weight, SIRM, and grain size of the particles deposited on the needle surface facilitates monitoring the removal of PM by urban forests under different environmental conditions (e.g., in closed dense urban forests and in open roadside spaces), different distances to roads, and different sampling heights above the ground.