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Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.

Background: Magnetocardiography (MCG) is a novel non-invasive technique that detects subtle magnetic fields generated by cardiomyocyte electrical activity, offering sensitive detection of myocardial ischemia. This study aimed to assess the ability of MCG to predict impaired myocardial perfusion using single-photon emission computed tomography (SPECT). Methods: A total of 112 patients with chest pain underwent SPECT and MCG scans, from which 65 MCG output parameters were analyzed. Using least absolute shrinkage and selection operator (LASSO) regression to screen for significant MCG variables, three machine learning models were established to detect impaired myocardial perfusion: random forest (RF), decision tree (DT), and support vector machine (SVM). The diagnostic performance was evaluated based on the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: Five variables, the ratio of magnetic field amplitude at R-peak and positive T-peak (RoART+), R and T-peak magnetic field angle (RTA), maximum magnetic field angle (MAmax), maximum change in current angle (CCAmax), and change positive pole point area between the T-wave beginning and peak (CPPPATbp), were selected from 65 automatic output parameters. RTA emerged as the most critical variable in the RF, DT, and SVM models. All three models exhibited excellent diagnostic performance, with AUCs of 0.796, 0.780, and 0.804, respectively. While all models showed high sensitivity (RF = 0.870, DT = 0.826, SVM = 0.913), their specificity was comparatively lower (RF = 0.500, DT = 0.300, SVM = 0.100). Conclusions: Machine learning models utilizing five key MCG variables successfully predicted impaired myocardial perfusion, as confirmed by SPECT. These findings underscore the potential of MCG as a promising future screening tool for detecting impaired myocardial perfusion. Clinical Trial Registration: ChiCTR2200066942, https://www.chictr.org.cn/showproj.html?proj=187904.

Introduction: Highly clustered color steel buildings (CSB) have become new impervious surfaces and heat island areas in cities due to their materials, especially in the Northwest. However, the extent of the influence of CSB on land surface temperature (LST) and how they are quantitatively related to each other have not been explored.Methods: Therefore, this paper takes Urumqi city, which is densely populated with CSB and has a unique topography, as an example. We obtained LST data and CSB data based on Landsat and Google Earth images, and quantitatively analyzed the warming effect of the CSB on the urban LST and the characteristics of regional variability.Results and discussion: The results of the study showed that from 2005 to 2020, the scale of CSB in Urumqi City has been expanding, with high-density aggregation shifting from urban areas to the suburbs. The urban area of Urumqi city shows a “cool island effect”, with higher temperatures in CSB. The high-density distribution of CSB roughly coincides with the high temperature areas of the city. The density of CSB is highly positively correlated with LST, with the correlation coefficient reaching 0.973 in 2010. For every 0.2 increase in cluster density, the corresponding temperature can rise by 0.4°C. At the same density of aggregation, the average temperature of urban CSB is lower than that in the suburbs. The contribution index of suburban CSB to LST reaches 0.512, significantly higher than the surrounding other buildings, marked it one of the main contributors to the thermal environment in the suburbs.The research results can provide a reference and data support for the sustainable development and planning of cities.

Abstract Objective Researches about the association between serum albumin-to-globulin ratio (AGR) and the prognosis of lung cancer are limited. We aimed to investigate the relationship between AGR and overall survival (OS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with anlotinib. Methods A retrospective cohort study was conducted on 196 advanced NSCLC patients with anlotinib treatment between June 1, 2018 and June 1, 2021. The exposure was AGR, calculated by baseline serum albumin / (serum total protein - serum albumin). The outcome was OS, defined as the period from the date of initial treatment with anlotinib to death or the last follow-up. The univariate and multivariate linear regression models and generalized additive models (GAM) were used to analyze the relationship between AGR and OS. The Kaplan-Meier method was used to analyze the OS. Results After adjusting for potential confounders, a non-linear relationship was observed between AGR and OS, which had an inflection point of 1.24. The hazard ratio and the confidence intervals on the left and the right sides of the inflection point were 13.05 (0.52 to 327.64) and 0.20 (0.07 to 0.57), respectively. It suggested that AGR was positively associated with OS when AGR was larger than 1.24, for every 1 unit increase in AGR, the risk of death lowered approximately by 80%. Conclusions The relationship between AGR and the OS for advanced NSCLC patients with anlotinib is non-linear. AGR level is an independent protective factor for OS in advanced NSCLC patients who received anlotinib therapy.

The detection of change in remote-sensing images is broadly applicable to many fields. In recent years, both supervised and unsupervised methods have demonstrated excellent capacity to detect changes in high-resolution images. However, most of these methods are sensitive to noise, and their performance significantly deteriorates when dealing with remote-sensing images that have been contaminated by mixed random noises. Moreover, supervised methods require that samples are manually labeled for training, which is time-consuming and labor-intensive. This study proposes a new unsupervised change-detection (CD) framework that is resilient to mixed random noise called self-supervised denoising network-based unsupervised change-detection coupling FCM_SICM and EMD (SSDNet-FSE). It consists of two components, namely a denoising module and a CD module. The proposed method first utilizes a self-supervised denoising network with real 3D weight attention mechanisms to reconstruct noisy images. Then, a noise-resistant fuzzy C-means clustering algorithm (FCM_SICM) is used to decompose the mixed pixels of reconstructed images into multiple signal classes by exploiting local spatial information, spectral information, and membership linkage. Next, the noise-resistant Earth mover’s distance (EMD) is used to calculate the distance between signal-class centers and the corresponding fuzzy memberships of bitemporal pixels and generate a map of the magnitude of change. Finally, automatic thresholding is undertaken to binarize the change-magnitude map into the final CD map. The results of experiments conducted on five public datasets prove the superior noise-resistant performance of the proposed method over six state-of-the-art CD competitors and confirm its effectiveness and potential for practical application.

The pavement is vulnerable to damage from natural disasters, accidents and other human factors, resulting in the formation of cracks. Periodic pavement monitoring can facilitate prompt detection and repair the pavement diseases, thereby minimizing casualties and property losses. Due to the presence of numerous interferences, recognizing highway pavement cracks in complex environments poses a significant challenge. Nevertheless, several computer vision approaches have demonstrated notable success in tackling this issue. We have employed a novel approach for crack recognition utilizing the ResNet34 model with a convolutional block attention module (CBAM), which not only saves parameters and computing power but also ensures seamless integration of the module as a plug-in. Initially, ResNet18, ResNet34, and ResNet50 models were trained by employing transfer learning techniques, with the ResNet34 network being selected as a fundamental model. Subsequently, CBAM was integrated into ResBlock and further training was conducted. Finally, we calculated the precision, average recall on the test set, and the recall of each class. The results demonstrate that by integrating CBAM into the ResNet34 network, the model exhibited improved test accuracy and average recall compared to its previous state. Moreover, our proposed model outperformed all other models in terms of performance. The recall rates for transverse crack, longitudinal crack, map crack, repairing, and pavement marking were 88.8%, 86.8%, 88.5%, 98.3%, and 99.9%, respectively. Our model achieves the highest precision of 92.9% and the highest average recall of 92.5%. However, the effectiveness in detecting mesh cracks was found to be unsatisfactory, despite their significant prevalence. In summary, the proposed model exhibits great potential for crack identification and serves as a crucial foundation for highway maintenance.

Abstract Shanghai is susceptible to land subsidence due to its unique geological environment and frequent human activities. Traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. Interferometric Synthetic Aperture Radar (InSAR) technology is a widely used method for monitoring ground subsidence due to its low cost, high efficiency, and ability to cover large areas. To monitor the surface sink condition of Shanghai over the past 2 years, monitoring data were obtained through the technical processing of 24 images from Sentinel-1A data covering Shanghai from 2019 to 2020 using the Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) technique. The ground subsidence (GS) results were extracted via PS and SBAS interferometry processing, while Shuttle Radar Topography Mission data were used to correct the residual phase. According to PS and SBAS methods, the maximum ground subsidence in the study area reached 99.8 mm and 47.2 mm, respectively. The subsidence rate and the accumulated amount of subsidence derived from the monitoring results revealed the urban area in Shanghai to be principally characterized by uneven GS, with multiple settlement funnels being found to be distributed across the main urban area. Moreover, when compared with the historical subsidence data, geological data, and urban construction distribution data, the individual settlement funnels were observed to correspond to those data concerning the historical surface settlement funnel in Shanghai. By randomly selecting GS time-series data regarding three feature points, it was determined that the morphological variables of the GS remained largely consistent at all time points and that their change trends exhibited a high degree of consistency, which verified the reliability of the PS-InSAR and SBAS-InSAR monitoring method. The results can provide data support for decision making in terms of geological disaster prevention and control in Shanghai.

Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used change detection methods. However, CVA requires sound radiometric correction to achieve optimal performance, and PCC is susceptible to accumulated classification errors. Although change vector analysis in the posterior probability space (CVAPS) was developed to resolve the limitations of PCC and CVA, the uncertainty of remote sensing imagery limits the performance of CVAPS owing to three major problems: 1) mixed pixels; 2) identical ground cover type with different spectra; and 3) different ground cover types with the same spectrum. To address this problem, this article proposes the FCM-CSBN-CVAPS approach under the CVAPS framework. The proposed approach decomposes the mixed pixels into multiple signal classes using the fuzzy C means (FCM) algorithm. Although the mixed pixel problem is less severe in the high-resolution image, the change detection performance is still enhanced because, as a soft clustering algorithm, FCM is less susceptible to cumulative clustering error. Then, a context-sensitive Bayesian network (CSBN) is constructed to establish multiple-to-multiple stochastic linkages between signal pairs and ground cover types by incorporating spatial information to resolve problems 2) and 3) discussed above. Finally, change detection is performed using CVAPS in the posterior probability space. The effectiveness of the proposed approach is evaluated on three bitemporal remote sensing datasets with different spatial sizes and resolutions. The experimental results confirm the effectiveness of FCM-CSBN-CVAPS in addressing the uncertainty problems of change detection and its superiority over other relevant change detection techniques.

We, the Editor and Publisher of the Journal Of Experimental Nanoscience, have retracted the following article, which is part of the Special Issue titled Adaptive Nanotechnology for Environmental Sustainability:Nan Li, Shuwen Yang & Yongming Hua (2023) Exploration on inheritance and innovation of material technology and material culture in folk arts, Journal of Experimental Nanoscience, 18:1, DOI: 10.1080/17458080.2023.2170360Following publication, concerns were raised about the peer review and decision-making processes for this special issue.After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief, it was confirmed that the article was not reviewed appropriately, in line with the Journal’s peer review standards and policy.As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract the article. The authors have been informed of this decision.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Osmanthus fragrans has a long history of cultivation in Asia and is widely used in food production for its unique aroma, which has important cultural and economic values. It is rich in flavonoids with diverse pharmacological properties, such as antioxidant, anti-tumor, and anti-lipid activities. However, little is known regarding the effects of Osmanthus fragrans flavonoid extract (OFFE) on adipogenesis and pre-adipocyte transdifferentiation. Herein, this research aimed to investigate the effect of OFFE on the differentiation, adipogenesis, and beiging of 3T3-L1 adipocytes and to elucidate the underlying mechanism. Results showed that OFFE inhibited adipogenesis, reduced intracellular reactive oxygen species levels in mature adipocytes, and promoted mitochondrial biogenesis as well as beiging/browning in 3T3-L1 adipocytes. This effect was accompanied by increased mRNA and protein levels of the brown adipose-specific marker gene Pgc-1a, and the upregulation of the expression of UCP1, Cox7A1, and Cox8B. Moreover, the research observed a dose-dependent reduction in the mRNA expression of adipogenic genes (C/EBPα, GLUT-4, SREBP-1C, and FASN) with increasing concentrations of OFFE. Additionally, OFFE activated the AMPK signaling pathway to inhibit adipogenesis. These findings elucidate that OFFE has an inhibitory effect on adipogenesis and promotes browning in 3T3-L1 adipocytes, which lays the foundation for further investigation of the lipid-lowering mechanism of OFFE in vivo in the future.

Background: As a population ages, blood pressure levels gradually increase, leading to a higher incidence of hypertension and increased cardiovascular diseases risk. This study examines factors affecting hypertension grading among centenarians in the Hainan Province. Methods: Data from 2014 to 2016 were accessed from the cross-sectional database “Hypertension Levels and Epidemiological Characteristics of the Elderly and Centenarians in Hainan province of China”. This study included 690 centenarians with hypertension. Hypertension grading was the dependent variable, analyzed against independent variables including demographic information (sex, age, ethnicity, education level, marital status, cohabitation, and regional distribution), lifestyle factors (smoking, alcohol consumption, and physical activity), body mass index (BMI), and comorbid conditions (diabetes and hyperlipidemia). Logistic regression models, adjusted for these factors, were used to assess the determinants of hypertension grading among the participants. Results: Multivariate regression analysis, after adjusting for other variables, revealed significant associations between BMI, low-density lipoprotein (LDL) levels, and hypertension grades. Individuals with BMI below 18.5 kg/m2 had a 0.614-fold lower risk of developing grade III hypertension (odds ratio [OR]: 0.614, 95% confidence interval [CI]: 0.390–0.966, p = 0.0350) and a 0.586-fold lower risk for grade II hypertension (OR: 0.586, 95% CI: 0.402–0.852, p = 0.0052). Furthermore, individuals with elevated LDL levels had a 6.087-fold greater risk of progressing from grade I to grade III hypertension (OR: 6.087, 95% CI: 1.635–22.660, p = 0.0071) and a 4.356-fold greater risk of progressing from grade II to grade III hypertension (OR: 4.356, 95% CI: 1.052–18.033, p = 0.0423). Additionally, individuals of Li ethnicity had 1.823-fold greater risk of progressing from grade I to grade II hypertension compared to those of Han ethnicity (OR: 1.823, 95% CI: 1.033–3.218, p = 0.0383). Conclusions: A BMI below 18.5 kg/m2, elevated LDL, and ethnicity emerged the primary factors associated with hypertension grading in centenarians. To reduce the risk of hypertension, it is crucial for centenarians to maintain a healthy weight, normal LDL levels, and adopt dietary habits including a low-cholesterol and low-fat diet.

Background: Fulminant myocarditis (FM) is associated with high mortality and an unfavorable long-term prognosis. However, noninvasive, rapid diagnostic and monitoring methods for FM are lacking. Case summary: This report details the case of a patient diagnosed with FM through a comprehensive assessment involving typical clinical symptoms, laboratory analyses, echocardiographic evidence, and cardiac magnetic resonance (CMR) findings. Before the patient underwent CMR, optical pumped magnetometer magnetocardiography (OPM-MCG) revealed abnormalities characteristic of substantial myocardial injury. After invasive coronary angiography, the patient’s coronary arteries were determined not to exhibit significant stenosis. Additionally, a myocardial bridge was observed in the middle segment of the anterior descending branch of the left coronary artery. Over a 7-month follow-up, the patient’s symptoms showed marked improvement, and the OPM-MCG eventually normalized, aligning with the findings indicated by the CMR. Conclusion: OPM-MCG is a valuable tool for therapy monitoring in FMs, offering a safe, non-invasive, and highly effective test for ancillary diagnostics and long-term prognostic follow-up. Additionally, MCG may potentially assist in clinical decision-making regarding the need for further interventions in patients with myocardial bridge.

Heavy metal(loid)s (HMs) and disinfection by-products (DBPs) in drinking water pose risks to human health and jeopardize drinking water. Water-related behaviors vary significantly among different age groups and regions. In this study, the carcinogenic and non-carcinogenic risks of HMs (As, Cd, Cr6+, Cu, Pb, and Zn) and DBPs (bromodichloromethane (BDCM), bromoform, chloroform, dibromochloromethane (DBCM), dichloroacetic acid (DCAA), and trichloroacetic acid (TCAA)) in drinking water in two Chinese megacities (Beijing in North China and Guangzhou in South China) via multiple exposure pathways were assessed. The results showed that children aged 9 months to 2 years had a total carcinogenic risk (TCR) and hazard index (HI) above acceptable levels, indicating that despite the drinking water quality in the selected megacities meeting the current Chinese national standards (GB 5749–2022), the health risks of exposure to HMs and DBPs in drinking water for local young children should not be neglected. Specifically, the carcinogenic risk (CR) of exposure to As in drinking water for children

Soil contamination with heavy metal(loid)s may influence microbial activities in the soil, and consequently jeopardize soil health. Microbial responses to soil contamination play an important role in ecological risk assessment. This study investigated the effect of heavy metal(loid)s contamination on microbial community structure and abundance in the surrounding soil of an abandoned antimony (Sb) smelter in Qinglong county, Guizhou province, Southwest China. A total of 46 soil samples were collected from ten sampling sites (labelled as A–I, and CK) across the study area at depths of 0–2, 2–10, 10–20, 20–30, 30–40, and 40–50 cm. The soil samples were analyzed for total and bioavailable heavy metal(loid) concentrations, bacterial, fungal, and archaeal community structures, diversities, and functions, together with soil basic physicochemical properties. Much greater ecological risk of Sb and arsenic (As) was present in the surface soil (0–2 cm) compared to that in the subsoils. The activities of dominant microorganisms tended to be associated with soil pH and heavy metal(loid)s (i.e., Sb, As, lead (Pb), cadmium (Cd), and chromium (Cr)). Bacteria associated with IMCC26256, Rhizobiales, Burkholderiales, and Gaiellales, and archaea associated with Methanocellales were estimated to be tolerant to high concentrations of Sb and As in the soil. In addition, the magnitude of soil microbial responses to Sb and As contamination was in the order of archaea > bacteria > fungi. In contrast to the negligible response of fungi and negative response of bacteria to Sb and As contamination, there was a strongly positive correlation between archaeal activity and total Sb and As concentrations in the soil. Our findings provide a theoretical basis for the remediation of Sb smelter-affected soil.

Abstract Background Anaplastic thyroid cancer (ATC) is considered the most lethal thyroid cancer, with an overall 5‐year survival rate below 10%. The FDA approved a BRAF/MEK inhibitor combination for the treatment of patients with BRAF‐mutated ATC. However, effective therapeutic options for patients with wild‐type BRAF are lacking. Case In our phase II study, patients having advanced/metastatic solid ATCs were treated with famitinib and camrelizumab, a combination therapy involving a multi‐targeted kinase inhibitor and an anti‐PD‐1 antibody. We report a case of a patient with locally advanced unresectable ATC who underwent this combination therapy, allowing us to perform complete surgical resection followed by post‐operative radiation therapy. Conclusion To the best of our knowledge, this is the first report describing the use of famitinib and camrelizumab as a neoadjuvant treatment for ATC with wild‐type BRAF. Clinical trial for a novel neoadjuvant approach for ATC are currently open for enrollment.

The satellite visibility number of GNSS is an important indicator for evaluating its availability for positioning and navigation. In urban areas, urban canyons cause serious satellite signals block, resulting in positioning uncertainty. Many studies used 3D city models to evaluate the visible satellites in some areas at a certain time. Nevertheless, this kind of method is difficult to apply because 3D models are not widely available. This paper thus proposes an easy method to evaluate the visibility of satellites with widely available street view panoramic imagery and GNSS ephemeris. The proposed method utilizes the locations of street view panoramic imagery and the associated GNSS ephemeris to calculate the visible satellite number at different times. Hence, the visible satellite number at a specific time can be mapped. Moreover, the visibility of satellites can be predicted according to its orbit parameters. To evaluate the effectiveness of the proposed method, Wuhan and Shanghai were taken to map post-event, real-time and forecast GNSS visibility. The experiments demonstrated that the proposed method provides a light weighted and easy to use solution to map the spatio-temporal visibility of satellites in urban areas, which is an important reference for GNSS stations layout and positioning qualities evaluation.

Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and temporal characteristics of five main atmospheric pollutants (PM2.5, PM10, SO2, NO2, and CO) near potential source of natural aerosols, affecting seven cities (Wuhai, Alashan, Wuzhong, Zhongwei, Wuwei, Jinchang, Zhangye), located in immediate proximity to the South Gobi deserts. The results, obtained for the period from 1 January 2016 to 31 December 2018, demonstrate total concentrations of PM2.5 and PM10 are 38.2 ± 19.5 and 101 ± 80.7 μg/m3 exceeding the same established by the Chinese National Ambient Air Quality Standard (CNAAQS), being 35 and 70 μg/m3, respectively. Based on the data from Moderate Resolution Imaging Spectroradiometer (MODIS) for the whole period, Clean Сontinental (71.49%) and Mixed (22.29%) types of aerosols prevail in the region. In the spring and winter seasons maximum concentrations of pollutants and high values of Aerosol Optical Depth (AOD) in the region atmosphere are observed. PM2.5 and PM10 ratio shows the presence of coarse aerosols in the total content with value 0.43. The highest concentrations of pollutants were in the period of dust storms activity, when PM2.5 and PM10 content exceeded 200 and 1000 µg/m3, and AOD value exceeded 1. UV Aerosol Index (UVAI), Aerosol Absorbing Optical Depth (AAOD), and Single Scattering Albedo (SSA), obtained from Ozone Monitoring Instrument (OMI), demonstrate the high content of dust aerosols in the period of sandstorms. Analysis of backward trajectories shows that dust air masses moved from North to Northwest China, affecting large deserts such as Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.

Objective: To evaluate the association between aspirin use and the risks of unruptured intracranial aneurysm (UIA) growth and aneurysmal subarachnoid hemorrhage (aSAH).Methods: We searched PubMed and Scopus from inception to 1 September 2020. Studies evaluating the associations between aspirin prescription and the risk of UIA growth or the risk of aSAH were included. The study only included patients with intracranial aneurysms. We assessed the quality of included studies using the Newcastle-Ottawa scale. Random-effects meta-analysis was conducted to pool the estimates of effect size quantitatively. Sensitivity analyses using the leave-one-out strategy were performed to identify any potential source of heterogeneity.Results: After a review of 2,226 citations, five cohort studies, two case-control studies, and one nested case-control study involving 8,898 participants were included. Pooled analyses showed that aspirin use, regardless of frequency and duration, was associated with a statistically significantly lower risk of UIA growth (OR 0.25, 95% CI 0.11–0.54; I2 = 0.0%, p = 0.604) and aSAH (OR, 0.37, 95% CI, 0.23–0.58; I2 = 79.3%, p = 0.001) in patients presented with intracranial aneurysms. The results did not significantly change in sensitivity analyses.Conclusions: Summarizing available evidence in the literature, our findings indicate that aspirin use, regardless of frequency and duration, was associated with a statistically significantly lower risk of UIA growth and aSAH in patients with UIA. Well-designed and large-scale clinical trials are needed to help define the role of aspirin as a protective pharmaceutical for UIAs.