Do socioeconomic factors modify the effects of PM1 and SO2 on lung cancer incidence in China?

Does ambient particulate matter 1 increase the risk of gastric cancer in the northwest of China?

Gastric cancer (GC) remains a significant health concern in Gansu province, China, with morbidity and mortality rates surpassing national averages. Despite the recognized health risks associated with ambient particulate matter with an aerodynamic diameter <1 μm (PM1), the relationship between PM1 exposure and GC incidence has not been extensively studied. Data on GC cases from 2013 to 2021 were gathered from 262 hospitals in Gansu, China. Concurrently, data on the normalized vegetation index (NDVI), gross domestic product (GDP), drinking and smoking behavioral index (DSBI), PM1, PM2.5, and PM2.5-1 were collected. Utilizing a Bayesian conditional autoregressive (CAR) combined generalized linear model (GLM) with quasi-Poisson regression, we evaluated the impact of PM1, PM2.5, PM2.5-1, NDVI, DSBI, and GDP on GC morbidity while adjusting for potential confounders. Our analysis indicated that exposure to PM1 (μg/m3) is significantly positively correlated with GC incidence in regions with an overall age-standardized incidence rate (ASIR) >40 (relative risks [RR]: 1.023, 95% confidence intervals [CI, 1.007, 1.039]), male ASIR >50 (RR: 1.014, 95% CI [1.009, 1.019]), and female ASIR >20 (RR: 1.010, 95% CI [1.002, 1.018]). PM2.5, PM2.5-1, DSBI, and GDP were positively correlated with GC incidence, while NDVI was negatively correlated in the study regions. Our findings provided evidence of a positive correlation between PM1 exposure and GC incidence in high-risk areas of GC within arid regions. Further research is warranted to elucidate the complex nonlinear relationships between environmental factors and GC. These insights could inform strategies for improving the control and prevention of GC in Gansu and similar regions.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China

BACKGROUND

Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods.

AIMS

We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods.

METHODS

We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality.

RESULTS

With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results.

CONCLUSION

This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

Influence of the atmospheric environment on spatial variation of lung cancer incidence in China

Conducting this research contributes to a deeper understanding of the correlation between atmospheric environmental quality and lung cancer incidence, and provides the scientific basis for formulating effective environmental protection and lung cancer prevention and control strategies. Lung cancer incidence in China has strong spatial variation. However, few studies have systematically revealed the characteristics of the spatial variation in lung cancer incidence, and have explained the causes of this spatial variation in lung cancer incidence from the perspectives of multiple components of the atmospheric environment to explain this spatial variation in lung cancer incidence. To address research limitations, we first analyze the spatial variation and spatial correlation characteristics of lung cancer incidence in China. Then, we build a spatial regression model using GeoDa software with lung cancer incidence as the dependent variable, five atmospheric environment factors-particulate matter 2.5 (PM2.5) concentration, temperature, atmospheric pressure, and elevation as explanatory variables, and four socio-economic characteristics as control variables to systematically analyze the influence and intensity of these factors on lung cancer incidence. The results show that lung cancer incidence in China has apparent changes in geographical and spatial unevenness, and spatial autocorrelation characteristics. In China, the lung cancer incidence is relatively high in Northeast China, while some areas of high lung cancer incidence still exist in Central China, Southwest China and South China, although the overall lung cancer incidence is relatively low. The atmospheric environment significantly affects lung cancer incidence. Different elements of the atmospheric environment vary in the direction and extent of their influence on the development of lung cancer. A 1% increase in PM2.5 concentration is associated with a level of 0.002975 increase in lung cancer incidence. Atmospheric pressure positively affects lung cancer incidence, and an increase in atmospheric pressure by 1% increases lung cancer incidence by a level of 0.026061. Conversely, a 1% increase in temperature is linked to a level of 0.006443 decreases in lung cancer incidence, and a negative correlation exists between elevation and lung cancer incidence, where an increase in elevation by 1% correlates with a decrease in lung cancer incidence by a level of 0.000934. The core influencing factors of lung cancer incidence in the seven geographical divisions of China exhibit variations. This study facilitates our understanding of the spatial variation characteristics of lung cancer incidence in China on a finer scale, while also offering a more diverse perspective on the impact of the atmospheric environment on lung cancer incidence.

Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

Exploring the association of PM2.5 with lung cancer incidence under different climate zones and socioeconomic conditions from 2006 to 2016 in China

Air pollution generated by urbanization and industrialization poses a significant negative impact on public health. Particularly, fine particulate matter (PM2.5) has become one of the leading causes of lung cancer mortality worldwide. The relationship between air pollutants and lung cancer has aroused global widespread concerns. Currently, the spatial agglomeration dynamic of lung cancer incidence (LCI) has been seldom discussed, and the spatial heterogeneity of lung cancer's influential factors has been ignored. Moreover, it is still unclear whether different socioeconomic levels and climate zones exhibit modification effects on the relationship between PM2.5 and LCI. In the present work, spatial autocorrelation was adopted to reveal the spatial aggregation dynamic of LCI, the emerging hot spot analysis was introduced to indicate the hot spot changes of LCI, and the geographically and temporally weighted regression (GTWR) model was used to determine the affecting factors of LCI and their spatial heterogeneity. Then, the modification effects of PM2.5 on the LCI under different socioeconomic levels and climatic zones were explored. Some findings were obtained. The LCI demonstrated a significant spatial autocorrelation, and the hot spots of LCI were mainly concentrated in eastern China. The affecting factors of LCI revealed an obvious spatial heterogeneity. PM2.5 concentration, nighttime light data, 2 m temperature, and 10 m u-component of wind represented significant positive effects on LCI, while education-related POI exhibited significant negative effects on LCI. The LCI in areas with low urbanization rates, low education levels, and extreme climate conditions was more easily affected by PM2.5 than in other areas. The results can provide a scientific basis for the prevention and control of lung cancer and related epidemics.

Effects of fine ambient particulate matters on de novo hypertensive disorders of pregnancy and blood pressure before 20 weeks

The effects of fine particulate matter (PM) on de novo hypertensive disorders of pregnancy (HDP) were inconsistent during the first and second trimesters. This study aimed to assess the trimester-specific effects of PM_2.5 and PM₁ prior to diagnosis of de novo HDP. The exposure of fine PM was predicted by satellite remote sensing data according to maternal residential addresses. De novo HDP was defined as gestational hypertension and preeclampsia during the current pregnancy. A logistic regression model was performed to assess the association of PM_2.5 and PM₁ with HDP during the first and early second trimesters (0-13 weeks and 14-20 weeks). The generalized estimating equation model was conducted to assess the effect of PM_2.5 and PM₁ on blood pressure. The present study included 22,821 pregnant women (mean age, 29.1 years) from 2013 to 2017. PM_2.5 and PM₁ were significantly associated with an increased risk of de novo HDP during the first trimester (OR = 1.070, 95% CI: 1.013-1.130; OR = 1.264, 95% CI: 1.058-1.511 for per 10 μg/m³) and early second trimester (OR = 1.045, 95% CI: 1.003-1.088; OR = 1.170, 95% CI: 1.002-1.366 for per 10 μg/m³). Significant trends of increased de novo HDP risk was also observed with the increment of PM (all P for trend <0.05). The stratified analyses demonstrated that the associations between exposure to fine PM and the risk of HDP were more pronounced among the pregnant women with maternal age above 35 and low maternal education level (all OR >1.047). Each 10 μg/m³ increase of PM₁ and PM_2.5 before diagnosis of de novo HDP elevated 0.204 (95% CI: 0.098-0.310) and 0.058 (95%CI: 0.033-0.083) mmHg of systolic blood pressure. Exposure to PM_2.5 and PM₁ during the first and early second trimester were positively associated with the risk of de novo HDP. The fine PM before diagnosis of de novo HDP elevated the systolic blood pressure.

Cross-level interaction between individual education and regional chemical fertilizer consumption on the risk of hypertension: evidence from the China hypertension survey

Hypertension is a common and costly public health burden in China, while the interaction effects of individual and contextual level factors on the risk of hypertension remain unclear. This study aimed to investigate whether the interaction effects between individual education and regional chemical fertilizer consumption are associated with the risk of hypertension based on a cross-level interaction perspective. Data was from the China Hypertension Survey (CHS) study, which used a nationally representative sample, and was conducted between 2012 and 2015. A total of 379 467 participants aged over 18 years from 221 counties in 31 provinces were included. A cross-sectional study design and two-level logistic models were conducted to investigate the cross-level interaction between individual education and regional chemical fertilizer consumption on the risk of hypertension after adjusting for potential confounding variables. Compared to individuals with high education (college or more), the adjusted OR (95%CI) for hypertension among those in the high school, middle school, and elementary school or less was 1.16 (1.12-1.21), 1.25 (1.20-1.30), and 1.49 (1.43-1.55), respectively. The association between regional chemical fertilizer consumption and hypertension was not significant. Interaction analysis showed that regional chemical fertilizer consumption was associated with an increased risk of hypertension for individuals with lower education. These patterns were consistent after stratified by sex. This study provided evidence from the Chinese population that interaction between individual education and regional chemical fertilizer was associated with risk of hypertension. Future research and policy aimed to improve population health and reduce hypertension could address the regional context of population as well as individual factors.

Impacts of traffic-related particulate matter pollution on semen quality: A retrospective cohort study relying on the random forest model in a megacity of South China

BACKGROUND

Emerging evidence shows the detrimental impacts of particulate matter (PM) on poor semen quality. High-resolution estimates of PM concentrations are conducive to evaluating accurate associations between traffic-related PM exposure and semen quality.

METHODS

In this study, we firstly developed a random forest model incorporating meteorological factors, land-use information, traffic-related variables, and other spatiotemporal predictors to estimate daily traffic-related PM concentrations, including PM_2.5, PM₁₀, and PM₁. Then we enrolled 1310 semen donors corresponding to 4912 semen samples during the study period from January 1, 2019, and December 31, 2019 in Guangzhou city, China. Linear mixed models were employed to associate individual exposures to traffic-related PM during the entire (0-90 lag days) and key periods (0-37 and 34-77 lag days) with semen quality parameters, including sperm concentration, sperm count, progressive motility and total motility.

RESULTS

The results showed that decreased sperm concentration was associated with PM₁₀ exposures (β: -0.21, 95 % CI: -0.35, -0.07), sperm count was inversely related to both PM_2.5 (β: -0.19, 95 % CI: -0.35, -0.02) and PM₁₀ (β: -0.19, 95 % CI: -0.33, -0.05) during the 0-90 days lag exposure window. Besides, PM_2.5 and PM₁₀ might diminish sperm concentration by mainly affecting the late phase of sperm development (0-37 lag days). Stratified analyses suggested that PBF and drinking seemed to modify the associations between PM exposure and sperm motility. We did not observe any significant associations of PM₁ exposures with semen parameters.

CONCLUSION

Our results indicate that exposure to traffic-related PM_2.5 and PM₁₀ pollution throughout spermatogenesis may adversely affect semen quality, especially sperm concentration and count. The findings provided more evidence for the negative associations between traffic-related PM exposure and semen quality, highlighting the necessity to reduce ambient air pollution through environmental policy.

An interpretable prediction of FCM driven by small samples for energy analysis based on air quality prediction

In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance.Implications: At present, China's air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions.

How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model

To alleviate the ecological and environmental problems caused by rapid urban development, China has formulated and implemented the new-type urbanization strategy. However, there is insufficient empirical research on the specific relationship between new-type urbanization and air pollution. Therefore, based on the panel data of 30 provinces in China from 2005 to 2018, this paper constructs a comprehensive evaluation index system of new-type urbanization using the five dimensions of population, economy, space, society, and green. The spatial Durbin model and the spatial mediating model are used to discuss the spatial effect, transmission mechanism, and regional heterogeneity of new-type urbanization on air pollution. The results show that China's air pollution mainly presents a spatial pattern of high-high agglomeration and low-low agglomeration, and there are spatial fluctuations. The construction of new-type urbanization significantly reduces local air pollution, and the industrial structure optimization, technological innovation, and energy structure adjustment are considered as important transmission mechanisms. However, under the fiscal decentralization and political tournament system in China, the policy implementation deviation may weaken the emission reduction effect of new-type urbanization, which is not conducive to regional environmental governance. From the sub-regional level, the impact of new-type urbanization on air pollution has regional heterogeneity. A robustness test confirms the reliability of our research conclusions. This study also proposes some policy suggestions that the government can utilize in grasping the policy focus of new-type urbanization construction to discover effective ways of controlling air pollution.

Interactive effects of maternal exposure to chemical fertilizer and socio-economic status on the risk of low birth weight

Background

Maternal exposure to chemical fertilizer and disadvantaged maternal socio-economic status (SES) have been found to associate with increased risk of low birth weight (LBW). However, whether the two factors would interact to elevate the risk of LBW remains unknown. The present study aimed to explore the interactive effects of maternal exposure to chemical fertilizer during pregnancy and low SES on the risk of term LBW (tLBW).

Methods

In this population-based case–control study, 179 tLBW cases (birthweight < 2500 g and gestational age ≥ 37 weeks) and 204 controls (birthweight ≥ 2500 g and gestational age ≥ 37 weeks) were chosen from the Perinatal Health Care Surveillance System of Pingding County, Shanxi Province, China between 2007 and 2012. Data on basic socio-demographic, dietary and lifestyle characteristics and environmental exposure were directly extracted from the system. Maternal exposure to chemical fertilizer was measured at both household level and village level. Household-level exposure was indicated by household chemical fertilizer use in farming during pregnancy and the data was collected by trained healthcare workers after the selection of cases and controls in 2013. Village-level exposure was indicated by annual amount of village chemical fertilizer consumption per acre and the data came from the Annals of National Economics Statistics of Pingding County in 2010. Interactions between maternal exposure to chemical fertilizer and SES were assessed in logistic regressions using relative excess risk due to interaction (RERI), which indicates an additive interaction if larger than 0.

Results

The combination of low maternal SES and high exposure to village-level chemical fertilizer consumption was associated with increased risk of tLBW (aOR = 2.62, 95%CI: 1.44 ~ 4.77); The combination of low maternal SES and exposure to household chemical fertilizer use was associated with elevated risk of tLBW (aOR = 2.18, 95%CI: 1.24 ~ 3.83). Additive interactions were detected between high exposure to village-level chemical fertilizer consumption and low maternal SES (RERI:1.79, P < 0.001) and between exposure to household chemical fertilizer use and low maternal SES (RERI:0.77, P < 0.05).

Conclusions

Our study suggested negative impacts of potential agricultural pollutants on adverse pregnancy outcomes, especially in disadvantaged socio-economic populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13604-z.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021)

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO2, O3, PM10, PM2.5, and SO2, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O3). Pollution change in susceptibility is more likely to occur in NO2 decrease and O3 increase, but unsusceptibility is more likely to occur in CO and SO2, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM10 and PM2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.

Efficacy and Safety of Anlotinib Combined with PD-1 Blockades for Patients with Previously Treated Epithelial Ovarian Cancer: A Retrospective Study

Purpose

This study was to investigate the efficacy and safety of anlotinib combined with programmed cell death protein 1 (PD-1) blockades for patients with previously treated advanced epithelial ovarian cancer (EOC).

Patients and Methods

Present study was designed as a retrospective study, a total of 32 patients with advanced EOC who progressed after at least two lines previously available standard therapy were included in this study. All the patients were administered with anlotinib combined with PD-1 blockades administration. Clinical activity was implemented and analyzed, which was assessed according to the change of target lesion by imaging evidence and all the subjects were followed up regularly. Safety profile were collected and documented during the treatment. Univariate analysis was carried out using log rank test and multivariate analysis were adjusted by Cox regression analysis.

Results

The best overall response suggested that partial response was noted in 12 patients, stable disease was observed in 14 patients, progressive disease was found in 6 patients. Therefore, the objective response rate (ORR) of the 32 patients was 37.5% (95% CI: 21.1–56.3%), disease control rate (DCR) of the patients was 81.3% (95% CI: 63.6–92.8%). The median follow-up duration of this study was 17.5 months (follow-up range: 0.9–33.5 months). And the median PFS and OS of the 32-patient cohort was 6.8 months (95% CI: 2.64–10.96) and 18.5 months (95% CI: 14.08–22.92), respectively. The most common treatment-related adverse reactions were fatigue (68.8%), nausea and vomiting (56.3%), hypertension (50.0%) and diarrhea (40.6%). Multivariate Cox regression analysis for PFS indicated that ECOG performance status and FIGO stage were independent factors to predict PFS of patients with previously treated EOC.

Conclusion

Anlotinib combined with PD-1 blockades demonstrated promising efficacy and tolerable safety profile for patients with previously treated advanced EOC preliminarily. The conclusion should be confirmed in more patients with advanced EOC subsequently.

Smaller particular matter, larger risk of female lung cancer incidence? Evidence from 436 Chinese counties

BACKGROUND

Many studies have reported the effects of PM2.5 and PM10 on human health, however, it remains unclear whether particular matter with finer particle size has a greater effect.

OBJECTIVES

This work aims to examine the varying associations of the incidence rate of female lung cancer with PM1, PM2.5 and PM10 in 436 Chinese cancer registries between 2014 and 2016.

METHODS

The effects of PM1, PM2.5 and PM10 were estimated through three regression models, respectively. Mode l only included particular matter, while Model 2 and Model 3 further controlled for time and location factors, and socioeconomic covariates, respectively. Moreover, two sensitivity analyses were performed to investigate the robustness of three particular matte effects. Then, we examined the modifying role of urban-rural division on the effects of PM1, PM2.5 and PM10, respectively.

RESULTS

The change in the incidence rate of female lung cancer relative to its mean was 5.98% (95% CI: 3.40, 8.56%) for PM1, which was larger than the values of PM2.5 and PM10 at 3.75% (95% CI: 2.33, 5.17%) and 1.57% (95% CI: 0.73, 2.41%), respectively. The effects of three particular matters were not sensitive in the two sensitivity analyses. Moreover, urban-rural division positively modified the associations of the incidence rate of female lung cancer with PM1, PM2.5 and PM10.

CONCLUSIONS

The effect on the incidence rate of female lung cancer was greater for PM1, followed by PM2.5 and PM10. There were positive modifying roles of urban-rural division on the effects of three particular matters. The finding supports the argument that finer particular matters are more harmful to human health, and also highlights the great significance to develop guidelines for PM1 control and prevention in Chinese setting.

Clinical Activity and Safety of Anlotinib Combined with PD-1 Blockades for Patients with Previously Treated Small Cell Lung Cancer

Objective

Anlotinib was the standard monotherapy for patients with previously treated small cell lung cancer (SCLC) in recent years. Programmed cell death protein 1 (PD-1) blockade combined with antiangiogenic targeted drugs have proved to play a synergistic action for cancer treatment clinically. Consequently, the present study was to investigate the efficacy and safety of anlotinib combined with PD-1 blockades for patients with previously treated SCLC.

Methods

A total of 36 patients with SCLC who were treated with at least one previous systemic chemotherapy regimen participated in this study retrospectively. All the patients were administered with anlotinib plus PD-1 blockades therapy. Clinical activity was assessed according to the change of target lesion by imaging evidence and all the subjects were followed up regularly. Safety profiles were collected and documented during the treatment. Univariate analysis was carried out using Log rank test and multivariate analysis was adjusted by Cox regression analysis.

Results

All the 36 patients with previously treated SCLC were able to have their efficacy and safety profile evaluated. The best overall response of the combination regimen showed that complete response was observed in one patient, partial response was noted in 9 patients, stable disease was reported in 19 patients, progressive disease was seen in 7 patients. Therefore, the objective response rate (ORR) of the 36 patients was 27.8% (95% CI: 14.2-45.2%), disease control rate (DCR) was 80.6% (95% CI: 64.0-91.8%). Regarding the prognostic data, the median PFS and OS of the 36 patients was 4.6 months (95% CI: 3.13-6.07) and 9.3 months (95% CI: 3.30-15.30), respectively. The most common treatment-related adverse reactions were hypertension (52.8%), fatigue (47.2%), diarrhea (38.9%), hand and foot reaction (38.9%) and dermal toxicity (33.3%). Furthermore, multivariate Cox regression analysis for PFS indicated that ECOG performance status was an independent factor to predict PFS.

Conclusion

Anlotinib combined with PD-1 blockades regimen preliminarily demonstrated encouraging efficacy and tolerable safety for patients with previously treated SCLC. The conclusion should be validated in prospective clinical trials subsequently.

A novel near-infrared fluorescent probe for the detection of sulfur dioxide derivatives and its application in biological imaging

SO2 plays an important role in our life and is also associated with many diseases, so it is a double-edged sword. Therefore, it is necessary to develop a probe for...

Spatio-Temporal Characteristics of SO2 across Weifang from 2008 to 2020

China has achieved good results in SO₂ pollution control, but SO₂ pollution still exists in some areas. Analyzing the spatio-temporal distribution of SO₂ is critical for regional SO₂ pollution prevention and control. Compared with existing air pollution studies that paid more attention to PM₂.₅, NO₂ and O₃, and focused on the macro scale, this study took the small-scale Weifang city as the research area, analyzed the temporal and spatial changes in SO₂, discussed the migration trajectory of SO₂ pollution and explored the impact of wind on SO₂ pollution. The results show that the average annual concentration of SO₂ in Weifang has exhibited a downward trend in the past 13 years, showing the basic characteristics of “highest in winter, lowest in summer and slightly higher in spring and autumn”, “highest on Sunday, lowest on Thursday and gradually decreasing from Monday to Thursday” and “highest at 9 a.m., lowest at 4 p.m. and gradually increasing from midnight to 9 a.m.”. SO₂ concentration showed obvious spatial heterogeneity: higher in the north and lower in the south. In addition, Shouguang, Changyi and Gaomi were seriously polluted. The SO₂ pollution shifted from south to northeast. The clean wind direction (southeast wind and northeast wind) of Weifang city accounted for about 41%, and the pollution wind direction (northwest wind and west wind) accounted for about 7%. Drawing from the multi-scale analysis, vegetation, precipitation, temperature, transport situation and human activity were the most relevant factors. Limited to data collection, more quantitative research is needed to gain insight into the influence mechanism in the future.

Efficacy and Safety of Anlotinib Monotherapy as Third-Line Therapy for Elderly Patients with Non-Small Cell Lung Cancer: A Real-World Exploratory Study

Purpose

The present study was to investigate the real-world efficacy and safety of anlotinib monotherapy as third-line therapy for elderly patients with advanced non-small cell lung cancer (NSCLC).

Methods

A total of 83 elderly patients (>65 years) with NSCLC who were progressed at least two lines of previous systemic therapy were recruited retrospectively. Patients were treated with anlotinib monotherapy (12 mg or 10 mg). Efficacy of anlotinib was assessed and all the patients were followed up regularly. Adverse reactions were collected during anlotinib administration. Univariate analysis was performed using Log rank test and multivariate analysis was adjusted by Cox regression analysis. Additionally, prognostic analysis according to common adverse reactions was implemented to identify the potential clinical significance.

Results

The best overall response of the 83 elderly patients during anlotinib monotherapy indicated that partial response (PR) was observed in six patients, stable disease (SD) was noted in 59 patients, and progressive disease (PD) was reported in 18 patients. Consequently, the objective response rate (ORR) was 7.2% (95% CI=2.7–15.1%) and the disease control rate (DCR) was 78.3% (95% CI=67.9–86.6%). The median progression-free survival (PFS) of the 83 elderly patients with NSCLC was 4.2 months (95% CI=3.51–4.89). Furthermore, the median overall survival (OS) of the 83 patients was 9.6 months (95% CI=6.65–12.55). The safety profile suggested that the relatively common adverse reactions of the elderly patients with ES-SCLC receiving anlotinib therapy were hypertension (49.4%), fatigue (45.8%), and hand–foot syndrome (39.8%). Interestingly, association analysis exhibited that the median PFS of patients with hypertension and non-hypertension was 4.5 and 3.0 months, respectively (χ²=6.306, P=0.012).

Conclusion

Anlotinib monotherapy demonstrated a satisfactory efficacy and tolerable safety profile in third-line settings for elderly patients with advanced NSCLC. Patients who experienced a hypertension adverse reaction induced by anlotinib therapy might confer superior PFS. The conclusion should be validated in prospective clinical trials subsequently.

Urbanization, ambient air pollution, and prevalence of chronic kidney disease: A nationwide cross-sectional study

An increasing number of studies have linked ambient air pollution to chronic kidney disease (CKD) prevalence. However, its potential effect modification by urbanization has not been investigated. Based on data of 47,204 adults from the China National Survey of Chronic Kidney Disease (CKSCKD) dataset, night light satellite remote sensing data and high-resolution air pollution inversion products, the present cross-sectional study investigated the association between fine particulate matter <2.5 mm in diameter (PM_2.5), nitrogen dioxide (NO₂), night light index (NLI) and CKD prevalence in China, and the effect modification by urbanization characterized by administrative classification and NLI on the pollutant-health associations. Our results showed that a 10-μg/m³ increase in PM_2.5 at 3-year moving average, a 10-μg/m³ increase in NO₂ at 5-year moving average, and a 10-U increase in NLI at 5-year moving average were significantly associated with increased odds of CKD prevalence [OR = 1.24 (95 %CI:1.14, 1.35); OR = 1.12 (95 %CI:1.09, 1.15); OR = 1.05 (95 %CI:1.02, 1.07)]. Meanwhile, the pollutant-health associations were more apparent in medium-urbanized areas compared to low- and high-urbanized areas. For instance, a 10-μg/m³ increase in PM_2.5 concentration at 2-year moving average was associated with increased odds of CKD in the areas with NLI level in the second [OR = 2.78 (95 %CI:1.77, 4.36)] and third quartiles [OR = 1.49 (95 %CI:1.14, 1.95)], compared to the lowest [OR = 0.96 (95% CI: 0.73, 1.26)] and highest [OR = 0.63 (95% CI: 0.39-1.02)] quartiles. PM_2.5 and NO₂ were associated with increased odds of CKD prevalence, especially in areas with medium NLI levels, suggesting the necessity of strengthening environmental management in medium-urbanized regions.

Ambient Ozone, PM1 and Female Lung Cancer Incidence in 436 Chinese Counties

Ozone air pollution has been increasingly severe and has become another major air pollutant in Chinese cities, while PM₁ is more harmful to human health than coarser PMs. However, nationwide studies estimating the effects of ozone and PM₁ are quite limited in China. This study aims to assess the spatial associations between ozone (and PM₁) and the incidence rate of female lung cancer in 436 Chinese cancer registries (counties/districts). The effects of ozone and PM₁ were estimated, respectively, using statistical models controlling for time, location and socioeconomic covariates. Then, three sensitivity analyses including the adjustments of smoking covariates and co-pollutant (SO₂) and the estimates of ozone, PM1 and SO₂ effects in the same model, were conducted to test the robustness of the effects of the two air pollutants. Further still, we investigated the modifying role of urban-rural division on the effects of ozone and PM₁. According to the results, a 10 μg/m³ increase in ozone and PM₁ was associated with a 4.57% (95% CI: 4.32%, 16.16%) and 4.89% (95% CI: 4.37%, 17.56%) increase in the incidence rate of female lung cancer relative to its mean, respectively. Such ozone and PM₁ effects were still significant in three sensitivity analyses. Regarding the modifying role of urban-rural division, the effect of PM₁ was greater by 2.98% (95% CI: 1.01%, 4.96%) in urban than in rural areas when PM1 changed by 10 μg/m³. However, there was no modification effect of urban-rural division for ozone. In conclusion, there were positive associations between ozone (and PM₁) and the incidence rate of female lung cancer in China. Urban-rural division may modify the effect of PM₁ on the incidence rate of female lung cancer, which is seldom reported. Continuous and further prevention and control measures should be developed to alleviate the situation of the two air pollutants.