Clinicopathological features, prognostic factor analysis, and survival nomogram of patients with double primary cancers involving lung cancer.

BACKGROUND: Although the incidence of double primary cancers (DPCs) involving lung cancer is rising, they have not been studied sufficiently. This study retrospectively analyzed the clinicopathological and prognostic characteristics of DPC patients with lung cancer and developed a survival nomogram to predict the individual OS rates. METHODS: We included 103 DPC patients with lung cancer from Shengjing Hospital between 2016 and 2021. Based on the 6-month cancer occurrence interval, the cases were categorized as synchronous DPCs (sDPCs) or metachronous DPCs (mDPCs). Furthermore, the mDPCs were subdivided based on whether the lung cancer occurred first (LCF cohort) or the other cancer occurred first (OCF cohort). RESULTS: Among the patients, 35 (33.98%) and 68 (66.02%) had sDPCs and mDPCs, respectively. In the mDPCs cohort, 18 (26.47%) belonged to the LCF cohort and 50 (73.53%) to the OCF cohort. The most frequent primary cancer sites were the breast (27.18%), colorectum (22.33%), and urinary system (18.45%). Independent risk factors for progression-free survival were Stage IV lung cancer (p = 0.008) and failure to undergo radical lung cancer surgery (p = 0.028). The risk factors for OS included squamous carcinoma (p = 0.048), Stage IV lung cancer (p = 0.001), single cancer resection plus drug therapy (p < 0.001), drug therapy alone (p = 0.002), failure to undergo radical lung cancer surgery (p = 0.014), and chemotherapy (p = 0.042). The median OS was 37 months, with 3- and 5-year rates of 50.9% and 35.9%, respectively. CONCLUSION: DPCs involving lung cancer account for 1.11% of cases. The breast, colorectum, and urinary system were the most common extra-pulmonary sites, and mDPCs were more frequent than sDPCs. Radical lung cancer surgery significantly affects prognosis, and drug therapy alone may be preferable when only one tumor is operable. The developed nomogram can accurately predict individual 3-year and 5-year OS rates.

Invert global and China's terrestrial carbon fluxes over 2019-2021 based on assimilating richer atmospheric CO2 observations.

With China's commitment to reach carbon peak by 2030 and achieve carbon neutrality by 2060, it is particularly important to obtain terrestrial ecosystem carbon fluxes with low uncertainty both globally and in China. The use of more observation data may help reduce the uncertainty of inverting carbon fluxes. This study uses the observation data from global stations, background stations and provincial stations in China, as well as the OCO-2 satellite, and uses the China Carbon Monitoring, Verification and Supporting System for Global (CCMVS-G) to estimate the carbon fluxes of global and Chinese terrestrial ecosystems from 2019 to 2021. The results revealed that the global terrestrial ecosystem carbon sink was approximately -3.40 Pg C/yr from 2019 to 2021. The carbon sinks in the Northern Hemisphere are large, especially in Asia, North America, and Europe. From 2019 to 2021, the carbon sink of China's terrestrial ecosystem was approximately -0.44 Pg C/yr. Carbon sinks exhibit significant seasonal and interannual variations in China. After assimilating the observation data, the uncertainty of the posterior flux is smaller than that of the prior flux, a more reasonable distribution of carbon sources and sinks can be obtained, and more accurate boundary conditions can be provided for the China Carbon Monitoring, Verification and Supporting System for Regional (CCMVS-R). In the future, it is important to establish a well-designed CO2 ground-based observation network.

Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013-2022 in Anhui, China.

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 µg m-3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

Aerosol forcing regulating recent decadal change of summer water vapor budget over the Tibetan Plateau.

The Tibetan Plateau (TP), known as the Asian water tower, has been getting wetter since the 1970s. However, the primary drivers behind this phenomenon are still highly controversial. Here, we isolate the impacts of greenhouse gases (GHG), aerosols, natural forcings and internal climate variability on the decadal change of summer water vapor budget (WVB) over the TP using multi-model ensemble simulations. We show that an anomalous Rossby wave train in the upper troposphere travelling eastward from central Europe and equatorward temperature gradient in eastern China due to the inhomogeneous aerosol forcing in Eurasia jointly contribute to anomalous easterly winds over the eastern TP. Such anomalous easterly winds result in a significant decrease in water vapor export from the eastern boundary of the TP and dominate the enhanced summer WVB over the TP during 1979-2014. Our results highlight that spatial variation of aerosol forcing can be used as an important indicator to project future WVB over the TP.

Spatially resolved land and grid model of carbon neutrality in China.

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals.

Bronchopulmonary foregut malformation with severe hemoptysis in advanced age: a case report and literature review.

Bronchopulmonary foregut malformation (BPFM) is a rare developmental malformation disease due to embryonic defects, with an even rarer occurrence in adults. We report a diagnosed case in an adult patient, and notably, this is the first reported case of such advanced age. Additionally, she experienced coughing up approximately 1 liter of blood and partial lung tissue, accompanied by respiratory failure and shock. Following treatment with transcatheter arterial embolization, her condition improved, and she has remained stable during follow-up. We present a case report and conducted a systematic review on this particular case.

Food-grade silica-loaded gallic acid nanocomposites: Synthesis and mechanism for enhancing water-based biological activity.

As the low water solubility of gallic acid (GA), its biological activities such as water-based antioxidant effect may be greatly reduced. Therefore, GA-loaded nanocomposites (F-SiO2@GA) with high water solubility were synthesized via solvent evaporation using food-grade silica (F-SiO2) as carriers in this work. The assessment of antioxidant capacity revealed that F-SiO2@GA exhibited considerably greater free-radical scavenging ability than free GA and the physical mixture of F-SiO2 and GA. In the photooxidation experiment of food-grade gardenia yellow pigment (GYP), F-SiO2@GA showed a notable antioxidant effect on GYP solution. Additionally, in the storage experiment on chilled whiteleg shrimp (Litopenaeus vannamei) treated with F-SiO2@GA, pH, total volatile basic nitrogen (TVBN), and thiobarbituric acid reactive substance (TBARS) values were effectively inhibited. In conclusion, the internal encapsulation of GA effectively prevented the self-aggregation phenomenon, thereby facilitating the exposure of its active phenolic hydroxyl group and significantly enhancing its water-based biological activity.

Columnar optical-radiative properties and components of aerosols in the Arctic summer from long-term AERONET measurements.

Aerosols as an external factor have an important role in the amplification of Arctic warming, yet the geography of this harsh region has led to a paucity of observations, which has limited our understanding of the Arctic climate. We synthesized the latest decade (2010-2021) of data on the microphysical-optical-radiative properties of aerosols and their multi-component evolution during the Arctic summer, taking into consideration the important role of wildfire burning. Our results are based on continuous observations from eight AERONET sites across the Arctic region, together with a meteorological reanalysis dataset and satellite observations of fires, and utilize a back-trajectory model to track the source of the aerosols. The summer climatological characteristics within the Arctic Circle showed that the aerosols are mainly fine-mode aerosols (fraction >0.95) with a radius of 0.15-0.20 µm, a slight extinction ability (aerosol optical depth âˆ¼ 0.11) with strong scattering (single scattering albedo ∼0.95) and dominant forward scattering (asymmetry factor âˆ¼ 0.68). These optical properties result in significant cooling at the Earth's surface (∼-13 W m-2) and a weak cooling effect at the top of the atmosphere (∼-5 W m-2). Further, we found that Arctic region is severely impacted by wildfire burning events in July and August, which primarily occur in central and eastern Siberia and followed in subpolar North America. The plumes from wildfire transport aerosols to the Arctic atmosphere with the westerly circulation, leading to an increase in fine-mode aerosols containing large amounts of organic carbon, with fraction as high as 97-98 %. Absorptive carbonaceous aerosols also increase synergistically, which could convert the instantaneous direct aerosol radiative effect into a heating effect on the Earth-atmosphere system. This study provides insights into the complex sources of aerosol loading in the Arctic atmosphere in summer and emphasizes the important impacts of the increasingly frequent occurrence of wildfire burning events in recent years.

A Review of Recent Advances in the Molecular Mechanisms Underlying Brain Metastasis in Lung Cancer.

Brain metastasis from lung cancer is a prevalent mode of treatment failure associated with a poor prognosis. The incidence of brain metastasis has recently shown a dramatic increase. The early detection and risk stratification of lung cancer-related brain metastasis would be highly advantageous for patients. However, our current knowledge and comprehension of the underlying mechanisms driving brain metastasis in lung cancer pose significant challenges. This review summarizes the mechanisms underlying brain metastasis, focusing on the intricate interplay between lung cancer-derived tumor cells and the unique characteristics of the brain, recent advancements in the identification of driver genes, concomitant genes, epigenetic features, including miRNAs and long noncoding RNAs, as well as the molecular characterization of brain metastasis originating from other organs, which may further enhance risk stratification and facilitate precise treatment strategies.

Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China.

Black carbon (BC) constitutes a pivotal component of atmospheric aerosols, significantly impacting regional and global radiation balance, climate, and human health. In this study, we evaluated BC data in two prominent atmospheric composition reanalysis datasets: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service (CAMS), and analyzed the causes of their deviations. This assessment is based on observational data collected from 34 monitoring stations across China from 2006 to 2022. Our research reveals a significant and consistent decline in BC concentrations within China, amounting to a reduction exceeding 67.33%. However, both MERRA-2 and CAMS reanalysis data fail to capture this declining trend. The average annual decrease of BC in MERRA-2 from 2006 to 2022 is only 0.06 µg/m3 per year, while the BC concentration in CAMS even increased with an average annual value of 0.014 µg/m3 per year. In 2022, MERRA-2 had overestimated BC concentration by 20% compared to observational data, while CAMS had overestimated it by approximately 66%. In the regional BC concentration analysis, the data quality of the reanalysis data is better in the South China (RM = 0.59, RC = 0.53), followed by the North China (RM = 0.50, RC = 0.42). Reanalysis BC data in Northwest China and the Tibetan Plateau are difficult to use for practical analysis due to their big difference with observation. In a comparison of the anthropogenic BC emissions inventory used in the two atmospheric composition reanalysis datasets with the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) emissions inventory, we found that: Despite the significant decline in China's BC emissions, MERRA-2 still relies on the 2006 emissions inventory, while CAMS utilizes emission inventories that even show an increasing trend. These factors will undoubtedly lead to greater deviations between reanalysis and observational data.

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy.

OBJECTIVES: This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment. MATERIALS AND METHODS: In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients). RESULTS: TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS. CONCLUSION: By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018-2021.

Accurate estimating CO2 emissions and sinks is crucial in achieving carbon neutrality in China. However, CO2 emissions from bottom-up inventories are uncertain at regional scales and lack independent verification from atmospheric perspectives. Here we integrated 39 high-precision CO2 stations in China to top-down invert emission-sink variations at 45 km × 45 km and achieved a full range of inventories verification. The results show that China's CO2 emissions are 15% higher than those of five bottom-up inventories, to an annual total of 3.40 Pg C a-1 for 2018-2021. After deducting human and livestock respiration, the annual CO2 emissions were 3.13 Pg C a-1 (11.48 Pg CO2 a-1). The annual increase in emissions slowed from 3.7% in 2019 to 1.1% in 2020 and resumed growth to 4.0% in 2021, consistent with observed CO2 growth rates in China. China's land CO2 sink, deducting farmland sinks and lateral fluxes, was 0.57 Pg C a-1 (2.09 Pg CO2 a-1) for 2018-2021 (higher than most global inverse models), accounting for ∼16.9% of anthropogenic CO2 emissions. The land sink in China decreased by -19.3% in 2019 due to a weak El Niño event and increased by 3.2% in 2020 and 13.7% in 2021. It is worth noting that inverse CO2 emissions and sinks in western China still face large uncertainty due to limited CO2 monitoring. Overall, our top-down estimates demonstrate spatiotemporal variations in CO2 emissions and sinks from atmospheric perspectives and highlight challenges for different provinces in achieving 2060 carbon neutrality with verified estimates.

Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer.

BACKGROUND: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. OBJECTIVES: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. MATERIALS AND METHODS: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0-3, 3-6, 6-9, 9-12, 12-15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere-shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. RESULTS: Among the five partitions, the model of 9-12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77-0.94). The AUC was 0.94 (0.85-0.98) for the feature fusion model and 0.91 (0.82-0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88-0.99) for the feature fusion method and 0.94 (0.85-0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81-0.97) and 0.89 (0.79-0.93) in two validation sets, respectively. CONCLUSIONS: This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.

Mallory-Weiss syndrome in four hemodialysis patients: a case study.

BACKGROUND: Hemodialysis patients are prone to gastrointestinal bleeding, and Mallory-Weiss syndrome (MWS) is one of the causes. Mallory-Weiss syndrome is often induced by severe vomiting, manifests as upper gastrointestinal bleeding, and is self-limited with a good prognosis. However, mild vomiting in hemodialysis patients can lead to the occurrence of MWS, and the mild early symptoms are easy to misdiagnose, leading to the aggravation of the disease. CASE PRESENTATION: In this paper, we report four hemodialysis patients with MWS. All patients displayed symptoms of upper gastrointestinal bleeding. The diagnosis of MWS was confirmed by gastroscopy. One patient had a history of severe vomiting; however, the other three reported histories of mild vomiting. Three patients received the conservative hemostasis treatment, and the gastrointestinal bleeding stopped. One patient underwent the gastroscopic and interventional hemostasis treatments. The conditions of three of the patients improved. Unfortunately, one of the patients died due to the cardia insufficiency. CONCLUSIONS: We think that the mild symptoms of MWS are easily covered up by other symptoms. This may lead to delays in diagnosis and treatment. For patients with severe symptoms, gastroscopic hemostasis is still the first choice, and interventional hemostasis can also be considered. For patients with mild symptoms, drug hemostasis is the first consideration.

Complexity of carbon dioxide flux in urban areas: A comparison with natural surfaces.

The characteristics of turbulent CO2 transport and its dissimilarity with heat and water vapor are investigated over both natural and urban areas. A novel index TS is proposed to effectively quantify the transport similarity between two scalars. By comparison, it is found that the transport of CO2 shows great complexity in urban areas. It is ideal in natural areas that heat, water vapor, and CO2 are efficiently transported by thermal plumes (i.e., the dominant coherent structures under unstable conditions), and that the transport similarity among them becomes increasingly evident with the increase of atmospheric instability. However, in urban areas, the transport of CO2 shows significant dissimilarity from that of heat and water vapor, and it is hard to detect the role of thermal plumes. Furthermore, it is observed that the sector-average CO2 flux in urban areas changes largely with the wind blowing from different urban functional areas. Specially, for a given direction, there might be contrasting characteristics in CO2 transport under different unstable conditions. These features can be explained by the flux footprint. Since the CO2 sources and sinks are distributed heterogeneously in urban areas, the variation of footprint areas with wind direction or atmospheric instability, causes the alternation between source-dominated (i.e., upward) and sink-dominated (i.e., downward) CO2 transport. Therefore, the role of coherent structures in CO2 transport is substantially confused by spatially-confined sources/sinks in urban areas, leading to significant transport dissimilarity between CO2 and heat or water vapor and thus the great complexity in CO2 transport. The findings in this study are helpful to promote the understanding of the global carbon cycle in depth.

Quantifying the contribution of local drivers to observed weakening of spring dust storm frequency over northern China (1982-2017).

Recent studies have suggested that spring dust storm (SDS) events in northern China (NC) have exhibited substantial decline over the past 30 years. However, it is unclear which local factors are most responsible for the decline in SDS events, and the contribution of each dominant factor remains to be determined. This study utilized high-density DS records and collocated homogenized surface meteorological observations from 1982 to 2017, in conjunction with land surface products, to examine the local drivers that influence the long-term variation in SDS frequency (SDSF) over the entire NC area and its three dust-source areas: northwestern China (NWC), north-central China (NCC), and northeastern China (NEC). Results indicated that the observed SDSF averaged over NC, NWC, NCC, and NEC has decreased by 144.4 %, 109.3 %, 166.4 %, and 92.2 %, respectively, during 1982-2017. The variation in SDSF is largely explained by variation in wind speed (WS), precipitation, volumetric soil moisture, and surface bareness. A multivariable linear regression model incorporating these local drivers accounted for 81.0 %, 74.0 %, and 46.9 % of the variance in SDSF in NWC, NCC, and NEC, respectively. Statistical analyses on the local drivers suggested that weakening of WS was the dominant factor in the reduction in SDSF over recent decades, contributing 76.9 %, 54.7 %, and 33.6 % of the variation in NWC, NCC, and NEC, respectively. More importantly, we revealed that the interannual variation in regional SDSF was not only controlled by local drivers, but also influenced by cross-regional transport of dust aerosols emitted from upstream source areas.

[Chemerin mediated neutrophil infiltration in oral squamous cell carcinoma and predicted poor prognosis].

PURPOSE: To explore the effect of Chemerin in oral squamous cell carcinoma (OSCC) tissue on neutrophils infiltration and its possible molecular mechanism. METHODS: The relationship between Chemerin expression and neutrophils density was assessed via double immunohistochemistry staining.The chemotactic effect of Chemerin on neutrophils in OSCC was detected by transwell assay, real-time quantitative PCR(qRT-PCR), Western blot, enzyme-linked immunosorbent assay(ELISA) and flow cytometry. The data were statistically analyzed using SPSS 23.0 software package. The relationship between Chemerin expression and neutrophils density was assessed using Spearman rank correlation analysis. ChemR23 knockout efficiency and chemotactic index were calculated by ANOVA. The relationship between Chemerin expression, neutrophils density and clinicopathological factors was analyzed by Mann-Whitney test. Kaplan-Meier test and Log rank test were used for survival analysis, and risk factors affecting the survival of OSCC patients was assessed using Cox regression model. RESULTS: Double immunohistochemistry staining showed that overexpression of Chemerin was significantly correlated with increased neutrophils infiltration in OSCC(P=0.023), and strong Chemerin expression and high neutrophils density were associated with higher clinical stage(P＜0.001), cervical lymph node metastasis (P＜0.001) and tumor recurrence (P=0.002). Kaplan-Meier survival analysis showed that patients in the strong Chemerin expression + high neutrophils density group had shortened cancer-related overall survival time and disease-free survival time compared with the other two groups. Transwell assay results showed that both OSCC cells and R-Chemerin had a significant chemotactic effect on dHL-60 cells; knockdown of ChemR23 suppressed Chemerin-induced chemotaxis to dHL-60 cells. CONCLUSIONS: Overexpression of Chemerin in OSCC tissue chemoattracts more neutrophils to tumor sites through its receptor ChemR23 and is related to poor clinical prognosis.

Progesterone prevents HGSOC by promoting precancerous cell pyroptosis via inducing fibroblast paracrine.

High-grade serous ovarian carcinoma (HGSOC) is one of the most fatal gynecological cancers and has no effective prevention strategies. Herein, we demonstrated that progesterone significantly inhibited the occurrence, metastasis, and ascites of ovarian cancer in vivo, and the tumor inhibition effect of progesterone was in the tubo-ovarian intrabursal model than in the intraperitoneal or subcutaneous models. Further data demonstrated that progesterone-treated fallopian tube fibroblasts conditioned medium significantly inhibit HGSOC precancerous cell viability by inducing pyroptosis via the IL-6/ROS/NLRP3/GSDMD pathway, implying that the oviduct microenvironment may enhance progesterone's protective effects on ovarian cancer. This study elucidated progesterone inhibiting ovarian cancer mechanism and provided evidence for progesterone as a chemo-preventive role for HGSOC.

Description and evaluation of a newly developed emission inventory processing system (EMIPS).

Commonly available emission inventories are often incompatible with the input requirements of atmospheric chemistry models due to temporal and spatial resolution, pollutant types, etc. We present the Emission Inventory Processing System (EMIPS) version 1, an open-source and multi-scale atmospheric emission modeling framework that prepares specific emissions inputs for atmospheric chemistry models. EMIPS is a multifunctional and user-friendly system, coded in Jython and developed on the MeteoInfo software platform. It allows users to freely combine and process emission inventories to generate model-ready emissions data. The core functions of EMIPS include preprocessing, temporal allocation, spatial allocation, chemical speciation, and vertical allocation. We detail the implementation of each function in the body of this paper, and several examples are provided for illustration. The emission outputs obtained with EMIPS have been evaluated by simulating four pollutants (PM2.5, PM10, NO2, and O3) concentrations in January 2017 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and comparison of model results with the observations indicates the model can reproduce the temporal and spatial patterns of pollutants, suggesting that EMIPS is capable of supporting atmospheric chemistry modeling. We expect this work could help to improve air quality research and forecast.

Environment and chronic disease in rural areas of Heilongjiang, China (ECDRAHC).

PURPOSE: Environmental factors such as long-term exposure to cold can increase the risk of chronic diseases. However, few studies have focused on the impact of environmental factors and lifestyle changes on chronic diseases. To fully explore the association between exposure to environmental factors and the prevalent risk of various chronic diseases, we conducted a large cohort study (Environment and Chronic Disease in Rural Areas of Heilongjiang, China (ECDRAHC)). The ECDRAHC collected detailed questionnaire data covering 10 sections, physical measurements and blood and urine samples. In this study, we describe the design and implementation of the cohort study and present the findings for the first 10 000 participants. PARTICIPANTS: The ECDRAHC study was carried out in rural areas where the annual average temperature is 2.9°C, and aimed to recruit 40 000 participants who are long-term residents aged 35-74 years. The participants will be followed up every 5 years. Currently, ECDRAHC has reached 26.7% (n=10 694) of the targeted population. FINDINGS TO DATE: A total of 10 694 adults aged 35-74 years were recruited, including 61.7% women. The prevalence of current smokers was 46.8% in men and 35.4% in women. The mean blood pressure was 140.2/89.9 mm Hg and 135.7/85.0 mm Hg in men and women, respectively. The mean body mass index was 24.74 kg/m2 in men and 24.65 kg/m2 in women, with >7.3% being obese (>30 kg/m2). The main non-communicable diseases found in phase 1 were hypertension, diabetes, hypertriglyceridaemia and metabolic syndrome, with a higher prevalence of 51.0%, 21.6%, 46.8% and 42.6%, respectively. FUTURE PLANS: We plan to complete the follow-up for the first phase of the ECDRAHC in 2024. The second and third phase of the cohort will be carried out steadily, as planned. This cohort will be used to investigate the relationship between environmental factors, lifestyle, and genetic and common chronic diseases.