Inversed albumin-to-globulin ratio and underlying liver disease severity as a prognostic factor for survival in hepatocellular carcinoma patients undergoing transarterial chemoembolization

PURPOSEPrevious studies have shown that an inversed albumin-to-globulin ratio (IAGR) is a predictor of the prognosis of many cancers. However, the prognostic value of an IAGR for patients with hepatocellular carcinoma (HCC) who undergo transarterial chemoembolization (TACE) is still unclear. This study aims to evaluate the predictive value of an IAGR for the prognosis of those patients.METHODSThis study retrospectively analyzed 396 patients with HCC who received TACE. Using a cut-off value of 1.0 for the albumin-to-globulin ratio, patients were divided into a normal albumin-to-globulin ratio (NAGR) (≥1) and an IAGR (<1) group. Univariate and multivariate analyses and time-dependent receiver operating characteristic analyses were performed to identify risk factors of overall survival (OS) and cancer-specific survival (CSS). Survival nomograms were constructed based on the multivariable analysis results and further evaluated using the consistency index (C-index) and calibration curve.RESULTSA total of 396 patients were included in the final analysis and were divided into the NAGR group (n = 298, 75.3%) and the IAGR (n = 98, 24.7%) group. The median OS and CSS were significantly worse in the IAGR group than in the NAGR group (OS: 8 vs. 26 months, CSS: 10 vs. 41 months, both P < 0.001). Multivariate analyses demonstrated that an IAGR was an independent risk factor for predicting worse OS [hazard ratio (HR), 2.024; 95% confidence interval (CI): 1.460–2.806] and CSS (HR: 2.439; 95% CI: 1.651–3.601). The nomogram-based model-related C-indexes for OS and CSS prediction were 0.715 (95% CI: 0.697–0.733) and 0.750 (95% CI: 0.729–0.771), and the calibration of the nomogram showed good consistency.CONCLUSIONThe IAGR along with underlying liver disease severity were the useful prognostic predictors of OS and CSS among patients with HCC undergoing TACE and might be useful to identify high-risk patients

The impact of monthly air pollution exposure and its interaction with individual factors: Insight from a large cohort study of comprehensive hospitalizations in Guangzhou area

BackgroundAlthough the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited.MethodA total of 68,416 people in South China were enrolled and followed up during 2019–2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM10 and O3 exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated.ResultsOverall, each 10 μg/m3 increase in PM10 concentration was associated with a 3.1% (95%CI: 1.3%−4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O3 exposure (6.8%, 5.5%−8.2%). Furthermore, each 10 μg/m3 increase in PM10 was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O3 was relevant to a 4.7%−22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM10 exposure (Pinteraction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O3 (Pinteraction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O3 exposure (Pinteraction: 0.032).ConclusionWe provide comprehensive evidence on the hospitalization hazard of monthly PM10 and O3 exposure and their interaction with individual factors

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R2 values ranging from 0.46 to 0.80

GADTI:graph autoencoder approach for DTI prediction from heterogeneous network

Identifying drug-target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision-recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI

Assessment of Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in China

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD &lt; 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms

Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis

CONTEXTProcess-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components.OBJECTIVEThis study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance.METHODSWe analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty.RESULTS AND CONCLUSIONSOur analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth