Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population

BackgroundThis study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM).MethodsPatients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM.ResultsFor the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set.ConclusionThe ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM

Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with non-alcoholic fatty liver disease (NAFLD) by bioinformatics analysis and machine learning

BackgroundChronic kidney disease (CKD) and non-alcoholic fatty liver disease (NAFLD) are closely related to immune and inflammatory pathways. This study aimed to explore the diagnostic markers for CKD patients with NAFLD.MethodsCKD and NAFLD microarray data sets were screened from the GEO database and analyzed the differentially expressed genes (DEGs) in GSE10495 of CKD date set. Weighted Gene Co-Expression Network Analysis (WGCNA) method was used to construct gene coexpression networks and identify functional modules of NAFLD in GSE89632 date set. Then obtaining NAFLD-related share genes by intersecting DEGs of CKD and modular genes of NAFLD. Then functional enrichment analysis of NAFLD-related share genes was performed. The NAFLD-related hub genes come from intersection of cytoscape software and machine learning. ROC curves were used to examine the diagnostic value of NAFLD related hub genes in the CKD data sets and GSE89632 date set of NAFLD. CIBERSORTx was also used to explore the immune landscape in GSE104954, and the correlation between immune infiltration and hub genes expression was investigated.ResultsA total of 45 NAFLD-related share genes were obtained, and 4 were NAFLD-related hub genes. Enrichment analysis showed that the NAFLD-related share genes were significantly enriched in immune-related pathways, programmed cell death, and inflammatory response. ROC curve confirmed 4 NAFLD-related hub genes in CKD training set GSE104954 and other validation sets. Then they were used as diagnostic markers for CKD. Interestingly, these 4 diagnostic markers of CKD also showed good diagnostic value in the NAFLD date set GSE89632, so these genes may be important targets of NAFLD in the development of CKD. The expression levels of the 4 diagnostic markers for CKD were significantly correlated with the infiltration of immune cells.Conclusion4 NAFLD-related genes (DUSP1, NR4A1, FOSB, ZFP36) were identified as diagnostic markers in CKD patients with NAFLD. Our study may provide diagnostic markers and therapeutic targets for CKD patients with NAFLD

Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China

Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (aph (675)) and Chlorophyll a (Chla) concentration in different regions (R2:0.60-0.82). The non-algal particle (NAP) absorption coefficient (aNAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs

SPATIAL DISTRIBUTION AND THE POSSIBLE SOURCE OF CDOM FOR INLAND WATER IN SUMMER IN THE NORTHEAST CHINA

The availability of underwater light is a critical factor in the growth and abundance of primary producers in shallow embayments. The aim of this study was to examine the spatial distribution and the possible source of CDOM for inland water in summer in the northeast china. Absorption spectra of inland water samples were measured from 200nm to 800nm. Highest mean-value of a(375) occurred in Chagan Lake. A significant spatial difference was found among four different inland waters, and evident spatial variation was in Chagan Lake. A consistent negative non-linear relationship was recorded between S value and CDOM absorption coefficient. Furthermore, S value was used as a proxy for CDOM composition and source. Fulvic acids is primary contribution for CDOM absorption in Songhua Lake and Shitoukoumen Reservoir, but humic acids in Nanhu Lake and Chagan Lake. The relationships between CDOM absorption and total suspended matter concentration and chlorophyll-a concentration were analyzed. It demonstrated the biological processes source for Nanhu Lake, Shitoukoumen Reservoir and Chagan Lake. But for Songhua Lake, the dominating source is from river inputs, but biological process was also an important portion for CDOM concentration

SPATIAL MAPPING OF ACTUAL EVAPOTRANSPIRATION AND WATER DEFICIT WITH MODIS PRODUCTS IN THE SONGNEN PLAIN, NORTHEAST CHINA

Analysis of spatial patterns of evapotranspiration (ET) and water deficit (WD) is significant in the evaluation of crop growth status and water use efficiency for the Songnen Plain, an important commodity grain product base of China. Spatial patterns of ET and WD in the Songnen Plain of 2008 growing season (from May to September) were mapped by using MODIS products and meteorological data. The results indicated that ET and WD exhibited obvious spatial variation and gradually increased from southwest to east and northeast. Total ET over the Songnen Plain during the 2008 growing season ranged from 182.7mm to 1002.4mm with the mean value of 591.1mm, and WD ranged from -163.0mm to 645.9mm with the mean value of 195.9mm. Average ET and WD for different land covers varied significantly, water-body and wetlands obtained the highest ET and WD values, while grassland got the lowest ET and WD values. Through this study, it would provide some supports for the assessment of crop growth in arid environments of Songnen Plain