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The mineral zircon has a robust crystal structure, preserving a wealth of geological information through deep time. Traditionally, trace elements in magmatic and hydrothermal zircon have been employed to distinguish between different primary igneous or metallogenic growth fluids. However, classical approaches based on mineral geochemistry are not only time consuming but often ambiguous due to apparent compositional overlap for different growth environments. Here, we report a compilation of 11 004 zircon trace element measurements from 280 published articles, 7173 from crystals in igneous rocks, and 3831 from ore deposits. Geochemical variables include Hf, Th, U, Y, Ti, Nb, Ta, and the REEs. Igneous rock types include kimberlite, carbonatite, gabbro, basalt, andesite, diorite, granodiorite, dacite, granite, rhyolite, and pegmatite. Ore types include porphyry Cu-Au-Mo, skarntype polymetallic, intrusion-related Au, skarn-type Fe-Cu, and Nb-Ta deposits. We develop Decision Tree, XGBoost, and Random Forest algorithms with this zircon geochemical information to predict lithology or deposit type. The F1-score indicates that the Random Forest algorithm has the best predictive performance for the classification of both lithology and deposit type. The eight most important zircon elements from the igneous rock (Hf, Nb, Ta, Th, U, Eu, Ti, Lu) and ore deposit (Y, Eu, Hf, U, Ce, Ti, Th, Lu) classification models, yielded reliable F1-scores of 0.919 and 0.891, respectively. We present a web page portal (http://60.205.170.161:8001/) for the classifier and employ it to a case study of Archean igneous rocks in Western Australia and ore deposits in Southwest China. The machine learning classifier successfully determines the known primary lithology of the samples, demonstrating significant promise as a classification tool where host rock and ore deposit types are unknown. [ABSTRACT FROM AUTHOR]

Background: Esophageal cancer is associated with substantial disease burden in China, and data on the economic burden are fundamental for setting priorities in cancer interventions. The medical expenditure for the diagnosis and treatment of esophageal cancer in China has not been fully quantified. This study aimed to examine the medical expenditure of Chinese patients with esophageal cancer and the associated trends., Methods: From 2012 to 2014, a hospital-based multicenter retrospective survey was conducted in 37 hospitals in 13 provinces/municipalities across China as a part of the Cancer Screening Program of Urban China. For each esophageal cancer patient diagnosed between 2002 and 2011, clinical information and expense data were extracted by using structured questionnaires. All expense data were reported in Chinese Yuan (CNY; 1 CNY = 0.155 USD) based on the 2011 value and inflated using the year-specific health care consumer price index for China., Results: A total of 14,967 esophageal cancer patients were included in the analysis. It was estimated that the overall average expenditure per patient was 38,666 CNY, and an average annual increase of 6.27% was observed from 2002 (25,111 CNY) to 2011 (46,124 CNY). The average expenditures were 34,460 CNY for stage I, 39,302 CNY for stage II, 40,353 CNY for stage III, and 37,432 CNY for stage IV diseases (P < 0.01). The expenditure also differed by the therapy type, which was 38,492 CNY for surgery, 27,933 CNY for radiotherapy, and 27,805 CNY for chemotherapy (P < 0.05). Drugs contributed to 45.02% of the overall expenditure., Conclusions: These conservative estimates suggested that medical expenditures for esophageal cancer in China substantially increased in the last 10 years, treatment for early-stage esophageal cancer costs less than that for advanced cases, and spending on drugs continued to account for a considerable proportion of the overall expenditure.

The long non-coding RNAs (lncRNAs) regulating encoding transcripts/genes involved in Wnt signalling pathway in keloids is largely unclear. We used a pathway-focused lncRNA microarray to detect the differentiated expression profiles of both lncRNAs and genes involved in Wnt pathway, thus a total of 116 Wnt-targeted genes and 69 Wnt-related lncRNAs aberrantly expressed in keloids were initially identified. A stepwise bioinformatics was further performed to find skin-related lncRNA/gene pairs in Wnt pathway in keloids. Firstly, an lncRNA/gene co-expression network with clustered functional modules was constructed; simultaneously, 114 Wnt-genes regarding to dermis were online enriched using Phenotype Enrichment. Secondly, 17 skin-related keloid-aberrant Wnt-genes were acquired by overlapping the 114 skin-related Wnt-genes with the 116 keloid-aberrant Wnt-genes. Thirdly, after co-expression coefficient of each lncRNA/gene profile being ranked respectively, 11 top co-expressed lncRNAs characterized with the highest co-expression coefficients to the 17 genes were identified. Fourthly, seven of the 11 top co-expressed lncRNAs exhibiting array-detected aberrant expression in keloids, together with their 12 most interactive Wnt-genes, were selected to undergo in-pair intracellularly quantitative PCR validation in keloids. As a result, four lncRNAs including CACNA1G-AS1, HOXA11-AS, LINC00312 and RP11-91I11.1 with their six paired Wnt-genes undergoing both array-and-qPCR as well as lncRNA-and-gene double validation were finally identified as skin-related lncRNA/gene pairs that involved in Wnt signalling pathway in keloids. In conclusion, in-depth exploration on these easily-accessible lncRNAs in keloids might aid to find the novel target on how to maintain highly recurrent tumours benign via Wnt-involved network regulation.