The short- and long-term readmission of four major categories of digestive system cancers: does obesity or metabolic disorder matter?

Purpose: Patients with digestive system cancers (DSCs) are at a high risk for hospitalizations; however, the risk factors for readmission remain unknown. Here, we established a retrospective cohort study to assess the association between metabolic obesity phenotypes and readmission risks of DSC. Experimental design: A total of 142,753 and 74,566 patients at index hospitalization were ultimately selected from the Nationwide Readmissions Database (NRD) 2018 to establish the 30-day and 180-day readmission cohorts, respectively. The study population was classified into four groups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). Multivariate Cox regression analysis was used to estimate the effect of metabolic obesity phenotypes on DSC readmission. Results: The MUNO phenotype had 1.147-fold (95% CI: 1.066, 1.235; p < 0.001) increased 180-day readmission risks in patients with neoplasm of the upper digestive tract. The MUNO phenotype had 1.073-fold (95% CI: 1.027, 1.121; p = 0.002) increased 30-day readmission risks and 1.067-fold (95% CI: 1.021, 1.115; p = 0.004) increased 180-day readmission risks in patients with neoplasm of the lower digestive tract. The MUNO and MUO phenotypes were independent risk factors of readmission in patients with liver or pancreatic neoplasm. Metabolic obesity status was independently associated with a high risk of severe and unplanned hospitalization within 30 days or 180 days. Conclusion: Both obesity and metabolic abnormalities are associated with a high risk for the poor prognosis of DSC patients. The effect of metabolic categories on the short- or long-term readmission of liver or pancreas cancers may be stronger than that of obesity.

Activation of the Nrf-2 pathway by pinocembrin safeguards vertebral endplate chondrocytes against apoptosis and degeneration caused by oxidative stress.

AIM: The occurrence and progression of intervertebral disc degeneration (IDD) are significantly influenced by the cartilaginous endplate (CEP). Pinocembrin (PIN), a type of flavonoid present in propolis and botanicals, demonstrates both antioxidant and anti-inflammatory characteristics, which could potentially be utilized in management. Therefore, it is crucial to investigate how PIN protects against CEP degeneration and its mechanisms, offering valuable insights for IDD therapy. MATERIALS AND METHODS: To investigate the protective impact of PIN in vivo, we created the IDD mouse model through bilateral facet joint transection. In vitro, an IDD pathological environment was mimicked by applying TBHP to treat endplate chondrocytes. KEY FINDINGS: In vivo, compared with the IDD group, the mouse in the PIN group effectively mitigates IDD progression and CEP calcification. In vitro, the activation of the Nrf-2 pathway improves the process of Parkin-mediated autophagy in mitochondria and decreases ferroptosis in chondrocytes. This enhancement promotes cell survival by addressing the imbalance of redox during pathological conditions related to IDD. Knocking down Nrf-2 with siRNA fails to provide protection to endplate chondrocytes against apoptosis and degeneration. SIGNIFICANCE: The Nrf-2-mediated activation of mitochondrial autophagy and suppression of ferroptosis play a crucial role in safeguarding against oxidative stress-induced degeneration and calcification of CEP through the protective function of PIN. To sum up, this research offers detailed explanations about how PIN can protect against apoptosis and calcification in CEP, providing valuable information about the development of IDD and suggesting possible treatment approaches.

Geological Characteristics and Control Mechanism of Uranium Enrichment in Coal-Bearing Strata in the Yili Basin, Northwest China-Implications for Resource Development and Environmental Protection.

Uranium enrichment is considerably prevalent in Jurassic coal-bearing strata in the Yili Basin. A large amount of uranium deposits (occurrences) have been discovered in recent decades. Previous studies have found that uranium deposits and coal seam have a certain correlation in their genesis and spatial distribution or sometimes uranium deposits develop directly in the coal seam. What are the geological characteristics of uranium enrichment? How is uranium enriched? How to strengthen the cooperative development of uranium and coal and environmental protection? In order to explain the aforementioned questions, the characteristics of uranium deposits, rock minerals, and geochemical and metallogenic chronology are summarized herein, and the geological control mechanism of uranium enrichment in coal-bearing strata is discussed. It is found that uranium enrichment (including sandstone uranium deposits and coal uranium deposits) has multistage genetic characteristics and is mainly spread over the gentle slope of the southern margin of the Yili basin, with its host rock possibly being sandstone, coal, and sometimes even mudstone. The uranium concentration has a considerable correlation with the reductant, and the occurrence state of uranium has both inorganic and organic affinities. In addition, uranium enrichment is believed to be a comprehensive effect of high uranium source rocks, tectonic activity, sedimentary facies, hydrogeology conditions, paleoclimate, and reductant. The difference is that uranium enrichment in sandstone is often generated in a mud-sand-mud stratigraphic structure, while uranium enrichment in coal usually develops as coal-sand-mud. What is more, strengthening the study of physical and chemical properties of the host rock, strengthening the study of uranium occurrence state, and sharing geological data are important ways for the cooperative development of coal and uranium resources and environmental protection.

A complete atmospheric emission inventory of F, As, Se, Cd, Sb, Hg, Pb, and U from coal-fired power plants in Anhui Province, eastern China.

Anhui Province is the most important energy production base for eastern China. Many large pithead coal-fired power plants are being operated in the coal-rich Huainan and Huaibei coalfields in northern Anhui. To assess the environmental risks of local coal-fired power plants, a complete atmospheric emission inventory of F, As, Se, Cd, Sb, Hg, Pb, and U from coal-fired power plants in Anhui was compiled by a simple mass-balance-based method. The results indicated that the atmospheric emissions of F, As, Se, Cd, Sb, Hg, Pb, and U in 2017 from the Anhui coal-fired power plants were 578 t, 2.01 t, 15.3 t, 0.57 t, 0.18 t, 2.80 t, 23.7 t, and 0.099 t, respectively. The emission factor is the major contributor to the uncertainties in this inventory. With increasing energy demand by the more developed eastern China region, the atmospheric emissions of volatile hazardous elements will continue to increase in the near future.

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative.

Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.

Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype.

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages ( https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB ), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers ( https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu ). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

IntLIM: integration using linear models of metabolomics and gene expression data.

BACKGROUND: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. RESULTS: The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. CONCLUSIONS: IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.