A novel prognostic model for patients with colon adenocarcinoma

BackgroundColon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.MethodsUsing the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan–Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).ResultsA total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.ConclusionIn this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies

CUTS: Neural Causal Discovery from Irregular Time-Series Data

Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from unstructured time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.Comment: https://openreview.net/forum?id=UG8bQcD3Em

Adipose tissues of MPC1± mice display altered lipid metabolism-related enzyme expression levels

Mitochondrial pyruvate carrier 1 (MPC1) is a component of the MPC1/MPC2 heterodimer that facilitates the transport of pyruvate into mitochondria. Pyruvate plays a central role in carbohydrate, fatty, and amino acid catabolism. The present study examined epididymal white adipose tissue (eWAT) and intrascapular brown adipose tissue (iBAT) from MPC1± mice following 24 weeks of feeding, which indicated low energy accumulation as evidenced by low body and eWAT weight and adipocyte volume. To characterize molecular changes in energy metabolism, we analyzed the transcriptomes of the adipose tissues using RNA-Sequencing (RNA-Seq). The results showed that the fatty acid oxidation pathway was activated and several genes involved in this pathway were upregulated. Furthermore, qPCR and western blotting indicated that numerous genes and proteins that participate in lipolysis were also upregulated. Based on these findings, we propose that the energy deficiency caused by reduced MPC1 activity can be alleviated by activating the lipolytic pathway

Analyzing Dynamic Changes of Laboratory Indexes in Patients with Acute Heart Failure Based on Retrospective Study

Background. Changes of N-terminal probrain natriuretic peptide (NT-proBNP) have been studied whether in the long term or the short term in patients of acute heart failure (AHF); however, changes of NT-proBNP in the first five days and their association with other factors have not been investigated. Aims. To describe the dynamic changes of relevant laboratory indexes in the first five days between different outcomes of AHF patients and their associations. Methods and Results. 284 AHF with dynamic values recorded were analyzed. Changes of NT-proBNP, troponin T, and C-reactive protein were different between patients with different outcomes, with higher values in adverse group than in control group at the same time points ( < 0.05). Then, prognostic use and risk stratification of NT-proBNP were assessed by receiver-operating characteristic curve and logistic regression. NT-proBNP levels at day 3 showed the best prognostic power (area under the curve = 0.730, 95% confidence interval (CI): 0.657 to 0.794) and was an independent risk factor for adverse outcome (odds ratio, OR: 2.185, 95% CI: 1.584-3.015). Classified changes of NT-proBNP may be predictive for adverse outcomes in AHF patients. Conclusions. Sequential monitoring of laboratory indexes within the first 5 days may be helpful for management of AHF patients

Identification of novel urine proteomic biomarkers for high stamina in high-altitude adaptation

Introduction: We aimed to identify urine biomarkers for screening individuals with adaptability to high-altitude hypoxia with high stamina levels. Although most non-high-altitude natives experience rapid decline in physical ability when ascending to high altitudes, some individuals with high-altitude adaptability continue to maintain high endurance levels.Methods: We divided the study population into two groups: the LC group (low change in endurance from low to high altitude) and HC group (high change in endurance from low to high altitude). We performed blood biochemistry testing for individuals at high altitudes and sea level. We used urine peptidome profiling to compare the HH (high-altitude with high stamina) and HL (high-altitude with low stamina) groups and the LC and HC groups to identify urine biomarkers.Results: Routine blood tests revealed that the concentration of white blood cells, lymphocytes and platelets were significantly higher in the HH group than in the HL group. Urine peptidome profiling showed that the proteins ITIH1, PDCD1LG2, NME1-NME2, and CSPG4 were significantly differentially expressed between the HH and HL groups, which was tested using ELISA. Urine proteomic analysis showed that LRG1, NID1, VASN, GPX3, ACP2, and PRSS8 were urine proteomic biomarkers of high stamina during high-altitude adaptation.Conclusion: This study provides a novel approach for identifying potential biomarkers for screening individuals who can adapt to high altitudes with high stamina

Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease

Coronary heart disease (CHD) is top risk factor for health in modern society, causing high mortality rate each year. However, there is no reliable way for early diagnosis and prevention of CHD so far. So study the mechanism of CHD and development of novel biomarkers is urgently needed. In this study, metabolomics and metagenomics technology are applied to discover new biomarkers from plasma and urine of 59 CHD patients and 43 healthy controls and trace their origin. We identify GlcNAc-6-P which has good diagnostic capability and can be used as potential biomarkers for CHD, together with mannitol and 15 plasma cholines. These identified metabolites show significant correlations with clinical biochemical indexes. Meanwhile, GlcNAc-6-P and mannitol are potential metabolites originated from intestinal microbiota. Association analysis on species and function levels between intestinal microbes and metabolites suggest a close correlation between Clostridium sp. HGF2 and GlcNAc-6-P, Clostridium sp. HGF2, Streptococcus sp. M143, Streptococcus sp. M334 and mannitol. These suggest the metabolic abnormality is significant and gut microbiota dysbiosis happens in CHD patients

Molecular Evolution and Stress and Phytohormone Responsiveness of SUT Genes in Gossypium hirsutum

Sucrose transporters (SUTs) play key roles in allocating the translocation of assimilates from source to sink tissues. Although the characteristics and biological roles of SUTs have been intensively investigated in higher plants, this gene family has not been functionally characterized in cotton. In this study, we performed a comprehensive analysis of SUT genes in the tetraploid cotton Gossypium hirsutum. A total of 18 G. hirsutum SUT genes were identified and classified into three groups based on their evolutionary relationships. Up to eight SUT genes in G. hirsutum were placed in the dicot-specific SUT1 group, while four and six SUT genes were, respectively, clustered into SUT4 and SUT2 groups together with members from both dicot and monocot species. The G. hirsutum SUT genes within the same group displayed similar exon/intron characteristics, and homologous genes in G. hirsutum At and Dt subgenomes, G. arboreum, and G. raimondii exhibited one-to-one relationships. Additionally, the duplicated genes in the diploid and polyploid cotton species have evolved through purifying selection, suggesting the strong conservation of SUT loci in these species. Expression analysis in different tissues indicated that SUT genes might play significant roles in cotton fiber elongation. Moreover, analyses of cis-acting regulatory elements in promoter regions and expression profiling under different abiotic stress and exogenous phytohormone treatments implied that SUT genes, especially GhSUT6A/D, might participate in plant responses to diverse abiotic stresses and phytohormones. Our findings provide valuable information for future studies on the evolution and function of SUT genes in cotton

The gut microbiome in atherosclerotic cardiovascular disease

The gut microbiota may play a role in cardiovascular diseases. Here, the authors perform a metagenome-wide association study on stools from individuals with atherosclerotic cardiovascular disease and healthy controls, identifying microbial strains and functions associated with the disease

A Mitochondria-Dependent Pathway Mediates the Apoptosis of GSE-Induced Yeast

Grapefruit seed extract (GSE), which has powerful anti-fungal activity, can induce apoptosis in S. cerevisiae. The yeast cells underwent apoptosis as determined by testing for apoptotic markers of DNA cleavage and typical chromatin condensation by Terminal Deoxynucleotidyl Transferase–mediated dUTP Nick End Labeling (TUNEL) and 4,6′-diaminidino-2-phenylindole (DAPI) staining and electron microscopy. The changes of ΔΨmt (mitochondrial transmembrane potential) and ROS (reactive oxygen species) indicated that the mitochondria took part in the apoptotic process. Changes in this process detected by metabonomics and proteomics revealed that the yeast cells tenaciously resisted adversity. Proteins related to redox, cellular structure, membrane, energy and DNA repair were significantly increased. In this study, the relative changes in the levels of proteins and metabolites showed the tenacious resistance of yeast cells. However, GSE induced apoptosis in the yeast cells by destruction of the mitochondrial 60 S ribosomal protein, L14-A, and prevented the conversion of pantothenic acid to coenzyme A (CoA). The relationship between the proteins and metabolites was analyzed by orthogonal projections to latent structures (OPLS). We found that the changes of the metabolites and the protein changes had relevant consistency

Analysis of Blue Infrastructure Network Pattern in the Hanjiang Ecological Economic Zone in China

As a crucial part of urban development, blue infrastructure (BI) provides multiecosystem services. Using the Hanjiang Ecological Economic Zone as the study area, the potential benefits of a BI network were constructed using morphological spatial pattern analysis (MSPA) and minimum cumulative resistance model (MCR) for three periods in order to assess network structure. The main conclusions are: (1) The total BI area of the study location increased at first and then decreased from 2010 to 2020, during which the area of the core and loop was continually rising while the islet and bridge were gradually dropping. These results reveal that landscape fragmentation was well controlled; (2) Both the Integral Index of Connectivity(IIC) and Probability of Connectivity(PC) of the landscape showed an increasing trend, but the integral connectivity level was still low; (3) The comprehensive resistance value decreased gradually from west to east. The potential corridors were concentrated in the middle and lower reaches of the Hanjiang and extended upstream. The amount decreased first and then increased. (4) The structure of the BI network was simple first and then complex, which is in line with changes in the number of BI sources. Thus, changes in the BI network pattern are closely linked to the changes in the study area and the number of BI sources