Glutathione species and metabolomic prints in subjects with liver disease as biological markers for the detection of hepatocellular carcinoma.

BACKGROUND: The incidence of liver disease is increasing in USA. Animal models had shown glutathione species in plasma reflects liver glutathione state and it could be a surrogate for the detection of hepatocellular carcinoma (HCC). METHODS: The present study aimed to translate methods to the human and to explore the role of glutathione/metabolic prints in the progression of liver dysfunction and in the detection of HCC. Treated plasma from healthy subjects (n = 20), patients with liver disease (ESLD, n = 99) and patients after transplantation (LTx, n = 7) were analyzed by GC- or LC/MS. Glutathione labeling profile was measured by isotopomer analyzes of 2H2O enriched plasma. Principal Component Analyzes (PCA) were used to determined metabolic prints. RESULTS: There was a significant difference in glutathione/metabolic profiles from patients with ESLD vs healthy subjects and patients after LTx. Similar significant differences were noted on patients with ESLD when stratified by the MELD score. PCA analyses showed myristic acid, citric acid, succinic acid, l-methionine, d-threitol, fumaric acid, pipecolic acid, isoleucine, hydroxy-butyrate and glycolic, steraric and hexanoic acids were discriminative metabolites for ESLD-HCC+ vs ESLD-HCC- subject status. CONCLUSIONS: Glutathione species and metabolic prints defined liver disease severity and may serve as surrogate for the detection of HCC in patients with established cirrhosis.

Modeling acute care utilization: practical implications for insomnia patients.

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.

Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange.

This study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011-2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: -1 to -365 days and -180 to -365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011-2013, 2011-2015, and 2011-2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011-2017 data from -1 to -365 and -180 to -365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.

[Correlation and path analysis of artemisininum content and related factor on Artemisiae annie in Three Gorges reservoir area].

OBJECTIVE: Study on the correlation and path analysis of artemisininum comtent and related factor on Artemisiae annie. In order to obtain high artemisininum content Artemisiae annie. METHODS: On the natural condition, choose 36 area where Artemisiae annie growth in three gorges reservoir area. The related factor on artemisininum content are studies through correlation and path analysis. RESULTS: The artemisininum content had significant correlations with biomass, over cover degree, K, P, and N in soil. P in soil had the nost positive influence on the artemisininum content with the direct path coefficinent 0.3439, over cover degree had the nost negative in fluence on the artemisininum content with the direct path coefficient -0.1421. The influence order of other factor was N in soil (0.3180), K in soil (0.2352), biomass ( -0.0084), and plant height (-0.0347). CONCLUSION: Artemisininum content in Artemisiae annie leaves are correlated with biomass, over cover degree, K in soil, P in soil, N in soil.

Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.

BACKGROUND: GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease. RESULTS: Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype. CONCLUSIONS: Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery. REVIEWERS: This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.