Urine Metabolites as a Predictor of Warfarin Response Based on INR in Atrial Fibrillation.

BACKGROUND: Warfarin is an anticoagulant with wide inter-individual variations in drug responses monitored based on the International Normalized Ratio (INR). It is commonly prescribed for Atrial Fibrillation (AF) and stroke. Oral anticoagulants (e.g., warfarin) reduce the risk of getting a stroke but increase the risk of hemorrhage. The proton Nuclear Magnetic Resonance (1H-NMR) pharmacometabonomics technique is useful for determining drug responses. Furthermore, pharmacometabonomics analysis can help identify novel biomarkers of warfarin outcome/ INR stability in urine. OBJECTIVES: The focus of this research was to determine if urine metabolites could predict the warfarin response based on INR in patients who were already taking warfarin (identification; phase I) and to determine if urine metabolites could distinguish between unstable and stable INR in patients who had just started taking warfarin (validation; phase II). METHODS: A cross-sectional study was conducted. Ninety urine samples were collected for phase 1, with 49 having unstable INR and 41 having stable INR. In phase II, 21 urine samples were obtained, with 13 having an unstable INR and eight having a stable INR. The metabolites associated with unstable INR and stable INR could be determined using univariate and multivariate logistic regression analysis. RESULTS: Multivariate Logistic Regression (MVLR) analysis showed that unstable INR was linked with seven regions. DISCUSSION: The urine pharmacometabonomics technique utilized could differentiate between the urine metabolite profiles of the patients on warfarin for INR stability. CONCLUSION: 1H-NMR-based pharmacometabonomics can help lead to a more individualized, controlled side effect for warfarin, thus minimizing undesirable effects in the future.

Plasma Metabolites as Predictors of Warfarin Outcome in Atrial Fibrillation.

BACKGROUND: Warfarin is prescribed as an oral anticoagulant to treat/prevent thromboembolism in conditions such as atrial fibrillation. As there is a narrow therapeutic window, treatment with warfarin is challenging. Pharmacometabonomics using nuclear magnetic resonance (NMR) spectroscopy may provide novel techniques for the identification of novel biomarkers of warfarin. PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation). EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers. KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%. CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.

Bemiparin as a Prophylaxis After an Unexplained Stillbirth: Open-Label Interventional Prospective Study.

Stillbirth is a devastating event to the parents, relatives, friends, and families. The role of anticoagulants in the prevention of unexplained stillbirths is uncertain. An open-label interventional prospective cohort study was conducted on 144 women with a history of unexplained stillbirths. The intervention group had a high umbilical artery resistance index (RI) and received bemiparin. The nonintervention group had a normal RI and did not receive any intervention. We measured the adjusted odds ratio (OR) and 95% confidence interval (CI) of the main outcome for these variables using logistic regression analysis. Fresh stillbirth and early neonatal death rates were lower (P = .005, OR = 11.949 and 95% CI = 2.099-68.014) and newborn weight was higher (P = .015, OR = 0.048, 95% CI = 0.004-0.549) in the group that received bemiparin. Bemiparin is effective in decreasing the rate of stillbirth in women with a history of previous unexplained stillbirths.

Systems responses to progressive water stress in durum wheat.

Durum wheat is susceptible to terminal drought which can greatly decrease grain yield. Breeding to improve crop yield is hampered by inadequate knowledge of how the physiological and metabolic changes caused by drought are related to gene expression. To gain better insight into mechanisms defining resistance to water stress we studied the physiological and transcriptome responses of three durum breeding lines varying for yield stability under drought. Parents of a mapping population (Lahn x Cham1) and a recombinant inbred line (RIL2219) showed lowered flag leaf relative water content, water potential and photosynthesis when subjected to controlled water stress time transient experiments over a six-day period. RIL2219 lost less water and showed constitutively higher stomatal conductance, photosynthesis, transpiration, abscisic acid content and enhanced osmotic adjustment at equivalent leaf water compared to parents, thus defining a physiological strategy for high yield stability under water stress. Parallel analysis of the flag leaf transcriptome under stress uncovered global trends of early changes in regulatory pathways, reconfiguration of primary and secondary metabolism and lowered expression of transcripts in photosynthesis in all three lines. Differences in the number of genes, magnitude and profile of their expression response were also established amongst the lines with a high number belonging to regulatory pathways. In addition, we documented a large number of genes showing constitutive differences in leaf transcript expression between the genotypes at control non-stress conditions. Principal Coordinates Analysis uncovered a high level of structure in the transcriptome response to water stress in each wheat line suggesting genome-wide co-ordination of transcription. Utilising a systems-based approach of analysing the integrated wheat's response to water stress, in terms of biological robustness theory, the findings suggest that each durum line transcriptome responded to water stress in a genome-specific manner which contributes to an overall different strategy of resistance to water stress.

Training neural networks with heterogeneous data.

Data pruning and ordered training are two methods and the results of a small theory that attempts to formalize neural network training with heterogeneous data. Data pruning is a simple process that attempts to remove noisy data. Ordered training is a more complex method that partitions the data into a number of categories and assigns training times to those assuming that data size and training time have a polynomial relation. Both methods derive from a set of premises that form the 'axiomatic' basis of our theory. Both methods have been applied to a time-delay neural network-which is one of the main learners in Microsoft's Tablet PC handwriting recognition system. Their effect is presented in this paper along with a rough estimate of their effect on the overall multi-learner system. The handwriting data and the chosen language are Italian.