Targeted and untargeted serum NMR metabolomics to reveal initial kidney disease in diabetes mellitus.

Serum H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe.

Using nuclear magnetic resonance urine metabolomics to develop a prediction model of early stages of renal disease in subjects with type 2 diabetes.

Type 2 diabetes mellitus (DM2) is a multimorbidity, long-term condition, and one of the worldwide leading causes of chronic kidney disease (CKD) -a silent disease, usually detected when non-reversible renal damage have already occurred. New strategies and more effective laboratory methods are needed for more opportune diagnosis of DM2-CKD. This study comprises clinical parameters and nuclear magnetic resonance (NMR)-based urine metabolomics data from 60 individuals (20-65 years old, 67.

Metabolomic and biochemical characterization of a new model of the transition of acute kidney injury to chronic kidney disease induced by folic acid.

Background: Renal diseases represent a major public health problem. The demonstration that maladaptive repair of acute kidney injury (AKI) can lead to the development of chronic kidney disease (CKD) and end-stage renal disease has generated interest in studying the pathophysiological pathways involved. Animal models of AKI-CKD transition represent important tools to study this pathology.

Silicon in 'pure gold'? Theoretical contributions and observations on teleanalysis by videoconference.

Ideas about psychoanalysis via videoconference-videoconference teleanalysis (VT)-are presented with the general understanding that these settings produce a twofold split between various degrees of recognition/negativisation of the absence of the other, on one hand, and the expectation of physical co-presence, on the other. This split has been put forward as dismantling the here, now, with me pre-reflexive unity of the analytic experience. This article suggests that both members of the analytic dyad will seek to reappropriate the experience through a forced ego integration that interferes with accessing states of unintegration in the analytic treatment and produces subtle alterations to symbolisation work.

Multi-wavelength high-performance liquid chromatographic fingerprints and chemometrics to predict the antioxidant activity of Turnera diffusa as part of its quality control.

The determination of the antioxidant activity of Turnera diffusa using partial least squares regression (PLSR) on chromatographic data is presented. The chromatograms were recorded with a diode array detector and, for each sample, an enhanced fingerprint was constructed by compiling into a single data vector the chromatograms at four wavelengths (216, 238, 254 and 345 nm). The wavelengths were selected from a contour plot, in order to obtain the greater number of peaks at each of the wavelengths.

Geometric orientation by humans: angles weigh in.

Human participants were trained to navigate to two geometrically equivalent corners of a parallelogram-shaped virtual environment. The unique shape of the environment combined three distinct types of geometric information that could be used in combination or in isolation to orient and locate the goals: the angular amplitudes of the corners, the relative wall length relationships, and the principal axis of symmetry. In testing, participants were placed in manipulated versions of the training environment that tested which types of geometry they had encoded and how angular information weighed in against the other two geometric properties.

Enhanced chromatographic fingerprinting of herb materials by multi-wavelength selection and chemometrics.

A strategy for multi-wavelength chromatographic fingerprinting of herbal materials, using high performance liquid chromatography with a UV-Vis diode array detector is presented. Valeriana officinalis was selected to show the proposed methodology since it is a widely used commercially available herbal drug, and because misfit with other valerian species is a current issue. The enhanced fingerprints were constructed by compiling into a single data vector the chromatograms from four wavelengths (226, 254, 280 and 326 nm), at which characteristic chemical constituents of studied herbs presented maximum absorbance.