Free-breathing gradient recalled echo-based CMR in a swine heart failure model.

In swine models, there are well-established protocols for creating a closed-chest myocardial infarction (MI) as well as protocols for characterization of cardiac function with cardiac magnetic resonance (CMR). This methods manuscript outlines a novel technique in CMR data acquisition utilizing smart-signal gradient recalled echo (GRE)-based array sequences in a free-breathing swine heart failure model allowing for both high spatial and temporal resolution imaging. Nine male Yucatan mini swine weighing 48.7 ± 1.6 kg at 58.2 ± 3.1 weeks old underwent the outlined imaging protocol before and 1-month after undergoing closed chest left anterior descending coronary artery (LAD) occlusion/reperfusion. The left ventricular ejection fraction (LVEF) at baseline was 59.3 ± 2.4% and decreased to 48.1 ± 3.7% 1-month post MI (P = 0.029). The average end-diastolic volume (EDV) at baseline was 55.2 ± 1.7 ml and increased to 74.2 ± 4.2 ml at 1-month post MI (P = 0.001). The resulting images from this novel technique and post-imaging analysis are presented and discussed. In a Yucatan swine model of heart failure via closed chest left anterior descending coronary artery (LAD) occlusion/reperfusion, we found that CMR with GRE-based array sequences produced clinical-grade images with high spatial and temporal resolution in the free-breathing setting.

Early developmental electroencephalography abnormalities, neonatal seizures, and induced spasms in a mouse model of tuberous sclerosis complex.

OBJECTIVE: Tuberous sclerosis complex (TSC) is one of the most common genetic causes of epilepsy. Seizures in TSC typically first present in infancy or early childhood, including focal seizures and infantile spasms. Infantile spasms in TSC are particularly characteristic in its strong responsiveness to vigabatrin. Although a number of mouse models of epilepsy in TSC have been described, there are very limited electroencephalographic (EEG) or seizure data during the preweanling neonatal and infantile-equivalent mouse periods. Tsc1GFAP CKO mice are a well-characterized mouse model of epilepsy in TSC, but whether these mice have seizures during early development has not been documented. The objective of this study was to determine whether preweanling Tsc1GFAP CKO mice have developmental EEG abnormalities or seizures, including spasms. METHODS: Longitudinal video-EEG and electromyographic recordings were performed serially on Tsc1GFAP CKO and control mice from postnatal days 9-21 and analyzed for EEG background abnormalities, sleep-wake vigilance states, and spontaneous seizures. Spasms were also induced with varying doses of N-methyl-D-aspartate (NMDA). RESULTS: The interictal EEG of Tsc1GFAP CKO mice had excessive discontinuity and slowing, suggesting a delayed developmental progression compared with control mice. Tsc1GFAP CKO mice also had increased vigilance state transitions and fragmentation. Tsc1GFAP CKO mice had spontaneous focal seizures in the early neonatal period and a reduced threshold for NMDA-induced spasms, but no spontaneous spasms were observed. SIGNIFICANCE: Neonatal Tsc1GFAP CKO mice recapitulate early developmental aspects of EEG abnormalities, focal seizures, and an increased propensity for spasms. This mouse model may be useful for early mechanistic and therapeutic studies of epileptogenesis in TSC.

Neurofibromatosis type 1 (Nf1)-mutant mice exhibit increased sleep fragmentation.

Neurofibromatosis type 1 (NF1) is a neurodevelopmental disorder in which affected children and adults are at a higher risk of sleep disorders. In an effort to identify potential sleep disturbances in a small animal model, we used a previously reported Nf1 conditional knockout (Nf1CKO ) mouse strain. In contrast to Nf1 mutant flies, the distribution of vigilance states was intact in Nf1CKO mice. However, Nf1CKO mice exhibited increased non-REM sleep (NREM)-to-wake and wake-to-NREM transitions. This sleep disruption was accompanied by decreased bout durations during awake and NREM sleep states under both light and dark conditions. Moreover, Nf1CKO mice have higher percentage delta power during awake and NREM sleep states under all light conditions. Taken together, Nf1CKO mice phenocopy some of the sleep disturbances observed in NF1 patients and provide a tractable platform to explore the molecular mechanisms governing sleep abnormalities in NF1.

Minimizing thermal degradation in gas chromatographic quantitation of pentaerythritol tetranitrate.

An analytical method for establishing calibration curves for the quantitation of pentaerythriol tetranitrate (PETN) from sorbent-filled thermal desorption tubes by gas chromatography with electron capture detection (TDS-GC-ECD) was developed. As PETN has been demonstrated to thermally degrade under typical GC instrument conditions, peaks corresponding to both PETN degradants and molecular PETN are observed. The retention time corresponding to intact PETN was verified by high-resolution mass spectrometry with a flowing atmospheric pressure afterglow (FAPA) ionization source, which enabled soft ionization of intact PETN eluting the GC and subsequent accurate-mass identification. The GC separation parameters were transferred to a conventional GC-ECD instrument where analytical method-induced PETN degradation was further characterized and minimized. A method calibration curve was established by direct liquid deposition of PETN standard solutions onto the glass frit at the head of sorbent-filled thermal desorption tubes. Two local, linear relationships between detector response and PETN concentration were observed, with a total dynamic range of 0.25-25ng.

PTP1B suppresses prolactin activation of Stat5 in breast cancer cells.

Basal levels of nuclear localized, tyrosine phosphorylated Stat5 are present in healthy human breast epithelia. In contrast, Stat5 phosphorylation is frequently lost during breast cancer progression, a finding that correlates with loss of histological differentiation and poor patient prognosis. Identifying the mechanisms underlying loss of Stat5 phosphorylation could provide novel targets for breast cancer therapy. Pervanadate, a general tyrosine phosphatase inhibitor, revealed marked phosphatase regulation of Stat5 activity in breast cancer cells. Lentiviral-mediated shRNA allowed specific examination of the regulatory role of five tyrosine phosphatases (PTP1B, TC-PTP, SHP1, SHP2, and VHR), previously implicated in Stat5 regulation in various systems. Enhanced and sustained prolactin-induced Stat5 tyrosine phosphorylation was observed in T47D and MCF7 breast cancer cells selectively in response to PTP1B depletion. Conversely, PTP1B overexpression suppressed prolactin-induced Stat5 tyrosine phosphorylation. Furthermore, PTP1B knockdown increased Stat5 reporter gene activity. Mechanistically, PTP1B suppression of Stat5 phosphorylation was mediated, at least in part, through inhibitory dephosphorylation of the Stat5 tyrosine kinase, Jak2. PTP1B knockdown enhanced sensitivity of T47D cells to prolactin phosphorylation of Stat5 by reducing the EC(50) from 7.2 nmol/L to 2.5 nmol/L. Immunohistochemical analyses of two independent clinical breast cancer materials revealed significant negative correlations between levels of active Stat5 and PTP1B, but not TC-PTP. Collectively, our data implicate PTP1B as an important negative regulator of Stat5 phosphorylation in invasive breast cancer.

Prolactin inhibits BCL6 expression in breast cancer through a Stat5a-dependent mechanism.

BCL6 is a transcriptional repressor that recognizes DNA target sequences similar to those recognized by signal transducer and activator of transcriptions 5 (Stat5). BCL6 disrupts differentiation of breast epithelia, is downregulated during lactation, and is upregulated in poorly differentiated breast cancer. In contrast, Stat5a mediates prolactin-induced differentiation of mammary epithelia, and loss of Stat5 signaling in human breast cancer is associated with undifferentiated histology and poor prognosis. Here, we identify the mammary cell growth factor prolactin as a potent suppressor of BCL6 protein expression in human breast cancer through a mechanism that requires Stat5a, but not prolactin-activated Stat5b, MEK-ERK, or PI3K-AKT pathways. Prolactin rapidly suppressed BCL6 mRNA in T47D, MCF7, ZR75.1, and SKBr3 breast cancer cell lines, followed by prolonged reduction of BCL6 protein levels within 3 hours. Prolactin suppression of BCL6 was enhanced by overexpression of Stat5a but not Stat5b, was mimicked by constitutively active Stat5a, but did not require the transactivation domain of Stat5a. Stat5 chromatin immunoprecipitation demonstrated physical interaction with a BCL6 gene regulatory region, and BCL6 transcript repression required histone deacetylase activity based on sensitivity to trichostatin A. Functionally, BCL6 overexpression disrupted prolactin induction of Stat5 reporter genes. Prolactin suppression of BCL6 was extended to xenotransplant tumors in nude mice in vivo and to freshly isolated human breast cancer explants ex vivo. Quantitative immunohistochemistry revealed elevated BCL6 in high-grade and metastatic breast cancer compared with ductal carcinoma in situ and nonmalignant breast, and cellular BCL6 protein levels correlated negatively with nuclear Stat5a (r = -0.52; P < 0.001) but not with Stat5b. Loss of prolactin-Stat5a signaling and concomitant upregulation of BCL6 may represent a regulatory switch facilitating undifferentiated histology and poor prognosis of breast cancer.

Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis.

A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis.

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel profiles obtained using gas chromatography (GC). Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current chemometric techniques to correctly model information that shifts from variable to variable within a dataset. The alignment algorithm developed is shown to increase the efficacy of pattern recognition methods applied to diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retention time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical. Two sets of diesel fuel gas chromatograms were studied using the novel alignment algorithm followed by principal component analysis (PCA). In the first study, retention times for corresponding chromatographic peaks in 60 chromatograms varied by as much as 300 ms between chromatograms before alignment. In the second study of 42 chromatograms, the retention time shifting exhibited was on the order of 10 s between corresponding chromatographic peaks, and required a coarse retention time correction prior to alignment with the algorithm. In both cases, an increase in retention time precision afforded by the algorithm was clearly visible in plots of overlaid chromatograms before and then after applying the retention time alignment algorithm. Using the alignment algorithm, the standard deviation for corresponding peak retention times following alignment was 17 ms throughout a given chromatogram, corresponding to a relative standard deviation of 0.003% at an average retention time of 8 min. This level of retention time precision is a 5-fold improvement over the retention time precision initially provided by a state-of-the-art GC instrument equipped with electronic pressure control and was critical to the performance of the chemometric analysis. This increase in retention time precision does not come at the expense of chemical selectivity, since the PCA results suggest that essentially all of the chemical selectivity is preserved. Cluster resolution between dissimilar groups of diesel fuel chromatograms in a two-dimensional scores space generated with PCA is shown to substantially increase after alignment. The alignment method is robust against missing or extra peaks relative to a target chromatogram used in the alignment, and operates at high speed, requiring roughly 1 s of computation time per GC chromatogram.

Comprehensive two-dimensional gas chromatography of volatile and semi-volatile components using a diaphragm valve-based instrument.

A high-temperature configuration for a diaphragm valve-based gas chromatography (GCXGC) instrument is demonstrated. GCxGC is a powerful instrumental tool often used to analyze complex mixtures. Previously, the temperature limitations of valve-based GCxGC instruments were set by the maximum operating temperature of the valve, typically 175 degrees C. Thus, valve-based GCxGC was constrained to the analysis of mainly volatile components; however, many complex mixtures contain semi-volatile components as well. A new configuration is described that extends the working temperature range of diaphragm valve-based GCxGC instruments to significantly higher temperatures, so both volatile and semi-volatile compounds can be readily separated. In the current investigation, separations at temperatures up to 250 degrees C are demonstrated. This new design features both chromatographic columns in the same oven with the valve interfacing the two columns mounted in the side of the oven wall so the valve is both partially inside as well as outside the oven. The diaphragm and the sample ports in the valve are located inside the oven while the temperature-restrictive portion of the valve (containing the O-rings) is outside the oven. Temperature measurements on the surface of the valve indicate that even after a sustained oven temperature of 240 degrees C, the portions of the valve directly involved with the sampling from the first column to the second column track the oven temperature to within 1.2% while the portions of the valve that are temperature-restrictive remain well below the maximum temperature of 175 degrees C. A 26-component mixture of alkanes, ketones, and alcohols whose boiling points range from 65 degrees C (n-hexane) to 270 degrees C (n-pentadecane) is used to test the new design. Peak shapes along the first column axis suggest that sample condensation or carry-over in the valve is not a problem. Chemometric data analysis is performed to demonstrate that the resulting data have a bilinear structure. After over 6 months of use and temperature conditions up to 265 degrees C, no deterioration of the valve or its performance has been observed.