Examining the U-shaped relationship of sleep duration and systolic blood pressure with risk of cardiovascular events using a novel recursive gradient scanning model

BackgroundObservational studies have suggested U-shaped relationships between sleep duration and systolic blood pressure (SBP) with risks of many cardiovascular diseases (CVDs), but the cut-points that separate high-risk and low-risk groups have not been confirmed. We aimed to examine the U-shaped relationships between sleep duration, SBP, and risks of CVDs and confirm the optimal cut-points for sleep duration and SBP.MethodsA retrospective analysis was conducted on NHANES 2007–2016 data, which included a nationally representative sample of participants. The maximum equal-odds ratio (OR) method was implemented to obtain optimal cut-points for each continuous independent variable. Then, a novel “recursive gradient scanning method” was introduced for discretizing multiple non-monotonic U-shaped independent variables. Finally, a multivariable logistic regression model was constructed to predict critical risk factors associated with CVDs after adjusting for potential confounders.ResultsA total of 26,691 participants (48.66% were male) were eligible for the current study with an average age of 49.43 ± 17.69 years. After adjusting for covariates, compared with an intermediate range of sleep duration (6.5–8.0 h per day) and SBP (95–120 mmHg), upper or lower values were associated with a higher risk of CVDs [adjusted OR (95% confidence interval) was 1.20 (1.04–1.40) for sleep duration and 1.17 (1.01–1.36) for SBP].ConclusionsThis study indicates U-shaped relationships between SBP, sleep duration, and risks of CVDs. Both short and long duration of sleep/higher and lower BP are predictors of cardiovascular outcomes. Estimated total sleep duration of 6.5–8.0 h per day/SBP of 95–120 mmHg is associated with lower risk of CVDs

Comparing gene discovery from Affymetrix GeneChip microarrays and Clontech PCR-select cDNA subtraction: a case study

BACKGROUND: Several high throughput technologies have been employed to identify differentially regulated genes that may be molecular targets for drug discovery. Here we compared the sets of differentially regulated genes discovered using two experimental approaches: a subtracted suppressive hybridization (SSH) cDNA library methodology and Affymetrix GeneChip(® )technology. In this "case study" we explored the transcriptional pattern changes during the in vitro differentiation of human monocytes to myeloid dendritic cells (DC), and evaluated the potential for novel gene discovery using the SSH methodology. RESULTS: The same RNA samples isolated from peripheral blood monocyte precursors and immature DC (iDC) were used for GeneChip microarray probing and SSH cDNA library construction. 10,000 clones from each of the two-way SSH libraries (iDC-monocytes and monocytes-iDC) were picked for sequencing. About 2000 transcripts were identified for each library from 8000 successful sequences. Only 70% to 75% of these transcripts were represented on the U95 series GeneChip microarrays, implying that 25% to 30% of these transcripts might not have been identified in a study based only on GeneChip microarrays. In addition, about 10% of these transcripts appeared to be "novel", although these have not yet been closely examined. Among the transcripts that are also represented on the chips, about a third were concordantly discovered as differentially regulated between iDC and monocytes by GeneChip microarray transcript profiling. The remaining two thirds were either not inferred as differentially regulated from GeneChip microarray data, or were called differentially regulated but in the opposite direction. This underscores the importance both of generating reciprocal pairs of SSH libraries, and of real-time RT-PCR confirmation of the results. CONCLUSIONS: This study suggests that SSH could be used as an alternative and complementary transcript profiling tool to GeneChip microarrays, especially in identifying novel genes and transcripts of low abundance

Contributions to classification and calibration with high-dimensional data

Statistical classification and calibration with high-dimensional data are studied. We have proposed new classification and calibration procedures for high-dimensional data and have established dimensional consistency for certain high-dimensional classification and calibration procedures. Strong dimensional consistency are obtained in certain cases.By treating data as discretization of random functions, we have proposed smoothing based classification and calibration procedures. We have established consistency and strong consistency for those new procedures and have shown that smoothing based procedures improve the performance when random time-shift exists.U of I OnlyETDs are only available to UIUC Users without author permissio

Development of Self-Healing d‑Gluconic Acetal-Based Supramolecular Ionogels for Potential Use as Smart Quasisolid Electrochemical Materials

Formation of supramolecular ionic liquid (IL) gels (ionogels) induced by low-molecular-mass gelators (LMMGs) is an efficient strategy to confine ILs, and the negligible influence of LMMGs on the electrochemical properties of ILs makes ionogels ideal quasisolid electrochemical materials. Furthermore, the stimuli-responsive and self-healing characters of the supramolecular gel can be utilized for the potential development of smart electrochemical materials. However, the poor mechanical properties of supramolecular ionogels reported so far limit their practical applications. Herein, we investigated a series of efficient d-gluconic acetal-based gelators (Gn, PG16, and B8) that can harden a wide variety of ILs at low concentrations. It was shown that both alkyl chain length and the number of hydrogen bonding sites of a certain gelator, as well as the nature of the IL anion, significantly influenced the gelation abilities. The resulting ionogels were thermally reversible, and most of them were stable at room temperature. Interestingly, a PG16-based supramolecular ionogel showed rapid self-healing properties upon mechanical damage. Furthermore, the PG16-based ionogel demonstrated unprecedented performances including the favorable ionic conductivity, excellent mechanical strength, and enhanced viscoelasticity, which make it a great self-healing electrochemical material. The ionogel formation mechanism was proposed based on the analysis of Fourier transform infrared, ¹HNMR, and X-ray diffraction, indicating that a combination of hydrogen bonding, π–π stacking, and interactions between alkyl chains was responsible for the self-assembly of gelators in ILs. Overall, our present studies on exploring the structure–property relationship of gelators for the formation of practically useful supramolecular ionogels shed light for future development of more functionalized ionogels

Using arterial–venous analysis to characterize cancer metabolic consumption in patients

Cellular metabolism is altered in many cancer types and the advent of metabolomics has allowed us to understand more about how this is dysregulated. Here, the authors report a method named CARVE to analyse the arterial supply and venous drainage of glioma patients during surgery and identify the metabolites that may be consumed and produced by the cancer