22 research outputs found

    Prognostic Impact of Genetic Polymorphism in Mineralocorticoid Receptor and Comorbidity With Hypertension in Androgen-Deprivation Therapy

    Get PDF
    Mineralocorticoid receptor (MR) signaling which is closely associated with hypertension plays important roles in resistance to antiandrogen therapy in prostate cancer. However, its impact on the prognosis in androgen-deprivation therapy (ADT) has not been elucidated. Then, we investigated the impact of genetic variation in MR and comorbidity with hypertension on the prognosis in ADT. This study included 182 Japanese patients with prostate cancer treated with ADT, whose comorbidity status with hypertension were available. The associations of MR polymorphism (rs5522) and comorbidity with hypertension with clinicopathological parameters as well as progression-free survival and overall survival were examined. Clinicopathological characteristics were comparable between genetic variation in MR. However, homozygous variant in MR was associated with shorter time to castration resistance (P = 0.014) and any-cause death (P = 0.024). In patients' background, presence of comorbidity with hypertension showed the trend with lower PSA level at diagnosis and lower biopsy Gleason score, as well as significant association with less incidence of N1. Comorbidity with hypertension was associated with longer time to castration resistance (P = 0.043) and any-cause death (P = 0.046), which was diminished on multivariate analysis including age, PSA level at diagnosis, biopsy Gleason score, clinical stage, and the modality of hormonal therapy. Genetic variation in MR (rs5522) and comorbidity with hypertension were significantly and potentially associated with prognosis when treated with ADT, respectively. This suggests that the individual intensity of MR signaling may be associated with resistance to ADT and a promising biomarker in ADT

    Properties and roles of ion channels in urinary bladder smooth muscle cells

    No full text
    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Levcromakalim and MgGDP Activate Small Conductance ATP-Sensitive K +

    No full text

    Spatial Analysis of Slowly Oscillating Electric Activity in the Gut of Mice Using Low Impedance Arrayed Microelectrodes

    No full text
    <div><p>Smooth and elaborate gut motility is based on cellular cooperation, including smooth muscle, enteric neurons and special interstitial cells acting as pacemaker cells. Therefore, spatial characterization of electric activity in tissues containing these electric excitable cells is required for a precise understanding of gut motility. Furthermore, tools to evaluate spatial electric activity in a small area would be useful for the investigation of model animals. We thus employed a microelectrode array (MEA) system to simultaneously measure a set of 8×8 field potentials in a square area of ∼1 mm<sup>2</sup>. The size of each recording electrode was 50×50 µm<sup>2</sup>, however the surface area was increased by fixing platinum black particles. The impedance of microelectrode was sufficiently low to apply a high-pass filter of 0.1 Hz. Mapping of spectral power, and auto-correlation and cross-correlation parameters characterized the spatial properties of spontaneous electric activity in the ileum of wild-type (WT) and <i>W/W<sup>v</sup></i> mice, the latter serving as a model of impaired network of pacemaking interstitial cells. Namely, electric activities measured varied in both size and cooperativity in <i>W/W<sup>v</sup></i> mice, despite the small area. In the ileum of WT mice, procedures suppressing the excitability of smooth muscle and neurons altered the propagation of spontaneous electric activity, but had little change in the period of oscillations. In conclusion, MEA with low impedance electrodes enables to measure slowly oscillating electric activity, and is useful to evaluate both histological and functional changes in the spatio-temporal property of gut electric activity.</p></div

    Decitabine Inhibits Bone Resorption in Periodontitis by Upregulating Anti-Inflammatory Cytokines and Suppressing Osteoclastogenesis.

    No full text
    DNA methylation controls several inflammatory genes affecting bone homeostasis. Hitherto, inhibition of DNA methylation in vivo in the context of periodontitis and osteoclastogenesis has not been attempted. Ligature-induced periodontitis in C57BL/6J mice was induced by placing ligature for five days with Decitabine (5-aza-2'-deoxycytidine) (1 mg/kg/day) or vehicle treatment. We evaluated bone resorption, osteoclast differentiation by tartrate-resistant acid phosphatase (TRAP) and mRNA expression of anti-inflammatory molecules using cluster differentiation 14 positive (CD14+) monocytes from human peripheral blood. Our data showed that decitabine inhibited bone loss and osteoclast differentiation experimental periodontitis, and suppressed osteoclast CD14+ human monocytes; and conversely, that it increased bone mineralization in osteoblastic cell line MC3T3-E1 in a concentration-dependent manner. In addition to increasing IL10 (interleukin-10), TGFB (transforming growth factor beta-1) in CD14+ monocytes, decitabine upregulated KLF2 (Krüppel-like factor-2) expression. Overexpression of KLF2 protein enhanced the transcription of IL10 and TGFB. On the contrary, site-directed mutagenesis of KLF2 binding site in IL10 and TFGB abrogated luciferase activity in HEK293T cells. Decitabine reduces bone loss in a mouse model of periodontitis by inhibiting osteoclastogenesis through the upregulation of anti-inflammatory cytokines via KLF2 dependent mechanisms. DNA methyltransferase inhibitors merit further investigation as a possible novel therapy for periodontitis

    Cross-correlation analyses.

    No full text
    <p>Panel A shows a cross-correlation function constructed from field potential recordings at Ch28 and Ch52 in an ileal musculature of WT mice (the same preparation shown in Fig. 1E). Panels B and C show the distribution of the phase-shift (x-axis) and the amplitude (y-axis), respectively, of the peak in cross-correlation functions derived from all 64Ch MEA data, in the same ileal preparation shown in A. Ch28 was used as the base channel. Panels D–F show cross-correlation function, phase-shift map and cross-correlation map, respectively, in the presence of nifedipine and TTX in the same preparation shown in A–C. Panels G–I were constructed from MEA data recorded from an ileal musculature of <i>W/W<sup>v</sup></i> mice (the same preparation in Fig. 5D–F).</p

    Auto-correlation analyses.

    No full text
    <p>Panel A shows an auto-correlation function constructed from the field potential recording at Ch28 in an ileal musculature of WT mice (the same preparation shown in Fig. 1E and Fig. 4A). Panels B and C show the distribution of the period (x-axis) and amplitude (y-axis), respectively, estimated from the adjacent peak of autocorrelation function, in the same ileal preparation shown in A. Panels D–F show auto-correlation function, period map and auto-correlation map, respectively, in the ileal musculature of <i>W/W<sup>v</sup></i> mice (the same preparation in Fig. 1F and Fig. 4D). The period and amplitude of autocorrelation were set to 10 in the period map, and to 0 in the autocorrelation map, respectively, in recording channels in which no peak was observed (the peak was below nought) in the frequency range of 9.4–27.0 cpm.</p

    Procedures used in MEA analyses.

    No full text
    <p>8×8 array data of field potentials were normally thinned by a 1000-fold time domain, thereby the sampling interval was increased to 50 ms. 1) In power spectrum analysis, Fourier transformation was applied to each field potential recording (1024 points for ∼52 s), and the spectral power in the frequency between 9.4 to 27.0 cpm was plotted as a 2D pseudo-colour image (Power map). 2) Auto-correlation function was derived from each field potential recording (1024 points) of 8×8 array data, after digital band-pass filtering (DBPF) of 0.05–0.5 Hz. The shift to the next peak was used as a period of spontaneous electric activity. 3) Cross-correlation function was derived from each field potential recording (1024 points) of 8×8 array data using Ch28 as a base channel, after DBPF of 0.05–0.5 Hz. The shifts (time-lags) and cross-correlation values of the peak in cross-correlation curves were plotted as 2D pseudo-colour images (Phase-shift map and Cross-correlation map, respectively).</p
    corecore