The nephrotoxicity of a number of aminophenols, quinols and catechols has been assessed from the extent of necrosis of proximal convoluted tubules produced by intravenous injection in rats, and the toxicity correlated with the oxidation-reduction potentials of the compounds.
4044 Background: To aid in decisions regarding adjuvant therapy (AT) for resected high-risk CC, two prog models are in common use: the Mayo Clinic NUM calculator developed from a pooled data analysis of 7 randomized 5FU-based AT trials, and ADJ! developed using SEER data. This study examines the accuracy of NUM and ADJ! utilizing a cohort of patients (pts) referred to the BC Cancer Agency (BCCA). Methods: Demographic, disease and treatment data for pts with stage II/III CC referred to the BCCA from 1995–1996 + 1999–2003 were collected. Observed (obs) 5-year relapse free survival (RFS) and overall survival (OS) were compared to predicted estimates (pred) using NUM and ADJ!, both overall and for all prog subgroups with ≥ 10pts, as stratified by T stage, N stage, tumor grade and age. Data are presented in a descriptive manner and using confidence intervals. Results: Median follow-up was 5.6 yrs for 2,033 pts - 53% male, median age 68y, 40% N0. The mean percentages of 5 year pred outcomes for each of the two models and the actual Kaplan Meier mean survivals are presented in the table . The percentage correct predictions of 5 y status is also presented, with correctness deemed accurate if the pt was alive and predicted to be alive by ≥ 50% as determined by each model or dead while the respective tool predicted < 50% possibility of being alive. For surgery alone pts, ADJ!pred were more often closer to what was observed, as compared to NUMpred, in the prog subgroups (for RFS 56%, OS 88%). For surgery + 5-FU pts, within these subgroups, NUMpred were more often closer to what was observed, as compared to ADJ!pred, for RFS (62%) and for OS (55%). Conclusions: In this independent population-based validation, NUM and ADJ! have acceptable and similar reliability with modest over-estimations of 5y RFS and OS. Both models thus appear to be useful adjuvant decision-aids. [Table: see text] No significant financial relationships to disclose.
Purpose: To quantify in vivo GABA concentration levels using a novel parametric proton magnetic resonance signal quantification algorithm called the Fast Pade Transform. Methods: Single voxel MEGA‐PRESS data were collected on a Siemens 3T Tim Trio system using 12‐channel phased‐array head coil (TE=68 ms, TR=2000 ms, Voxel size=30×30×30 mm3, Vector size=1024, NEX=64). Both water reference and water suppressed spectra were obtained. A total of three phantoms (containing NAA, Cr, Cho, GABA, and Lac) were created with different GABA concentration levels of 1 mMol, 2 mMol and 3 mMol. In vivo data were collected from the anterior cingulate cortex region in 5 human volunteers. The fast Pade transform algorithm was implemented in MATLAB. Spectral data were processed with this FPT program to obtain parametric information such as frequency, line‐width, phase and amplitude. GABA concentration levels were calculated using the ratio of metabolite and water amplitude information, since signal amplitudes are directly proportional to the amount of protons present in the acquisition volume. Results: The results suggest that the Fast Pade Transform algorithm measured GABA concentrations in phantoms are very close to the actual known values. And this method is capable of quantifying in vivo MEGA‐PRESS proton spectra in 5 human brain data with a mean measurement of 2.01 mMol, which agrees with literature reported human brain in vivo GABA concentration level. Conclusion: This work demonstrates the potential of the fast Pade transform technique to quantify the GABA C‐4 doublet resonance signals with the MEGA‐PRESS acquisition. We plan to test this method with more in vivo data as well as with other MRS acquisition techniques in the future. NIH funding – R01MH092301
Background
Although all women who live into late life experience reproductive aging (menopause), there is little understanding about how reproductive aging, as distinct from chronologic aging, influences the development of AD. Studies indicate that some AD biomarkers differ between peri‐ and postmenopausal women, but no studies to date have examined AD biomarkers across (sub)stages of the postmenopause. Here we compared plasma Ab42/40 levels in Early versus Late Postmenopausal women. We hypothesized that plasma levels of Ab42/40 would be lower in the Late versus Early Postmenopause group.
Method
Participants included women from the Human Connectome Aging Project who had plasma Ab42/40 and were either in the Early Postmenopause (i.e., Stages +1a, +1b or +1c; >0 and <6 years after the final menstrual period; FMP) or Late Postmenopause (Stage +2; > 6 years after the FMP) according to the Stages of Reproductive Aging Workshop +10 criteria. Plasma Aβ42/40 was measured using an immunoprecipitation and liquid chromatography‐mass spectrometry assay. A univariate analysis of variance was conducted to compare plasma Ab42/40 in Early versus Late Postmenopause groups controlling for age, education, MOCA score, and ApoE carrier status.
Result
The sample included 42 women (mean age=66.2 years; mean MoCA=25.88; 73.8% white). Plasma Ab42/40 were significantly higher in the Early (n=10; mean age=55.77) versus Late Postmenopause (n=32; mean age=69.44) groups with mean values of 0.11 (SE=.002) and 0.103 (SE=.001), respectively, F(1,28)=7.23, p=.01. We restricted a second analysis to women age 60 years and under to optimally control for age differences between the groups (n=18, mean age=56.56 years; mean MOCA=26.04; mean education=17.17 years; 56% white, Early n=10). Again, plasma Ab42/40 were significantly higher in the Early (n=10; mean age=55.77) versus Late Postmenopause (n=32; mean age=57.56) groups with mean values of 0.11 (SE=.002) and 0.105 (SE=.003), respectively, F(1,13)=6.52, p =.025.
Conclusion
Advanced reproductive stage, independent of chronologic age, may be associated with a lower plasma Ab42/40, a pattern shown to be predictive of the development of AD. These findings add to a body of AD biomarker studies showing differences between perimenopausal and postmenopausal women, and suggest that AD biomarkers also differ within distinct stages of the postmenopause.
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