17 research outputs found
Shortening of atrioventricular delay at increased atrial paced heart rates improves diastolic filling and functional class in patients with biventricular pacing
<p>Abstract</p> <p>Background</p> <p>Use of rate adaptive atrioventricular (AV) delay remains controversial in patients with biventricular (Biv) pacing. We hypothesized that a shortened AV delay would provide optimal diastolic filling by allowing separation of early and late diastolic filling at increased heart rate (HR) in these patients.</p> <p>Methods</p> <p>34 patients (75 ± 11 yrs, 24 M, LVEF 34 ± 12%) with Biv and atrial pacing had optimal AV delay determined at baseline HR by Doppler echocardiography. Atrial pacing rate was then increased in 10 bpm increments to a maximum of 90 bpm. At each atrial pacing HR, optimal AV delay was determined by changing AV delay until best E and A wave separation was seen on mitral inflow pulsed wave (PW) Doppler (defined as increased atrial duration from baseline or prior pacemaker setting with minimal atrial truncation). Left ventricular (LV) systolic ejection time and velocity time integral (VTI) at fixed and optimal AV delay was also tested in 13 patients. Rate adaptive AV delay was then programmed according to the optimal AV delay at the highest HR tested and patients were followed for 1 month to assess change in NYHA class and Quality of Life Score as assessed by Minnesota Living with Heart Failure Questionnaire.</p> <p>Results</p> <p>81 AV delays were evaluated at different atrial pacing rates. Optimal AV delay decreased as atrial paced HR increased (201 ms at 60 bpm, 187 ms at 70 bpm, 146 ms at 80 bpm and 123 ms at 90 bpm (ANOVA F-statistic = 15, p = 0.0010). Diastolic filling time (P < 0.001 vs. fixed AV delay), mitral inflow VTI (p < 0.05 vs fixed AV delay) and systolic ejection time (p < 0.02 vs. fixed AV delay) improved by 14%, 5% and 4% respectively at optimal versus fixed AV delay at the same HR. NYHA improved from 2.6 ± 0.7 at baseline to 1.7 ± 0.8 (p < 0.01) 1 month post optimization. Physical component of Quality of Life Score improved from 32 ± 17 at baseline to 25 ± 12 (p < 0.05) at follow up.</p> <p>Conclusions</p> <p>Increased heart rate by atrial pacing in patients with Biv pacing causes compromise in diastolic filling time which can be improved by AV delay shortening. Aggressive AV delay shortening was required at heart rates in physiologic range to achieve optimal diastolic filling and was associated with an increase in LV ejection time during optimization. Functional class improved at 1 month post optimization using aggressive AV delay shortening algorithm derived from echo-guidance at the time of Biv pacemaker optimization.</p
Comparison of Pathology and Prevalence of Prostate Cancer in Patients with PSA between 4-10 ng/ml
ABSTRACT:
Introduction & Objective: Prostate cancer is the forth common cancer in the world with different prevalence rate based on age, race and geographic area. Prevalence of prostate cancer in patients with PSA between 4-10ng/ml is reported to be about 20% in some studies. The aim of this study was to define the prevalence of prostate cancer in patients with 4-10 ng/ml PSA, in different age groups, based on Gleason grade.
Materials & Methods: In this cross sectional descriptive-analytic study, after taking informed consent, TRUS (Tran rectal ultrasound biopsy) was done in all patients with PSA of 4-10 ng/ml who referred to Labafinejad hospital from 2005 to 2006. Their pathology was classified into 3 groups, from 1-10, according to Gleason grade. Patients with cancers were divided into three groups 50-60, 60-70 and 70-80 years old, according to their age. Collected data were analyzed with SPSS software and descriptive tests.
Results: Prevalence of prostate cancer in 155 studied patients that underwent TRUS biopsy was 17.4%. Considering the age of patients, the rate of prostate cancer was 29%, 48.1% and 22.2% in 50-60, 60-70, and 70-80 years old patients. According to Gleason grade, the rate of prostate cancer was 3,7%, 74% and 22.2% in grades 1-4, 5-7 and 8-10, respectively.
Conclusion: Prevalence of prostate cancer in patients with PSA between 4-10 ng/ml in this study was 17.4 percent and most of these patients were in 50-60 year-old age groups and have Gleason grade less than 7. The majority of these patients are curable by radical prostatectomy
Variation in inflammatory cytokine/growth-factor genes and mammographic density in premenopausal women Aged 50-55
Background Mammographic density (MD) has been found to be an independent risk factor for breast cancer. Although data from twin studies suggest that MD has a strong genetic component, the exact genes involved remain to be identified. Alterations in stromal composition and the number of epithelial cells are the most predominant histopathological determinants of mammographic density. Interactions between the breast stroma and epithelium are critically important in the maturation and development of the mammary gland and the cross-talk between these cells are mediated by paracrine growth factors and cytokines. The potential impact of genetic variation in growth factors and cytokines on MD is largely unknown. Methods We investigated the association between 89 single nucleotide polymorphisms (SNPs) in 7 cytokine/growth-factor genes (FGFR2, IGFBP1, IGFBP3, TGFB1, TNF, VEGF, IL6) and percent MD in 301 premenopausal women (aged 50 to 55 years) participating in the Norwegian Breast Cancer Screening Program. We evaluated the suggestive associations in 216 premenopausal Singapore Chinese Women of the same age. Results We found statistically significant associations between 9 tagging SNPs in the IL6 gene and MD in Norwegian women; the effect ranged from 3â5% in MD per variant allele (p-valuesâ=â0.02 to 0.0002). One SNP in the IL6 (rs10242595) significantly influenced MD in Singapore Chinese women. Conclusion Genetic variations in IL6 may be associated with MD and therefore may be an indicator of breast cancer risk in premenopausal women
Polymorphisms in hormone metabolism and growth factor genes and mammographic density in Norwegian postmenopausal hormone therapy users and non-users
Introduction: Mammographic density (MD) is one of the strongest known breast cancer risk factors. Estrogen and progestin therapy (EPT) has been associated with increases in MD. Dense breast tissue is characterized by increased stromal tissue and (to a lesser degree) increased numbers of breast epithelial cells. It is possible that genetic factors modify the association between EPT and MD, and that certain genetic variants are particularly important in determining MD in hormone users. We evaluated the association between MD and 340 tagging single nucleotide polymorphisms (SNPs) from about 30 candidate genes in hormone metabolism/growth factor pathways among women who participated in the Norwegian Breast Cancer Screening Program (NBCSP) in 2004. Methods: We assessed MD on 2,036 postmenopausal women aged 50 to 69 years using a computer-assisted method (Madena, University of Southern California) in a cross-sectional study. We used linear regression to determine the association between each SNP and MD, adjusting for potential confounders. The postmenopausal women were stratified into HT users (EPT and estrogen-only) and non-users (never HT). Results: For current EPT users, there was an association between a variant in the prolactin gene (PRL; rs10946545) and MD (dominant model, Bonferroni-adjusted P (Pb) = 0.0144). This association remained statistically significant among current users of norethisterone acetate (NETA)-based EPT, a regimen common in Nordic countries. Among current estrogen-only users (ET), there was an association between rs4670813 in the cytochrome P450 gene (CYP1B1) and MD (dominant model, Pb = 0.0396). In never HT users, rs769177 in the tumor necrosis factor (TNF) gene and rs1968752 in the region of the sulfotransferase gene (SULT1A1/SULT1A2), were significantly associated with MD (Pb = 0.0202; Pb = 0.0349). Conclusions: We found some evidence that variants in the PRL gene were associated with MD in current EPT and NETA users. In never HT users, variants in the TNF and SULT1A1/SULT1A2 genes were significantly associated with MD. These findings may suggest that several genes in the hormone metabolism and growth factor pathways are implicated in determining MD
Polymorphisms in hormone metabolism and growth factor genes and mammographic density in Norwegian postmenopausal hormone therapy users and non-users
Introduction
Mammographic density (MD) is one of the strongest known breast cancer risk factors. Estrogen and progestin therapy (EPT) has been associated with increases in MD. Dense breast tissue is characterized by increased stromal tissue and (to a lesser degree) increased numbers of breast epithelial cells. It is possible that genetic factors modify the association between EPT and MD, and that certain genetic variants are particularly important in determining MD in hormone users. We evaluated the association between MD and 340 tagging single nucleotide polymorphisms (SNPs) from about 30 candidate genes in hormone metabolism/growth factor pathways among women who participated in the Norwegian Breast Cancer Screening Program (NBCSP) in 2004.
Methods
We assessed MD on 2,036 postmenopausal women aged 50 to 69 years using a computer-assisted method (Madena, University of Southern California) in a cross-sectional study. We used linear regression to determine the association between each SNP and MD, adjusting for potential confounders. The postmenopausal women were stratified into HT users (EPT and estrogen-only) and non-users (never HT).
Results
For current EPT users, there was an association between a variant in the prolactin gene (PRL; rs10946545) and MD (dominant model, Bonferroni-adjusted P (Pb) = 0.0144). This association remained statistically significant among current users of norethisterone acetate (NETA)-based EPT, a regimen common in Nordic countries. Among current estrogen-only users (ET), there was an association between rs4670813 in the cytochrome P450 gene (CYP1B1) and MD (dominant model, Pb = 0.0396). In never HT users, rs769177 in the tumor necrosis factor (TNF) gene and rs1968752 in the region of the sulfotransferase gene (SULT1A1/SULT1A2), were significantly associated with MD (Pb = 0.0202; Pb = 0.0349).
Conclusions
We found some evidence that variants in the PRL gene were associated with MD in current EPT and NETA users. In never HT users, variants in the TNF and SULT1A1/SULT1A2 genes were significantly associated with MD. These findings may suggest that several genes in the hormone metabolism and growth factor pathways are implicated in determining MD
Perinatal outcomes based on the institute of medicine guidelines for weight gain in twin pregnancies.
Association between 9 IL6 tagging SNPs (with P-value less than 0.05) and MD after adjustment for age and BMI, based on an additive genetic model (Nâ=â301).
a<p>based on map to Genome Build 37.3.</p>b<p>Number of women with wild-wild genotype.</p>c<p>Number of women with wild-variant genotype.</p>d<p>Number of women with variant-variant genotype.</p>e<p>Minor allele frequency.</p>f<p>Percent MD per variant allele based on additive model adjusted for age at mammogram (continuous) and BMI at mammogram (continuous).</p
Short-term neonatal outcomes in diamniotic twin pregnancies delivered after 32 weeks and indications of late preterm deliveries.
Association between 8 IL6 tagging SNPs from table 3 and MD in low and high BMI groups.
a<p>Percent MD per variant allele based on additive model adjusted for age at mammogram (continuous) and BMI at mammogram (continuous).</p>b<p>Number of women with wild-wild genotype.</p>c<p>Number of women with wild-variant genotype.</p>d<p>Number of women with variant-variant genotype.</p>e<p>P-value for interaction.</p
The association between the most significant SNP within each growth factor gene and MD in Norwegian women (Nâ=â310).
a<p>Number of women with wild-wild genotype.</p>b<p>Number of women with wild-variant genotype.</p>c<p>Number of women with variant-variant genotype.</p>d<p>Minor allele frequency.</p>e<p>Percent MD per variant allele based on additive model adjusted for age at mammogram (continuous) and BMI at mammogram (continuous).</p