Demographics and survivial of a cohort of blood and breast cancer patients who developed heart failure following cancer treatment

Background/Aim: Cardiotoxicity resulting in heart failure is a devastating complication of cancer therapy. It is possible that a patient may survive cancer only to develop heart failure (HF), which is more deadly than cancer. The aim of this project was to profile the characteristics of patients at risk of cancer treatment induced heart failure. Methods: Linked Health Data Analysis of Queensland Cancer Registry (QCR) from 1996-2009, Death Registry and Hospital Administration records for HF and chemotherapy admissions were reviewed. Index heart failure admission must have occurred after the date of cancer registry entry. Results: A total of 15,987 patients were included in this analysis; 1,062 (6.6%) had chemotherapy+HF admission (51.4% Female) and 14,925 (93.4%) chemotherapy_no HF admission. Median age of chemotherapy+HF patients was 67 years (IQR 58 to 75) vs. 54 years (IQR 44 to 64) for chemotherapy_no HF admission. Chemotherapy+HF patients had increased risk of all cause mortality (HR 2.79 [95% CI 2.58-3.02] and 1.67 [95% CI, 1.54 to 1.81] after adjusting for age, sex, marital status, country of birth, cancer site and chemotherapy dose). Index HF admission occurred within one year of cancer diagnosis in 47% of HF patients with 80% of patinets having there index admission with 3 years. The number of chemotherapy cycles was not associated with significant reduction in survival time in chemotherapy+HF patients. Mean survival for heart failure patients was 5.3 years (95% CI, 4.99 - 5.62) vs.9.57 years (95% CI, 9.47-9.68) for chemotherapy_no HF admission patients. Conclusion: All-cause mortality was 67% higher in patients diagnosed with HF following chemotherapy in adjusted analysis for covariates. Methods to improve and better coordinate of the interdisciplinary care for cancer patients with HF involving cardiologists and oncologists are required, including evidence-based guidelines for the comprehensive assessment, monitoring and management of this cohort.

Voluntary exercise decreases atherosclerosis in nephrectomised ApoE knockout mice

Cardiovascular disease is the main cause of morbidity and mortality in patients with kidney disease. The effectiveness of exercise for cardiovascular disease that is accelerated by the presence of chronic kidney disease remains unknown. The present study utilized apolipoprotein E knockout mice with 5/6 nephrectomy as a model of combined kidney disease and cardiovascular disease to investigate the effect of exercise on aortic plaque formation, vascular function and systemic inflammation. Animals were randomly assigned to nephrectomy or control and then to either voluntary wheel running exercise or sedentary. Following 12-weeks, aortic plaque area was significantly (p<0.05, d=1.2) lower in exercising nephrectomised mice compared to sedentary nephrectomised mice. There was a strong, negative correlation between average distance run each week and plaque area in nephrectomised and control mice (r=–0.76, p=0.048 and r=–0.73, p=0.062; respectively). In vitro aortic contraction and endothelial-independent and endothelial-dependent relaxation were not influenced by exercise (p>0.05). Nephrectomy increased IL-6 and TNF-α concentrations compared with control mice (p<0.001 and p<0.05, respectively), while levels of IL-10, MCP-1 and MIP-1α were not significantly influenced by nephrectomy or voluntary exercise (p>0.05). Exercise was an effective non-pharmacologic approach to slow cardiovascular disease in the presence of kidney disease in the apolipoprotein E knockout mouse.

Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

Statistical modelling and power analysis for detecting trends in total suspended sediment loads

The export of sediments from coastal catchments can have detrimental impacts on estuaries and near shore reef ecosystems such as the Great Barrier Reef. Catchment management approaches aimed at reducing sediment loads require monitoring to evaluate their effectiveness in reducing loads over time. However, load estimation is not a trivial task due to the complex behaviour of constituents in natural streams, the variability of water flows and often a limited amount of data. Regression is commonly used for load estimation and provides a fundamental tool for trend estimation by standardising the other time specific covariates such as flow. This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error. To achieve this, we propose a new regression model that includes an innovative compounding errors model structure and uses two additional predictive variables (average discounted flow and turbidity). By combining this modelling approach with a new, regularly optimised, sampling strategy, which adds uniformity to the event sampling strategy, the predictive power was increased to 90%. Using the enhanced regression model proposed here, it was possible to detect a trend of 20% over 20 years. This result is in stark contrast to previous conclusions presented in the literature. (C) 2014 Elsevier B.V. All rights reserved.

Pattern visual evoked potentials in dyslexic versus normal children

Purpose: Presence of neurophysiological abnormalities in dyslexia has been a conflicting issue. This study was performed to evaluate the role of sensory visual deficits in the pathogenesis of dyslexia. Methods: Pattern visual evoked potentials (PVEP) were recorded in 72 children including 36 children with dyslexia and 36 children without dyslexia (controls) who were matched for age, sex and intelligence. Two check sizes of 15 and 60 min of arc were used with temporal frequencies of 1.5 Hz for transient and 6 Hz for steady‑state methods. Results: Mean latency and amplitude values for 15 min arc and 60 min arc check sizes using steady state and transient methods showed no significant difference between the two study groups (P values: 0.139/0.481/0.356/0.062).Furthermore, no significant difference was observed between two methods of PVEPs in dyslexic and normal children using 60min arc with high contrast(Pvalues: 0.116, 0.402, 0.343 and 0.106). Conclusion: The sensitivity of PVEP has high validity to detect visual deficits in children with dyslexic problem. However, no significant difference was found between dyslexia and normal children using high contrast stimuli.

High-density fine-mapping of a chromosome 10q26 linkage peak suggests association between endometriosis and variants close to CYP2C19

OBJECTIVE To refine a previously reported linkage peak for endometriosis on chromosome 10q26, and conduct follow-up analyses and a fine-mapping association study across the region to identify new candidate genes for endometriosis. DESIGN Case-control study. SETTING Academic research. PATIENT(S) Cases=3,223 women with surgically confirmed endometriosis; controls=1,190 women without endometriosis and 7,060 population samples. INTERVENTION(S) Analysis of 11,984 single nucleotide polymorphisms on chromosome 10. MAIN OUTCOME MEASURE(S) Allele frequency differences between cases and controls. RESULT(S) Linkage analyses on families grouped by endometriosis symptoms (primarily subfertility) provided increased evidence for linkage (logarithm of odds score=3.62) near a previously reported linkage peak. Three independent association signals were found at 96.59 Mb (rs11592737), 105.63 Mb (rs1253130), and 124.25 Mb (rs2250804). Analyses including only samples from linkage families supported the association at all three regions. However, only rs11592737 in the cytochrome P450 subfamily C (CYP2C19) gene was replicated in an independent sample of 2,079 cases and 7,060 population controls. CONCLUSION(S) The role of the CYP2C19 gene in conferring risk for endometriosis warrants further investigation.

Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1

Migraine is a common episodic neurological disorder, typically presenting with recurrent attacks of severe headache and autonomic dysfunction. Apart from rare monogenic subtypes, no genetic or molecular markers for migraine have been convincingly established. We identified the minor allele of rs1835740 on chromosome 8q22.1 to be associated with migraine (P = 5.38 x 10(-)(9), odds ratio = 1.23, 95% CI 1.150-1.324) in a genome-wide association study of 2,731 migraine cases ascertained from three European headache clinics and 10,747 population-matched controls. The association was replicated in 3,202 cases and 40,062 controls for an overall meta-analysis P value of 1.69 x 10(-)(1)(1) (odds ratio = 1.18, 95% CI 1.127-1.244). rs1835740 is located between MTDH (astrocyte elevated gene 1, also known as AEG-1) and PGCP (encoding plasma glutamate carboxypeptidase). In an expression quantitative trait study in lymphoblastoid cell lines, transcript levels of the MTDH were found to have a significant correlation to rs1835740 (P = 3.96 x 10(-)(5), permuted threshold for genome-wide significance 7.7 x 10(-)(5). To our knowledge, our data establish rs1835740 as the first genetic risk factor for migraine.