FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium.

BACKGROUND: Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium. METHODS: Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression. RESULTS: Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95% confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2. CONCLUSION: Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2.

Investigation of gait cycle deviation over surface irregularities utilizing muscle activities.

BACKGROUND: Human beings regularly walk over even and uneven surfaces during their daily activities. A human being with lower limb disability needs an exoskeleton to walk independently. However, walking surface irregularities increase the risk of falling of exoskeleton users. This falling tendency can be minimized by balancing the exoskeleton on irregular surface profiles against the gait cycle variation. Gait variation is studied using quality EMG signals obtained from the gastrocnemius and hamstring muscle activity during uneven surface walking. OBJECTIVE: The present study compares the activity of hamstring and gastrocnemius muscles during walking on a treadmill, utilizing both even and uneven planes. METHODS: Integrated electromyography signals from eight healthy male subjects are collected while walking on a treadmill, even and uneven planes. Muscle activity variation on these planes is studied using two-way ANOVA with replications. RESULTS: The results show that hamstring muscle activity registers a sound variation in swing phase but has no variation in stance phase over all three planes, whereas gastrocnemius muscle activity changes between swing and stance phases over even and uneven planes during forward walking. CONCLUSIONS: The results illustrate that the gait cycle variation depends on surface irregularities which indicates the importance of surface consideration.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

BACKGROUND: Estimation of elbow dynamics has been the object of numerous investigations. OBJECTIVE: In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. METHODS: Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. RESULTS: The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. CONCLUSION: The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

FOXM1 is a transcriptional target of ERalpha and has a critical role in breast cancer endocrine sensitivity and resistance.

In this study, we investigated the regulation of FOXM1 expression by estrogen receptor alpha (ERalpha) and its role in hormonal therapy and endocrine resistance. FOXM1 protein and mRNA expression was regulated by ER-ligands, including estrogen, tamoxifen (OHT) and fulvestrant (ICI182780; ICI) in breast carcinoma cell lines. Depletion of ERalpha by RNA interference (RNAi) in MCF-7 cells downregulated FOXM1 expression. Reporter gene assays showed that ERalpha activates FOXM1 transcription through an estrogen-response element (ERE) located within the proximal promoter region. The direct binding of ERalpha to the FOXM1 promoter was confirmed in vitro by mobility shift and DNA pull-down assays and in vivo by chromatin immunoprecipitation (ChIP) analysis. Our data also revealed that upon OHT treatment ERalpha recruits histone deacetylases to the ERE site of the FOXM1 promoter, which is associated with a decrease in histone acetylation and transcription activity. Importantly, silencing of FOXM1 by RNAi abolished estrogen-induced MCF-7 cell proliferation and overcame acquired tamoxifen resistance. Conversely, ectopic expression of FOXM1 abrogated the cell cycle arrest mediated by the anti-estrogen OHT. OHT repressed FOXM1 expression in endocrine sensitive but not resistant breast carcinoma cell lines. Furthermore, qRT-PCR analysis of breast cancer patient samples revealed that there was a strong and significant positive correlation between ERalpha and FOXM1 mRNA expression. Collectively, these results show FOXM1 to be a key mediator of the mitogenic functions of ERalpha and estrogen in breast cancer cells, and also suggest that the deregulation of FOXM1 may contribute to anti-estrogen insensitivity.