Effects of Preoperative Isokinetic Eccentric Training and Whey Protein Isolate Supplement on Quadriceps Strength and Knee Function in Patients with Anterior Cruciate Ligament Rupture

Objective To explore the effect of preoperative isokinetic eccentric training with or not whey protein isolate supplement before operation on lower limb muscle strength and knee function in patients with anterior cruciate ligament (ACL) rupture. Methods A total of 22 male volunteers aged 18-40 years with ACL rupture were recruited in outpatient service. With randomized block design,subjects were randomly assigned to isokinetic eccentric training (IE) group and isokinetic eccentric training with whey protein isolate supplement (IE+WPI) group. The IE group received isokinetic eccentric training of the injured limb on an isokinetic dynamometer under the guidance of physiatrist in laboratory before operation. There were 3-4 sets per day with 8-10 repetitions for each set,twice a week,with at least one day between sessions. The IE+WPI group were supplied with whey protein isolate 22 g per day on the basis of isokinetic eccentric training,taking breakfast or 30-60 minutes after the training. The intervention lasted for 6 weeks. Isokinetic muscle strength of limbs,the function and laxity of knee,the circumferences of thigh and knee,and the body composition were measured before and after the treatment. Results Compared with baseline,the peak torque (PT) of isokinetic-eccentric contraction (IE group:41.0%,P=0.018;IE+WPI group:46.7%,P=0.008) and the concentric contraction (IE group:29.6%,P=0.018;IE+WPI group:38.9%,P=0.038) of quadriceps in the two training groups significantly increased after isokinetic eccentric training. The Lysholm score increased significantly in IE+WPI group compared with baseline (P=0.018). Conclusions Isokinetic eccentric training before operation for ACL rupture patients can increase the strength of quadriceps and improve the function of knees. Protein isolate supplement can improve such effect.

Prioritizing genomic applications for action by level of evidence: a horizon-scanning method

As evidence accumulates on the use of genomic tests and other health-related applications of genomic technologies, decision makers may increasingly seek support in identifying which applications have sufficiently robust evidence to suggest they might be considered for action. As an interim working process to provide such support, we developed a horizon-scanning method that assigns genomic applications to tiers defined by availability of synthesized evidence. We illustrate an application of the method to pharmacogenomics tests.

Potential increased risk of cancer from commonly used medications: an umbrella review of meta-analyses

Several commonly used medications have been associated with increased cancer risk in the literature. Here, we evaluated the strength and consistency of these claims in published meta-analyses. We carried out an umbrella review of 74 meta-analysis articles addressing the association of commonly used medications (antidiabetics, antihyperlipidemics, antihypertensives, antirheumatics, drugs for osteoporosis, and others) with cancer risk where at least one meta-analysis in the medication class included some data from randomized trials. Overall, 51 articles found no statistically significant differences, 13 found some decreased cancer risk, and 11 found some increased risk (one reported both increased and decreased risks). The 11 meta-analyses that found some increased risks reported 16 increased risk estimates, of which 5 pertained to overall cancer and 11 to site-specific cancer. Six of the 16 estimates were derived from randomized trials and 10 from observational data. Estimates of increased risk were strongly inversely correlated with the amount of evidence (number of cancer cases) (Spearman's correlation coefficient = -0.77, P < 0.001). In 4 of the 16 topics, another meta-analysis existed that was larger (n = 2) or included better controlled data (n = 2) and in all 4 cases there was no statistically significantly increased risk of malignancy. No medication or class had substantial and consistent evidence for increased risk of malignancy. However, for most medications we cannot exclude small risks or risks in population subsets. Such risks are unlikely to be possible to document robustly unless very large, collaborative studies with standardized analyses and no selective reporting are carried out.

Gene selection for brain cancer classification

With the introduction of microarray, cancer classification, diagnosis and prediction are made more accurate and effective. However, the final outcome of the data analyses very much depend on the huge number of genes with relatively small number of samples present in each experiment. It is thus crucial to select relevant genes to be used for future specific cancer markers. Many feature selection methods have been proposed but none is able to classify all kinds of microarray data accurately, especially on those multi-class datasets. We propose a one-versus-one comparison method for selecting discriminatory features instead of performing the statistical test in a one-versus-all manner. Brain cancer is chosen as an example. Here, 3 types of statistics are used: signal-to-noise ratio (SNR), t-statistics and Pearson correlation coefficient. Results are verified by performing hierarchical and k-means clustering. Using our one-versus-one comparisons, best performance accuracies of 90.48% and 97.62% can be obtained by hierarchical and k-means clustering respectively. However best performance accuracies of 88.10% and 80.95% can be obtained respectively when using one-versus-all comparison. This shows that one-versus-one comparison is superior.

Fast extraction of somatosensory evoked potential based on second order blind identification

Second order blind identification (SOBI) technique is a promising independent component analysis (ICA) method to extract somatosensory evoked potential (SEP). This simulation study focused on SEP extraction from EEG and power-line noise contaminated SEP signals at signal to noise ratio (SNR) of -10dB and -20dB. The correlation coefficients between template SEP and SOBI extracted SEP showed significant high similarity (r>0.76) at -10dB and mild acceptable similarity (r>0.6) at -20dB EEG contaminated SEP. However, SOBI extracted SEP showed good performance in power-line noise situation to achieve high correlation coefficients with template SEP (r=0.96). The fast extracted SEP showed stable amplitude and latency, which are almost identical with the SEP template. The results suggested that SOBI is an appropriate method to extract SEP from noisy background.

An Improved Method for Discriminating ECG Signals using Typical Nonlinear Dynamic Parameters and Recurrence Quantification Analysis in Cardiac Disease Therapy

The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (&#955;<inf>max</inf>) and correlation dimension (D<inf>2</inf>) alone are somewhat limited in recognition rate. In this paper, improved methods for computing &#955;<inf>max</inf>and D<inf>2</inf>are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with &#955;<inf>max</inf>and D<inf>2</inf>so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (V<inf>max</inf>) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals.

Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only

Event-related potentials (ERP) are in general masked by various kinds of artifacts. To attenuate the effects of artifacts, various schemes have been introduced, such as epoch rejection, electro-oculogram (EOG) regression and independent component analysis (ICA). However, none of the existing techniques can automatically remove various kinds of artifacts from a single ERP epoch. EOG regression cannot handle artifacts other than ocular ones. ICA incorporating higher order statistics (HOS) normally requires data with large number of time samples in order that the solution is robust. In this paper we blindly separate the multi-channel ERP into source components by estimating the correlation matrices of the data. Since only second order statistics (SOS) is involved, the process performs well at the single epoch level. Automatic artifact identification is performed in the source domain by introducing objective criteria for various artifacts. Criteria are based on time domain signal amplitude for blink and spurious peak artifact, scalp distribution of signal power for eye movement artifact and power distribution of frequency components for muscle artifact. The correction procedure can be completed by removing the identified artifactual sources from the raw multi-channel ERP.

Adaptive signal enhancement of somatosensory evoked potential for spinal cord compression detection: an experimental study

The objective of this study was to assess the efficacy of adaptive signal enhancement (ASE) as a means of indicating intraoperative spinal cord impingement. ASE technique was used to determine the changes in the somatosensory evoked potential (SEP) elicited from eighteen rats with varying levels of spinal cord compression. ASE technique was found to be able to effectively extract SEP signals for the detection of spinal cord injury. Furthermore, while the traditional ensemble averaging (EA) technique requires more than 500 trials for meaningful signal processing in severe noisy SEP recordings, the ASE method required only 50 trials to provide similar information. Because of its fast and reliable SEP detection, the ASE method is ideal for spinal cord monitoring in the clinical setting.

Multi-adaptive filtering technique for surface somatosensory evoked potentials processing

Somatosensory evoked potential (SEP) testing has been widely applied to diagnosis of various neurological disorders. However, SEP recorded using surface electrodes is buried in noises, which makes the signal-to-noise ratio (SNR) very poor. Conventional averaging method usually requires up to thousands of raw SEP input trials to increase the SNR so that an identifiable waveform can be produced for latency and amplitude measurement. In this study, a multi-adaptive filtering (MAF) technique, emerging from the combination of well-developed adaptive noise canceller and adaptive signal enhancer, is introduced for fast and accurate surface SEP extraction. The MAF technique first processes the raw surface recorded SEP by the Canceller with a reference noise channel of background noise for adaptive subtraction before entering the Enhancer. The MAF was verified by filtering simulated SEP signals in which electroencephalography and Gaussian noise of different SNRs were added. It was found that the MAF could effectively suppress the noise and enhance the SEP components such that the SNR of the SEP is improved. Results showed that MAF with 50 input trials could provide similar performance in SEP detection to those extracted by the conventional averaging method with 1000 trials even at an SNR of -20 dB.

Free radical EPR spectroscopy analysis using blind source separation

In this paper, we propose a novel approach for electron paramagnetic resonance (EPR) mixture spectra analysis based on blind source separation (BSS) technique. EPR spectrum of a free radical is often superimposed by overlapping spectra of other species. It is important and challenging to accurately identify and quantify the 'pure' spectra from such mixtures. In this study, an automated BSS method implementing independent component analysis is used to extract the components from mixed EPR spectra that contain overlapping components of different paramagnetic centers. To apply this method, there is no requirement to know the component spectra or the number of components in advance. The method is applied to analyze free radical EPR spectra which are collected from standard chemical system, cultured cell suspense, and ex vivo rat kidneys by spin trapping EPR technique. Results show that the BSS method proposed here is capable of identifying the component EPR spectra from mixtures with unknown compositions. The BSS technique can offer powerful aids in resolving spectral overlapping problems in general EPR spectroscopy analysis.