[Measurement and evaluation of the quadriceps muscle mass in young men based on magnetic resonance imaging].

OBJECTIVE: To investigate the correlation between the quadriceps cross-sectional area (CSA) and quadriceps muscle volume (QMV) at different horizontal levels from the upper edge of the patella, and to determine the best observation position. METHODS: Thigh magnetic resonance imaging (MRI) images of 22 Chinese young men [age: (29±6) years] with anterior cruciate ligament (ACL) rupture were examined. The CSA was measured at 18, 15, and 12 cm above the upper edge of the pate-lla (denoted by CSA-18, CSA-15 and CSA-12 respectively), and the QMV and CSA were determined by semiautomatic segmentation. A curve model was established to estimate QMV. Bland-Altman analysis was performed to determine the confidence limits of the volumes. RESULTS: On the unaffected side, the mean QMV was (1 944.45±323.77) cm3. The quadriceps CSA at the upper edge of the patella at 18, 15, and 12 cm was (80.80±12.16) cm2, (77.53±12.03) cm2, and (72.68±10.51) cm2, respectively. The coefficients of determination (R2), ascertained using curve estimation models, for the 3 positions were 0.819, 0.755, and 0.684 (P < 0.001), and the standard deviations of the volume estimated value (SEE) were 7.4%, 8.7%, and 9.8%. The fitting equations of the three horizontal positions were all good, but the fitting degree of CSA-18 was the highest. The Bland-Altman scatter plot showed that the arithmetic means of the QMV at 18, 15 and 12 cm from the upper edge of the patella 0.8 cm3, -1.1 cm3, and 0.9 cm3 and 95% limits of agreement (LoA) were (-268.8, 270.5), (-315.2, 313.1), and (-355.7, 357.5), respectively. The estimated QMV was in good agreement with the measured value. The difference between the estimated CSA-18 and measured values was the smallest. The results on the affected side were consistent. CONCLUSION: The correlation between QMV and CSA in the young men with the upper edge of patella as baseline was reliable and consistent. Among them, CSA-18 had the highest correlation with the QMV. However, different observation sites could be selected for different injuries of the quadriceps.

[Establishment of high-fat diet-induced obesity and insulin resistance model in rats].

OBJECTIVE: To investigate the appropriate conditions and duration for establishing a high-fat diet-induced obesity and insulin resistance model in rats. METHODS: Forty-five 6-week-old male Sprague-Dawley (SD) rats were randomly assigned into 2 groups: (1) control group (CON), (2) high-fat diet group (HFD). HFD was fed with a high-fat diet (45% kcal from fat) while CON with chow diet. After four-weeks of high-fat diet feeding, the rats of obesity resistance (OR) were eliminated according to body weight sorting, whereas obese (OB) rats were continued feeding a high-fat diet until 12 weeks. Body weight and food intake were recorded weekly. Glucose tolerance was evaluated by oral glucose tolerance test (OGTT) in 4 weeks, 8 weeks and 12 weeks. At the end of 12 weeks, insulin releasing test and visceral fat mass were measured and HE staining of the liver, adipose tissue and pancreatic tissue were conducted. RESULTS: After 4 weeks of a high-fat diet, the body weight of HFD was 17.8% higher than that of CON (P=0.001), and the rate of obesity was 67.6%-78.4%. Glucose tolerance of OB rats was impaired with a higher blood glucose concentration at 120 min (P<0.001) and a higher area under the curve (AUC, P=0.037) in OGTT compared with CON. The rate of obesity and insulin-resistance rats was 79.3%. After 8 weeks of feeding, the body weight in OB was 30.4% higher than CON (P<0.001). In OGTT, blood glucose levels at 60 min and 120 min were 35.6% and 36.4% higher than those in CON (both P<0.001), and AUC was 21.7% (P<0.001) higher than that of CON. The rate of obesity and insulin-resistance rats was 100.0%. After 12 weeks of feeding, the body weight in OB was 36.9% higher than that in CON (P<0.001). In OGTT, the blood glucose levels at 60 min and 120 min were 24.8% (P=0.001) and 34.6% (P<0.001) higher than those in CON, and AUC was 16.1% (P=0.019) higher than that of CON. The rate of obesity and insulin-resistance rats was 93.3%. The insulin releasing test showed that serum insulin concentration at each time point (0, 30, 60, 120 min) was higher than that in CON, with a 6.3-times higher than that in CON at 120 min (P=0.008). Pathological changes were observed in islets and liver in the OB rats. CONCLUSION: After 4 weeks of a high-fat diet (45% kcal from fat) feeding in six-weeks SD rats, the rats of OR were eliminated. Impaired glucose tolerance was found in OB rats after 4 weeks of feeding, and the rate was higher after 8-12 weeks of high-fat diet feeding.

[Effects of chlorogenic acid on glucose tolerance and its curve characteristics in high-fat diet-induced obesity rats].

OBJECTIVE: To observe the effect of chlorogenic acid (chlorogenic acid, CGA) on the glucose tolerance and its curve characteristics in high fat diet-induced obesity (diet-induced-obesity, DIO) rats, so as to provide scientific grounds for the development and utilization of CGA in early prevention and reversal of prediabetes. METHODS: Eight of forty-six male Sprague-Dawley rats were randomly selected as the normal diet group (CON group), and the rest were fed with high-fat diet. After 4 weeks, 24 high-fat-induced obese rats were screened according to the criteria and then randomly divided into high fat diet group (HFD group), 50% CGA group and 98% CGA group. The CGA groups received intragastric administrations of 50% CGA and 98% CGA orally via a gavage needle once a day for 8 weeks, respectively, while the CON and HFD groups received a carrier solution (phosphate buffer saline, PBS). Their body weights were measured weekly and oral glucose tolerance test (OGTT) was performed every 4 weeks. Fasting insulin and insulin release were measured at the end of the study. Meanwhile, HOMA-IR and visceral fat percentage were calculated. Histopathological examination by hematoxylin and eosin staining method were evaluated in the pancreatic tissues. RESULTS: Before the intervention of chlorogenic acid, blood glucose levels 120 min after glucose loading (P<0.05) and AUC-G (P<0.05) were increased in the HFD group when compared with the CON group, and the time to glucose peak was delayed after 4 weeks of chlorogenic acid intervention (P<0.05). After 8 weeks of intervention, the HOMA-IR index, the insulin levels at 0 min, 30 min, 60 min, and 120 min after glucose loading and AUC-I increased (P<0.05), and the histopathological examination showed obvious hyperplasia of pancreatic islets (P<0.05). Compared with the HFD group, there was no significant change in glucose tolerance and glucose peak time in 50%CGA and 98%CGA groups at the end of 4 weeks of intervention. However, after 8 weeks of intervention, OGTT-60min,OGTT-120min blood glucose (P<0.05) were lower, HOMA-IR index and OGTT-0min, OGTT-120min serum insulin level decreased (P<0.05), the time to glucose peak shifted to an earlier timepoint (P<0.05), abnormal islet hyperplasia attenuated (P<0.05) in 50% CGA and 98% CGA groups. Also, the OGTT-30min serum insulin level was decreased (P<0.05) in 50% CGA group. CONCLUSION: Delay in time to glucose peak during the OGTT was one of the manifestations of impaired glucose tolerance in DIO rats, and 50% and 98% CGA could improve the glucose tolerance and delay in glucose peak time.

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.

Comparison of blind source separation methods in fast somatosensory-evoked potential detection.

Blind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. The algorithms we compared included second-order blind identification, estimation of signal parameters via rotation invariance technique, algorithm for multiple unknown signals extraction, joint approximate diagonalization of eigenmatrices, extended infomax, and fast independent component analysis. The performances of these BSS algorithms were determined by the correlation coefficients between the true and the extracted SEP signals. There were significant differences in the performances of the various BSS algorithms in a simulation study. In summary, second-order blind identification using six covariance matrix denoting SOBI6 was recommended as the most appropriate BSS method for fast SEP extraction from noisy backgrounds.

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.

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.

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.

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.

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 (λmax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λmaxand D2are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λmaxand D2so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (Vmax) 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.

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.