TC-relay latency distribution of detected cortical spikes.

<p>TC-relay latency distribution of detected cortical spikes.</p

Waveform comparison of TS- and ES-evoked cortical AP responses.

<p>(A) The individual traces with selected spikes. Bold lines show the individual 1-ms spike signals contributing to the averaged signals in (B). Scale: 0.2 mV. (B) The 1-ms averaged spike signals and the correlation coefficients yielded by them. Scale: 0.5 ms, 0.2 mV.</p

TC-relay latency obtainment.

<p>The two traces in (A) are thalamus and cortex traces of the same TS trial. The trace in (B) is the cortex trace of an ES trial. Single arrows indicate detected AP spikes (for the thalamus trace only the first). Double arrows represent the obtained TC-relay latencies.</p

Theta glass electrode.

<p>(A) The glass was pulled to produce the tip for microstimulation. (B) The tip of the glass. With two isolated channels (as indicated by the arrows), the tip could be used as a bipolar microelectrode.</p

<i>In Vitro</i> Differential Diagnosis of Clavus and Verruca by a Predictive Model Generated from Electrical Impedance

<div><p>Background</p><p>Similar clinical appearances prevent accurate diagnosis of two common skin diseases, clavus and verruca. In this study, electrical impedance is employed as a novel tool to generate a predictive model for differentiating these two diseases.</p><p>Materials and Methods</p><p>We used 29 clavus and 28 verruca lesions. To obtain impedance parameters, a LCR-meter system was applied to measure capacitance (<i>C</i>), resistance (<i>R_e</i>), impedance magnitude (<i>Z</i>), and phase angle (<i>θ</i>). These values were combined with lesion thickness (<i>d</i>) to characterize the tissue specimens. The results from clavus and verruca were then fitted to a univariate logistic regression model with the generalized estimating equations (GEE) method. In model generation, log <i>Z_SD</i> and <i>θ_SD</i> were formulated as predictors by fitting a multiple logistic regression model with the same GEE method. The potential nonlinear effects of covariates were detected by fitting generalized additive models (GAM). Moreover, the model was validated by the goodness-of-fit (GOF) assessments.</p><p>Results</p><p>Significant mean differences of the index <i>d, R_e, Z,</i> and <i>θ</i> are found between clavus and verruca (<i>p</i><0.001). A final predictive model is established with <i>Z</i> and <i>θ</i> indices. The model fits the observed data quite well. In GOF evaluation, the area under the receiver operating characteristics (ROC) curve is 0.875 (>0.7), the adjusted generalized <i>R</i>² is 0.512 (>0.3), and the <i>p</i> value of the Hosmer-Lemeshow GOF test is 0.350 (>0.05).</p><p>Conclusions</p><p>This technique promises to provide an approved model for differential diagnosis of clavus and verruca. It could provide a rapid, relatively low-cost, safe and non-invasive screening tool in clinic use.</p></div

Multivariate analysis of the predictors of verruca at 80(GEE) method.

<p>Goodness-of-fit assessment: Number of clusters  = 57, number of observations  = 166, the estimated area under the Receiver Operating Characteristic (ROC) curve  = 0.875>0.7, adjusted generalized <i>R</i>² = 0.512>0.3, and Hosmer-Lemeshow goodness-of-fit <i>F</i> test <i>p</i> = 0.350>0.05 (df = 9, 156).</p><p>Prediction: To calculate the estimated probability of being verruca (i.e., the <i>predicted value</i>,) given the observed covariate values, one can use the following formula. According to the above fitted multiple logistic regression model</p><p>the <i>predicted value</i> of observation <i>i</i> is</p><p>where log <i>Z_SD</i> =  logarithmized standardized <i>Z</i> value, and θ<i>_SD</i> =  standardized θ value.</p

Comparison of impedance data between clavus and verruca at 80

<p>Notes: The measured variables, <i>d</i>, <i>C</i>, <i>R_e</i>, <i>Z</i>, and <i>θ</i>, indicate thickness, capacitance, resistance, impedance magnitude, and phase angle, respectively. The transformed variables, <i>C_SD</i>, <i>R_eSD</i>, <i>Z_SD</i>, and <i>θ_SD</i>, signify standardized <i>C</i>, <i>R_e</i>, <i>Z</i>, and <i>θ</i> values, respectively. The transformed variables, log <i>d</i> and log <i>Z_SD</i> denote logarithmized <i>d</i> value and standardized logarithmized <i>Z</i> value. The listed values were mean ± standard deviation (SD) on the upper row and median (range) on the lower one. All <i>p</i>-values of group comparisons are obtained by fitting univariate logistic regression models with the generalized estimating equations (GEE) method to account for the correlations between repeated measurements.</p

The Receiver Operating Characteristic (ROC) curve for the prediction of verruca.

<p>The estimated area under the ROC curve (AUC) is 0.875.</p

The GAM plots of the predictors, log <i>Z_SD</i> (A), θ<i>_SD</i> (B), and log <i>d</i> (C) respectively.

<p>The generalized additive models plots reveal the smoothed partial effects of the predictors in modeling the probability of being verruca. The distribution of the observed values of log <i>Z_SD</i>, <i>θ_SD</i>, and log <i>d</i> are shown by the rugs on the <i>X</i>-axes. The <i>Y</i>-axes are the <i>logit</i> of the estimated probability of being verruca (), i.e., log. The horizontal green line indicates the place where = 0.5.</p

High-Resolution Structural and Functional Assessments of Cerebral Microvasculature Using 3D Gas ΔR₂*-mMRA

<div><p>The ability to evaluate the cerebral microvascular structure and function is crucial for investigating pathological processes in brain disorders. Previous angiographic methods based on blood oxygen level-dependent (BOLD) contrast offer appropriate visualization of the cerebral vasculature, but these methods remain to be optimized in order to extract more comprehensive information. This study aimed to integrate the advantages of BOLD MRI in both structural and functional vascular assessments. The BOLD contrast was manipulated by a carbogen challenge, and signal changes in gradient-echo images were computed to generate ΔR₂* maps. Simultaneously, a functional index representing the regional cerebral blood volume was derived by normalizing the ΔR₂* values of a given region to those of vein-filled voxels of the sinus. This method is named 3D gas ΔR₂*-mMRA (microscopic MRA). The advantages of using 3D gas ΔR₂*-mMRA to observe the microvasculature include the ability to distinguish air–tissue interfaces, a high vessel-to-tissue contrast, and not being affected by damage to the blood–brain barrier. A stroke model was used to demonstrate the ability of 3D gas ΔR₂*-mMRA to provide information about poststroke revascularization at 3 days after reperfusion. However, this technique has some limitations that cannot be overcome and hence should be considered when it is applied, such as magnifying vessel sizes and predominantly revealing venous vessels.</p></div