[Mechanisms and applications of transcutaneous electrical nerve stimulation in analgesia].

Transcutaneous electrical nerve stimulation (TENS), as a non-pharmacological and non-invasive analgesic therapy with low-cost, has been widely used to relieve pain in various clinical applications, by delivering current pulses to the skin area to activate the peripheral nerve fibers. Nevertheless, analgesia induced by TENS varied in the clinical practice, which could be caused by the fact that TENS with different stimulus parameters has different biological mechanisms in relieving pain. Therefore, to advance our understanding of TENS in various basic and clinical studies, we discussed (1) neurophysiological and biochemical mechanisms of TENS-induced analgesia; (2) relevant factors that may influence analgesic effects of TENS from the perspectives of stimulus parameters, including stimulated position, pulse parameters (current intensity, frequency, and pulse width), stimulus duration and used times in each day; and (3) applications of TENS in relieving clinical pain, including post-operative pain, chronic low back pain and labor pain. Finally, we propose that TENS may involve multiple and complex psychological neurophysiological mechanisms, and suggest that different analgesic effects of TENS with different stimulus parameters should be taken into consideration in clinical applications. In addition, to optimize analgesic effect, we recommend that individual-based TENS stimulation parameters should be designed by considering individual differences among patients, e.g., adaptively adjusting the stimulation parameters based on the dynamic ratings of patients' pain.

[Contrastive study on dynamic spectrum extraction method].

Dynamic spectrum method extracts the absorbance of the artery pulse blood with some wavelengths. The method can reduce some influence such as measurement condition, individual difference and spectrum overlap. It is a new way for noninvasive blood components detection However, how to choose a dynamic spectrum extraction method is one of the key links for the weak ingredient spectrum signal. Now there are two methods to extract the dynamic spectral signal-frequency domain analysis and single-trial estimation in time domain In the present research, comparison analysis and research on the two methods were carrued out completely. Theoretical analysis and experimental results show that the two methods extract the dynamic spectrum from different angles. But they are the same in essence--the basic principle of dynamic spectrum, the signal statistical and average properties. With the pulse wave of relative stable period and amplitude, high precision dynamic spectrum can be obtained by the two methods. With the unstable pulse wave due to the influence of finger shake and contact-pressure change, the dynamic spectrum extracted by single-trial estimation is more accurate than the one by frequecy domain analysis.

[Non-invasive measurement of human blood cholesterol concentration based on dynamic spectrum method].

For non-invasive measurement of human blood cholesterol concentration, this experiment was carried out on 80 volunteers clinically. In vivo dynamic spectra of fingers were achieved and biochemical examinations of blood components contents including cholesterol were get as soon as possible. BP artificial neural network with inputs of dynamic spectra plus energy of harmonic waves processed by Principal Components Analysis(PCA) was used to establish the model of the total cholesterol values. The correlation between the predicted value and the true value of cholesterol is 96.48%. The maximum relative error is 25.44% and root-mean-square error of prediction is 0.242 6 mmol x L(-1). The results show that PCA can make the process of computing faster and this study is another advance of dynamic spectra.

[Noninvasive measurement of the human RBC concentration based on BP NN model].

In the present article, the BP neural network's arithmetic model is applied to the noninvasive detection of the concentration of the red blood cell of human body. Due to the use of BP neural network in the modeling and analysis of the dynamic spectrum data and the actual measured value of the red blood cell, the authors get a better result which refers to that the output value tracks the expected result very well. The related coefficient R can reach 0.993. When predicting the output value in the way of the BP neural network model, the maximal relative error is only 4.7%, average relative error is 2.1%, so the authors can say that it has more ideal prediction ability. The experimental result shows that the BP neural network model can be accurate in dealing with the nonlinear relation between the dynamic spectrum data and human erythrocyte practical value and it can make the method of noninvasive blood analysis more useful in clinical application. So it has a high application value.

[The effects of signal to noise ratio of instrument and number of wavelengths on the accuracy of spectral analysis].

In order to study the influence of the two factors, the signal to noise ratio (SNR) of instrument and number of wavelengths, on the accuracy of quantitative analysis in spectral modeling analysis, the influences of different SNRs, the number of wavelengths participating in partial least square regression, and multi-wavelength modeling in different SNR bands were taken into account. The relationships between these factors and quantitative analysis accuracy were explored by modeling analysis experiments. The results show that the analysis accuracy is directly related to the SNR of instrument. And increasing wavelengths, especial the ones with high SNR, reflects a phenomenon that four times of wavelength number will bring 2 times accuracy increasing, which can compensate for the lack of SNR. The research provides the experimental basis and theoretical guidance to improve the model's quality and reduce the prediction error by the utilization and improvement of the instrument's SNR and the reasonable choice of wavelengths and bands of modeling.

[The analysis for improving the SNR of blood components noninvasive measurement with DS method].

In order to increase the accuracy of blood components measurement and enhance the stability of prediction model, the quantitative signal-noise-ratio (SNR) analysis of measuring instruments based on dynamic spectrum (DS) and preprocessing method was conducted. The SNR of DS is increased after adding boxcar integrator, decreasing wavelength revolution, balancing the DS's SNR and excluding gross errors in preprocessing according to experiment results. Two volunteers were tested continuously for many times using the DS data acquiring system. The correlation coefficients of the each volunteer's DS data was increased from 0.934 and 0.953 to 0.991 and 0.987, respectively. Moreover, the gap between the correlation coefficient of the same volunteer's DS and different volunteers' DS is increased too, which shows that the SNR can be improved by these methods. The quantitative SNR analysis can guide the way of choosing preprocessing method efficiently, which will create the condition for clinical application of the blood components noninvasive measurement.

[Equalization of whole-band signal's SNR in the blood components noninvasive measurement].

To fully extend the category of blood components that can be noninvasively measured by dynamic spectrum (DS) method and to increase its measuring precision, an overall consideration of light source, tissue absorption and sensor's sensitivity was made. Compensating the light source and adding the telecentric lens not only expand the spectral effective detecting range, but also balance the signal-to-noise ratio (SNR) of the photoelectric pulse in the whole band equalization. The integral SNR of DS signal was increased and the measurement bandwidth was expanded. The effectiveness of this method was validated by the quality evaluation criterion of DS data: the effective detecting range of visible DS was widened from 600-1 000 nm to 500-1 135 nm; the effective detecting range of near-infrared DS was widened from 900-1 100 nm to 900-1 700 nm. The results show that the design can create the condition for detection of new blood components noninvasively, and enhance the prediction accuracy of the blood components, for which noninvasive measuring using DS method has been achieved.

[Effect of different distribution of components concentration on the accuracy of quantitative spectral analysis].

In order to discuss the effect of different distribution of components concentration on the accuracy of quantitative spectral analysis, according to the Lambert-Beer law, ideal absorption spectra of samples with three components were established. Gaussian noise was added to the spectra. Correction and prediction models were built by partial least squares regression to reflect the unequal modeling and prediction results between different distributions of components. Results show that, in the case of pure linear absorption, the accuracy of model is related to the distribution of components concentration. Not only to the component we focus on, but also to the non-tested components, the larger covered and more uniform distribution is a significant point of calibration set samples to establish a universal model and provide a satisfactory accuracy. This research supplies a theoretic guidance for reasonable choice of samples with suitable concentration distribution, which enhances the quality of model and reduces the prediction error of the predict set.

[The concentration resolution of complex liquid spectrometry analysis and the effect of concentration distribution].

In order to ensure the feasibility of complex liquid spectroscopy analysis, to analyze the accuracy gain of modeling by multi-wavelength, and to determine the appropriate distribution of concentration to obtain the high quality and universal quantitative analysis model, the precision of the detection of composition concentration by spectral analysis is illustrated through a error analysis which takes into account the following three contributions: spectral instrument noise, multi-wavelength modeling and the distribution of composition concentration. By concentration resolution analysis, the concentration resolution can be achieved when the spectrometer noise is available, but also the theoretical basis is provided to select a suitable spectrometer to meet the resolution requirement of quantitative analysis. Over-sampling technique indicates that the precision improvement by modeling with multi-wavelength can obtain higher concentration detection sensitivity. The sparse-dense-ratio and Euclidean distance of both measured and non-measured components provide the theoretic guidance for choosing the suitable concentration distribution which improves the model's quality and reduces the prediction error of the sample set.

[Single-trial estimation of dynamic spectrum].

To improve the efficiency and accuracy of the dynamic spectrum data processing, the method of single-trial estimation was adopted. First, the rising edge of the whole band PPG was extracted, which was calculated by averaging superimposed collected photoelectric plethysmography (PPG) at all wavelengths as a template per single pulse; Second, this template was used to correct the rising edge of PPG at all wavelengths, and the difference of absorbance was calculate, and then a single-trial DS was obtained; finally, the single-trial DS which contained the gross error under the 3sigma criterion was removed, and then the remaining superimposed single-trial DS was averaged as the final output of the DS. Data measured from 10 volunteers were compared with the results of the extraction in frequency domain: the correlation coefficient distribution of the DS from the same finger of the same individual was improved from 0.006775 to 0.0003840; the correlation coefficient distribution of the DS from the different fingers of the same individual was improved from 0.01393 to 0.002205, whereas the differences of DS between different indivisuals with high quality, and accelerate the process of DS put into practical application.

[Application of wavelet transform on improving detecting precision of the non-invasive blood components measurement based on dynamic spectrum method].

Time-varying noises in spectra collection process have influence on the prediction accuracy of quantitative calibration in the non-invasive blood components measurement which is based on dynamic spectrum (DS) method. By wavelet transform, we focused on the absorbance wave of fingertip transmission spectrum in pulse frequency band. Then we increased the signal to noise ratio of DS data, and improved the detecting precision of quantitative calibration. After carrying out spectrum data continuous acquisition of the same subject for 10 times, we used wavelet transform de-noising to increase the average correlation coefficient of DS data from 0.979 6 to 0.990 3. BP neural network was used to establish the calibration model of subjects' blood components concentration values against dynamic spectrum data of 110 volunteers. After wavelet transform de-noising, the correlation coefficient of prediction set increased from 0.677 4 to 0.846 8, and the average relative error was decreased from 15.8% to 5.3%. Experimental results showed that the introduction of wavelet transform can effectively remove the noise in DS data, improve the detecting precision, and accelerate the development of non-invasive blood components measurement based on DS method.

[Research on the measurement of urinary albumin by visible-near infrared spectroscopy].

The urinary albumin (UMALB) is the most reliable diagnostic indicator of renal injury in clinical. Attempting to realize the rapid and free reagent measurement of UMALB, the visible-near infrared multiple optical path length spectra of 207 urine samples were collected. By the nonlinear characteristics of multiple optical path length spectra, more information about the component of sample contents can be obtained. The PLS model of the spectra and UMALB was firstly established. Based on it, the PLS-ANN modeling method was built to introduce nonlinear information. By contrast, the PLS-ANN modeling method can obtain a better model to improve the accuracy of quantitative analysis. The R2 of predicted model was 0.951 1 and the RMSEP was 5.02 mg x L(-1). The results showed the feasibility of the visible-near infrared multiple optical path length spectroscopy technique for urinary albumin analysis. This research establishes the foundation of detecting the urinary albumin and other components free of reagent conveniently and rapidly.

[Increasing the precision of the noninvasive blood components measurement based on DS method using harmonic waves].

To improve the signal-to-noise ratio (SNR) of the dynamic spectrum (DS) data and to increase the stability of the model and the prediction accuracy, the harmonic waves of DS data were introduced into DS method. Sixty samples were determined as the research objects according to the quality of the pulse wave and the distribution of the harmonic waves after further analysis of 110 volunteers' data acquired in vivo. This paper took whether adding the energy of harmonic waves into the DS data as the division standard to generate two groups. BP artificial neural network was used to establish the calibration model of subjects' hemoglobin values against DS. The correlation coefficients of the predicted values and the true values in experimental group, containing the energy of harmonic waves, was 0.91, much higher than 0.80 in the control group. Other indexes were all improved too. The results showed that the modified method can enhance the SNR of DS method and accelerate the development of noninvasive blood components measurement based on DS method.

[The quality evaluation of dynamic spectrum data].

To eliminate the influence of all factors on non-invasive measurement precision of blood components by dynamic spectrum method during data acquisition process, a quality evaluation criterion of dynamic spectrum data needs to be established to improve the stability of the model and precision of the prediction. The number of stable wavelength as a quality evaluation index for the dynamic spectrum data was presented in this article after further analysis of 110 samples, which were all obtained by in vivo measurements, and 60 samples were picked up as the satisfactory samples. BP artificial neural network was used to establish the calibration model of subjects' total cholesterol, glucose and hemoglobin values against dynamic spectrum data. The prediction result of the experiment group was improved compared with the control group. The average relative error was decreased from 13.8%, 15.8% and 5.4% to 6.5%, 6.5% and 2.1% respectively, by which the effectiveness of the number of stable wavelength as a quality evaluation index could be proved. By evaluating the quality of the dynamic spectrum data measured, more reliable prediction result can be obtained, which can make the non-invasive measurement of the blood components come to the clinical application sooner.

[Research on the blood components detecting by multi-optical path length spectroscopy technique].

To discuss the feasibility of using the serum's multi-optical path length spectroscopy information for measuring the concentration of the human blood components, the automatic micro-displacement measuring device was designed, which can obtain the near-infrared multi-optical path length from 0 to 4.0 mm (interval is 0.2 mm) spectra of 200 serum samples with multioptical path length spectrum of serum participated in building the quantitative analysis model of four components of the human blood: glucose (GLU), total cholesterol (TC), total protein (TP) and albumin (ALB), by mean of the significant non-linear spectral characteristic of blood. Partial least square (PLS) was used to set up the calibration models of the multi-optical path length near-infrared absorption spectrum of 160 experimental samples against the biochemical analysis results of them. The blood components of another 40 samples were predicted according to the model. The prediction effect of four blood components was favorable, and the correlation coefficient (r) of predictive value and biochemical analysis value were 0.9320, 0.9712, 0.9462 and 0.9483, respectively. All of the results proved the feasibility of the multi-optical path length spectroscopy technique for blood components analysis. And this technique established the foundation of detecting the components of blood and other liquid conveniently and rapidly.

Construction of ICG encapsulated W18O49@MSN as a fluorescence carrier for real-time tracked photothermal therapy.

Photothermal therapy (PTT) has drawn tremendous attention because of its high therapeutic efficiency in targeting cells while minimizing the damage to normal tissues and organs. Tungsten oxide (W18O49, WO) plays a pivotal role in PTT development and its use in PTT systems has been extensively studied. However, it is difficult to control morphology of WO through conventional hydrothermal method. Which make its related researches have been limited up to now. In this study, we describe the construction and effects on tumor of a novel nanoplatform based on WO and indocyanine green (ICG) loaded in mesoporous silica nanoparticles (MSN) for dual-modal PTT and near-infrared imaging. (WO+ICG)@MSN could efficiently control WO shape without the need of surface modification due to its water-soluble of MSN. (WO+ICG)@MSN produced a PTT synergistic effect under irradiation of a single 808nm near-infrared (NIR) laser. Notably, an enhanced lethal effect of the 808nm laser triggering dual-modal therapy on B16 tumor cells was observed. The in vivo animal experiments showed that (WO+ICG)@MSN induced an effective solid tumor reduction under 808nm NIR light irradiation, revealing the potential of these nanocomposites as a NIR-mediated dual-modal therapeutic platform for cancer treatment.

Simulation and experimental study of DC electric field distribution characteristics of rat hippocampal slices in vitro.

Direct current (DC) electric field is a noninvasive neuromodulation tool that can inhibit or facilitate excitability of neurons. Despite its efficacy, the dielectric constant of artificial cerebrospinal fluid and the position and direction of brain slices and other factors can affect the field intensity and distribution acting on the surface of rat hippocampus slices, thus causing errors. In this study, we describe a new analytical method optimized for DC electric fields acting on brain slices, and the design of an external DC electric field stimulator to allow scientific evaluation of brain slices. We investigated parameters regarding the uniformity of electric field distribution and identified the maximal parameters using the finite element method. Then, we selected and simplified slice images using magnetic resonance imaging data and calculated the electric field intensity of the original and simplified models. The electric field simulator induced action potential and excitatory postsynaptic current with intensities of 1, 5, and 10 V/m. This study describes the development of a new electric field stimulator and successfully demonstrates its practicability for scientific evaluation of tissue slices.