Objective To further comprehend the definition， molecular mechanism， and clinical significance of perineural invasion （PNI） so as to explore new therapy for the tumors. Methods The literatures about the definition， molecular mechanism， and clinical study of PNI were reviewed and analyzed. Results At present， widely accepted definition of PNI was that at least 33% of the circumference of the nerve should be surrounded by tumor cells or tumor cells within any of three layers of the nerve sheath. The newest theory on molecular mechanism of PNI was that PNI was more like infiltration， invasion， not just diffusion. “Path of low-resistance” and “Reciprocal signaling interactions” were the main theories. More recently， the studies had demonstrated that “Reciprocal signaling interactions” could more clearly explain the mechanism of PNI. Stromal elements， including fibroblasts， seemed to play a key role in the complex signaling interactions driving PNI. Neurotrophins and axonal guidance molecules had been implicated in promoting the progress of PNI. PNI was a prognosis index in the cancers of the head and neck， stomach， pancreas， colon and rectum， and prostate， which was positive indicated that the patients would have a poor prognosis and a low 5-year survival rate. Conclusions The mechanism of PNI is very complex， and its clear mechanism is still undefined. Keeping on researching the mechanism of PNI could provide theoretical foundation to disclose the mechanism and the therapy of PNI.
Partial least square (PLS) combining with Raman spectroscopy was applied to develop predictive models for plasma paclitaxel concentration detection. In this experiment, 312 samples were scanned by Raman spectroscopy. High performance liquid chromatography (HPLC) was applied to determine the paclitaxel concentration in 312 rat plasma samples. Monte Carlo partial least square (MCPLS) method was successfully performed to identify the outliers and the numbers of calibration set. Based on the values of degree of approach (Da), moving window partial least square (MWPLS) was used to choose the suitable preprocessing method, optimum wavelength variables and the number of latent variables. The correlation coefficients between reference values and predictive values in both calibration set (Rc2) and validation set (Rp2) of optimum PLS model were 0.933 1 and 0.926 4, respectively. Furthermore, an independent verification test was performed on the prediction model. The results showed that the correlation error of the 20 validation samples was 9.36%±2.03%, which confirmed the well predictive ability of established PLS quantitative analysis model.