【Abstract】 Objective To explore a method to identify the sensory and motor fascicles in peri pheral nervetrunk. Methods Thirty Wistar rats were selected to obtain whole spine. The spinal gangl ion, its dorsal root and ventral root,and sciatic nerve were harvested, Annexin V and Agrin specificities were observed with Western blot. In the experimental group,anterior branch and posterior branch of spinal nerve, sciatic nerve, and its muscular branch and cutaneous branch were harvested from 15 rats to make the observation of immunohistochemistry. In the other 15 rats, first antibody was replaced by PBS as control group. Different nerve fascicles were studied with Micro Raman scattering technique in 16 12-month-old New Zealand rabbits. Results The Annexin V and Agrin were special substances of sensory and motor nerves respectively and can act as specific antigens for identifying different nerve fascicles. There were significant differences in the intensity and breadth of the peak of the spectral properties between motor and sensory fascicles at frequencies of 1 088, 1 276, 1 439, 1 579 and 1 659 cm-1 .The peak intensity ratios of 1 276 to 1 439 cm-1 were 0.95±0.06 in motor nerve fascicles and 1.17±0.08 in sensory fascicles, showing significant differences (P lt; 0.05). Conclusion The Micro Raman spectra is more effective than immunohistochemistry in identifying different nerve fascicles, and it possesses as feasibil ity for cl inical appl ication.
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.
ObjectiveTo systematically review the clinical significance of Raman spectroscopy (RS) in the auxiliary diagnosis of colon cancer (CC). MethodsPubMed, Web of Science, The Cochrane Library, CNKI, VIP and WanFang Data databases were electronically searched to collect diagnostic tests related to RS in the auxiliary diagnosis of CC from inception to October 1st, 2021. Two reviewers independently screened the literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed using Stata 12.0 and Meta-Disc 1.4 software. ResultsA total of 21 studies involving 1 419 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and positive posttest probability (PPP) for CC screening applying RS were 0.94 (95%CI 0.93 to 0.95), 0.91 (95%CI 0.90 to 0.92), 157.50 (95%CI 74.44 to 333.21), 10.40 (95%CI 6.62 to 16.33), 0.08 (95%CI 0.05 to 0.12) and 77%, respectively. The area under the curve (AUC) of summary receiver operating characteristic (SROC) curve was 0.98 (95%CI 0.96 to 0.99). ConclusionCurrent evidence shows that RS is a potentially useful tool for CC screening. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.