Objective:To develop a simple, rapid, and convenient analysis method for the identification of breast cancer and its molecular sub-types.Methods:A laser confocal Raman spectrometer was used to collect Raman spectrograms of normal breast cells and different molecular sub-types of breast cancer cells, and assign the material origin of the Raman spectral peaks. First, Savitzky-Golay smoothing (with a window size of 9) was selected to perform smoothing and denoising on the Raman spectrogram. Subsequently, an iterative adaptive weighted penalty least squares method was employed for baseline correction, and principal component analysis was used to eliminate outliers. The recognition model of normal breast cells and breast cancer cells and the recognition model of different molecular sub-types of breast cancer cells were established by using three algorithms with different principles, including partial least squares discriminant analysis (PLS-DA), K-nearest neighbor (KNN), and support vector machine (SVM).Results:The Raman spectrogram and Raman peak shifts of normal breast cells and breast cancer cells were similar, but there were significant differences in intensity. The results of the machine learning models showed that the recognition accuracy of PLS-DA and SVM algorithms for distinguishing between normal breast cells and breast cancer cells was above 92.03% and 90.67%, respectively. The recognition accuracy of PLS-DA and SVM algorithms for different molecular sub-types of breast cancer cells was (83.66 ± 2.77)% and (90.55 ± 0.06)%, respectively.Conclusions:The combination of Raman spectroscopy and machine learning algorithms can achieve accurate identification of normal breast cells, breast cancer cells, and different molecular sub-types of breast cancer cells.

OBJECTIVE： To study the effects of Capparis spinosa total alkaloid on Notch pathways related protein Notch2， Delta-like 3 （DLL3）， Jagged1 and Notch intracellular domain 1 （NICD1） in mice with systemic sclerosis （SSc）. METHODS： BALB/c mice were randomly divided into blank control group， model group， positive control group （penicillamine 125 mg/kg）， C. spinosa total alkaloid low-dose， medium-dose and high-dose groups （225， 450， 900 mg/kg）， with 16 mice in each group. Except for blank control group， other groups were given bleomycin subcutaneously for 4 weeks to induce SSc model. C. spinosa total alkaloid groups were given relevant dose of C. spinosa total alkaloid cream for external use. Positive control group was given relevant dose of penicillamine intragastrically. Blank control group and model groups were given cream matrix without drug， once a day， for consecutive 8 weeks. 4 h after last administration， the skin of the administration area of each group of mice was collected. mRNA expression of Notch2 and NICD1 was detected by real-time PCR； the content of DLL3 was measured by ELISA； the protein expression of Jagged1 in skin tissue was detected by immunohistochemstry. RESULTS： Compared with blank control group， mRNA expression of Notch2 and NICD1， DLL3 content， protein expression of Jagged1 were markedly increased， with statistical significance （P＜0.01）. Compared with model group， mRNA expression of NICD1， DLL3 content and protein expression of Jagged1 were decreased significantly in C. spinosa total alkaloid medium-dose and high-dose groups， positive control group， mRNA expression of Notch2 in skin tissue were decreased significantly in C. spinosa total alkaloid high-dose group and positive control group， with statistical significance （P＜0.05 or P＜0.01）. CONCLUSIONS： C. spinosa total alkaloid can inhibit the abnormal expression of Notch2， NICD1， DLL3 and Jagged1 in skin tissue of SSc model mice， and inhibit over activation of Notch pathway in SSc model mice.