Objective:To establish a serum detection method of surface-enhanced Raman spectroscopy (SERS) combining with machine learning for early screening of colorectal cancer (CRC).Methods:Serum samples were collected from 150 CRC patients diagnosed at Jiangdu People′s Hospital, Affiliated to Yangzhou University, and also from 37 healthy subjects. Gold nanohexapod (AuNHs) arrays were prepared using an oil-water interface self-assembly method. A 5 μl serum sample was applied onto the AuNHs array. Scatheless and rapid detection for serum were performed using a Renishaw inVia Raman spectrometer at room temperature with a laser wavelength of 785 nm, exposure time of 10 s, and power of 5 mW. The raw SERS spectra were preprocessed using Savitzky-Golay smoothing, AsLS baseline correction, and Min-Max normalization with Origin 2019 software. Furthermore, the principal component analysis (PCA)-support vector machine (SVM) model was constructed using Python′s scikit-learn library. Leave-One-Out Cross-Validation (LOOCV) was used to evaluate the model′s accuracy, sensitivity, specificity, and area under the curve (AUC).Results:The AuNHs arrays exhibited uniform morphology. The relative standard deviation (RSD) of the SERS intensity at 1 080 cm -1 was 5.69%, and the RSD of the SERS intensity at 1 340 cm -1 was 6.20%. The limit of detection (LOD) of the AuNHs array was 9.42×10 -12 mol/L. The PCA-SVM model achieved an accuracy of 90.91% (170/187), sensitivity of 96.79% (181/187), specificity of 99.47% (186/187), and an AUC of 0.98. The most significant characteristic peaks distinguishing different CRC stages were at 747, 940, 1 000, 1 447, and 1 612 cm -1. Conclusion:The serum detection method based on SERS combined with machine learning can accurately screen CRC with higher accuracy, sensitivity, and specificity, demonstrating potential clinical application value.

Objective:To establish a serum detection method of surface-enhanced Raman spectroscopy (SERS) combining with machine learning for early screening of colorectal cancer (CRC).Methods:Serum samples were collected from 150 CRC patients diagnosed at Jiangdu People′s Hospital, Affiliated to Yangzhou University, and also from 37 healthy subjects. Gold nanohexapod (AuNHs) arrays were prepared using an oil-water interface self-assembly method. A 5 μl serum sample was applied onto the AuNHs array. Scatheless and rapid detection for serum were performed using a Renishaw inVia Raman spectrometer at room temperature with a laser wavelength of 785 nm, exposure time of 10 s, and power of 5 mW. The raw SERS spectra were preprocessed using Savitzky-Golay smoothing, AsLS baseline correction, and Min-Max normalization with Origin 2019 software. Furthermore, the principal component analysis (PCA)-support vector machine (SVM) model was constructed using Python′s scikit-learn library. Leave-One-Out Cross-Validation (LOOCV) was used to evaluate the model′s accuracy, sensitivity, specificity, and area under the curve (AUC).Results:The AuNHs arrays exhibited uniform morphology. The relative standard deviation (RSD) of the SERS intensity at 1 080 cm -1 was 5.69%, and the RSD of the SERS intensity at 1 340 cm -1 was 6.20%. The limit of detection (LOD) of the AuNHs array was 9.42×10 -12 mol/L. The PCA-SVM model achieved an accuracy of 90.91% (170/187), sensitivity of 96.79% (181/187), specificity of 99.47% (186/187), and an AUC of 0.98. The most significant characteristic peaks distinguishing different CRC stages were at 747, 940, 1 000, 1 447, and 1 612 cm -1. Conclusion:The serum detection method based on SERS combined with machine learning can accurately screen CRC with higher accuracy, sensitivity, and specificity, demonstrating potential clinical application value.

Objective:To analyze the epidemiological and clinical characteristics of human brucellosis in Sichuan Province, and to provide a scientific basis for prevention and control of brucellosis.Methods:Retrospective analysis methods were used to collect the data of reported cases of brucellosis in Sichuan Province from 2018 to 2020 from the "Infectious Disease Reporting Information Management System" in the "China Disease Prevention and Control Information System". The distribution (population, time and region) and case investigation data (epidemiological contact history and clinical manifestations) were analyzed by descriptive epidemiological.Results:A total of 309 cases of brucellosis were reported in Sichuan Province in 2018 - 2020, and no deaths occurred. The annual incidence rate was 0.123/100 000. Among the 309 reported cases of brucellosis, the sex ratio of men to women was 2.4 ∶ 1.0 (218 ∶ 91); the age of onset was mainly 20 - 60 years old, accounting for 79.9% (247/309); the occupation was mainly farmers, accounting for 51.8% (160/309). Brucellosis cases were reported in every month of the year, mainly from March to August, accounting for 72.5% (224/309) of the total number of cases. Cases were reported in 21 cities (autonomous prefectures) in Sichuan Province from 2018 to 2020. The top 5 cities (autonomous prefectures) in terms of cumulative number of cases were Chengdu, Liangshan, Zigong, Neijiang and Luzhou in order, accounting for 60.5% (187/309) of the total number of cases. Among the 309 reported cases of brucellosis, 279 case investigations were completed. Among them, 258 cases had a history of contact with livestock and their products, accounting for 92.5%; all contacted livestock were sheep; the main mode of transmission was direct contact, accounting for 88.5% (247/279); the main routes of transmission were sheep farming (50.2%, 140/279), sheep slaughtering (18.6%, 52/279), selling mutton or cutting sheep products in mutton restaurants (11.1%, 31/279); the main clinical manifestations were fever, fatigue, hyperhidrosis, and muscle and joint pain, accounting for 91.4% (255/279), 89.2% (249/279), 80.3% (224/279) and 65.2% (182/279), respectively.Conclusions:The spread of brucellosis in Sichuan Province is increasing, and spring and summer are the epidemic seasons. Brucellosis cases are most common in sheep farms, and the infection method is mainly direct contact infection; the clinical manifestations are diverse and non-specific. Relevant departments should strengthen the prevention and control of the disease in key populations and regions to control the outbreak of brucellosis.

Objective To analyze the expressions of p27 and PCNA and cell apoptosis in ulcerative colitis and colon cancer.Methods The expressions of p27 and proliferation cell nuclear antigen(PCNA)proteins in colorectal cancer,ulcerative colitis and normal colon were analyzed by StrptAvidin-Biotin Enzyme Complex immunohistochemical methods.The apoptosis was detected using terminal uridine deoxynucleiotide nick end labeling(TUNEL) histochemistry.Results The level of PCNA expression in colorectal cancer and ulcerative colitis was markedly higher than that in normal colon(P