Objective To explore the current status and influencing factors of oral health knowledge and behavior among the elderly living in Gongshu District of Hangzhou City.Methods A total of 600 elderly individuals were selected by multi-stage stratified cluster random sampling and interviewed with a self-designed questionnaire which included demographic characteristics and oral health knowledge and behavior.The status and influencing factors of oral health behavior were analyzed by single and multiplefactor analysis methods.Results A questionnaire survey was conducted among 600 elderly residents,with a response rate of 99.3％ (596/600) and an effective response rate of 94.1％ (561/600).The rate of good oral hygiene was 50.4％.Those elderly with different age,education level,medical insurance and oral hygiene showed significant difference in qualified rate of oral health behavior (x2 values were 10.79,21.32,5.72 and 16.33,respectively; all P＜0.05).In Logistic regression model,education level was positively correlated with oral health behavior among the elderly,and the qualified rate of oral health behavior of the elderly with education level of junior school or above was 2.69 times higher than that of illiterate elderly (x2=10.53,P=0.001).Conclusion The awareness rate of oral health knowledge among the elderly living in Gongshu District of Hangzhou City is at a relatively higher level,though oral health behavior is moderate.Age,education level,medical insurance and oral hygiene could be impacting factors of oral health behavior.

Objective:To explore the application value of artificial intelligence (AI) in image post-processing of reconstructed CTA based on CT cerebral perfusion (CTP).Methods:Clinical and radiological data of 100 patients suspected of cerebrovascular diseases in Hebei General Hospital from January to July 2020 were retrospectively selected. All patients were divided into A and B group on average according to the different examination schemes. Cerebral CTP examination was performed in group A (the temporal maximum intensity projective data set generated by the first 5 time phases in the maximum period of the difference between arteriovenous CT values selected as subgroup A1, and the corresponding original thin-layer images selected as subgroup A2), single phase CTA examination was performed in group B, manual and AI image post-processing were performed respectively. Subjective scoring of the image data was performed, and the objective bid evaluation indexes such as CT value, noise (SD), signal-to-noise ratio (SNR), contrast to noise ratio (CNR) were measured, the qualified rate of artificial and AI vascular segmentation was counted, and post-processing time were recorded. The objective evaluation indexes were compared between three groups using one-way ANOVA, and the Kruskal-Wallis H test was used to compare the difference of subjective scores.Results:Statistically significant differences were observed in subjective score and objective evaluation index of original images among group A1, group A2 and group B (all P<0.05). Among them, arterial enhancement, arteriolar detail display score, cerebral artery CT value, SNR and CNR in group A1 were higher than those in group A2 and group B (all P<0.05). In a total of 100 patients with 1 100 blood vessels, the qualified rates of AI vascular segmentation in group A1 [98.4% (541/550)] and group B [98.7% (543/550)] were higher than those of manual [82.9% (456/550), 87.1% (479/550), χ2=77.392, 56.521, P<0.001], but the qualified rate of AI vascular segmentation of group A2 [78.4% (431/550)] was lower than that of manual [85.6% (471/550), χ2=9.855, P=0.002]. The completion time of AI post-processing were reduced by 56.30%, 49.63%, 50.81%, respectively than those with manual. Conclusion:Compared with manual image post-processing, AI has certain advantages in image quality and work efficiency of reconstructed CTA post-processing based on CTP de-noising dataset, and it is worth popularizing and applying in the image post-processing of cerebrovascular disease, combined with artificial quality control.

Objective:To investigate the predictive value of atherogenic index of plasma (AIP) in the assessment of acute pancreatitis (AP).Methods:598 patients diagnosed with AP admitted to the Affiliated Hospital of Yangzhou University between January 2016 and December 2020 were recruited and divided into severe acute pancreatitis group (SAP group, n=57) and non-severe acute pancreatitis group (non SAP group, n=541) according to the Atlanta Classification (2012 revision). General clinical data and related biochemical indicators of all enrolled patients were collected, and Bedside Index of Acute Pancreatitis Severity (BISAP) score, Ranson score and CT Severity Index (CTSI) score were performed. The risk factors of SAP were analyzed by logistic regression. Receiver operating characteristic (ROC) curve was used to analyze the evaluation value of AIP and various scoring systems on the severity of pancreatitis. Results:The AIP, white blood cell (WBC), neutrophil count (NEUT), fasting blood glucose (FBG), serum total cholesterol (TC) level, proportion of hyperlipidemia, proportion of diabetes, Ranson score, BISAP score, CTSI score of patients in SAP group were higher than those in non SAP group, and the difference was statistically significant (all P<0.05). Multivariate logistic regression analysis showed that AIP was an independent risk factor for SAP ( P<0.05). ROC curve showed that the are under the curve (AUC) of SAP predicted by AIP was 0.706(95% CI: 0.631-0.782, P<0.001). Conclusions:AIP is an independent risk factor for SAP, which helps to assess the severity of AP.

Background: The systemic immune inflammation index (SII) is a reproducible biomarker of inflammatory process. Aims: To explore the predictive value of SII for severe acute pancreatitis (SAP). Methods: A total of 406 patients with acute pancreatitis (AP) from Jan. 2013 to Dec. 2020 at Affiliated Hospital of Yangzhou University were collected, and were divided into SAP group and non SAP group. ROC curve was drawn to evaluate the value of SII, NLR, PLR, CAR for predicting SAP. Results: Compared with non‑SAP group, SII, NLR, PLR, CAR were significantly increased in SAP group (P<0.05). When the best cut‑off value was 1 705.83, AUC of SII for predicting SAP was 0.754, the sensitivity was 75.47%, and the specificity was 69.12%. AUC of SII for predicting SAP was higher than that of PLR, CAR (Z=2.647, P=0.007; Z= 2.616, P=0.008), while no significant difference was found between SII and NLR (P>0.05). And no significant difference in AUC was found between PLR and CAR (P>0.05). Conclusions: SII is a good new hematological index that can be used to predict the severity of AP, its predictive ability is similar to NLR, better than PLR and CAR.