Objective To determine the content of paeoniflorin with different medicinal parts in Radix Paeoniae Alba and offer a reference for collection and processing. Methods The content of paeoniflorin was measured by HPLC method. Combining air drying with weight relief condition, the content of paeoniflorin was calculated in fresh Radix Paeoniae Alba. Results There was the highest content of paeoniflorin in Radix Paeoniae Alba in 24～48 hours after collection, and the another increasing of the content was founded 120 hours after collection. Conclusion Radix Paeoniae Alba should be processed in 48 hours after collection.

BACKGROUND:The peri-implant bone absorption is closely related to the repair effect. OBJECTIVE:To compare the effects of three kinds of dental implant systems on the peri-implant bone absorption. METHODS:116 patients who underwent the dental implant systems were col ected, including 46 cases with 3I implant system, 40 cases with ITI implant system and 30 cases with BLB implant system. The peri-implant bone absorption, sulcus bleeding index and periodontal probing depth of three groups were detected at 1, 3, 6, 9 and 12 months after implantation, respectively. RESULTS AND CONCLUSION:The peri-implant bone absorption of three groups within 1 year after implantation was in a rise, and the bone absorption of BLB group was significantly higher than that of ITI and 3I groups at 3 and 12 months after implantation (P<0.05). Compared with the natural teeth, the gingival sulcus bleeding index of three groups were al increased at different time points after implantation;the gingival sulcus bleeding index of BLB group was significantly higher than that of natural teeth at 6 months after implantation (P<0.05);the gingival sulcus bleeding index of three groups were significantly higher than that of natural teeth at 9 months after implantation (P<0.05). The periodontal probing depth of three groups showed an ascending trend at 6 months after implantation;the periodontal probing depth of three groups was higher than that of natural teeth at different time points after implantation, which exhibited significant differences at 6 and 9 months after implantation (P<0.05). In conclusion, three kinds of dental implant systems exhibit differet effects on the peri-implant bone absorption, but al achieve excel ent clinical efficacies.

Objective:To develop and validate a nomogram model for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on preoperative enhanced computed tomography imaging features and clinical data.Methods:The clinical data of 210 patients with HCC undergoing surgery in the Second Affiliated Hospital of Anhui Medical University from May 2018 to May 2022 were retrospectively analyzed, including 172 males and 38 females, aged (59±10) years old. Patients were randomly divided into the training group ( n=147) and validation group ( n=63) by systematic sampling at a ratio of 7∶3. Preoperative enhanced computed tomography imaging features and clinical data of the patients were collected. Logistic regression was conducted to analyze the risk factors for HCC with MVI, and a nomogram model containing the risk factors was established and validated. The diagnostic efficacy of predicting MVI status in patients with HCC was assessed by receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) of the subjects in the training and validation groups. Results:The results of multifactorial analysis showed that alpha fetoprotein ≥400 μg/ml, intra-tumor necrosis, tumor length diameter ≥3 cm, unclear tumor border, and subfoci around the tumor were independent risk factors predicting MVI in HCC. A nomogram model was established based on the above factors, in which the area under the curve (AUC) of ROC were 0.866 (95% CI: 0.807-0.924) and 0.834 (95% CI: 0.729-0.939) in the training and validation groups, respectively. The DCA results showed that the predictive model thresholds when the net return is >0 ranging from 7% to 93% and 12% to 87% in the training and validation groups, respectively. The CIC results showed that the group of patients with predictive MVI by the nomogram model are highly matched with the group of patients with confirmed MVI. Conclusion:The nomogram model based on the imaging features and clinical data could predict the MVI in HCC patients prior to surgery.