Objective To study the pharmacokinetics and detection window of clozapine and its metabolites in human blood, so as to provide experimental basis for forensic cases of identification of clozapine poisoning. Methods 29 Taiyuan Han people's elbow venous blood was collected after given oral administration of 12.5mg clozapine at different time point, in which clozapine and its metabolites were extracted with solid phase extraction (SPE) and determined by HPLC-MS-MS. The qualitative analysis was based on retention time and MRM ions. The quantitative analysis was based on an internal standard method and calibration curve. Using the 3p97 pharmacokinetic software, pharmacokinetic equation of clozapine in the blood were imitated from the C-T data, and pharmacokinetic parameters were calculated. Results The pharmacokinetics of clozapine met a two compartment open model with a first kinetics absorption. The Tmax of clozapine(CLP), demethylclozapine(DMCLP), N-oxidation-clozapine(NO-CLP) respectively were 2.96±1.32h, 8.65±3.00h, 9.31±26.38h; The Cmax of CLP, DMCLP, NO-CLP respectively were 34.68±9.32ng/mL, 11.16±4.15ng/mL, 9.62±13.88ng/mL;The t1/2 of CLP, DMCLP, NO-CLP respectively were 17.02±23.63h, 27.06±12.58h, 41.27±29.75h; The detection window of CLP, DMCLP, NO-CLP respectively were 81.72±26.19h, 93.21±29.40h and 19.93±14.62h. Conclusion The pharmacokinetics of clozapine in blood of Han people is consistent with two compartment open model with a first kinetics absorption. The pharmacokinetics model and parameters of clozapine can provide expirimental basis for forensic identification of clozapine poisoning cases.

Objective: To explore the influence of nursing intervention on bone health of breast cancer patients in aromatase inhibitors (AIs) treatment. Methods: Totally 120 breast cancer patients receiving AIs treatment were randomly divided into the observation group (60 cases) and the control group (60 cases), and bone mineral density was measured. After that, the control group was routinely instructed to take calcium supplements, given health lectures, and real-time management. The observation group were given whole-process bone health management intervention based on "Internet+" on the basis of routine nursing. After 1 year, osteoporosis knowledge questionnaire and bone mineral density test were conducted in the two groups. Results: Score of Osteoporosis Knowledge Test of observation group was 21.38±3.29, and 12.54±5.03 of control group, comparing the two groups was statistically significant (t=10.27, P < 0.05); before intervention bone mineral density of observation group was (1.042±0.138) g/cm², and (1.058±0.127) g/cm² of control group, comparing the two groups has no statistical significance, comparable (t=1.053, P > 0.05); after intervention the bone mineral density of observation group was (0.951±0.108) g/cm², and (0.854±0.100) g/cm² of control group, the comparison between the two groups was statistically significant (t=5.937, P<0.05). After intervention, the incidence of osteoporosis in the observation group was 21.67% (13/60), which was significantly higher than 35.00% (21/60) in the control group (χ²=4.279, P<0.05). Conclusion Whole-course nursing intervention can promote bone health of AIs patients with breast cancer.

Objective     To explore the association between the preoperative systemic immune-inflammation index (SII) and prognosis in non-small cell lung cancer (NSCLC) patients. Methods     A comprehensive literature survey was performed on PubMed, Web of Science, EMbase, The Cochrane Library, Wanfang, and CNKI databases to search the related studies from inception to December 2021. The hazard ratio (HR) and 95% confidence interval (CI) were combined to evaluate the correlation of the preoperative SII with overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS) in NSCLC patients. Results     A total of 11 studies involving 9 180 patients were eventually included. The combined analysis showed that high SII levels were significantly associated with worse OS (HR=1.61, 95%CI 1.36-1.90, P<0.001), DFS (HR=1.50, 95%CI 1.34-1.68, P<0.001), and RFS (HR=1.17, 95%CI 1.04-1.33, P<0.001). Subgroup analyses also further verified the above results. Conclusion     Preoperative SII is a powerful prognostic biomarker for predicting outcome in patients with operable NSCLC and contribute to prognosis evaluation and treatment strategy formulation. However, more well-designed and prospective studies are warranted to verify our findings.