Objective:To investigate the ways of pharmaceutical care performed by clinical pharmacists for the patients with severe infection. Methods:Through deciding the anti-infection therapeutic regimen, providing drug counselling and pharmacy education and focusing on adverse drug reactions, pharmacists offered suggestions for one patient with severe legionella pneumonia complicated with AECOPD. Results:The pharmaceutical care performed by clinical pharmacists could solve the problems and improve the compliance, safety, effectiveness and rationality in the drug treatment of the patient. Conclusion:According to the individual condition of patients, clinical pharmacists can realize their own values through looking for the breakthrough points of pharmaceutical care and participating in clinical practice.

Objective:To prepare compound fluconazole gel and to establish a method for its quality control. Methods: Flucon-azole and baicalin were the main ingredients in the gel, and their contents were determined by HPLC. Moreover, the drug release in vitro, irritation and stability of the gel were tested. Results:The fluconazole and baicalin had a good linear relationship within the range of 12. 5-150. 0 μg·ml-1 and 25. 0-175. 0 μg·ml-1, respectively. The average recovery rate was 99. 45%(RSD=0. 40%, n=9) and 99. 31%(RSD=0. 31%, n=9), respectively. The in vitro drug release of the gel was accordance with the first order release e-quation, and the cumulative release rate of fluconazole and baicalin in 12 h was 87. 6% and 80. 03%, respectively. The gel was stable without irritation. Conclusion:The formula, preparation technology and stability of compound fluconazole gel are promising and the quality standard is controllable.

OBJECTIVE To prepare Angelica•Cinnamomum（Angelica sinensis-Cinnamomum cassia ）self•microemulsion drug delivery system （AC•SMEDDS），and to optimize its formulation and characterize its preparation . METHODS Using Angelica• Cinnamomum mixed volatile oil as oil phase and model drug ，on the basis of selecting emulsifier and co -emulsifier and the optimization of their mass ratio range ，the formulation was optimized with central composite design •response surface methodology using the ratio of oil phase （Angelica•Cinnamomum mixed volatile oil ），mass ratio of emulsifier and co -emulsifier as factors ，the comprehensive score of volatile oil content ，particle size and emulsifying time as index . Morphology，particle size ，drug loading ， entrapped efficiency and stability of optimized AC•SMEDDS were characterized . RESULTS The optimum formulation of AC•SMEDDS contained the ratio of oil phase was 30%，and the mass ratio of emulsifier （EL•40）and co -emulsifier（ethanol）was 9∶1. Results of validation tests showed that the average particle size of AC•SMEDDS was （148.33±1.53）nm，and emulsifying time was （18.44±0.11）s. The comprehensive score was 0.68，relative deviation of which from the predicted value （0.70）was 2.86%. AC•SMEDDS prepared by optimal formulation was faint yellow ，uniform and transparent liquid ，and spherical particals with translucent edge were observed under transmission electron microscope . Calculated by ligustilide and cinnamaldehyde ，the drug loading was （7.58±0.03） and （4.17±0.01） mg/g，and entrapped efficiency was （93.25±0.01）% and （88.89±0.02）% ， respectively. No stratification or precipitation occurred after centrifugation at the speed of 10 000 r/min or placing within 7 （No.2019-0520） days at 4 and 25 ℃ . The contents of ligustilide and cinnamaldehyde were stable . Its particle size had no significant change after 50，100 and 200 times dilution by purified water . CONCLUTIONS AC•SMEDDS is prepared successfully and its formulation is optimized . The stability of the preparation is good .

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac®) at a 28-day interval. Using TMT-based proteomics, we identified 1,715 serum and 7,342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) at baseline using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers before vaccination for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.
Humans ; COVID-19 Vaccines ; Leukocytes, Mononuclear ; Proteomics ; COVID-19/prevention & control* ; Vaccination ; Antibodies ; Antibodies, Viral ; Antibodies, Neutralizing