Objective:To establish a practical patient-based internal quality control method for valproic acid drug concentration monitoring.Methods:Observational Study. A PBRTQC model based on the exponentially weighted moving average (EWMA) method was established using Python. All results of a total of 28, 757 valproic acid concentration data from February 1, 2023 to January 31, 2024 were collected and split into training set and validation set at a ratio of 80% and 20% respectively. The truncation limit (TL) was optimized by using the winsorized mean method and the trimmed mean method. Different weighting coefficients λ were set. Different TL and different λ were combined with the EWMA algorithm into different patient-based real-time quality control (PBRTQC) models. The optimized models were verified by introducing simulated constant errors (CE) and proportional errors (PE) respectively. The false positive alarm rate (FAR) was used to evaluate specificity, and the average number of patients before error detection (ANPed) was used to evaluate sensitivity. According to the daily test volume and quality target requirements, we comprehensively judged whether the performance evaluation indicators of FAR and ANPed meet the laboratory requirements. Bias detection curve was used for determination of the best model.Results:The parameters of the best PBRTQC model for valproic acid drug concentration monitoring are: trimmed mean method with 1.5 standard deviations (i.e., truncating data outside 1.5 standard deviations of the data mean), λ=0.01. The performance verification result shows that ANPed of CE and PE of this model are both less than 100. The comparison between the EQA results and the EWMA results show that the EWMA method results are comparable to the EQA results.Conclusion:A PBRTQC model for the valproic acid drug concentration monitoring project based on the EWMA method has been successfully established. It is comparable with both IQC and EQA results, which means PBRTQC may be used as a supplement to the quality control of daily quality control products.

Psychotropic medication plays a crucial role in the field of mental illness,and the issues of drug efficacy and safety due to individual differences cannot be ignored.Genetic factors,especially the genetic poly-morphisms related to drug-metabolizing enzymes,drug action targets,and risk,have a significant impact on drug responses.Pharmacogenomics,by detecting genetic polymorphisms,can reveal a patient's inherited tend-encies towards drug efficacy,pharmacokinetic characteristics,and potential toxicity,thereby predicting the therapeutic effects and adverse reactions of drug treatment,and providing guidance for personalized therapy.Therefore,individualized medication based on pharmacogenomics helps to improve cure rates,reduce relapse rates,and decrease medical costs,which is of great significance to clinical medication in mental illness.

Objective:To establish a practical patient-based internal quality control method for valproic acid drug concentration monitoring.Methods:Observational Study. A PBRTQC model based on the exponentially weighted moving average (EWMA) method was established using Python. All results of a total of 28, 757 valproic acid concentration data from February 1, 2023 to January 31, 2024 were collected and split into training set and validation set at a ratio of 80% and 20% respectively. The truncation limit (TL) was optimized by using the winsorized mean method and the trimmed mean method. Different weighting coefficients λ were set. Different TL and different λ were combined with the EWMA algorithm into different patient-based real-time quality control (PBRTQC) models. The optimized models were verified by introducing simulated constant errors (CE) and proportional errors (PE) respectively. The false positive alarm rate (FAR) was used to evaluate specificity, and the average number of patients before error detection (ANPed) was used to evaluate sensitivity. According to the daily test volume and quality target requirements, we comprehensively judged whether the performance evaluation indicators of FAR and ANPed meet the laboratory requirements. Bias detection curve was used for determination of the best model.Results:The parameters of the best PBRTQC model for valproic acid drug concentration monitoring are: trimmed mean method with 1.5 standard deviations (i.e., truncating data outside 1.5 standard deviations of the data mean), λ=0.01. The performance verification result shows that ANPed of CE and PE of this model are both less than 100. The comparison between the EQA results and the EWMA results show that the EWMA method results are comparable to the EQA results.Conclusion:A PBRTQC model for the valproic acid drug concentration monitoring project based on the EWMA method has been successfully established. It is comparable with both IQC and EQA results, which means PBRTQC may be used as a supplement to the quality control of daily quality control products.

Objective To observe the effect of over-expressed amyloid procursor protein(APP) gene on the cholinergic receptor binding and the ChAT activity in human neuroblastoma cell line SH-SY5Y.Methods SH-SY5Y cells were transfected with pCMV695 plasmid containing wild type human APP695 gene by Lipofectamine 2000 method.The expression of APP was detected by Western blot.A? contents were test by ELISA assay in over-expression SH-SY5Y cell clones(SH-SY5Y-APP).The special binding of muscarinic and nicotinic cholinergic receptors in those A?-overproducing cell clones was determined by radio-ligand binding method.The cholinergic acetyl transferase(ChAT) activity was assayed by radiao-immunoassay.Results No evident morphologic changes of cytotoxicity were detected after transfection.When A? production was 2～2.6 times as much as that of normal cells,muscarinic receptor binding was decreased from 18.5 % to 21.9 %(P