ABSTRACT
Objective:To establish and evaluate a new real-time quality control method that can identify the random errors by using the backpropagation neural network (BPNN) algorithm and taking blood glucose test as an example.Methods:A total of 219 000 blood glucose results measured by Siemens advia 2 400 analytical system from January 2019 to July 2020 and derived from Laboratory Information System of Beijing Chaoyang Hospital Laboratory Department was regarded as the unbiased data of our study. Six deviations with different sizes were introduced to generate the corresponding biased data. With each biased data, BPNN and MovSD algorithms were used and tested, and then evaluated by traceability method and clinical method.Results:For BPNN algorithm, the block size was pre-set to 10 and the false-positive rate in all biases was within 0.1%. For MovSD, however, the optimal block size and exclusive limit were 150 and 10% separately and its false-positive rate in all biases was 0.38%, which was 0.28% higher than BPNN. Especially, for the least two error factors of 0.5 and 1, all the random errors were not detected by MovSD; for the error factor larger than 1, random errors could be detected by MovSD but the MNPed was higher than that of BPNN under all deviations. The difference was up to 91.67 times. 460 000 reference data were produced by traceability procedure. The uncertainty of BPNN algorithm evaluated by these reference data was only 0.078%.Conclusion:A real-time quality control method based on BPNN algorithm was successfully established to identify random errors in analytical phase, which was more efficient than MovSD method and provided a new idea and method for the identification of random errors in clinical practice.
ABSTRACT
Objective:To investigate the application value of establishing the differential diagnosis model of pulmonary tuberculosis using routine laboratory data.Methods:The retrospective study was conducted. The routine laboratory data of newly diagnosed patients with pulmonary tuberculosis and other pulmonary diseases in Beijng Jishuitan Hospital and Beijing Hepingli Hospital from May 2015 to November 2021were collected. According to the random numbers showed in the computer, all the 11516 patients were divided into training dataset and test dataset with a ratio of 9∶1. Four machine learning algorithms, Support Vector Machine, Random Forest, K-Nearest Neighbor and Logistic Regression, were used to build models and select features. The diagnostic accuracy of each model was verified by using the 10-fold cross-validation method and the performance of each model was evaluated by using the receptor operator of characteristic (ROC) curve.Results:Random Forest was selected as the optimal machine learning algorithm to build the best feature model in the study. According to importance scale of factors, the differential diagnosis model of pulmonary tuberculosis consisting of 37 non-specific test indexes. In the validation set and test set the accuracy and area under curve (AUC) of the models were 0.747 and 0.736, the sensitivity, specificity and accuracy were 68.03% and 68.75%, 70.91% and 67.90%, 70.30% and 68.12%, respectively.Conclusion:A key tool in the differential diagnosis model of pulmonary tuberculosis was established by routine laboratory data in combination with machine learning. The results of this study need to be further verified by more data from medical institutions.
ABSTRACT
Objective To investigate the value of glycated hemoglobin(HbA1c)and urine trace albumin (u -ALB)content in early diagnosis of type 2 diabetic nephropathy.Methods 200 patients with type 2 diabetes were selected as diabetes group,and 30 cases of healthy people as control group.According to the content of HbA1c, the diabetes patients were re -divided into low group (HbA1c 10.1%).The levels of HbA1c and u -ALB were detected and the correlation between them was calculated.Results The values of HbA1c (8.85 ±1.22)% and u -ALB (88.3 ±12.4)mg/L in the diabetes group were significantly higher than those of the control group (t =10.88,54.25,all P <0.05);The level of HbA1c in the high,medium and low group[(11.02 ±1.37)%,(8.45 ±2.01)%,(6.88 ±1.23)%]were consistent with the levels of the respective levels of fasting glucose[(13.22 ±2.05)mmol/L,(9.25 ±1.28)mmol/L, (6.27 ±0.63)mmol/L].HbA1c and constituting the u -ALB levels were positively correlated in the high,medium and low group(r =0.452,0.512,0.452,all P <0.05).Conclusion The combined detection of HbA1c and u -ALB levels has important value for early diagnosis of type 2 diabetic nephropathy.
ABSTRACT
AIM:To determine the effect of microRNA-363(miR-363) on HepG2 cells.METHODS:Bioin-formatic analysis was conducted to identify if the Mcl-1 was regulated by miR-363.The expression of Mcl-1 and miR-363 was detected by real-time PCR in normal liver cell line LO2 and hepatocellular carcinoma cell line HepG2, Huh7 and PLC. miR-363 was transfected into the HepG2 cells, and then the level of Mcl-1 was measured.The relative viability was evalua-ted by MTT assay after the HepG2 cells were transfected with miR-363, and the apoptosis was analyzed by flow cytometry with Annexin V/PI staining.RESULTS:Bioinformatic analysis identified that there was a putative target site in the Mcl-1 mRNA for miR-363.Transfection of miR-363 mimics suppressed Mcl-1 expression in the HepG2 cells.Transfection of miR-363 mimics inhibited the cell viability as well as inducing cell apoptosis in HepG2 cells.CONCLUSION: Over-ex-pression of miR-363 significantly inhibits the cell viability and induces apoptosis in HepG2 cells, and the mechanism may be related to the downregulation of Mcl-1 caused by miR-363 transfection.