Objective To analyse correlation between automatic quantification of emphysema and lung function based on artificial intelligence(AI)model algorithm by chest computed tomography(CT)in patients with chronic obstructive pulmonary disease(COPD).Methods The clinical and imaging data of hospitalized COPD patients who received chest CT plain scan in Taizhou Hospital of Zhejiang Province,Enze Hospital of Taizhou Enze Medical Center(Group)from December 2020 to May 2021 were retrospectively collected,patients were classified into five levels of ventilator-function decline.By using the AI model,the extent of emphysema lesions in COPD patients were calculated,low-attenuation areas below-950HU were identified and their low attenuation area percentage(LAA％)were calculated.Combined with the output results of AI model and whether each variable met the characteristics of normal distribution,Pearson correlation coefficient between percentage of measured forced expiratory volume at the end of 1 second to estimated value(FEV1％)and LAA％of each lung lobe,and the Spearman correlation coefficient between FEV1 as a percentage of forced vital capacity(FEV1/FVC)and LAA％of each lung lobe in patients with different COPD grades were calculated respectively.Results There was a negative correlation between total lung LAA％and FEV1/FVC in moderate COPD(r=-0.632,P=0.001).Total lung LAA％in very severe COPD was negatively correlated with both FEV1/FVC and FEV1％(r=-0.562,P=0.045 and r=-0.701,P=0.004).The results of lung segment analysis showed that LAA％of the left upper lung lobe was more strongly correlated with pulmonary function indicators in extremely severe COPD(r=-0.650,P=0.016 andr=-0.731,P=0.002).The correlation between left inferior lobe LAA％and FEV1/FVC was stronger correlation in patients with moderate COPD(r=-0.712,P=0.000).In smoking patients,LAA％was moderate correlated with FEV1(r=-0.534,P=0.006),and LAA％was moderate correlated with FEV1/FVC(r=-0.564,P=0.003).Conclusion AI-based emphysema quantification results have a good correlation with FEV1/FVC and FEV1％,which can provide strong support for the diagnosis and classification of COPD based on CT plain scan images.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective To analyse correlation between automatic quantification of emphysema and lung function based on artificial intelligence(AI)model algorithm by chest computed tomography(CT)in patients with chronic obstructive pulmonary disease(COPD).Methods The clinical and imaging data of hospitalized COPD patients who received chest CT plain scan in Taizhou Hospital of Zhejiang Province,Enze Hospital of Taizhou Enze Medical Center(Group)from December 2020 to May 2021 were retrospectively collected,patients were classified into five levels of ventilator-function decline.By using the AI model,the extent of emphysema lesions in COPD patients were calculated,low-attenuation areas below-950HU were identified and their low attenuation area percentage(LAA％)were calculated.Combined with the output results of AI model and whether each variable met the characteristics of normal distribution,Pearson correlation coefficient between percentage of measured forced expiratory volume at the end of 1 second to estimated value(FEV1％)and LAA％of each lung lobe,and the Spearman correlation coefficient between FEV1 as a percentage of forced vital capacity(FEV1/FVC)and LAA％of each lung lobe in patients with different COPD grades were calculated respectively.Results There was a negative correlation between total lung LAA％and FEV1/FVC in moderate COPD(r=-0.632,P=0.001).Total lung LAA％in very severe COPD was negatively correlated with both FEV1/FVC and FEV1％(r=-0.562,P=0.045 and r=-0.701,P=0.004).The results of lung segment analysis showed that LAA％of the left upper lung lobe was more strongly correlated with pulmonary function indicators in extremely severe COPD(r=-0.650,P=0.016 andr=-0.731,P=0.002).The correlation between left inferior lobe LAA％and FEV1/FVC was stronger correlation in patients with moderate COPD(r=-0.712,P=0.000).In smoking patients,LAA％was moderate correlated with FEV1(r=-0.534,P=0.006),and LAA％was moderate correlated with FEV1/FVC(r=-0.564,P=0.003).Conclusion AI-based emphysema quantification results have a good correlation with FEV1/FVC and FEV1％,which can provide strong support for the diagnosis and classification of COPD based on CT plain scan images.

Objective:To explore the application value of patient data-based real-time quality control (PBRTQC) in enhancing the effectiveness of internal quality control (IQC) management.Methods:From the PBRTQC real-time quality control intelligent monitoring platform integrated with the laboratory information system (LIS), a total of 35,631 test results of red blood cell (RBC) count, white blood cell (WBC) count, and dehydroepiandrosterone sulfate (DHEA-S) were collected from patients of the Department of General Xi'an Area Medical Laboratory Center from August 1, 2023, to April 1, 2024. The platform was used in patient data distribution characteristics test, EWMA real-time quality control chart procedure establishment, performance validation, effect evaluation, best procedure selection, and real-time operation. The performance evaluation indexes of the best PBRTQC procedure establishment, the cut-off limit range, weighting coefficient, cumulative mean, standard deviation (SD), coefficient of variation ( CV) of the EWMA real-time quality control chart, and the cumulative mean, SD, and CV of its internal quality control data in the same period were counted, and at the same time compared with the quality target (1/3TEa). Coefficient of variation analyses were performed to compare the quality control status of PBRTQC and conventional internal quality control in the presence of warning or alarm prompts based on quality control process records, and alarm messages. Results:The evaluation indexes of the optimal procedures for RBC count, WBC count, and DHEA-S were the probability of error detection (Ped) between 93%-97% and greater than 90%, the false positive rate (FPR) between 0.0%-0.5%, the false negative rate (FNR) between 3.0%-7.0%, and the average number of the patient sample until error detection (ANPed) between 5-11, which is in line with the optimal quality control efficacy quality requirements for the PBRTQC procedure. The patient outcome cut-off concentrations for the optimal procedure EWMA quality control charts ranged from RBC count (3.92-5.16)×10 12/L, WBC count (4.28-7.50)×10 9/L, and DHEA-S (830-2 160) μg/L; (2 160-4 210) μg/L. The weighting coefficients were 0.05, 0.03, and 0.03, respectively. The real-world application of the EWMA real-time quality control charts showed stable and excellent analytical performance of the measurement system, such as out-of-control alarm: RBC count, 1 true alarm, Ped of 95.85%, and FPR of 0%. The cumulative CV of EWMA was less than the quality target; the cumulative CV of DHEA-S was 7.66% and 9.47%, respectively, and the cumulative CV of low level was greater than the quality target (8.33%), and the cumulative CV of high and low levels were 4.12% and 6.25%. Conclusion:The PBRTQC EWMA method can monitor the patient data - in real-time and continuous way. It can also dynamically identify and provide early indication of small changes in analytical performance during the analysis process, and can be used as a supplement to quality control products to improve the efficacy of laboratory quality management.

【Objective】 To investigate the molecular mechanism of long non-coding RNA (lncRNA) TDRG1 in facilitating the malignant progression and poor prognosis of patients with cervical cancer. 【Methods】 Cervical cancer cell lines and normal cervical cell Ect1/E6E7 were collected. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression of TDRG1. Cervical cancer cell lines were transfected with TDRG1-siRNA, and the proliferation of cancer cells was detected by CCK-8 method and cell plate cloning experiment. The invasion and migration of cancer cells were measured by Transwell experiment. The apoptosis of cancer cells was examined by flow cytometry, and the expressions of relevant proteins were tested by Western blot. 【Results】 Compared with Ect1/E6E7, cervical cancer cell lines showed relatively increased expression of TDRG1. Downregulation of TDRG1 expression inhibited the proliferation and colony formation (162±21 vs. 411±33, P<0.05), as well as the invasion and migration (invasion: 86±13 vs. 315±38, P<0.01; migration: 177±22 vs. 406±41, P<0.01) of Hela cells. Meanwhile, the apoptosis of Hela cells increased [(28±1.5)% vs. (16±1.2)%, P<0.05] and the expression of Bcl-2 protein reduced. In addition, TDRG1 knockdown also decreased the activity of autophagy in Hela cells. 【Conclusion】 TDRG1 facilitates the malignant biological progression of cervical cancer by inhibiting the apoptosis and providing a protective autophagy in cervical cells.

0.05).Conclusion The SDF-1/CXCR4 protein may play a role in the malignant transformation of primary ovarian cancer,and it may participate in the initiation,progression and metastasis of ovarian cancer.