Objective To observe the value of T1 mapping combining diffusion weighted imaging(DWI)for noninvasive preoperative predicting tumor-infiltrating lymphocyte(TIL)level in invasive breast cancer.Methods Totally 143 patients with invasive breast cancer were retrospectively collected and divided into high group(TIL≥10％,n=73)and low group(TIL＜10％,n=70)according to TIL level by postoperation pathology.Clinicopathological information were collected,MRI features of breast cancer lesions were documented,mean T1 values(T1mean)and mean ADC values(ADCmean)were measured,and then were compared between groups.Multivariate logistic regression analysis was used to identify independent predictive factors of TIL levels,and a nomogram was constructed based on regression model.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to evaluate the predictive value for TIL levels.Results Compared with low group,high group had higher proportion of human epidermal growth factor receptor 2(HER2)positivity(P＜0.05),and showed more circular/oval shapes and more smooth margins but less peritumoral edema(all P＜0.05).Significant differences of lesions enhancement pattern was found between groups(P＜0.05).T1mean and ADCmean were both higher in high group than those in low group(both P＜0.05).Lesions enhancement pattern,T1mean and ADCmean were all independent predictors of TIL levels in breast cancer.The AUC of nomogram combining the above 3 factors for predicting TIL level was 0.848,significantly higher than that of lesions enhancement pattern(AUC=0.569,Z=5.384,P＜0.05)and T1mean(AUC=0.662,Z=3.876,P＜0.05),but not statistically different with that of ADCmean(AUC=0.814,Z=1.578,P=0.115).Decision curve analysis showed that this nomogram had good clinical application value.Conclusion Combining T1 mapping and DWI could effectively predict level of TIL level in breast cancer before surgery.

Objective To observe the value of T1 mapping combining diffusion weighted imaging(DWI)for noninvasive preoperative predicting tumor-infiltrating lymphocyte(TIL)level in invasive breast cancer.Methods Totally 143 patients with invasive breast cancer were retrospectively collected and divided into high group(TIL≥10％,n=73)and low group(TIL＜10％,n=70)according to TIL level by postoperation pathology.Clinicopathological information were collected,MRI features of breast cancer lesions were documented,mean T1 values(T1mean)and mean ADC values(ADCmean)were measured,and then were compared between groups.Multivariate logistic regression analysis was used to identify independent predictive factors of TIL levels,and a nomogram was constructed based on regression model.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to evaluate the predictive value for TIL levels.Results Compared with low group,high group had higher proportion of human epidermal growth factor receptor 2(HER2)positivity(P＜0.05),and showed more circular/oval shapes and more smooth margins but less peritumoral edema(all P＜0.05).Significant differences of lesions enhancement pattern was found between groups(P＜0.05).T1mean and ADCmean were both higher in high group than those in low group(both P＜0.05).Lesions enhancement pattern,T1mean and ADCmean were all independent predictors of TIL levels in breast cancer.The AUC of nomogram combining the above 3 factors for predicting TIL level was 0.848,significantly higher than that of lesions enhancement pattern(AUC=0.569,Z=5.384,P＜0.05)and T1mean(AUC=0.662,Z=3.876,P＜0.05),but not statistically different with that of ADCmean(AUC=0.814,Z=1.578,P=0.115).Decision curve analysis showed that this nomogram had good clinical application value.Conclusion Combining T1 mapping and DWI could effectively predict level of TIL level in breast cancer before surgery.

Objective To investigate the value of MRI radiomics for the preoperative noninvasive prediction of isocitrate dehydrogenase(IDH)mutation status in glioma.Methods Totally,306 glioma patients were retrospectively selected.All patients were randomly assigned into training group(n=214)and validation group(n=92)at a ratio of 7∶3.Region of interest(ROI)was manually delineated by two radiologists independently on the fluid attenuated inversion recovery(FLAIR)and contrast enhanced(CE)MRI images for obtaining whole volume of interest(VOI)of lesion.A total of 851 radiomics features were extracted from the VOI,respectively.The least absolute shrinkage and selection operator(LASSO)method was used for features dimension reduction combing 10-fold cross validation.Three Radiomics score(Radscore)were calculated by linear combination of retained features and their corresponding coefficients.The optimal Radscore and clinical characteristics were incorporated to perform logistic regression analysis for establishing the IDH mutation status noninvasive prediction model.A nomogram was plotted for realizing the visualization of model.The receiver operating characteristic(ROC)curve was plotted to evaluate the prediction performance of model.The calibration and clinical utility of the model were evaluated by calibration curve and decision curve.Results The area under the curve(AUC)of Radscore-combined based on combination of two sequences was 0.856 in the training group,which was superior to the Radscore-CE(AUC=0.821),Radscore-FLAIR(AUC=0.766)from single sequence,with consistent result in the validation group.The addition of clinical characteristics to the model improved predictive value with AUC,sensitivity and specificity of 0.898,79.59％,90.52％in the training group.Conclusion The radiomics model based on FLAIR and CE MRI contributes to preoperative noninvasive prediction of IDH mutation status in glioma.The combination of multi-sequence and the addition of clinical characteristics can improve the prediction performance.

Objective To investigate the value of MRI radiomics for the preoperative noninvasive prediction of isocitrate dehydrogenase(IDH)mutation status in glioma.Methods Totally,306 glioma patients were retrospectively selected.All patients were randomly assigned into training group(n=214)and validation group(n=92)at a ratio of 7∶3.Region of interest(ROI)was manually delineated by two radiologists independently on the fluid attenuated inversion recovery(FLAIR)and contrast enhanced(CE)MRI images for obtaining whole volume of interest(VOI)of lesion.A total of 851 radiomics features were extracted from the VOI,respectively.The least absolute shrinkage and selection operator(LASSO)method was used for features dimension reduction combing 10-fold cross validation.Three Radiomics score(Radscore)were calculated by linear combination of retained features and their corresponding coefficients.The optimal Radscore and clinical characteristics were incorporated to perform logistic regression analysis for establishing the IDH mutation status noninvasive prediction model.A nomogram was plotted for realizing the visualization of model.The receiver operating characteristic(ROC)curve was plotted to evaluate the prediction performance of model.The calibration and clinical utility of the model were evaluated by calibration curve and decision curve.Results The area under the curve(AUC)of Radscore-combined based on combination of two sequences was 0.856 in the training group,which was superior to the Radscore-CE(AUC=0.821),Radscore-FLAIR(AUC=0.766)from single sequence,with consistent result in the validation group.The addition of clinical characteristics to the model improved predictive value with AUC,sensitivity and specificity of 0.898,79.59％,90.52％in the training group.Conclusion The radiomics model based on FLAIR and CE MRI contributes to preoperative noninvasive prediction of IDH mutation status in glioma.The combination of multi-sequence and the addition of clinical characteristics can improve the prediction performance.

Objective:To explore the value of amide proton transfer weighted imaging (APTWI) combined with human epidermal growth factor receptor 2 (HER2) expression in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer.Methods:The study was a cross-sectional study. Clinicopathological [estrogen receptor (ER), progesterone receptor (PR), HER2, Ki-67 status, and molecular subtypes] and imaging data were retrospectively analyzed in 100 female patients who had invasive ductal carcinoma of the breast confirmed pathologically by preoperative puncture in the Henan Cancer Hospital from May 2023 to May 2024. All patients underwent MRI, including enhanced MRI, APTWI, and diffusion-weighted imaging (DWI) before NAC. The reference enhanced MRI images were segmented into lesions using the threshold extraction method, and the three-dimensional region of interest within the tumor was automatically outlined by the software and replicated in the amide proton transfer map generated by APTWI and the apparent diffuse coefficient (ADC) map generated by DWI. The magnetization transfer ratio asymmetry (MTRasym) value and the ADC value were measured, respectively. Tumor response to NAC was assessed using the Miller-Payne grading system, where Grade 5 indicated pCR and Grades 1-4 were classified as non-pCR. Independent sample t-tests and χ2 tests were used to compare clinical pathological and imaging parameters between pCR and non-pCR patients. Statistically significant variables were included in multivariate logistic regression to identify independent predictors of pCR. The diagnostic performance of individual and combined indicators for pCR was evaluated using receiver operating characteristic curves and the area under the curve (AUC). DeLong′s test was used to compare AUCs. Results:There were 39 pCR and 61 non-pCR patients. Significant differences were observed between the pCR and non-pCR patients in molecular subtypes, ER, PR, HER2, and Ki-67 statuses ( P<0.05). Pre-treatment MTRasym values were significantly higher in the pCR patients compared to the non-pCR patients ( P=0.005), whereas ADC values showed no statistical difference ( P=0.372). Multivariate logistic regression analysis showed HER2 positivity ( OR=5.87, 95% CI 1.99-17.30, P=0.001) and MTRasym values>2.61% (OR=4.39, 95% CI 1.37-14.08, P=0.013) was independent predictors of pCR after NAC. HER2 positivity combined with MTRasym value>2.61% predicted pCR after NAC in breast cancer with AUC of 0.819, which was superior to HER2 positivity and MTRasym value alone in predicting efficacy ( Z=3.91, P<0.001; Z=2.63, P=0.009). Conclusions:The MTRasym value of pre-treatment APTWI is valuable in predicting pCR after NAC in breast cancer. APTWI combined with HER2 expression status can further enhance the predictive efficacy.

Objective:To explore the value of amide proton transfer weighted imaging (APTWI) combined with human epidermal growth factor receptor 2 (HER2) expression in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer.Methods:The study was a cross-sectional study. Clinicopathological [estrogen receptor (ER), progesterone receptor (PR), HER2, Ki-67 status, and molecular subtypes] and imaging data were retrospectively analyzed in 100 female patients who had invasive ductal carcinoma of the breast confirmed pathologically by preoperative puncture in the Henan Cancer Hospital from May 2023 to May 2024. All patients underwent MRI, including enhanced MRI, APTWI, and diffusion-weighted imaging (DWI) before NAC. The reference enhanced MRI images were segmented into lesions using the threshold extraction method, and the three-dimensional region of interest within the tumor was automatically outlined by the software and replicated in the amide proton transfer map generated by APTWI and the apparent diffuse coefficient (ADC) map generated by DWI. The magnetization transfer ratio asymmetry (MTRasym) value and the ADC value were measured, respectively. Tumor response to NAC was assessed using the Miller-Payne grading system, where Grade 5 indicated pCR and Grades 1-4 were classified as non-pCR. Independent sample t-tests and χ2 tests were used to compare clinical pathological and imaging parameters between pCR and non-pCR patients. Statistically significant variables were included in multivariate logistic regression to identify independent predictors of pCR. The diagnostic performance of individual and combined indicators for pCR was evaluated using receiver operating characteristic curves and the area under the curve (AUC). DeLong′s test was used to compare AUCs. Results:There were 39 pCR and 61 non-pCR patients. Significant differences were observed between the pCR and non-pCR patients in molecular subtypes, ER, PR, HER2, and Ki-67 statuses ( P<0.05). Pre-treatment MTRasym values were significantly higher in the pCR patients compared to the non-pCR patients ( P=0.005), whereas ADC values showed no statistical difference ( P=0.372). Multivariate logistic regression analysis showed HER2 positivity ( OR=5.87, 95% CI 1.99-17.30, P=0.001) and MTRasym values>2.61% (OR=4.39, 95% CI 1.37-14.08, P=0.013) was independent predictors of pCR after NAC. HER2 positivity combined with MTRasym value>2.61% predicted pCR after NAC in breast cancer with AUC of 0.819, which was superior to HER2 positivity and MTRasym value alone in predicting efficacy ( Z=3.91, P<0.001; Z=2.63, P=0.009). Conclusions:The MTRasym value of pre-treatment APTWI is valuable in predicting pCR after NAC in breast cancer. APTWI combined with HER2 expression status can further enhance the predictive efficacy.

Objective:To investigate the role of dendritic cells (DC) in Chlamydia muridarum ( Cm) respiratory infection and their effect on adaptive immune response. Methods:C57BL/6 mice were exposed to 1×10 3 inclusion-forming units (IFU) of Cm through inhalation to establish the mouse model of Cm respiratory infection. The proportion of CD11c + MHCⅡ + DC and the expression of costimulatory molecules (CD40, CD80 and CD86) in spleen tissues were detected by flow cytometry on 0, 3 and 7 d after infection. The expression of IL-12p40, IL-10 and IL-6 at mRNA level in spleen tissues was detected by qPCR. Mouse splenic DC isolated on 7 d after Cm infection were sorted by magnetic beads and then transferred to recipient mice. Th1 response in the recipient mice was measured using intracellular cytokine staining 14 d after infection. Results:Cm respiratory infection induced massive infiltration of DC and promoted the expression of costimulatory molecules on splenic DC. The expression of IL-12 and IL-10 at mRNA level in splenic DC reached the peak on 3 d after infection. Transferring the splenic DC of Cm-infected mice into the recipient mice could alleviate the disease condition in the recipient mice after Cm infection with reduced Cm inclusion-forming units in lung tissues and significantly increased proportion of Th1 cells in lung and spleen tissues. Conclusions:Cm respiratory infection could induce the maturation and activation of DC, which promoted Th1 immune response. DC played an important role in Cm infection.

Objective:To investigate the relationship between transcription factors (TFs) and the prognosis of colon cancer, and to construct a prognosis model through TCGA and GEO dual databases, so as to quantify the risk of patients and guide clinical treatment decisions.Methods:The transcriptome and clinical data of colon cancer in TCGA and GEO databases were used in this study. The transcriptome data were annotated and the gene expression was calculated. The difference analysis of TFs in TCGA and GEO (log2FC > 1, P-value (Fdr) < 0.05) was performed. The difference TFs of double data intersection were used for correlation prognosis analysis ( P<0.01). The risk coefficient and risk value of prognosis-related TFs were calculated by COX multivariate analysis, and the prognosis model of TFs was constructed by COX model with "survival" and "glmnet" package. The survival curve ( P<0.001) and ROC curve (AUC>0.75) of the sequence set and verification set were drawn, and the distribution of risk value was visualized. After grouping according to risk value, GSEA enrichment analysis was calculated, gene set grid was constructed, target genes were predicted, and finally, pathway enrichment analysis of GO and KEGG was carried out. Results:387 TFs with different expressions in TCGA and GEO databases were used to draw heat map, volcanic map and TFs-related forest map, and the prognosis model of colon cancer was constructed according to COX multivariate analysis=0.310×HSF4+0.137×IRX3-0.127×ATOH1+0.290×OVOL3+0.137×HOXC6+0.155×SIX2+0.092×ZNF556-0.444×CXXC5+0.429×TIGD1+0.413×TCF7L1. Through enrichment analysis, our results showed that these prognostic factors may directly or indirectly act on cancer pathways, such as basic cell carcinoma and cancer signaling pathway, local tissue-cell adhesion, and extracellular matrix.Conclusions:The constructed TFs prognosis model of colon cancer can quantify the prognostic risk of colon cancer, and its high-risk group is an independent risk factor of colon cancer prognosis. This model is a new way to evaluate the prognosis of colon cancer.

Objective:To explore effects of different lower limb placement angles on venous drainage among patients undergoing thoracolumbar surgery in prone position.Methods:From January 2017 to January 2018, we selected patients underwent elective thoracolumbar surgery in prone position of Operating Room in Beijing Hospital as research objects. According to the need of surgery, it was divided into 51 cases in low-angle group and 54 cases in traditional-angle group. We compared effects of different lower limb placement angles on patients by taking the changes of lower limb venous blood flow under ultrasound before and after surgery as main observation indicators and taking changes of lower limb skin temperature and leg circumference as the secondary observation indicator.Results:There were statistical differences in the changes of blood flow on the left and right popliteal veins, the left and right posterior tibial veins of patients under ultrasound between two groups before and after surgery ( P<0.05) . There were no statistical differences in the changes of the leg circumference of 10 cm above the left and right ankles and the changes of the leg circumference of 15 cm above the left and right patellae of patients between two groups ( P>0.05) ; there was also no statistical difference in the change of bilateral skin temperature ( P>0.05) . Conclusions:The lower limb placement angles among patients undergoing thoracolumbar surgery in prone position may affect the venous drainage which nurses of Operating Room should pay attention to when placing positions.

Objective:To conduct periodic revalidation of the 15 items and 43 terms autoverification rules of blood analysis after 1 year of application, analyze the application suitability and make the rules improved.Methods:Track the results of 528 010 blood analysis samples of our hospital from August 1, 2019 to January 31, 2020, and analyze the pass rate and interception rate of autoverification; 600 specimens in total were selected randomly for microscope examination, including 300 specimens which touched autoverification rules (1 012 items of autoverification rules) and were intercepted by autoverification and 300 specimens which untouched autoverification rules and were released by autoverification. The abnormal characteristics and unacceptable Delta check of the specimens also need to be concerned at the same time.The false negative rate and false positive rate, true negative rate, true positive rate and pass correct rate of autoverification were verified and compared with the rate of the second phase verification when the autoverification rule was established. The false negative rate, false positive rate, true negative rate and true positive rate of the Delta check rule which 54 716 specimens touched were calculated and compared with the second phase verification rate when the autoverification rule was established.The results of microscopic examination were used as the gold standard for the calculation of the rates, and P<0.05 was considered as a significant difference. The false positive and true positive of 1 012 autoverification rules were analyzed item by item.The false positive and true positive of 108 specimens which touched blast cell autoverification rule were analyzed terms by terms. The mean TAT and median TAT of 528 010 specimens and 193 750 outpatient specimens were calculated respectively, and the report percentages of 528 010 samples that TAT<30, 30-60　and>60 min were calculated respectively. Analyze and evaluate the application suitability of autoverification rules to juge whether they meet the needs of doctors and laboratory. The design process and the rules and application process of autoverification were optimized and improved.Results:The autoverification pass rate was 63.06% (332 971/528 010), the interception rate was 36.94% (195 039/528 010). The false negative rate was 1.00% (1/600), the false positive rate was 12.67% (76/600), the true negative rate was 49% (294/600), the true positive rate was 37.33% (224/600), and the correct rate was 98% (294/300). The pass rate, true negative rate, true positive rate and correct rate of the periodic reverification group were higher than the second phase verification group, the false negative rate and false positive rate were lower than that the second phase verification group. The false negative rate and true positive rate of the Delta check of periodic verification group were lower than that the second phase verification group, the false positive rate and true negative rate were higher than the second phase verification group, there were significant differences in the comparition results. The mean TAT of 528 010 specimens was25 min, and the median TAT was 22 min. The mean TAT of 193 750 outpatient specimens was 23 min, and the median TAT was 20 min. The report percentages of 528 010 samples that TAT<30 min, 30 min-60 min and>60 min were 83.30% (439 819/528 010), 8.00% (42 250/528 010) and 8.70% (45 941/528 010), respectively.Conclusion:The results of periodic revalidation of autoverification after 1 years application show that the 15 items and 43 terms autoverification rules of blood analysis could meet requirements about the accuracy and efficiency of the laboratory, and have a good suitability for application.