Objective: To investigate the identification process of a case with anti-Fy combined with Rh blood group system antibodies and to review the transfusion strategy and epidemiological characteristics of Duffy blood group system antibodies. Methods: The antibody specificity of a patient diagnosed with liver cirrhosis, who exhibited unexpected antibodies, was determined using the microcolumn gel method, enzyme method, and elution test. A retrospective analysis was performed to assess the incidence and clinical characteristics of antibodies associated with the Duffy blood group system among a cohort of 652 003 patients treated at our hospital from 2014 to 2024. Results: The patient's serum contained anti-Fy , anti-c, and anti-E antibodies. Through the targeted recruitment of African international students, the patient successfully received four units of Fy -negative blood that matched the ABO and Rh phenotypes. Between 2014 and 2024, the incidence of Duffy blood group system antibodies was 0.005 7% (37 out of 652 003), with 9 cases (24.3%) combined with Rh antibodies. Conclusion: Patients with anti-Fy combined with Rh antibodies require Fy -negative blood with matched Rh phenotypes. Targeted recruitment based on racial antigen differences can effectively resolve rare blood type transfusion challenges.

Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.

Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.

【Objective】 To identify and propose blood transfusion suggestions for 3 children suspected to have red blood cell T polyagglutination. 【Methods】 According to the RBC reactions with phytohemagglutinin, adult serum and cord blood serum, aggregation test with polybrene reagent and MN antigen phenotype test were carried out on 3 children to confirm the presence of T polyagglutination. The donor serum with negative or weak reactions was selected by minor cross matching for the 3 children who needed therapeutic plasma exchange(TPE). 【Results】 Three cases of RBC T polyagglutination were caused by bacterial infection, with transient appearance of MN antigen; the samples were reactive to peanut agglutinin, soybean agglutinin, adult serum but nonreactive to cord blood serum, and didn′t aggregate after adding polybrene reagent. After receiving timely TPE, the T polyagglutination gradually disappeared. 【Conclusion】 Some bacteria, such as Streptococcus pneumoniae, may cause polyagglutination of red blood cells. The patients with suspected T polyagglutination should be diagnosed in time. For T polyagglutination patients, the minor matched plasma should be used for avoiding the random plasma with anti-T antibody transfusion.

To understand the adverse reactions of Kan,glaite injection and its incidenceand explore the factors affecting the safety of Kan,glaite Injection in clinical use.Methods A noninterventional and prospective hospital based monitoring study and nested case-control study were conducted.All hospitalized cancer patients receiving Kan.glaite injections from 28 hospitals in northeast, north, central,east, south, southwest, northwest regions of China from September 2013 to September 2014 were enrolled in this study. The main monitoring contents included the general status of patients, the drug administration (including indications and combined use), medication safety, and so on. Patients developing adverse reactions related to Kan,glaite injection distributed in the case group and patients who did not develop adverse reactions were randomly selected and distributed into the control group with a ratio of l : 3 , and the factors that affect the safe use of Kan,glaite injection were analyzed.Results A total of 5 022 cancer patients were entered into the study, including 2 926 males and 2 096 females with age from 9 t0 95 years and their average age was (60 + 12) years. The top five tumors were lung cancer (1 456 patients,28.99%) ,intestinal cancer (867 patients,17.26% ) , marmuary cancer ( 372 patients, 7.41% ) , gastric cancer ( 346 patients, 6.89%) , and liver cancer (335 cases,6.67%). There were 3 863 patients (76.92% ) with tumor stage III and IV, 348 patients with allergic history (6.93%) ; 2 524 patients were complicated with other diseases (50.26% ) ; 4 687 patients (93.33%) had combined drug therapy.During the monitoring period,751(14.95%) of 5 022 patients developed adverse events and of them, 18 cases met the criteria of causality of adverse reactions, including 7 cases of phlebitis, 3 cases of nausea and vomiting, 3 cases of chills, 2 cases of rash, 1 case of palpitation, 1 case of transammase increase, and l case of fever. The incidence of adverse reactions was 0.36%. Univariate analysis showed that the incidence of adverse reactions in patients with combined diseases was higher than that in patients without combined diseases (X2=5.4723,P=0.019),the incidence of adverse reactions in patients with combined western medication was higher than that in patients with combined westem and Chinese medication (P=0.002).Logistic regression analysis showed that the influencing factors of adverse reactions were coexisting diseases (OR=1. 636, 95%C/:1.100-2.433,P= 0.013)and combined medication (OR=1.475,95%C/:1.108-1.965,P=0.027).Nested case-control study showed that the influencing factors of adverse reactions induced by Kan.glaite injection were radiotherapy (OR= 1.864,95% C/:0.930-3.736,P<0.01)and corubined medication (OR=1.622,95% C/:1.102-2.389,P<0.01) .Conclusions The incidence of adverse reactions of Kan,glaite injection in clinical application is lower and the safety is good. Coexisting diseases, combined medication and radiotherapy are main factors affecting its clinical safety.

To understand the adverse reactions of Kan,glaite injection and its incidenceand explore the factors affecting the safety of Kan,glaite Injection in clinical use.Methods A noninterventional and prospective hospital based monitoring study and nested case-control study were conducted.All hospitalized cancer patients receiving Kan.glaite injections from 28 hospitals in northeast, north, central,east, south, southwest, northwest regions of China from September 2013 to September 2014 were enrolled in this study. The main monitoring contents included the general status of patients, the drug administration (including indications and combined use), medication safety, and so on. Patients developing adverse reactions related to Kan,glaite injection distributed in the case group and patients who did not develop adverse reactions were randomly selected and distributed into the control group with a ratio of l : 3 , and the factors that affect the safe use of Kan,glaite injection were analyzed.Results A total of 5 022 cancer patients were entered into the study, including 2 926 males and 2 096 females with age from 9 t0 95 years and their average age was (60 + 12) years. The top five tumors were lung cancer (1 456 patients,28.99%) ,intestinal cancer (867 patients,17.26% ) , marmuary cancer ( 372 patients, 7.41% ) , gastric cancer ( 346 patients, 6.89%) , and liver cancer (335 cases,6.67%). There were 3 863 patients (76.92% ) with tumor stage III and IV, 348 patients with allergic history (6.93%) ; 2 524 patients were complicated with other diseases (50.26% ) ; 4 687 patients (93.33%) had combined drug therapy.During the monitoring period,751(14.95%) of 5 022 patients developed adverse events and of them, 18 cases met the criteria of causality of adverse reactions, including 7 cases of phlebitis, 3 cases of nausea and vomiting, 3 cases of chills, 2 cases of rash, 1 case of palpitation, 1 case of transammase increase, and l case of fever. The incidence of adverse reactions was 0.36%. Univariate analysis showed that the incidence of adverse reactions in patients with combined diseases was higher than that in patients without combined diseases (X2=5.4723,P=0.019),the incidence of adverse reactions in patients with combined western medication was higher than that in patients with combined westem and Chinese medication (P=0.002).Logistic regression analysis showed that the influencing factors of adverse reactions were coexisting diseases (OR=1. 636, 95%C/:1.100-2.433,P= 0.013)and combined medication (OR=1.475,95%C/:1.108-1.965,P=0.027).Nested case-control study showed that the influencing factors of adverse reactions induced by Kan.glaite injection were radiotherapy (OR= 1.864,95% C/:0.930-3.736,P<0.01)and corubined medication (OR=1.622,95% C/:1.102-2.389,P<0.01) .Conclusions The incidence of adverse reactions of Kan,glaite injection in clinical application is lower and the safety is good. Coexisting diseases, combined medication and radiotherapy are main factors affecting its clinical safety.

Objective To explore effects of paclitaxel on proliferation and migration of breast cancer Michigan Cancer Foundation-7 (MCF-7) cells via mammalian target of rapamycin (mTOR) signaling pathway.Methods The cases were randomly divided into four groups,including control group,paclitaxel low-dose group (0.25 μmol/L),paclitaxel medium-dose group (0.5 μmol/L),and paclitaxel high-dose group (1 μmol/L).The viability of MCF-7 cells was measured with methyl thiazolyl tetrazolium (MTT) assay.MCF-7 cell cycle was examined with flow cytometry.MCF-7 cell migration was tested with transwell migration assay.The levels of mTOR signalling pathway-related protein were assayed with Western blot.Results Compared to the control group,MCF-7 cell viability was significantly decreased in paclitaxel low,medium and high-dose groups (P ＜ 0.05),and the inhibitory rate was highest at 48 h (P ＜ 0.05).MCF-7 cell migration was significantly inhibited in paclitaxel low,medium and high-dose groups [(98 ± 9.78) vs (86.21 ± 6.58),(53.41 ± 3.16) and (42.00 ± 4.69),P ＜ 0.05].Moreover,compared to the control group,the number of MCF-7 cells at G1 phase was significantly increased in paclitaxel low,medium and high-dose groups [(52.14±6.13)％ vs (67.93 ±8.16)％,(72.32 ±3.67)％ and (78.53 ± 6.28)％,P ＜ 0.01],the number of MCF-7 cells at G2 phase was significantly reduced in paclitaxel low,medium and high-dose group [(13.68 ± 0.85) ％ vs (8.57 ± 1.03) ％,(5.30 ± 0.89) ％ and (3.46 ± 0.78) ％,P ＜0.01].The phosphorylations of 4E binding protein (4EBP1) and mTOR proteins as well as the expressions of cell-cycle protein D1 (Cyclin D1) and matrix metalloproteinase-9 (MMP-9) were significantly inhibited in paclitaxel low,medium and high-dose groups (P ＜ 0.01).Conclusions These results suggested paclitaxel could inhibit proliferation and migration in breast cancer MCF-7 cells,which might be related to mTOR signal pathway.

[Objective] To investigate the variation of Q promoter (Qp) in nasopharyngeal carcinoma (NPC) cells, and to compare the existing two mutant sites [62 225 site(g→a)and 62 422 site (g→c) ] Qp in NPC cells with the Qp in B95.8 cell line in the functional and biological difference. [Methods] The Qp sequence was amplified in the samples from 29 cases of paraffin-embedded tissues of NPC suffers and 14 cases of peripheral blood of healthy adults by polymerase chain reaction (PCR) method (totally 43 cases). The point mutations on specified sites were analyzed and statistically compared from sequencing results. The sequences of variant and prototype Qp were amplified by PCR and cloned into luciferase reporter vector (pGL3-basic), then transfected into HaCat cells respectively. The transcriptional activity was compared between variant and prototype Qp using luciferase reporter system. The DNA binding affinity of mutant and prototype Qp to Sp1 was compared through chromatin immunoprecipitation (CHIP) method since mutation of nt 62 225 located in a Spl binding site. [Results] The mutation rate of Qp was significantly higher in NPC compared with healthy controls (P=0.039 5, <0.05), which suggested the variant Qp was closely associated with NPC. The transcription of the luciferase gene promoted by variant Qp was significant more than that of prototype Qp in transient transfection assay (2.5:1, P<0.05). The binding affinity of variant Qp to Sp1 was about 1.52 times higher than that of prototype Qp as determined by quantitative ChIP assay. [Conclusions] The transcriptional activity was enhanced in variant Qp in NPC cells compared with prototype, which possibly through the higher binding affinity to Sp1. We suggest that the mutated Qp may play an important role during the EBV infection and transformation of nasopharyngeal epithelium.

Objective To investigate the expression and their clinical significance of P?- glycoprotein (P?- gp), glutathione?- S?- transferase?- ?(GST?- ?)and DNA topoisomerase Ⅱ(TopoⅡ)in breast cancer. Methods The expressions of P?- gp, GST?- ? and TopoⅡ were detected in 60 cases of untreated primary breast cancer by using immunohistochemical S?- P method. Results The positive expression rates of P?- gp, GST?- ? and TopoⅡ in breast cancer were 37.1 %, 66.7 % and 55.0 % respectively. The expression of P?- gp and TopoⅡ had positive correlation with degree of differentiation(P