Objective:To report the blood group antigen and antibody specificity identification methods for a patient with high-frequency antibodies, and the process of finding and providing compatible blood for the patient.Methods:A patient sent from the Blood Transfusion Department of Shanxi Provincial People′s Hospital to Taiyuan Blood Center in November 2022 was selected for the study. Classical serological methods were used to determine the patient′s blood type, screen for unexpected antibodies, identify antibodies, and perform crossmatching. High-frequency antibody identification was carried out using red blood cells treated with various enzymes. Blood group genotyping was conducted using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF) and Sanger sequencing. Multiple strategies were employed to address the patient′s blood source problem. The study was approved by the Medical Ethics Committee of Taiyuan Blood Center [Ethics No. 2024 Ethics Review No.(2)].Results:①The patient′s blood type was B, RhD positive. Initial screening of the patient′s serum with multiple screening cells and antibody identification cells in saline medium was negative, but positive in antiglobulin medium. The patient′s serum showed varying reaction intensities with red blood cells treated with different enzymes. ②MALDI-TOF mass spectrometry and Sanger sequencing revealed a homozygous nonsense variant c. 376C>T (p.Gln126Ter) in the ABCG2 gene, resulting in the Jr(a-) phenotype. During family donor selection, the patient′s son was found to have a heterozygous variant c. 376C>T (p.Gln126Ter), and another heterozygous variant c. 421C>A (p.Gln141Lys), which predicted a Jr(a+ w) phenotype. ③Crossmatch tests confirmed the compatibility of blood from the patient′s son, which was used to address the urgent blood requirement. Later, rare blood from a Jr(a-) donor from the Guangzhou Blood Center was used for the patient′s ongoing treatment, saving the patient′s life. Conclusion:Combining classic serological testing with blood group gene typing techniques successfully identified the rare Jr(a-) blood type and high-frequency anti-Jra antibodies. Enzyme-treated red blood cell identification methods confirmed the presence of anti-Jra antibodies. By searching within the family and seeking help from other blood centers, compatible blood was found. This approach may provide insights for resolving similar complex blood matching problems in the future.

Objective:To assess the safety and efficacy of thiotepa, busulfan, and etoposide (TBE) conditioning followed by autologous hematopoietic stem-cell transplantation (TBE auto-HSCT) in primary central nervous system lymphoma (PCNSL) patients.Methods:Clinical data from 27 PCNSL patients who received TBE auto-HSCT at the Fifth Medical Center of PLA General Hospital between November 1, 2021, and April 30, 2024, were retrospectively analyzed.Results:Twenty-seven patients [16 males, 11 females; median age 57 (23–72) years] were included, with 12 (44.4%, 12/27) over 60. Twenty-five had newly diagnosed PCNSL and 2 were relapsed. Median time from diagnosis to transplantation was 6.9 (5.0–10.0) months. TBE auto-HSCT increased complete remission (CR) rate from 63.0 to 96.3% ( P= 0.005), and 9 of 10 patients in partial remission achieving CR post-transplant. Median follow-up was 24.5 months (range 2.0–36.0). Two-year progress-free and OS rates were (87.2±6.9) % and (88.6±6.2) %, respectively. Common grade 3 nonhematologic adverse events were diarrhea (18.5%, 5/27) and bacterial infections (14.8%, 4/27). One patient (64 years old) died from carbapenem-resistant Enterobacteriaceae infection within 2 months post-transplant, yielding a 100-day treatment-related mortality of 3.7% (1/27) . Conclusion:TBE-conditioned high-dose chemotherapy with auto-HSCT is effective, safe, and well-tolerated in PCNSL patients, including the elderly.

Hypoxia inducible factors(HIFs)are core molecules that enable the body to adapt to hypoxia environments.By sensing changes in intracellular oxygen pressure,HIFs regulate gene expression related to hypoxia adaptation,thereby enhancing the body's hypoxia tolerance at cellular,tissue and organ levels.On the other hand,HIFs promote the generation of red blood cells,angiogenesis,and regulate the body's energy metabolism,thereby improving its hypoxia tolerance.The enhancement of hypoxia tolerance is of great significance for the prevention and treatment of hypoxia-related diseases,upgrading of athletes'performance,enhancement of workers'efficiency at high-altitudes,and the improvement of individu-als'quality of life.This article reviews the relationships between HIFs and hypoxia tolerance as well as related mechanisms in order to provide strategies for enhancing hypoxia tolerance in the body.

OBJECTIVE: To report the blood group antigen and antibody specificity identification methods for a patient with high-frequency antibodies, and the process of finding and providing compatible blood for the patient. METHODS: A patient sent from the Blood Transfusion Department of Shanxi Provincial People's Hospital to Blood Transfusion Technology Research Laboratory of Taiyuan Blood Center in November 2022 was selected for the study. Classical serological methods were used to determine the patient's blood type, screen for unexpected antibodies, identify antibodies, and perform crossmatching. High-frequency antibody identification was carried out using red blood cells treated with various enzymes. Blood group genotyping was conducted using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF) and Sanger sequencing. Multiple strategies were employed to address the patient's blood source problem. The study was approved by the Medical Ethics Committee of Taiyuan Blood Center [Ethics No. 2024 Ethics Review No.(2)]. RESULTS: The patient's blood type was B, RhD positive. Initial screening of the patient's serum with multiple screening cells and antibody identification cells in saline medium was negative, but positive in antiglobulin medium. The patient's serum showed varying reaction intensities with red blood cells treated with different enzymes. MALDI-TOF mass spectrometry and Sanger sequencing revealed a homozygous nonsense variant c.376C>T (p.Gln126Ter) in the ABCG2 gene, resulting in the Jr(a-) phenotype. During family donor selection, the patient's son was found to have a heterozygous variant c.376C>T (p.Gln126Ter), and another heterozygous variant c.421C>A (p.Gln141Lys), which predicted a Jr(a+w) phenotype. Crossmatch tests confirmed the compatibility of blood from the patient's son, which was used to address the urgent blood requirement. Later, rare blood from a Jr(a-) donor from the Guangzhou Blood Center was used for the patient's ongoing treatment, saving the patient's life. CONCLUSION Combining classic serological testing with blood group gene typing techniques successfully identified the rare Jr(a-) blood type and high-frequency anti-Jra antibodies. Enzyme-treated red blood cell identification methods confirmed the presence of anti-Jra antibodies. By searching within the family and seeking help from other blood centers, compatible blood was found. This approach may provide insights for resolving similar complex blood matching problems in the future.
Humans ; Blood Grouping and Crossmatching/methods* ; Blood Group Antigens/immunology* ; Blood Transfusion ; Male ; Isoantibodies/blood* ; Female ; Genotype

Objective This study aimed to explore the feasibility of using a long short-term memory(LSTM)model based on empirical mode decomposition(EMD)and singular spectrum analysis(SSA)to predict the incidence of pulmonary tuberculosis in Shanxi Province.The goal was to provide a reliable prediction method to support the prevention and control of tuberculosis epidemics in the region.Methods Collecting and collating monthly data on the reported incidence of tuberculosis nationwide from January 2007 to December 2018.The LSTM、EMD-LSTM、SSA-LSTM models were established using the reported monthly incidence of tuberculosis reported in Shanxi Province from January 2007 to December 2017 as the training set and using the reported monthly incidence of tuberculosis from January to December 2018 as the test set.Mean squared error(MSE),mean absolute error(MAE),root mean squared error(RMSE),and mean absolute percentage error(MAPE)were used to evaluate the prediction effect of the models to determine the best model.Results The MSE,MAE,RMSE and MAPE of the EMD-LSTM model in predicting the incidence trend of pulmonary tuberculosis in the next year were 0.036,0.140,0.189 and 0.045,respectively.Compared with the LSTM model,the prediction performance increased by 66.36%,38.33%,42.38%and 41.56%,respectively.Compared with the SSA-LSTM model,it improved by 28.00%,9.68%,15.25%and 16.67%,respectively.Conclusion Compared with the single LSTM model,the fitting and prediction performance of EMD-LSTM and SSA-LSTM models are improved effectively.However,the prediction effect of EMD-LSTM model is better than that of SSA-LSTM model.Therefore,the EMD-LSTM model is more suitable for predicting the incidence trend of pulmonary tuberculosis in Shanxi Province,and can provide a theoretical basis for tuberculosis prevention and control policies.

Objective The prediction model of recurrence within two years after complete remission of diffuse large B-cell lymphoma(DLBCL)patients was constructed based on frienemy indecision region dynamic ensemble selection(FIRE-DES)to provide decision-making basis for the treatment of patients.Methods To collect data of 498 patients who achieved complete response after treatment from January 2010 to January 2020 in a Grade-A hospital in Shanxi Province.A FIRE-DES combination prediction model based on four common category-disequilibrium treatment methods was constructed and compared with five traditional single classifiers and two integrated classifiers.Results Among the four categories of unbalance algorithms,synthetic minority oversampling technique and edited nearest neighbor(SMOTE-ENN)algorithm has obtained the optimal classification performance.On this basis,the classification effect of dynamic ensemble selection performance(DESP),K-nearest oracle union(KNORAU)and meta-learning for dynamic ensemble selection(META-DES)dynamic integration selection algorithms is obviously superior to the traditional single classifier and ensemble classifier model.The classification effect of the improved DESP,KNORAU and META-DES dynamic selection algorithms based on Frienemy Indecision Region is further improved.The classification performance of FIRE-META-DES was the best(Accuracy=0.909,Precision=0.906,Recall=0.967,AUC=0.879,F1-Score=0.936,Brier Score=0.088).Conclusion Aiming at the actual DLBCL data set,SMOTE-ENN+FIRE-META-DES combined prediction model for recurrence used in this paper achieves the optimal performance and low computational complexity,which can provide a strong reference for DLBCL recurrence prediction.

Objective To explore the prediction performance of the autoregressive summation moving average(ARIMA)model based on empirical mode decomposition(EMD)for the prevalence trend of tuberculosis,to provide method support for the prediction of tuberculosis,and to provide ideas for the prediction of other infectious diseases.Methods The monthly data of pulmonary tuberculosis incidence in Shanxi Province from January 2008 to December 2018 were collected and sorted.The last three months,six months,nine months and one year of the data were used as the test set to evaluate the model prediction effect,and the training set was the remaining data of the corresponding sequence.The EMD-ARIMA model was constructed to predict and compared with the single ARIMA model.Results The predicted errors of EMD-ARIMA model for the next three months,six months,nine months and one year were all smaller than the errors of ARIMA model.Conclusion Compared with single ARIMA model,EMD-ARIMA model can improve the prediction accuracy and predict the incidence of pulmonary tuberculosis,and provide effective theoretical reference for disease control and prevention.

Objective This study aimed to explore the feasibility of using a long short-term memory(LSTM)model based on empirical mode decomposition(EMD)and singular spectrum analysis(SSA)to predict the incidence of pulmonary tuberculosis in Shanxi Province.The goal was to provide a reliable prediction method to support the prevention and control of tuberculosis epidemics in the region.Methods Collecting and collating monthly data on the reported incidence of tuberculosis nationwide from January 2007 to December 2018.The LSTM、EMD-LSTM、SSA-LSTM models were established using the reported monthly incidence of tuberculosis reported in Shanxi Province from January 2007 to December 2017 as the training set and using the reported monthly incidence of tuberculosis from January to December 2018 as the test set.Mean squared error(MSE),mean absolute error(MAE),root mean squared error(RMSE),and mean absolute percentage error(MAPE)were used to evaluate the prediction effect of the models to determine the best model.Results The MSE,MAE,RMSE and MAPE of the EMD-LSTM model in predicting the incidence trend of pulmonary tuberculosis in the next year were 0.036,0.140,0.189 and 0.045,respectively.Compared with the LSTM model,the prediction performance increased by 66.36%,38.33%,42.38%and 41.56%,respectively.Compared with the SSA-LSTM model,it improved by 28.00%,9.68%,15.25%and 16.67%,respectively.Conclusion Compared with the single LSTM model,the fitting and prediction performance of EMD-LSTM and SSA-LSTM models are improved effectively.However,the prediction effect of EMD-LSTM model is better than that of SSA-LSTM model.Therefore,the EMD-LSTM model is more suitable for predicting the incidence trend of pulmonary tuberculosis in Shanxi Province,and can provide a theoretical basis for tuberculosis prevention and control policies.

Objective The prediction model of recurrence within two years after complete remission of diffuse large B-cell lymphoma(DLBCL)patients was constructed based on frienemy indecision region dynamic ensemble selection(FIRE-DES)to provide decision-making basis for the treatment of patients.Methods To collect data of 498 patients who achieved complete response after treatment from January 2010 to January 2020 in a Grade-A hospital in Shanxi Province.A FIRE-DES combination prediction model based on four common category-disequilibrium treatment methods was constructed and compared with five traditional single classifiers and two integrated classifiers.Results Among the four categories of unbalance algorithms,synthetic minority oversampling technique and edited nearest neighbor(SMOTE-ENN)algorithm has obtained the optimal classification performance.On this basis,the classification effect of dynamic ensemble selection performance(DESP),K-nearest oracle union(KNORAU)and meta-learning for dynamic ensemble selection(META-DES)dynamic integration selection algorithms is obviously superior to the traditional single classifier and ensemble classifier model.The classification effect of the improved DESP,KNORAU and META-DES dynamic selection algorithms based on Frienemy Indecision Region is further improved.The classification performance of FIRE-META-DES was the best(Accuracy=0.909,Precision=0.906,Recall=0.967,AUC=0.879,F1-Score=0.936,Brier Score=0.088).Conclusion Aiming at the actual DLBCL data set,SMOTE-ENN+FIRE-META-DES combined prediction model for recurrence used in this paper achieves the optimal performance and low computational complexity,which can provide a strong reference for DLBCL recurrence prediction.

Objective To explore the prediction performance of the autoregressive summation moving average(ARIMA)model based on empirical mode decomposition(EMD)for the prevalence trend of tuberculosis,to provide method support for the prediction of tuberculosis,and to provide ideas for the prediction of other infectious diseases.Methods The monthly data of pulmonary tuberculosis incidence in Shanxi Province from January 2008 to December 2018 were collected and sorted.The last three months,six months,nine months and one year of the data were used as the test set to evaluate the model prediction effect,and the training set was the remaining data of the corresponding sequence.The EMD-ARIMA model was constructed to predict and compared with the single ARIMA model.Results The predicted errors of EMD-ARIMA model for the next three months,six months,nine months and one year were all smaller than the errors of ARIMA model.Conclusion Compared with single ARIMA model,EMD-ARIMA model can improve the prediction accuracy and predict the incidence of pulmonary tuberculosis,and provide effective theoretical reference for disease control and prevention.