Comparison of Mitoxantrone in Combination with Intermediate-dose Cytarabine versus High-dose Cytarabine as Consolidation Therapies for Young Non-APL Acute Myeloid Leukemia Patients with Favorable and Intermediate Cytogenetics

In this study,we compared the efficacy of mitoxantrone in combination with intermediate-dose cytarabine（HAM） with that of high-dose cytarabine alone（Hi DAC） as consolidation regimens in non-acute promyelocytic leukemia（APL） acute myeloid leukemia patients with favorable and intermediate cytogenetics.A total of 62 patients from Shenzhen People＇s Hospital were enrolled in this study.All patients enrolled received standard induction chemotherapy and achieved the first complete remission（CR1）.In these patients,24 received Hi DAC and 38 received HAM as consolidation.The median relapse free survival（RFS） and overall survival（OS） were similar between these two consolidation regimens.Even in subgroup analysis according to risk stratification,the combination regimen conferred no benefit in longterm outcome in patients with favorable or intermediate cytogenetics.However,in patients receiving HAM regimen,the lowest neutrophil count was lower,neutropenic period longer,neutropenic fever rate higher,and more platelet transfusion support was required.HAM group also tended to have higher rate of sepsis than Hi DAC group.According to our results,we suggest that combination treatment with mitoxantrone and intermediate-dose cytarabine has limited value as compared to Hi DAC,even in young non-APL AML patients with favorable and intermediate cytogenetics.

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

Objective: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer(PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine(TCM) syndromes.Methods: From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining10,060 electronic medical records, which were randomly divided into a training set and a test set.Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.Results: The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%,respectively. The classification accuracy rates of the models for all syndromes in this paper were between82.15% and 93.82%.Conclusion: Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Mining and Correlation Analysis of Association Rules between Properties and Therapeutic Efficacy of Chinese Materia Medica Based on Strategy Pattern

To the Editor:Chinese physicians often address the combination of the properties and therapeutic efficacy of Chinese materia medica(CMM).They believe that the properties and therapeutic efficacy of traditional Chinese medicines(TCMs)should be considered as an"organic whole.""Use based on therapeutic efficacy"can lead to omission of the properties of CMM.It also