BACKGROUND Mixed-phenotype acute leukemia(MPAL)is characterized by acute undifferentiated leukemia with blasts co-expressing myeloid and lymphoid antigens.However,consensus regarding the ideal management strategy for ...BACKGROUND Mixed-phenotype acute leukemia(MPAL)is characterized by acute undifferentiated leukemia with blasts co-expressing myeloid and lymphoid antigens.However,consensus regarding the ideal management strategy for MPAL is yet to be established,owing to its rarity.CASE SUMMARY A 55-year-old male was diagnosed with T/myeloid MPAL.Vincristine,prednisolone,daunorubicin,and L-asparaginase were administered as induction chemotherapy.Septic shock occurred 10 days after induction,and bone marrow examination following recovery from sepsis revealed refractory disease.Venetoclax and decitabine were administered as chemotherapy-free induction therapy to reduce the infection risk.There were no serious infections,including febrile neutropenia,at the end of the treatment.After receiving two additional cycles of venetoclax/decitabine,the patient underwent haploidentical peripheral blood stem-cell transplantation and achieved complete response(CR)to treatment.CONCLUSION CR was maintained in a patient with MPAL who underwent haploidentical peripheral blood stem-cell transplantation after additional venetoclax/decitabine cycles.展开更多
One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time.Usually,vaccine production preparation must be done at least six...One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time.Usually,vaccine production preparation must be done at least six months in advance,and accurate long-term influenza forecasting is essential for this.Although diverse machine learning models have been proposed for influenza forecasting,they focus on short-term forecasting,and their performance is too dependent on input variables.For a country’s longterm influenza forecasting,typical surveillance data are known to be more effective than diverse external data on the Internet.We propose a two-stage data selection scheme for worldwide surveillance data to construct a longterm forecasting model for influenza in the target country.In the first stage,using a simple forecasting model based on the country’s surveillance data,we measured the change in performance by adding surveillance data from other countries,shifted by up to 52 weeks.In the second stage,for each set of surveillance data sorted by accuracy,we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage.Using the selected surveillance data,we trained a new longterm forecasting model for influenza and perform influenza forecasting for the target country.We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method.We report some of the results.展开更多
文摘BACKGROUND Mixed-phenotype acute leukemia(MPAL)is characterized by acute undifferentiated leukemia with blasts co-expressing myeloid and lymphoid antigens.However,consensus regarding the ideal management strategy for MPAL is yet to be established,owing to its rarity.CASE SUMMARY A 55-year-old male was diagnosed with T/myeloid MPAL.Vincristine,prednisolone,daunorubicin,and L-asparaginase were administered as induction chemotherapy.Septic shock occurred 10 days after induction,and bone marrow examination following recovery from sepsis revealed refractory disease.Venetoclax and decitabine were administered as chemotherapy-free induction therapy to reduce the infection risk.There were no serious infections,including febrile neutropenia,at the end of the treatment.After receiving two additional cycles of venetoclax/decitabine,the patient underwent haploidentical peripheral blood stem-cell transplantation and achieved complete response(CR)to treatment.CONCLUSION CR was maintained in a patient with MPAL who underwent haploidentical peripheral blood stem-cell transplantation after additional venetoclax/decitabine cycles.
基金This research was supported by a government-wide R&D fund project for infectious disease research(GFID),Republic of Korea(Grant Number:HG19C0682).
文摘One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time.Usually,vaccine production preparation must be done at least six months in advance,and accurate long-term influenza forecasting is essential for this.Although diverse machine learning models have been proposed for influenza forecasting,they focus on short-term forecasting,and their performance is too dependent on input variables.For a country’s longterm influenza forecasting,typical surveillance data are known to be more effective than diverse external data on the Internet.We propose a two-stage data selection scheme for worldwide surveillance data to construct a longterm forecasting model for influenza in the target country.In the first stage,using a simple forecasting model based on the country’s surveillance data,we measured the change in performance by adding surveillance data from other countries,shifted by up to 52 weeks.In the second stage,for each set of surveillance data sorted by accuracy,we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage.Using the selected surveillance data,we trained a new longterm forecasting model for influenza and perform influenza forecasting for the target country.We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method.We report some of the results.
