CD19-specific chimeric antigen receptor T cell (CD19 CAR T) therapy has shown high remission rates in patients with refractory/relapsed B-cell acute lymphoblastic leukemia (r/r B-ALL). However, the long-term outcome and the factors that influence the efficacy need further exploration. Here we report the outcome of 51 r/r B-ALL patients from a non-randomized, Phase II clinical trial (ClinicalTrials.gov number: NCT02735291). The primary outcome shows that the overall remission rate (complete remission with or without incomplete hematologic recovery) is 80.9%. The secondary outcome reveals that the overall survival (OS) and relapse-free survival (RFS) rates at 1 year are 53.0 and 45.0%, respectively. The incidence of grade 4 adverse reactions is 6.4%. The trial meets pre-specified endpoints. Further analysis shows that patients with extramedullary diseases (EMDs) other than central nervous system (CNS) involvement have the lowest remission rate (28.6%). The OS and RFS in patients with any subtype of EMDs, higher Tregs, or high-risk genetic factors are all significantly lower than that in their corresponding control cohorts. EMDs and higher Tregs are independent high-risk factors respectively for poor OS and RFS. Thus, these patient characteristics may hinder the efficacy of CAR T therapy.

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Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.

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