Bone Marrow Transplant
Division of Hematology, Department of Internal Medicine, Mayo Clinic Rochester, Rochester, USA.
Published: March 2019
Myeloma patients failing to achieve a complete response post autologous stem cell transplantation are heterogeneous, some ultimately achieving deeper responses and prolonged remission, whilst others relapse rapidly with poor outcomes. We evaluated the prognostic impact of the plasma cell proliferative index (PCPI) post-therapy, in 382 patients with myeloma failing to achieve complete response at 100 days post-transplant. Sixty percent (n = 230) of patients had zero clonal or too few clonal plasma cells to accurately assess PCPI (No PCPI). The remaining 40% (n = 152) of patients had PCPI performed with 79% (n = 120) having a low PCPI and 21% (n = 32) having an elevated PCPI. Patients with an elevated PCPI had significantly shorter progression free and overall survival. The median PFS was 8 months for elevated PCPI vs. 19 months for low PCPI vs. 24 months for no PCPI (p < 0.0001). The median OS was 27 months for elevated PCPI vs. 79 months for low PCPI vs. not reached for no PCPI, p < 0.0001). On multivariable analysis post-therapy PCPI was an independent predictor of progression free and overall survival. The PCPI post-therapy is a powerful predictor of survival and risk stratifies myeloma patients failing to achieve complete response early in the disease course.
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http://dx.doi.org/10.1038/s41409-018-0280-8 | DOI Listing |
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Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.
Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios.
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School of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China.
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