2020
DOI: 10.21203/rs.3.rs-20815/v1
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Inhibition of acute leukemia with attenuated Salmonella typhimurium strain VNP20009

Abstract: Background Despite recent promising progress, the prognosis of acute leukemia (AL) patients remains to be improved. New therapies are therefore still needed. Spontaneous complete remission (SCR) of leukemia caused by severe bacterial infection in clinic suggests the possibility of bacterial treatment for AL. An engineered attenuated Salmonella typhimurium VNP20009 with good tolerance and safety has been shown to be highly effective as an anti-tumor agent in many solid cancer models, but it has not been applied… Show more

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Cited by 1 publication
(2 citation statements)
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“…[21] The number of nodes in the hidden layer is mainly determined empirically. The number of nodes in the hidden layer is determined empirically, and the empirical formula is shown as follows (14).…”
Section: Intelligent Recognition Model Classification Layermentioning
confidence: 99%
See 1 more Smart Citation
“…[21] The number of nodes in the hidden layer is mainly determined empirically. The number of nodes in the hidden layer is determined empirically, and the empirical formula is shown as follows (14).…”
Section: Intelligent Recognition Model Classification Layermentioning
confidence: 99%
“…Yin et al [13] (2019) proposed a method that combines mud-log and well-log data to achieve downhole overflow monitoring in deepwater HTHP wells with a LM-BP neural network model established by genetic algorithm. Li et al [14] (2022) proposed a new method for intelligent prediction of drilling overflow and Leakage based on multi-parameter fusion, using genetic algorithm to optimize a multilayer feedforward neural network and establish a GA-BP neural network drilling spill leakage prediction model based on multi-parameter fusion to predict spills by fusing data from multiple sources. However, in the process of downhole data acquisition, the influence of complex downhole environment on sensors and the excessively long data transmission distance of deep wells and ultra-deep wells will both lead to unstable monitoring data received at the surface, the change of missing data is random, and the monitoring data of downhole may have a single missing data at one moment and multiple missing data at the same moment, thus affecting the accuracy of overflow identification.…”
Section: Introductionmentioning
confidence: 99%