The present study aimed to determine the clinical characteristics of cytopenia in patients with relapsed and refractory B-cell non-Hodgkin lymphoma (B-NHL) who were treated with chimeric antigen receptor T-cell (CAR-T) therapy. Thus, a total of 63 patients with relapsed and refractory B-NHL who underwent CAR-T therapy between March 2017 and October 2021 were retrospectively selected for analysis. Neutropenia, anemia and thrombocytopenia at grade ≥3 occurred in 48 (76.19%), 16 (25.39%) and 15 (23.80%) cases, respectively. The results of a multivariate analysis demonstrated that the baseline absolute neutrophil count (ANC) and hemoglobin concentration were independent risk factors for grade ≥3 cytopenia. A total of 3 patients died early and were therefore excluded from the present study. Furthermore, cell recovery was examined at day +28 after infusion; 21 patients (35%) did not recover from cytopenia and 39 patients (65%) recovered. A multivariate analysis demonstrated that the baseline ANC <2.29x10 9 /l, baseline hemoglobin <114.50 g/l and baseline IL-6 >21.43 pg/l were independent risk factors affecting hemocyte recovery. In conclusion, patients with relapsed and refractory B-NHL exhibited an increased incidence of grade ≥3 hematologic toxicity following CAR-T cell therapy, while baseline blood cell and IL-6 levels are independent risk factors for hemocyte recovery.
The traditional point-cloud registration algorithms require large overlap between scans, which imposes strict constrains on data acquisition. To facilitate registration, the user has to strategically position or move the scanner to ensure proper overlap. In this work, we design a method of feature extraction based on high-level information to establish structure correspondences and an optimization problem. And we rewrite it as a fixed-point problem and apply the Lie algebra to parameterize the transform matrix. To speed up convergence, we introduce Anderson acceleration, an approach enhanced by heuristics. Our model attends to the structural features of the region of overlap instead of the correspondence between points. The experimental results show the proposed ICP method is robust, has a high accuracy of registration on point clouds with low overlap on a laser datasets, and achieves a computational time that is competitive with that of prevalent methods.
Compressed imaging reconstruction technology can reconstruct high-resolution images with a small number of observations by applying the theory of block compressed sensing to traditional optical imaging systems, and the reconstruction algorithm mainly determines its reconstruction accuracy. In this work, we design a reconstruction algorithm based on block compressed sensing with a conjugate gradient smoothed l0 norm termed BCS-CGSL0. The algorithm is divided into two parts. The first part, CGSL0, optimizes the SL0 algorithm by constructing a new inverse triangular fraction function to approximate the l0 norm and uses the modified conjugate gradient method to solve the optimization problem. The second part combines the BCS-SPL method under the framework of block compressed sensing to remove the block effect. Research shows that the algorithm can reduce the block effect while improving the accuracy and efficiency of reconstruction. Simulation results also verify that the BCS-CGSL0 algorithm has significant advantages in reconstruction accuracy and efficiency.
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