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While post-transplant cyclophosphamide (PTCy) is commonly used as graft-versus-host disease (GvHD) prophylaxis in haploidentical stem cell transplantation (haplo-HSCT), its dose remains a matter of debate due to side effect concerns. Standard dose of 100 mg/kg associated with tacrolimus and post-engraftment anti-thymocyte globulin (ATG) was used as the reference GvHD prophylaxis in our center and had demonstrated encouraging results. Though PTCy 80 mg/kg was shown to be feasible in patients in reduced-intensity conditioning, whether it exerts equivalent GvHD prophylactic efficacy in myeloablative conditioning (MAC) setting has not been confirmed. Here, we retrospectively analyzed the efficacy and safety of PTCy 80 mg/kg combined with tacrolimus and post-engraftment ATG as GvHD prophylaxis in patients aged more than 55 years or with cardiac antecedents or HCT-CI score >2 undergoing haplo-HSCT with MAC. The cumulative incidence of grade III-IV aGvHD at day 100 and moderate-to-severe cGvHD at 1 year was 4.8% ± 3.4% and 19.9% ± 7.0%, respectively. When compared with patients receiving the reference regimen, patients from the PTCy 80 mg/kg group had similar incidence of GvHDs and survival as their younger counterparts. Thus, PTCy 80 mg/kg seems to be feasible for patients treated with MAC conditioning regimens in haplo-HSCT, inviting further investigation notably in frail patients.

ABSTRACTMarine big data are characterized by a large amount and complex structures, which bring great challenges to data management and retrieval. Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database, this paper proposes a spatio-temporal grid index model (STGI) for efficient optimized query of marine big data. A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data. Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models. Through theoretical analysis and experimental verification, the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency, such as marine big data.