Switching from eltrombopag to hetrombopag in patients with primary immune thrombocytopenia: a post-hoc analysis of a multicenter, randomized phase III trial.

While studies have explored the feasibility of switching between various thrombopoietin receptor agonists in treating immune thrombocytopenia (ITP), data on the switching from eltrombopag to hetrombopag remains scarce. This post-hoc analysis of a phase III hetrombopag trial aimed to assess the outcomes of ITP patients who switched from eltrombopag to hetrombopag. In the original phase III trial, patients initially randomized to the placebo group were switched to eltrombopag. Those who completed this 14-week eltrombopag were eligible to switch to a 24-week hetrombopag. Treatment response, defined as a platelet count of ≥ 50 × 109/L, and safety were evaluated before and after the switch. Sixty-three patients who completed the 14-week eltrombopag and switched to hetrombopag were included in this post-hoc analysis. Response rates before and after the switch were 66.7% and 88.9%, respectively. Among those with pre-switching platelet counts below 30 × 109/L, eight out of 12 patients (66.7%) responded, while eight out of nine patients (88.9%) with pre-switching platelet counts between 30 × 109/L and 50 × 109/L responded post-switching. Treatment-related adverse events were observed in 50.8% of patients during eltrombopag treatment and 38.1% during hetrombopag treatment. No severe adverse events were noted during hetrombopag treatment. Switching from eltrombopag to hetrombopag in ITP management appears to be effective and well-tolerated. Notably, hetrombopag yielded high response rates, even among patients who had previously shown limited response to eltrombopag. However, these observations need to be confirmed in future trials.

Parainfluenza virus 5 is a next-generation vaccine vector for human infectious pathogens.

Parainfluenza virus 5 (PIV5) is a negative-sense, single-stranded RNA virus that can infect humans and many species of animals. Infection in these reservoir hosts is generally asymptomatic and has few safety concerns. Emerging evidence has shown that PIV5 is a promising vector for developing vaccines against human infectious diseases caused by coronaviruses, influenza, respiratory syncytial virus, rabies, HIV, or bacteria. In this review, we summarize recent progress and highlight the advantages and strategies of PIV5 as a vaccine vector to improve future vaccine design and application for clinical trials.

Oxymatrine triggers apoptosis by regulating Bcl-2 family proteins and activating caspase-3/caspase-9 pathway in human leukemia HL-60 cells.

With the objective of identifying promising antitumor agents for human leukemia, we carried out to determine the anticancer ability of oxymatrine on the human leukemia HL-60 cell line. In vitro experiments demonstrated that oxymatrine reduced the proliferation of HL-60 cells in a dose- and time-dependent manner via the induction of apoptosis and cell cycle arrest at G2/M and S phases. The proteins involved in oxymatrine-induced apoptosis in HL-60 cells were also examined using Western blot. The increase in apoptosis upon treatment with oxymatrine was correlated with downregulation of anti-apoptotic Bcl-2 expression and upregulation of pro-apoptotic Bax expression. Furthermore, oxymatrine induced the activation of caspase-3 and caspase-9 and the cleavage of poly(ADP-ribose) polymerase (PARP) in HL-60 cells. In addition, pretreatment with a specific caspase-3 (Z-DEVD-FMK) or caspase-9 (Z-LEHD-FMK) inhibitor significantly neutralized the pro-apoptotic activity of oxymatrine in HL-60 cells, demonstrating the important role of caspase-3 and caspase-9 in this process. Taken together, these results indicated that oxymatrine-induced apoptosis may occur through the activation of the caspase-9/caspase-3-mediated intrinsic pathway. Therefore, oxymatrine may be a potential candidate for the treatment of human leukemia.

Spatial Structure Constraints for Weakly Supervised Semantic Segmentation.

The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absence of specific categories of objects, visualization-based techniques have been widely adopted to provide object location clues. Considering class activation maps (CAMs) can only locate the most discriminative part of objects, recent approaches usually adopt an expansion strategy to enlarge the activation area for more integral object localization. However, without proper constraints, the expanded activation will easily intrude into the background region. In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion. Specifically, we propose a CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content. Moreover, we propose an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency. Without external saliency models to provide background clues, our approach achieves 72.7% and 47.0% mIoU on the PASCAL VOC 2012 and COCO datasets, respectively, demonstrating the superiority of our proposed approach. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SSC.

Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation.

Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts. However, to guarantee WSSS task feasible, we need to generate pseudo labels by expanding the seeds from CAMs which is complex and time-consuming, thus hindering the design of efficient end-to-end (single-stage) WSSS approaches. To tackle the above dilemma, we resort to the off-the-shelf and readily accessible saliency maps for directly obtaining pseudo labels given the image-level class labels. Nevertheless, the salient regions may contain noisy labels and cannot seamlessly fit the target objects, and saliency maps can only be approximated as pseudo labels for simple images containing single-class objects. As such, the achieved segmentation model with these simple images cannot generalize well to the complex images containing multi-class objects. To this end, we propose an end-to-end multi-granularity denoising and bidirectional alignment (MDBA) model, to alleviate the noisy label and multi-class generalization issues. Specifically, we propose the online noise filtering and progressive noise detection modules to tackle image-level and pixel-level noise, respectively. Moreover, a bidirectional alignment mechanism is proposed to reduce the data distribution gap at both input and output space with simple-to-complex image synthesis and complex-to-simple adversarial learning. MDBA can reach the mIoU of 69.5% and 70.2% on validation and test sets for the PASCAL VOC 2012 dataset. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MDBA.

Depth and Video Segmentation Based Visual Attention for Embodied Question Answering.

Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user's questions by exploring the real-world environment. It has attracted increasing research interests due to its broad applications in personal assistants and in-home robots. Most of the existing methods perform poorly in terms of answering and navigation accuracy due to the absence of fine-level semantic information, stability to the ambiguity, and 3D spatial information of the virtual environment. To tackle these problems, we propose a depth and segmentation based visual attention mechanism for Embodied Question Answering. First, we extract local semantic features by introducing a novel high-speed video segmentation framework. Then guided by the extracted semantic features, a depth and segmentation based visual attention mechanism is proposed for the Visual Question Answering (VQA) sub-task. Further, a feature fusion strategy is designed to guide the navigator's training process without much additional computational cost. The ablation experiments show that our method effectively boosts the performance of the VQA module and navigation module, leading to 4.9 % and 5.6 % overall improvement in EQA accuracy on House3D and Matterport3D datasets respectively.

Robust-EQA: Robust Learning for Embodied Question Answering With Noisy Labels.

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.

Hierarchical Co-Attention Propagation Network for Zero-Shot Video Object Segmentation.

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of introducing motion information, such as optical flow, can lead to overreliance on optical flow estimation. To address these challenges, we propose an encoder-decoder-based hierarchical co-attention propagation network (HCPN) capable of tracking and segmenting objects. Specifically, our model is built upon multiple collaborative evolutions of the parallel co-attention module (PCM) and the cross co-attention module (CCM). PCM captures common foreground regions among adjacent appearance and motion features, while CCM further exploits and fuses cross-modal motion features returned by PCM. Our method is progressively trained to achieve hierarchical spatio-temporal feature propagation across the entire video. Experimental results demonstrate that our HCPN outperforms all previous methods on public benchmarks, showcasing its effectiveness for ZS-VOS. Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HCPN.

Hierarchical Graph Pattern Understanding for Zero-Shot Video Object Segmentation.

The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene. The temporal consistency provided by the optical flow could be effectively supplemented by modeling in a structural form. This paper proposes a new hierarchical graph neural network (GNN) architecture, dubbed hierarchical graph pattern understanding (HGPU), for zero-shot video object segmentation (ZS-VOS). Inspired by the strong ability of GNNs in capturing structural relations, HGPU innovatively leverages motion cues (i.e., optical flow) to enhance the high-order representations from the neighbors of target frames. Specifically, a hierarchical graph pattern encoder with message aggregation is introduced to acquire different levels of motion and appearance features in a sequential manner. Furthermore, a decoder is designed for hierarchically parsing and understanding the transformed multi-modal contexts to achieve more accurate and robust results. HGPU achieves state-of-the-art performance on four publicly available benchmarks (DAVIS-16, YouTube-Objects, Long-Videos and DAVIS-17). Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HGPU.

Automated object recognition in high-resolution optical remote sensing imagery.

This paper reports the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, as well as summarize the challenges, champion solutions, and future directions.

Aberrant expression of GOLM1 protects ALK+ anaplastic large cell lymphoma from apoptosis by enhancing BCL-XL stability.

Golgi membrane protein 1 (GOLM1) is aberrantly expressed in many types of solid tumors and contributes to cancer development; however, its role in hematopoietic and lymphoid neoplasms remains unknown. Here, we report that GOLM1 was significantly upregulated in anaplastic large cell lymphoma (ALCL), particularly in anaplastic lymphoma kinase-positive (ALK+) ALCL. Mechanistically, the expression of GOLM1 was induced by nucleophosmin-ALK in both ALK-transformed T cells and ALCL cell lines through AKT/mTOR pathway. Knockdown of GOLM1 expression led to a reduction in the growth and viability of ALCL cells with increased spontaneous apoptosis, whereas ectopic expression of GOLM1 protected ALCL cells from apoptosis induced by staurosporine treatment. Moreover, GOLM1 directly interacted with B-cell lymphoma-extra large protein (a crucial anti-apoptosis regulator) and significantly prolonged its stability. Introduction of GOLM1 promoted ALK+ ALCL cells colony formation in vitro and tumor growth in a murine xenograft model. Taken together, our findings demonstrate, to our knowledge, for the first time that GOLM1 plays a critical role in suppressing apoptosis and promoting the progression of ALK+ ALCL and provide evidence that GOLM1 is a potential biomarker and therapeutic target in ALK-induced hematological malignancies.

Self-Supervised Multi-Modal Hybrid Fusion Network for Brain Tumor Segmentation.

Accurate medical image segmentation of brain tumors is necessary for the diagnosing, monitoring, and treating disease. In recent years, with the gradual emergence of multi-sequence magnetic resonance imaging (MRI), multi-modal MRI diagnosis has played an increasingly important role in the early diagnosis of brain tumors by providing complementary information for a given lesion. Different MRI modalities vary significantly in context, as well as in coarse and fine information. As the manual identification of brain tumors is very complicated, it usually requires the lengthy consultation of multiple experts. The automatic segmentation of brain tumors from MRI images can thus greatly reduce the workload of doctors and buy more time for treating patients. In this paper, we propose a multi-modal brain tumor segmentation framework that adopts the hybrid fusion of modality-specific features using a self-supervised learning strategy. The algorithm is based on a fully convolutional neural network. Firstly, we propose a multi-input architecture that learns independent features from multi-modal data, and can be adapted to different numbers of multi-modal inputs. Compared with single-modal multi-channel networks, our model provides a better feature extractor for segmentation tasks, which learns cross-modal information from multi-modal data. Secondly, we propose a new feature fusion scheme, named hybrid attentional fusion. This scheme enables the network to learn the hybrid representation of multiple features and capture the correlation information between them through an attention mechanism. Unlike popular methods, such as feature map concatenation, this scheme focuses on the complementarity between multi-modal data, which can significantly improve the segmentation results of specific regions. Thirdly, we propose a self-supervised learning strategy for brain tumor segmentation tasks. Our experimental results demonstrate the effectiveness of the proposed model against other state-of-the-art multi-modal medical segmentation methods.

Deep Metric Learning Based on Meta-Mining Strategy With Semiglobal Information.

Recently, deep metric learning (DML) has achieved great success. Some existing DML methods propose adaptive sample mining strategies, which learn to weight the samples, leading to interesting performance. However, these methods suffer from a small memory (e.g., one training batch), limiting their efficacy. In this work, we introduce a data-driven method, meta-mining strategy with semiglobal information (MMSI), to apply meta-learning to learn to weight samples during the whole training, leading to an adaptive mining strategy. To introduce richer information than one training batch only, we elaborately take advantage of the validation set of meta-learning by implicitly adding additional validation sample information to training. Furthermore, motivated by the latest self-supervised learning, we introduce a dictionary (memory) that maintains very large and diverse information. Together with the validation set, this dictionary presents much richer information to the training, leading to promising performance. In addition, we propose a new theoretical framework that can formulate pairwise and tripletwise metric learning loss functions in a unified framework. This framework brings new insights to society and facilitates us to generalize our MMSI to many existing DML methods. We conduct extensive experiments on three public datasets, CUB200-2011, Cars-196, and Stanford Online Products (SOP). Results show that our method can achieve the state of the art or very competitive performance. Our source codes have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MMSI.

An effective and chemotherapy-free strategy of all-trans retinoic acid and arsenic trioxide for acute promyelocytic leukemia in all risk groups (APL15 trial).

The combination of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) has been demonstrated to have comparable effectiveness or better to ATRA and chemotherapy (CHT) in non-high-risk acute promyelocytic leukemia (APL). However, the efficacy of ATRA-ATO compared to ATRA-ATO plus CHT in high-risk APL remains unknown. Here we performed a randomized multi-center non-inferiority phase III study to compare the efficacy of ATRA-ATO and ATRA-ATO plus CHT in newly diagnosed all-risk APL to address this question. Patients were assigned to receive ATRA-ATO for induction, consolidation, and maintenance or ATRA-ATO plus CHT for induction followed by three cycles of consolidation therapy, and maintenance therapy with ATRA-ATO. In the non-CHT group, hydroxyurea was used to control leukocytosis. A total of 128 patients were treated. The complete remission rate was 97% in both groups. The 2-year disease-free, event-free survival rates in the non-CHT group and CHT group in all-risk patients were 98% vs 97%, and 95% vs 92%, respectively (P = 0.62 and P = 0.39, respectively). And they were 94% vs 87%, and 85% vs 78% in the high-risk patients (P = 0.52 and P = 0.44, respectively). This study demonstrated that ATRA-ATO had the same efficacy as the ATRA-ATO plus CHT in the treatment of patients with all-risk APL.

Dose tapering to withdrawal stage and long-term efficacy and safety of hetrombopag for the treatment of immune thrombocytopenia: Results from an open-label extension study.

BACKGROUND: The efficacy of hetrombopag in Chinese patients with immune thrombocytopenia (ITP) has been demonstrated in a randomized, double-blind, placebo-controlled, multicenter, phase III trial (NCT03222843). OBJECTIVE: This study aimed to report comprehensive data on a ≤6-week dose tapering to withdrawal (Stage 3) and an additional 24-week long-term extension period (Stage 4) in this phase III trial. PATIENTS/METHODS: Patients who fulfilled the screening criteria were eligible to enter Stage 3 or 4. During Stage 3, hetrombopag was gradually tapered to withdrawal. During Stage 4, hetrombopag treatment was initiated at 2.5, 3.75, 5, or 7.5 mg once daily. The efficacy endpoints during Stage 3 or 4 and the safety profile during the entire treatment period were reported. RESULTS: Among 194 patients who entered Stage 3, 171 (88.1%) relapsed. The median time to the first relapse since the start of Stage 3 was 15.0 days (95% CI, 14.0-16.0). In Stage 4, 144 (42.5%) patients responded at ≥75% of their assessments and 254 (74.9%) patients achieved platelet count ≥30 × 109 /L at least once, which was at least twice their baseline platelet count in the hetrombopag group (n = 339). The most common adverse events were upper respiratory tract infection (53.1%), thrombocytopenia (27.1%), and urinary tract infection (21.2%) in the hetrombopag group. CONCLUSION: The majority of patients who experienced dose tapering to withdrawal experienced a relapse. Long-term treatment with hetrombopag was effective in increasing and maintaining platelet count within the desired range in Chinese adults with ITP. Hetrombopag was well tolerated.

VMAN: A Virtual Mainstay Alignment Network for Transductive Zero-Shot Learning.

Transductive zero-shot learning (TZSL) extends conventional ZSL by leveraging (unlabeled) unseen images for model training. A typical method for ZSL involves learning embedding weights from the feature space to the semantic space. However, the learned weights in most existing methods are dominated by seen images, and can thus not be adapted to unseen images very well. In this paper, to align the (embedding) weights for better knowledge transfer between seen/unseen classes, we propose the virtual mainstay alignment network (VMAN), which is tailored for the transductive ZSL task. Specifically, VMAN is casted as a tied encoder-decoder net, thus only one linear mapping weights need to be learned. To explicitly learn the weights in VMAN, for the first time in ZSL, we propose to generate virtual mainstay (VM) samples for each seen class, which serve as new training data and can prevent the weights from being shifted to seen images, to some extent. Moreover, a weighted reconstruction scheme is proposed and incorporated into the model training phase, in both the semantic/feature spaces. In this way, the manifold relationships of the VM samples are well preserved. To further align the weights to adapt to more unseen images, a novel instance-category matching regularization is proposed for model re-training. VMAN is thus modeled as a nested minimization problem and is solved by a Taylor approximate optimization paradigm. In comprehensive evaluations on four benchmark datasets, VMAN achieves superior performances under the (Generalized) TZSL setting.

A multicenter, randomized phase III trial of hetrombopag: a novel thrombopoietin receptor agonist for the treatment of immune thrombocytopenia.

BACKGROUND: Hetrombopag, a novel thrombopoietin receptor agonist, has been found in phase I studies to increase platelet counts and reduce bleeding risks in adults with immune thrombocytopenia (ITP). This phase III study aimed to evaluate the efficacy and safety of hetrombopag in ITP patients. METHODS: Patients who had not responded to or had relapsed after previous treatment were treated with an initial dosage of once-daily 2.5 or 5 mg hetrombopag (defined as the HETROM-2.5 or HETROM-5 group) or with matching placebo in a randomized, double-blind, 10-week treatment period. Patients who received placebo and completed 10 weeks of treatment switched to receive eltrombopag, and patients treated with hetrombopag in the double-blind period continued hetrombopag during the following open-label 14-week treatment. The primary endpoint was the proportion of responders (defined as those achieving a platelet count of ≥ 50 × 109/L) after 8 weeks of treatment. RESULTS: The primary endpoint was achieved by significantly more patients in the HETROM-2.5 (58.9%; odds ratio [OR] 25.97, 95% confidence interval [CI] 9.83-68.63; p < 0.0001) and HETROM-5 (64.3%; OR 32.81, 95% CI 12.39-86.87; p < 0.0001) group than in the Placebo group (5.9%). Hetrombopag was also superior to placebo in achieving a platelet response and in reducing the bleeding risk and use of rescue therapy throughout 8 weeks of treatment. The durable platelet response to hetrombopag was maintained throughout 24 weeks. The most common adverse events were upper respiratory tract infection (42.2%), urinary tract infection (17.1%), immune thrombocytopenic purpura (17.1%) and hematuria (15%) with 24-week hetrombopag treatment. CONCLUSIONS: In ITP patients, hetrombopag is efficacious and well tolerated with a manageable safety profile. Trial registration Clinical trials.gov NCT03222843 , registered July 19, 2017, retrospectively registered.

Exploiting Web Images for Multi-Output Classification: From Category to Subcategories.

Studies present that dividing categories into subcategories contributes to better image classification. Existing image subcategorization works relying on expert knowledge and labeled images are both time-consuming and labor-intensive. In this article, we propose to select and subsequently classify images into categories and subcategories. Specifically, we first obtain a list of candidate subcategory labels from untagged corpora. Then, we purify these subcategory labels through calculating the relevance to the target category. To suppress the search error and noisy subcategory label-induced outlier images, we formulate outlier images removing and the optimal classification models learning as a unified problem to jointly learn multiple classifiers, where the classifier for a category is obtained by combining multiple subcategory classifiers. Compared with the existing subcategorization works, our approach eliminates the dependence on expert knowledge and labeled images. Extensive experiments on image categorization and subcategorization demonstrate the superiority of our proposed approach.

Approximate Kernel Selection via Matrix Approximation.

Kernel selection is of fundamental importance for the generalization of kernel methods. This article proposes an approximate approach for kernel selection by exploiting the approximability of kernel selection and the computational virtue of kernel matrix approximation. We define approximate consistency to measure the approximability of the kernel selection problem. Based on the analysis of approximate consistency, we solve the theoretical problem of whether, under what conditions, and at what speed, the approximate criterion is close to the accurate one, establishing the foundations of approximate kernel selection. We introduce two selection criteria based on error estimation and prove the approximate consistency of the multilevel circulant matrix (MCM) approximation and Nyström approximation under these criteria. Under the theoretical guarantees of the approximate consistency, we design approximate algorithms for kernel selection, which exploits the computational advantages of the MCM and Nyström approximations to conduct kernel selection in a linear or quasi-linear complexity. We experimentally validate the theoretical results for the approximate consistency and evaluate the effectiveness of the proposed kernel selection algorithms.

Treatment of non-high-risk acute promyelocytic leukemia with realgar-indigo naturalis formula (RIF) and all-trans retinoid acid (ATRA): study protocol for a randomized controlled trial.

BACKGROUND: Acute promyelocytic leukemia (APL) is a highly curable disease when treated with all-trans retinoid acid (ATRA) and arsenic trioxide (ATO). The combination of ATO and ATRA has become the standard therapeutic protocol for induction therapy in non-high-risk APL. An oral arsenic realgar-indigo naturalis formula (RIF) has also showed high efficacy and it has a more convenient route of administration than the standard intravenous regimen. Unlike in previous trials, the arsenical agent was used simultaneously with ATRA during post-remission therapy in this trial. METHODS: This study was designed as a multicenter, randomized controlled trial. The trial has a non-inferiority design with superiority being explored if non-inferiority is identified. All patients receive ATRA-ATO during the induction therapy. After achieving hematologic complete remission (HCR), patients were randomly assigned (1:1) to receive treatment with ATRA-RIF (experimental group) or ATRA-ATO (control group) as the consolidation therapy. During the consolidation therapy, the two groups receive ATRA plus RIF or intravenous ATO 2 weeks on and 2 to ~ 4 weeks off until molecular complete remission (MCR), then ATRA and oral RIF 2 weeks on and 2 to ~ 4 weeks off giving a total of six courses. DISCUSSION: This trial aims to compare the efficacy of ATRA-ATO versus ATRA-RIF in non-high-risk patients with APL, to demonstrate that oral RIF application reduces the total hospitalization days and medical costs. The simple schedule was studied in this trial. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02899169. Registered on 14 September 2016.