Down-regulation of Musashi-2 exerts antileukemic effects on acute lymphoblastic leukemia cells and increases sensitivity to dexamethasone.

Musashi-2 (MSI2), implicated in the oncogenesis and propagation of a broad array of malignancies, inclusive of certain leukemia, remains a nascent field of study within the context of acute lymphoblastic leukemia (ALL). Using lentiviral transfection, ALL cells with stable MSI2 knockdown were engineered. A suite of analytic techniques - a CCK-8 assay, flow cytometry, qRT-PCR, and western blotting - were employed to evaluate cellular proliferation, cell cycle arrest, and apoptosis and to confirm differential gene expression. The suppression of MSI2 expression yielded significant results: inhibition of cell proliferation, G0/G1 cell cycle arrest, and induced apoptosis in ALL cell lines. Furthermore, it was noted that MSI2 inhibition heightened the responsiveness of ALL cells to dexamethasone. Significantly, the depletion of MSI2 prompted the translocation of GR from the cytoplasm to the nucleus upon dexamethasone treatment, consequently leading to enhanced sensitivity. Additionally, the FOXO1/4 signaling pathway contributed to the biological effects of ALL cells evoked by MSI2 silencing. Our study offers novel insight into the inhibitory effects of MSI2 suppression on ALL cells, positing MSI2 as a promising therapeutic target in the treatment of ALL.

A generalized multi-skill aggregation method for cognitive diagnosis.

Online education brings more possibilities for personalized learning, in which identifying the cognitive state of learners is conducive to better providing learning services. Cognitive diagnosis is an effective measurement to assess the cognitive state of students through response data of answering the problems(e.g., right or wrong). Generally, the cognitive diagnosis framework includes the mastery of skills required by a specified problem and the aggregation of skills. The current multi-skill aggregation methods are mainly divided into conjunctive and compensatory methods and generally considered that each skill has the same effect on the correct response. However, in practical learning situations, there may be more complex interactions between skills, in which each skill has different weight impacting the final result. To this end, this paper proposes a generalized multi-skill aggregation method based on the Sugeno integral (SI-GAM) and introduces fuzzy measures to characterize the complex interactions between skills. We also provide a new idea for modeling multi-strategy problems. The cognitive diagnosis process is implemented by a more general and interpretable aggregation method. Finally, the feasibility and effectiveness of the model are verified on synthetic and real-world datasets.

Decreased serum apolipoprotein A1 level predicts poor prognosis of patients with de novo myelodysplastic syndromes.

BACKGROUND: Myelodysplastic syndromes (MDS) is a group of heterogeneous myeloid clonal diseases originating from hematopoietic stem cells. It has been demonstrated that apolipoproteins A1(ApoA1) are associated with disease risk in many cancer types. However, there still lacks evidence regarding the link between ApoA1 and MDS. This study was designed to investigate the prognostic value of pretreatment ApoA1 levels in MDS patients. METHODS: We retrospectively analyzed a cohort of 228 MDS patients to explore the prognostic value of the serum ApoA1 levels at diagnosis. Patients were divided into the high ApoA1 group and the low ApoA1 group. The prognostic significance was determined by univariate and multivariate Cox hazard models. RESULTS: MDS patients with low ApoA1 levels had significantly shorter overall survival (OS, P < 0.0001) along with a higher frequency of TP53 mutation (P = 0.002). Based on univariate analysis, age (≥ 60 years), gender (male), lower levels of hemoglobin (< 10 g/dl), HDL (≤0.91 mmol/L), higher bone marrow blast percentage (> 5%), higher IPSS-R scores and poorer karyotype were significantly associated with decreased OS. However, low ApoA1 level did not influence leukemia-free survival (LFS, P = 0.367). Multivariate Cox proportional hazards regression analysis indicated that low ApoA1 level (≤ 1.02 g/L) was also an independent adverse prognostic factor for OS in MDS (P = 0.034). CONCLUSIONS: Decreased ApoA1 level predicts a poor prognosis of MDS patients and thus provides a novel evaluation factor for them that is independent of the IPSS-R system.

Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm.

Generating molecules with desired properties is an important task in chemistry and pharmacy. An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19. Data mining and artificial intelligence may be good ways to find an efficient method. Recently, both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule's properties. However, existing methods need to be improved in efficiency and performance. To solve these problems, we propose a method named the Chemical Genetic Algorithm for Large Molecular Space (CALM). Specifically, CALM employs a scalable and efficient molecular representation called molecular matrix. Then, we design corresponding crossover, mutation, and mask operators inspired by domain knowledge and previous studies. We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization. The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods, where the z tests performed on the results of several experiments show that our method is more than 99% likely to be significant. At the same time, based on the experimental results, we point out the insufficiency in the experimental evaluation standard which affects the fair evaluation of previous work. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-021-0970-3.

Elevated mature monocytes in bone marrow accompanied with a higher IPSS-R score predicts a poor prognosis in myelodysplastic syndromes.

BACKGROUND: Myelodysplastic syndromes (MDS) is a group of heterogeneous myeloid clonal diseases originating from hematopoietic stem cells. Clinically, elevated mature monocyte in bone marrow is often observed, but its clinical value still remains unclear. METHODS: We retrospectively analyzed a cohort of 216 MDS patients to explore the prognostic value of the percentage of mature monocyte in bone marrow (PMMBM). All patients were divided into elevated PMMBM group and the normal group by 6% PMMBM as the cut-off value. RESULTS: Our results showed that PMMBM> 6% was associated with inferior overall survival (OS) (P = 0.026) along with higher-risk IPSS-R (P = 0.025) and higher frequency of IDH2 mutation (P = 0.007). Multivariate analyses showed that besides older age (> 60 years) for OS, gender (male) for OS, lower neutrophil count (< 0.8 ×  109/L) for OS, higher bone marrow blast percentage (> 5%) for OS and LFS, poorer karyotype for OS, elevated PMMBM was also an independent adverse prognostic factor for OS in MDS (P < 0.0001) but not for LFS (P = 0.736). CONCLUSIONS: These findings indicate that increased PMMBM may assists Revised International Prognostic Scoring System (IPSS-R) to predict a poor outcome and provide a novel evaluation factor for MDS patients especially when their karyotype analyses fail.

Chyle Fat-Derived Stem Cells Conditioned Medium Inhibits Hypertrophic Scar Fibroblast Activity.

BACKGROUND: Hypertrophic scars (HSs) generally form after injury to the deep layers of the dermis and are characterized by excessive collagen deposition. An increasing amount of evidence has determined that human adipose tissue-derived mesenchymal stem cells attenuate fibrosis in various conditions. We explored the effect and possible mechanism of chyle fat-derived stem cells (CFSCs) on HS formation. METHODS: Hypertrophic scar-derived fibroblasts (HSFs) and CFSCs were isolated from individual patients. Third-passage CFSCs were isolated and cultured using a mechanical emulsification method, and their surface CD markers were analyzed by flow cytometry. The adipogenic and osteogenic differentiation capacity of the CFSCs was determined using oil red O staining and alizarin red S staining, respectively. Then, the effects of CFSCs on HSFs were assessed in vitro. Hypertrophic scar-derived fibroblasts were treated with starvation-induced conditioned medium from the CFSCs (CFSC-CM). The change in HSF cellular behaviors, such as cell proliferation, migration, and protein expression of scar-related molecules, was evaluated by cell counting assay, scratch wound assay, enzyme-linked immunosorbent assay, and western blotting. All data were analyzed using SPSS 17.0. RESULTS: The CFSCs expressed CD90, CD105, and CD73 but did not express CD34, CD45, or CD31. The CFSCs differentiated into adipocytes and osteoblasts under the appropriate induction conditions. Chyle fat-derived stem cells conditioned medium inhibited HSF proliferation and migration. The in vitro and ex vivo studies revealed that CFSC-CM decreased type I collagen, type III collagen, and α smooth muscle actin expression. CONCLUSIONS: Our results suggest that CFSCs are associated with the inhibition of fibrosis in HSFs by a paracrine effect. The use of CFSC-CM may be a novel therapeutic strategy for HSs.

The Effect of Chyle Fat Injection on Human Hypertrophic Scars in an Animal Model: A New Strategy for the Treatment of Hypertrophic Scars.

BACKGROUND: Chyle fat transplantation has shown positive effects on preexisting human hypertrophic scars (HSs) in a nude mouse HS graft model. METHODS: Hypertrophic scar fragments were obtained from 5 surgically treated burn patients and implanted into the backs of nude mice in 3 groups: group A, control; group B, triamcinolone; and group C, chyle fat. The specimens were implanted after the corresponding intralesional injection in each group, and the mice were observed for 4 weeks. In total, 18 mice and 72 scar specimens were studied. After 4 weeks, the HSs were removed from the mice. Then, the scar weights, histology, and decorin staining were assessed to evaluate the therapeutic efficacy. RESULTS: An obviously significant difference was observed in the HS weight reduction between groups A and C (P < 0.01), and a significant difference in the HS weight reduction was observed between groups A and B (P < 0.05). However, there was no significant difference between groups B and C. The treatment groups (groups B and C) showed strong decorin staining. Furthermore, the decorin staining was much stronger in group C than in group B (P < 0.05). Significant differences in extracellular matrix deposition were observed among the 3 groups, as determined by Masson trichrome staining. Both groups B and C showed significant therapeutic efficacy compared with group A, and group C exhibited a significant therapeutic effect compared with group B (P < 0.05). CONCLUSIONS: This study indicates that chyle fat grafting is beneficial for treating HSs.

Learning Relation-Enhanced Hierarchical Solver for Math Word Problems.

Automatically solving math word problems (MWPs) is a challenging task for artificial intelligence (AI) and machine learning (ML) research, which aims to answer the problem with a mathematical expression. Many existing solutions simply model the MWP as a sequence of words, which is far from precise solving. To this end, we turn to how humans solve MWPs. Humans read the problem part-by-part and capture dependencies between words for a thorough understanding and infer the expression precisely in a goal-driven manner with knowledge. Moreover, humans can associate different MWPs to help solve the target with related experience. In this article, we present a focused study on an MWP solver by imitating such procedure. Specifically, we first propose a novel hierarchical math solver (HMS) to exploit semantics in one MWP. First, to imitate human reading habits, we propose a novel encoder to learn the semantics guided by dependencies between words following a hierarchical "word-clause-problem" paradigm. Next, we develop a goal-driven tree-based decoder with knowledge application to generate the expression. One step further, to imitate human associating different MWPs for related experience in problem-solving, we extend HMS to the Relation-enHanced Math Solver (RHMS) to utilize the relation between MWPs. First, to capture the structural similarity relation, we develop a meta-structure tool to measure the similarity based on the logical structure of MWPs and construct a graph to associate related MWPs. Then, based on the graph, we learn an improved solver to exploit related experience for higher accuracy and robustness. Finally, we conduct extensive experiments on two large datasets, which demonstrates the effectiveness of the two proposed methods and the superiority of RHMS.

Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection.

Traffic anomalies, such as traffic accidents and unexpected crowd gathering, may endanger public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit citizens' quality of life and city planning. However, traffic anomaly detection faces two main challenges. First, it is challenging to model traffic dynamics due to the complex spatiotemporal characteristics of traffic data. Second, the criteria of traffic anomalies may vary with locations and times. In this article, we propose a spatiotemporal graph convolutional adversarial network (STGAN) to address these above challenges. More specifically, we devise a spatiotemporal generator to predict the normal traffic dynamics and a spatiotemporal discriminator to determine whether an input sequence is real or not. There are high correlations between neighboring data points in the spatial and temporal dimensions. Therefore, we propose a recent module and leverage graph convolutional gated recurrent unit (GCGRU) to help the generator and discriminator learn the spatiotemporal features of traffic dynamics and traffic anomalies, respectively. After adversarial training, the generator and discriminator can be used as detectors independently, where the generator models the normal traffic dynamics patterns and the discriminator provides detection criteria varying with spatiotemporal features. We then design a novel anomaly score combining the abilities of two detectors, which considers the misleading of unpredictable traffic dynamics to the discriminator. We evaluate our method on two real-world datasets from New York City and California. The experimental results show that the proposed method detects various traffic anomalies effectively and outperforms the state-of-the-art methods. Furthermore, the devised anomaly score achieves more robust detection performances than the general score.