Selinexor synergizes with azacitidine to eliminate myelodysplastic syndrome cells through p53 nuclear accumulation.

Myelodysplastic syndromes (MDS) are clonal malignancies of multipotent hematopoietic stem cells, characterized by ineffective hematopoiesis leading to cytopenia. Hypomethylating agents, including azacitidine, have been used for treating MDS with some success; however, the overall survival rate remains poor and, therefore, finding new therapies is necessary. Selinexor, which exerts anticancer effects against some hematologic tumors, is a nuclear export protein inhibitor that blocks cell proliferation and induces apoptosis in various cancer cell lines. We investigated the effects of combined selinexor and azacitidine administration on two MDS cell lines, namely SKM-1 and MUTZ-1. Cells were subjected to a proliferation assay, and the effects of each drug alone, and in combination, were compared. Changes in apoptosis and the cell cycle between groups were also analyzed. Western blotting was conducted to identify the underlying mechanism of action of combined selinexor and azacitidine therapy. The results revealed that the combination of selinexor and azacitidine synergistically inhibited MDS cell proliferation and arrested the cell cycle at the G2/M phase. This combination also promoted MDS cell apoptosis and enhanced p53 accumulation in the nucleus, thereby allowing p53 to be activated and to function as a tumor suppressor. Overall, our results indicate that the combination of selinexor and azacitidine may be a promising approach for treating MDS.

Bone marrow mesenchymal stem cells regulate the dysfunction of NK cells via the T cell immunoglobulin and ITIM domain in patients with myelodysplastic syndromes.

BACKGROUND: Myelodysplastic syndrome (MDS) is a clonal disease of hematopoietic cells, characterized by hematopoietic cell hematopoiesis and a high risk of transformation into acute myeloid leukemia (AML). Although the underlying mechanism is unclear, MDS is often associated with immune system disorders, especially cellular immune abnormalities. We analyzed the number of lymphocyte subsets by flow cytometry assay and explored the alteration of lymphocyte subsets in MDS. METHODS: Healthy controls, inpatients with primary MDS and patients with AML diagnosed from January 2017 to July 2021 were included. Flow cytometry assays were used to study lymphocyte subsets obtained from the bone marrow of the participants as well as changes in natural killer (NK) cell function. One-way analysis of variance and Student's t-test were used to analyze the data. RESULTS: We found a reduction in the number and function of NK cells in patients with MDS. By further measuring the activating and inhibitory receptors on the surface of NK cells, we found that the T cell immunoglobulin and ITIM domain (TIGIT) was the highest expressed marker on NK cells. Additionally, the expression of CD155, which is the ligand of TIGIT, was significantly higher than expressions of CD112 and CD113 on bone marrow mesenchymal stem cells (BMSCs). CONCLUSIONS: The co-culture results of BMSCs and NK cells demonstrated that BMSCs regulate NK cells through the TIGIT/CD155 interaction, indicating that NK cells play a vital role in MDS progression. BMSCs regulate the function of NK cells via TIGIT/CD155. Video Abstract.

An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE.

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

A Blade Defect Diagnosis Method by Fusing Blade Tip Timing and Tip Clearance Information.

Blade tip timing (BTT) technology is considered the most promising method for blade vibration measurements due to the advantages of its simplicity and non-contact measurement capacity. Nevertheless, BTT technology still suffers from two problems, which are (1) the requirements of domain expertise and prior knowledge of BTT signals analysis due to severe under-sampling; and (2) that the traditional BTT method can only judge whether there is a defect in the blade but it cannot judge the severity and the location of the defect. Thus, how to overcome the above drawbacks has become a big challenge. Aiming at under-sampled BTT signals, a feature learning method using a convolutional neural network (CNN) is introduced. In this way, some new fault-sensitive features can be adaptively learned from raw under-sampled data and it is therefore no longer necessary to rely on prior knowledge. At the same time, research has found that tip clearance (TC) is also very sensitive to the blade state, especially regarding defect severity and location. A novel analysis method fusing TC and BTT signals is proposed in this paper. The goal of this approach is to integrate tip clearance information with tip timing information for blade fault detection. The method consists of four key steps: First, we extract the TC and BTT signals from raw pulse data; second, TC statistical features and BTT deep learning features will be extracted and fused using the kernel principal component analysis (KPCA) method; then, model training and selection are carried out; and finally, 16 sets of experiments are carried out to validate the feasibility of the proposed method and the classification accuracy achieves 95%, which is far higher than the traditional diagnostic method.

N'-Prop-ylisonicotinohydrazide.

In the title compound, C(9)H(11)N(3)O, the crystal structure is stabilized by a bifurcated inter-molecular N-H⋯(N,O) hydrogen bond and a C-H⋯O inter-action, leading to chains of mol-ecules.

catena-Poly[[[(1-ethyl-6-fluoro-4-oxo-7-(piperazin-1-yl)-1,4-dihydro-quinoline-3-carboxyl-ato-κO,O)copper(II)]-µ-1-ethyl-6-fluoro-4-oxo-7-(piperazin-1-yl)-1,4-dihydro-quinoline-3-carboxyl-ato-κN:O,O] tetra-hydrate].

In the title compound, {[Cu(C(16)H(17)FN(3)O(3))(2)]·4H(2)O}(n), the Cu(II) atom is bonded to two O,O'-bidentate 1-ethyl-6-fluoro-4-oxo-7-(piperazin-1-yl)-1,4-dihydro-quinoline-3-carboxyl-ate (norf) monoanions and a symmetry-generated N-bonded norf anion, resulting in a distorted square-pyramidal coordination environ-ment with the N atom occupying the apical site. The bridging norf anion results in one-dimensional chains propogating along [010]. A network of O-H⋯O and N-H⋯O hydrogen bonds helps to establish the crystal structure.