The Differentiation Balance of Bone Marrow Mesenchymal Stem Cells Is Crucial to Hematopoiesis.

Bone marrow mesenchymal stem cells (BMSCs), the important component and regulator of bone marrow microenvironment, give rise to hematopoietic-supporting stromal cells and form hematopoietic niches for hematopoietic stem cells (HSCs). However, how BMSC differentiation affects hematopoiesis is poorly understood. In this review, we focus on the role of BMSC differentiation in hematopoiesis. We discussed the role of BMSCs and their progeny in hematopoiesis. We also examine the mechanisms that cause differentiation bias of BMSCs in stress conditions including aging, irradiation, and chemotherapy. Moreover, the differentiation balance of BMSCs is crucial to hematopoiesis. We highlight the negative effects of differentiation bias of BMSCs on hematopoietic recovery after bone marrow transplantation. Keeping the differentiation balance of BMSCs is critical for hematopoietic recovery. This review summarises current understanding about how BMSC differentiation affects hematopoiesis and its potential application in improving hematopoietic recovery after bone marrow transplantation

AMOBH: Adaptive Multiobjective Black Hole Algorithm

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called “adaptive multiobjective black hole algorithm” (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases

The oral microbiome of patients with ischemic stroke predicts their severity and prognosis

Background and objectivesStroke is a common group of cerebrovascular diseases that can lead to brain damage or death. Several studies have shown a close link between oral health and stroke. However, the oral microbiome profiling of ischemic stroke (IS) and its potential clinical implication are unclear. This study aimed to describe the oral microbiota composition of IS, the high risk of IS, and healthy individuals and to profile the relationship between microbiota and IS prognosis.MethodsThis observational study recruited three groups: IS, high-risk IS (HRIS), and healthy control (HC) individuals. Clinical data and saliva were collected from participants. The modified Rankin scale score after 90 days was used to assess the prognosis of stroke. Extracted DNA from saliva and performed 16S ribosomal ribonucleic acid (rRNA) gene amplicon sequencing. Sequence data were analyzed using QIIME2 and R packages to evaluate the association between the oral microbiome and stroke.ResultsA total of 146 subjects were enrolled in this study according to the inclusion criteria. Compared with HC, HRIS and IS demonstrated a progressive increase trend in Chao1, observed species richness, and Shannon and Simpson diversity index. On the basis of permutational multivariate analysis of variance, the data indicate a great variation in the saliva microbiota composition between HC and HRIS (F = 2.40, P < 0.001), HC and IS (F = 5.07, P < 0.001), and HRIS and IS (F = 2.79, P < 0.001). The relative abundance of g_Streptococcus, g_Prevotella, g_Veillonella, g_Fusobacterium, and g_Treponema was higher in HRIS and IS compared with that in HC. Furthermore, we constructed the predictive model by differential genera to effectively distinguish patients with IS with poor 90-day prognoses from those with good (area under the curve = 79.7%; 95% CI, 64.41%–94.97%; p < 0.01).DiscussionIn summary, the oral salivary microbiome of HRIS and IS subjects have a higher diversity, and the differential bacteria have some predictive value for the severity and prognosis of IS. Oral microbiota may be used as potential biomarkers in patients with IS

BGM-Net: Boundary-Guided Multiscale Network for Breast Lesion Segmentation in Ultrasound.

Automatic and accurate segmentation of breast lesion regions from ultrasonography is an essential step for ultrasound-guided diagnosis and treatment. However, developing a desirable segmentation method is very difficult due to strong imaging artifacts e.g., speckle noise, low contrast and intensity inhomogeneity, in breast ultrasound images. To solve this problem, this paper proposes a novel boundary-guided multiscale network (BGM-Net) to boost the performance of breast lesion segmentation from ultrasound images based on the feature pyramid network (FPN). First, we develop a boundary-guided feature enhancement (BGFE) module to enhance the feature map for each FPN layer by learning a boundary map of breast lesion regions. The BGFE module improves the boundary detection capability of the FPN framework so that weak boundaries in ambiguous regions can be correctly identified. Second, we design a multiscale scheme to leverage the information from different image scales in order to tackle ultrasound artifacts. Specifically, we downsample each testing image into a coarse counterpart, and both the testing image and its coarse counterpart are input into BGM-Net to predict a fine and a coarse segmentation maps, respectively. The segmentation result is then produced by fusing the fine and the coarse segmentation maps so that breast lesion regions are accurately segmented from ultrasound images and false detections are effectively removed attributing to boundary feature enhancement and multiscale image information. We validate the performance of the proposed approach on two challenging breast ultrasound datasets, and experimental results demonstrate that our approach outperforms state-of-the-art methods

Pore Structural and Fractal Analysis of the Effects of MgO Reactivity and Dosage on Permeability and F–T Resistance of Concrete

Currently, the MgO expansion agent is widely used to reduce the cracking risk of concrete. The influence of MgO reactivity (50 s and 300 s) and dosage (0, 4 wt.% and 8 wt.%, by weight of binder) on the air void, pore structure, permeability and freezing–thawing (F–T) resistance of concrete were studied. The results indicate (1) the addition of 4–8 wt.% reactive MgO (with reactivity of 50 s and termed as M50 thereafter) and weak reactive MgO (with reactivity of 300 s and termed M300 thereafter) lowers the concrete’s compressive strength by 4.4–17.2%, 3.9–16.4% and 1.9–14.6% at 3, 28 and 180 days, respectively. The increase in MgO dosage and reactivity tends to further reduce the concrete strength at all hydration ages. (2) Permeability of the concrete is closely related to the pore structure. M50 can densify the pore structure and lower the fraction of large capillary pores at an early age, thus it is beneficial for the impermeability of concrete. In contrast, M300 can enhance the 180-day impermeability of concrete since it can densify the pore structure only at a late age. (3) The influence of MgO on F–T resistance is minor since MgO could not change the air void parameters. (5) MgO concretes exhibit obvious fractal characteristics. The fractal dimension of the pore surface (Ds) exhibits a close relationship with the permeability property of concrete. However, no correlation can be found between F–T resistance and Ds.</jats:p

Self-doping effect in confined copper selenide semiconducting quantum dots for efficient photoelectrocatalytic oxygen evolution

Self-doping can not only suppress the photogenerated charge recombination of semiconducting quantum dots by self-introducing trapping states within the bandgap, but also provide high-density catalytic active sites as the consequence of abundant non-saturated bonds associated with the defects. Here, we successfully prepared semiconducting copper selenide (CuSe) confined quantum dots with abundant vacancies and systematically investigated their photoelectrochemical characteristics. Photoluminescence characterizations reveal that the presence of vacancies reduces the emission intensity dramatically, indicating a low recombination rate of photogenerated charge carriers due to the self-introduced trapping states within the bandgap. In addition, the ultra-low charge transfer resistance measured by electrochemical impedance spectroscopy implies the efficient charge transfer of CuSe semiconducting quantum dots-based photoelectrocatalysts, which is guaranteed by the high conductivity of their confined structure as revealed by room-temperature electrical transport measurements. Such high conductivity and low photogenerated charge carriers recombination rate, combined with high-density active sites and confined structure, guaranteeing the remarkable photoelectrocatalytic performance and stability as manifested by photoelectrocatalysis characterizations. This work promotes the development of semiconducting quantum dots-based photoelectrocatalysis and demonstrates CuSe semiconducting quantum confined catalysts as an advanced photoelectrocatalysts for oxygen evolution reaction

Pertinence of glioma and single nucleotide polymorphism of TERT, CCDC26, CDKN2A/B and RTEL1 genes in glioma: a meta-analysis

BackgroundPrevious genetic-epidemiological studies considered TERT (rs2736100), CCDC26 (rs4295627), CDKN2A/B (rs4977756) and RTEL1 (rs6010620) gene polymorphisms as the risk factors specific to glioma. However, the data samples of previous genetic-epidemiological studies are modest to determine whether they have definite association with glioma.MethodThe study paid attention to systematically searching databases of PubMed, Embase, Web of Science (WoS), Scopus, Cochrane Library and Google Scholars. Meta-analysis under 5 genetic models, namely recessive model (RM), over-dominant model (O-DM), allele model (AM), co-dominant model (C-DM) and dominant model (DM) was conducted for generating odds ratios (ORs) and 95% confidence intervals (CIs). That was accompanied by subgroup analyses according to various racial groups. The software STATA 17.0 MP was implemented in the study.Result21 articles were collected. According to data analysis results, in four genetic models (AM, RM, DM and C-DM) TERT gene rs2736100 polymorphism, CCDC26 gene rs4295627 polymorphism, CDKN2A/B gene rs4977756 polymorphism and RTEL1 gene rs6010620 polymorphisms increased the risk of glioma in Caucasians to different degrees. In Asian populations, the CCDC26 gene rs4295627 polymorphism and CDKN2A/B gene rs4977756 polymorphism did not exhibit a relevance to the risk of glioma. It is suggested to cautiously explain these results as the sample size is small.ConclusionThe current meta-analysis suggested that the SNP of TERT (rs2736100), CCDC26 (rs4295627), CDKN2A/B (rs4977756) and RTEL1 (rs6010620) genes in glioma might increase risk of glioma, but there are ethnic differences. Further studies evaluating these polymorphisms and glioma risk are warranted