The treatment of bone defects has plagued clinicians. Exosomes, the naturally secreted nanovesicles by cells, exhibit great potential in bone defect regeneration to realize cell-free therapy. In this work, we successfully revealed that human umbilical cord mesenchymal stem cells-derived exosomes could effectively promote the proliferation, migration, and osteogenic differentiation of a murine calvariae preosteoblast cell line in vitro. Considering the long period of bone regeneration, to effectively exert the reparative effect of exosomes, we synthesized an injectable hydroxyapatite (HAP)-embedded in situ cross-linked hyaluronic acid-alginate (HA-ALG) hydrogel system to durably retain exosomes at the defect sites. Then, we combined the exosomes with the HAP-embedded in situ cross-linked HA-ALG hydrogel system to repair bone defects in rats in vivo. The results showed that the combination of exosomes and composite hydrogel could significantly enhance bone regeneration. Our experiment provides a new strategy for exosome-based therapy, which shows great potential in future tissue and organ repair.

Layer-by-layer (LBL) assembly is a simple and effective method for the fabrication of a three-dimensional (3D) scaffold for nanotechnological and biomedical applications. Herein, a novel 3D scaffold based on an alternate LBL assembly of graphene oxide (GO) nanosheets and fibrinogen nanofibers (Fg NFs) on a silicon substrate was fabricated and utilized to create a 3D hydroxyapatite (HA) scaffold by biomimetic mineralization in 1.5Â simulated body fluid for different nucleation periods. The obtained 3D (GO-NF) n-HA scaffold was characterized using atomic force microscopy, scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The results demonstrate that the Fg NFs could promote the nucleation and growth of HA crystals along the axis. The 3D (GO-NF) 10-HA scaffold composed of 10 layers of GO alternating with 10 layers of NFs was successfully created by LBL assembly and subsequent biomimetic mineralization. The effects of the created 3D (GO-NF) 10-HA scaffolds on the adhesion, morphology, and proliferation of L-929 cells were investigated. The in vitro cell culture indicates that the 3D (GO-NF) 10-HA scaffold has a higher proliferation ability and better cytocompatibility than the other control samples.

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Event-related brain potentials were recorded to investigate electrophysiological correlates of aggression in high and low socioeconomic status (SES) participants who responded to violent and nonviolent images by using a choice reaction time paradigm. ERP data showed that violent images elicited a smaller N2 deflection than did nonviolent images in both high and low SES groups, but there was no difference in N2 amplitudes to aggressive and non-aggressive information as a function of SES. Notably, the latency of N2 in the low SES group was longer than that of the high SES group, suggesting slowness by the low SES group in deploying control responses. In addition, the low SES group exhibited significantly smaller P3 amplitudes to violent images, suggesting a reduction in brain activity known to reflect activation of the aversive motivational system, and this findings link this brain activity to aggressive behavior. As a whole the present findings show that participants low in SES seem to display similar psychophysiological responses to individuals high in aggression.

Additional sex combs‐like 1 (ASXL1) mutations, a hotspot in myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), have been frequently reported for their potential prognostic value, but the results are controversial. Therefore, a meta‐analysis was performed. Databases, including PubMed, Embase, and Cochrane Library, were searched for relevant studies published up to January 13, 2022. STATA v16.0 software was used to calculate the combined hazard ratios (HRs) and their 95% confidence intervals (CIs) for overall survival (OS) and AML transformation. Subgroup analysis was used to explore the effects of the grouping factors on heterogeneity.Ten studies on ASXL1 mutations and the prognosis of MDS were selected. Our results indicate that ASXL1 mutations have an adverse prognostic impact on OS (HR = 1.68,95%CI:1.45–1.94, p < .0001) and AML transformation (HR = 2.20,95% CI:1.68–2.87, p < .0001). The results for different age groups were not significantly different (HR = 1.87,95% CI: 1.31–2.67; HR = 1.62,95% CI:1.35–2.07). Ten studies covering 5816 patients with AML were included. The pooled HR for OS was 1.37 (95% CI:1.20–1.56, p < .0001). ASXL1 mutations were especially associated with a poorer OS in the subgroup aged ≥60 years (HR = 2.86, 95% CI:1.34–6.08, p = .006); when considering cytogenetically normal AML (CN‐AML), the HR was 1.78(95% CI:1.27–2.49, p = .001). This meta‐analysis indicates an independent, adverse prognostic impact of ASXL1 mutations in patients with MDS and AML, which also applies to patients with CN‐AML. Age was a risk factor for patients with AML and ASXL1 mutations but not for patients with MDS.

A magnetic separable Z-scheme composite based on ZnFe2O4, TiO2 nanosheets and RGO exhibits efficient photocatalytic degradation of p-NP.

Data imbalance is often encountered in deep learning process and is harmful to model training. The imbalance of hard and easy samples in training datasets often occurs in the segmentation tasks from Contrast Tomography (CT) scans. However, due to the strong similarity between adjacent slices in volumes and different segmentation tasks (the same slice may be classified as a hard sample in liver segmentation task, but an easy sample in the kidney or spleen segmentation task), it is hard to solve this imbalance of training dataset using traditional methods. In this work, we use a pre-training strategy to distinguish hard and easy samples, and then increase the proportion of hard slices in training dataset, which could mitigate imbalance of hard samples and easy samples in training dataset, and enhance the contribution of hard samples in training process. Our experiments on liver, kidney and spleen segmentation show that increasing the ratio of hard samples in the training dataset could enhance the prediction ability of model by improving its ability to deal with hard samples. The main contribution of this work is the application of pre-training strategy, which enables us to select training samples online according to different tasks and to ease data imbalance in the training dataset.