Mesenchymal Stem Cell Transplantation for Liver Cell Failure: A New Direction and Option

Background and Aims. Mesenchymal stem cell transplantation (MSCT) became available with liver failure (LF), while the advantages of MSCs remain controversial. We aimed to assess clinical advantages of MSCT in patients with LF. Methods. Clinical researches reporting MSCT in LF patients were searched and included. Results. Nine articles (n=476) related with LF patients were enrolled. After MSCT, alanine aminotransferase (ALT) baseline decreased largely at half a month (P<0.05); total bilirubin (TBIL) baseline declined to a certain stable level of 78.57 μmol/L at 2 and 3 months (P<0.05). Notably, the decreased value (D value) of Model for End-Stage Liver Disease score (MELD) of acute-on-chronic liver failure (ACLF) group was higher than that of chronic liver failure (CLF) group (14.93 ± 1.24 versus 4.6 ± 5.66, P<0.05). Moreover, MELD baseline of ≥20 group was a higher D value of MELD than MELD baseline of <20 group with a significant statistical difference after MSCT (P=0.003). Conclusion. The early assessment of the efficacy of MSCT could be based on variations of ALT at half a month and TBIL at 2 and 3 months. And it had beneficial effects for patients with LF, especially in ACLF based on the D value of MELD

Multiscale Study on the Effect of Nano-Organic Montmorillonite on the Performance of Rubber Asphalt

In order to improve the high-temperature performance, antiaging performance, and storage stability of rubber asphalt, nano-organic montmorillonite (NOMMT) was mixed with rubber asphalt. Macroscopic influences of NOMMT on rubber asphalt were measured through penetration, softening point, ductility, rotational viscosity tests, dynamic shear rheology test, and bending beam rheology test at low temperature and were conducted on rubber asphalt with different contents of NOMMT. Then, the microscopic mechanism of NOMMT on the microscopic performance of rubber asphalt was studied through using scanning electron microscopy (SEM), infrared spectroscopy (IR), and differential scanning calorimetry (DSC). The results showed that the rubber particles were smoother, uniform, and dispersed after NOMMT was introduced, and the compatibility between NOMMT and crumbed rubber powder was good. Some stable structures were formed in the composite modified asphalt. The disappearance of alcohol phenol and the increase in related groups such as alkane, benzene, and hydrocarbon indicated that chemical reaction occurred between NOMMT and rubber asphalt, resulting in the changes of the performance of the composite modified system, so that high-temperature stability, antiaging properties, and storage stability were improved but its low-temperature performance was decreased

The Value of Fecal Microbiota Transplantation in the Treatment of Ulcerative Colitis Patients: A Systematic Review and Meta-Analysis

Background and Aims. Fecal microbiota transplantation (FMT) has challenged the traditional management of ulcerative colitis (UC) in recent years, while it remained controversial. We aimed to provide a systematic protocol of FMT treatment on UC. Methods. Studies reporting on FMT treatment in UC patients were performed. A fixed-effect model was used to assess the efficacy of FMT. Results. Eighteen studies were enrolled (n=446). A pooled proportion of patients who received FMT had a significant efficacy compared to the placebo group (odds ratio (OR): 2.73, P=0.002) with a low risk of heterogeneity (P=0.59, I2=0%). The Mayo score decreased to 5 points in a state of mild–moderate activity after FMT treatment, and the optimal range of the Mayo score baseline was 6–9 for FMT administration. Then, the baseline of the Shannon diversity index (SDI) had a negative correlation with the clinical response rate (R=−0.992, P=0.08) or remission rate (R=−0.998, P=0.036), and the optimal diversity of bacteria was at 7 days to one month. Moreover, the colonoscopy delivery and unrelated fecal donor had slight superiorities of FMT treatment. Conclusion. FMT treatment had a higher efficacy and shorter time-point of early assessment of effectiveness on UC patients compared to traditional therapies. And the optimal FMT delivery and donor were colonoscopy delivery and unrelated donor in clinical practice

Comparative Analysis of Human Mesenchymal Stem Cells from Umbilical Cord, Dental Pulp, and Menstrual Blood as Sources for Cell Therapy

Although mesenchymal stem cells (MSCs) based therapy has been considered as a promising tool for tissue repair and regeneration, the optimal cell source remains unknown. Umbilical cord (UC), dental pulp (DP), and menstrual blood (MB) are easily accessible sources, which make them attractive candidates for MSCs. The goal of this study was to compare the biological characteristics, including morphology, proliferation, antiapoptosis, multilineage differentiation capacity, and immunophenotype of UC-, DP-, and MB-MSCs in order to provide a theoretical basis for clinical selection and application of these cells. As a result, all UC-, DP-, and MB-MSCs have self-renewal capacity and multipotentiality. However, the UC-MSCs seemed to have higher cell proliferation ability, while DP-MSCs may have significant advantages for osteogenic differentiation, lower cell apoptosis, and senescence. These differences may be associated with the different expression level of cytokines, including vascular endothelial growth factor, fibroblast growth factor, keratinocyte growth factor, and hepatocyte growth factor in each of the MSCs. Comprehensively, our results suggest DP-MSCs may be a desired source for clinical applications of cell therapy

Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images

Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method