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Meta-analysis of the effectiveness and safety of Shenyankangfu tablets combined with losartan potassium in the treatment of chronic glomerulonephritis
To conduct a systematic review of the efficacy and safety of Shenyankangfu tablets in combination with losartan potassium in the treatment of chronic glomerulonephritis. We searched PubMed, Embase, Cochrane Library, CNKI, WanFang Data, and WeiPu for comparative studies on Shenyankangfu tablets in combination with losartan potassium in the treatment of chronic glomerulonephritis. The search period runs from the establishment of the database until September 2021. Data extraction and quality evaluation were carried out on the documents that met the inclusion criteria, and a meta-analysis of the included literature was conducted using the RevMan5.3 software. A total of 17 randomized controlled trials that met the inclusion criteria were included, with a total sample size of 1680 patients (841 patients in the study group and 839 in the control group). The effective rate was significantly higher in the study group than in the control group [RR = 1.22, 95% CI (1.16, 1.27), P < 0.00001]. In addition, 24-hour urine protein levels [SMD = -1.11, 95% CI (-1.40, -0.83), P < 0.00001], urine NAG enzyme [SMD = -0.99, 95% CI (-1.27, -0.72), P < 0.00001], leukotactin-1 [SMD = -2.43, 95% CI (-3.50, -1.35), P < 0.00001], and the incidence of adverse reactions [RR = 0.43, 95% CI (0.28, 0.66), P < 0.00001] were lower in the study group when compared to the control group. It is safer to treat chronic glomerulonephritis with Shyenyankangfu tablets in combination with losartan potassium. At the same time, it alleviates disease-related symptoms, reduces the influence of cytokine levels, and has fewer adverse reactions, making it more conducive to disease recovery. However, additional multi-center, randomized, control trials with large sample sizes must be conducted to confirm the findings.
Journal Article
Meta-analysis of the efficacy and safety of Ginkgolide Meglumine Injection combined with Butylphthalide in the treatment of Acute Ischemic Stroke
To evaluate the efficacy and safety of Ginkgolide Meglumine Injection (GMI) combined with Butylphthalide in the treatment of Acute Ischemic Stroke (AIS), and provide reference for rational clinical medication.
PubMed, Embase, Web of science, CNKI, Wanfang, VIP and other databases were searched for published studies on the treatment of AIS with GMI combined with Butylphthalide in both Chinese and English. The search period was from the establishment of the database to July 2023. The included studies that met the inclusion criteria were analyzed using RevMan 5.3 software for Meta-analysis.
A total of 25 studies involving 2362 patients (experimental group = 1182, control group = 1180) were included. The results of meta-analysis showed that the overall effective rate of the experimental group was significantly higher than that of the control group [RR = 1.21, 95% CI (1.16, 1.26), P< 0.00001]. In addition, compared with the control group, the experimental group showed significant improvement in NIHSS score [SMD = -1.59, % CI (-2.00-1.18), P< 0.00001] and ADL score [SMD = 2.12, 95% CI (1.52, -2.72), P<0.00001], significant decrease in CRP [SMD = -2.24, 95% CI (-3.31, -1.18), P<0.0001] and TNF-α [SMD = -2.74, 95% CI (-4.45, -1.03), P< 0.005] levels, and improvement in plasma viscosity [SMD = -0.86, 95% CI (-1.07, -0.66), P< 0.00001]. However, the influence on homocysteine level remains inconclusive. Furthermore, there was no significant difference in the incidence of adverse reactions between the two groups [SMD = 0.95, 95% CI (0.71, 1.28), P> 0.05].
GMI combined with Butylphthalide shows good clinical application effects and good safety in the treatment of AIS. However, more large-sample, multicenter, randomized controlled are needed to confirm these findings.
Journal Article
Causal Effects of Gut Microbiome on Systemic Lupus Erythematosus: A Two-Sample Mendelian Randomization Study
The observational association between gut microbiome and systemic lupus erythematosus (SLE) has been well documented. However, whether the association is causal remains unclear. The present study used publicly available genome-wide association study (GWAS) summary data to perform two-sample Mendelian randomization (MR), aiming to examine the causal links between gut microbiome and SLE. Two sets of MR analyses were conducted. A group of single nucleotide polymorphisms (SNPs) that less than the genome-wide statistical significance threshold (5 × 10 -8 ) served as instrumental variables. To obtain a comprehensive conclusion, the other group where SNPs were smaller than the locus-wide significance level (1 × 10 -5 ) were selected as instrumental variables. Based on the locus-wide significance level, the results indicated that there were causal effects of gut microbiome components on SLE risk. The inverse variance weighted (IVW) method suggested that Bacilli and Lactobacillales were positively correlated with the risk of SLE and Bacillales , Coprobacter and Lachnospira were negatively correlated with SLE risk. The results of weighted median method supported that Bacilli , Lactobacillales , and Eggerthella were risk factors for SLE and Bacillales and Coprobacter served as protective factors for SLE. The estimates of MR Egger suggested that genetically predicted Ruminiclostridium6 was negatively associated with SLE. Based on the genome-wide statistical significance threshold, the results showed that Actinobacteria might reduce the SLE risk. However, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) detected significant horizontal pleiotropy between the instrumental variables of Ruminiclostridium6 and outcome. This study support that there are beneficial or detrimental causal effects of gut microbiome components on SLE risk.
Journal Article
Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles
Traffic sign detection is an important component of autonomous vehicles. There is still a mismatch problem between the existing detection algorithm and its practical application in real traffic scenes, which is mainly due to the detection accuracy and data acquisition. To tackle this problem, this study proposed an improved sparse R-CNN that integrates coordinate attention block with ResNeSt and builds a feature pyramid to modify the backbone, which enables the extracted features to focus on important information, and improves the detection accuracy. In order to obtain more diverse data, the augmentation method used is specifically designed for complex traffic scenarios, and we also present a traffic sign dataset in this study. For on-road autonomous vehicles, we designed two modules, self-adaption augmentation (SAA) and detection time augmentation (DTA), to improve the robustness of the detection algorithm. The evaluations on traffic sign datasets and on-road testing demonstrate the accuracy and effectiveness of the proposed method.
Journal Article
Transforming ground mica into high-performance biomimetic polymeric mica film
Biomimetic assembly of high-quality nanosheets into nacre-like structures can produce macroscopic films with favorable mechanical and optical performances due to the intrinsic properties and high level of ordering of the nanoscale building blocks. Natural ground mica is abundant and exhibits great application potential. However, large size and low aspect ratio greatly limit its biomimetic assembly. Moreover, exfoliation of ground mica into high-quality nanosheets remains a significant challenge. Here, we report that large-scale exfoliation of ground mica into mono- or few-layered mica nanosheets with a production rate of ~1.0 g h
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1
can be successfully achieved. The mica nanosheets are then assembled into strong biomimetic polymeric mica film that inherits the high electric insulation, excellent visible transmittance, and unique ultraviolet-shielding properties of natural mica. Its overall performance is superior to that of natural sheet mica and other biomimetic films, making the polymeric mica film a suitable substrate for flexible and transparent devices.
Biomimetic assembly of nanosheets into nacre-like structures and films is of interest for a range of applications; the abundance of mica makes it a good candidate. Here, the authors report on the large-scale exfoliation of ground mica into nanosheets and the assembly into polymeric mica films.
Journal Article
Preparation and Characterization of Electrospun PLCL/Poloxamer Nanofibers and Dextran/Gelatin Hydrogels for Skin Tissue Engineering
In this study, two different biomaterials were fabricated and their potential use as a bilayer scaffold for skin tissue engineering applications was assessed. The upper layer biomaterial was a Poly(ε-caprolactone-co-lactide)/Poloxamer (PLCL/Poloxamer) nanofiber membrane fabricated using electrospinning technology. The PLCL/Poloxamer nanofibers (PLCL/Poloxamer, 9/1) exhibited strong mechanical properties (stress/strain values of 9.37 ± 0.38 MPa/187.43 ± 10.66%) and good biocompatibility to support adipose-derived stem cells proliferation. The lower layer biomaterial was a hydrogel composed of 10% dextran and 20% gelatin without the addition of a chemical crosslinking agent. The 5/5 dextran/gelatin hydrogel displayed high swelling property, good compressive strength, capacity to present more than 3 weeks and was able to support cells proliferation. A bilayer scaffold was fabricated using these two materials by underlaying the nanofibers and casting hydrogel to mimic the structure and biological function of native skin tissue. The upper layer membrane provided mechanical support in the scaffold and the lower layer hydrogel provided adequate space to allow cells to proliferate and generate extracellular matrix. The biocompatibility of bilayer scaffold was preliminarily investigated to assess the potential cytotoxicity. The results show that cell viability had not been affected when cocultured with bilayer scaffold. As a consequence, the bilayer scaffold composed of PLCL/Poloxamer nanofibers and dextran/gelatin hydrogels is biocompatible and possesses its potentially high application prospect in the field of skin tissue engineering.
Journal Article
LncRNA BLACAT1 is involved in chemoresistance of non-small cell lung cancer cells by regulating autophagy
The aim of the present study was to determine the effect of the long non-coding RNA (lncRNA) bladder cancer-associated transcript 1 (BLACAT1) in chemoresistance of non-small cell lung cancer (NSCLC) cells. Expression of lncRNA BLACAT1, microRNA (miR)-17, autophagy-related protein 7 (ATG7), multidrug-resistance protein 1 (MRP1), and the autophagy-associated proteins light chain 3 (LC3)-II/LC3-I and Beclin 1 were detected using the reverse transcription-quantitative polymerase chain reaction and western blot analysis. Cell viability was determined using an MTT assay. The interaction between BLACAT1 and miR-17 was determined using RNA immunoprecipitation and RNA pull-down assays. A cisplatin (DDP)-resistant NSCLC cell A549/DDP xenograft model in nude mice was established to investigate the effect of BLACAT1 on the chemoresistance of NSCLC cells. Compared with in DDP-sensitive NSCLC cells, expression of BLACAT1, ATG7, MRP1, LC3-II/LC3-I and Beclin 1 was significantly upregulated in DDP-resistant NSCLC cells, whereas miR-17 was downregulated in DDP-resistant NSCLC cells. Short interfering RNA against BLACAT1 decreased the viability of DDP-resistant NSCLC cells. In addition, BLACAT1 interacted with miR-17, and negatively regulated miR-17. BLACAT1 promoted ATG7 expression through miR-17, and facilitated autophagy and promoted chemoresistance of NSCLC cells through miR-17/ATG7. Finally, in vivo experiments indicated that inhibition of BLACAT1 ameliorated the chemoresistance of NSCLC. BLACAT1 was upregulated in DDP-resistant NSCLC cells, and promoted autophagy and chemoresistance of NSCLC cells through the miR-17/ATG7 signaling pathway.
Journal Article
Multimodal detection of hateful memes by applying a vision-language pre-training model
Detrimental to individuals and society, online hateful messages have recently become a major social issue. Among them, one new type of hateful message, “hateful meme”, has emerged and brought difficulties in traditional deep learning-based detection. Because hateful memes were formatted with both text captions and images to express users’ intents, they cannot be accurately identified by singularly analyzing embedded text captions or images. In order to effectively detect a hateful meme, the algorithm must possess strong vision and language fusion capability. In this study, we move closer to this goal by feeding a triplet by stacking the visual features, object tags, and text features of memes generated by the object detection model named Visual features in Vision-Language (VinVl) and the optical character recognition (OCR) technology into a Transformer-based Vision-Language Pre-Training Model (VL-PTM) OSCAR+ to perform the cross-modal learning of memes. After fine-tuning and connecting to a random forest (RF) classifier, our model (OSCAR+RF) achieved an average accuracy and AUROC of 0.684 and 0.768, respectively, on the hateful meme detection task in a public test set, which was higher than the other eleven (11) published baselines. In conclusion, this study has demonstrated that VL-PTMs with the addition of anchor points can improve the performance of deep learning-based detection of hateful memes by involving a more substantial alignment between the text caption and visual information.
Journal Article