Background and Aim. Antituberculosis (anti-TB) drug-induced liver injury (ATLI) is a common and serious adverse drug reaction of tuberculosis treatment, and the incidence of ATLI has been reported to vary from 2.0% to...
Background and Aim. Antituberculosis (anti-TB) drug-induced liver injury (ATLI) is a common and serious adverse drug reaction of tuberculosis treatment, and the incidence of ATLI has been reported to vary from 2.0% to 28.0%. This study aims to estimate the incidence of ATLI in patients who receive anti-TB treatment and describe its temporal trend in the world. Methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards were followed, and the protocol was registered in PROSPERO (CRD42020200077). Five electronic databases were searched to identify eligible studies published between 1990 and 2022. Search terms included anti-TB treatment and drug-induced liver injury. Studies that reported the incidence of ATLI or provided sufficient data to calculate the incidence of ATLI were included, and duplicate studies were excluded. Meta-analysis was conducted on the basis of logit-transformed metrics for the incidence of ATLI with 95% confidence intervals (CIs), followed by a predefined subgroup meta-analysis. Temporal trend analyses were performed to describe the change in pooled incidence over time. A random effects metaregression was conducted to explore the source of heterogeneity. All statistical analyses were carried out using R 4.0.1. Results. A total of 160 studies from 156 records with 116147 patients were included in the meta-analysis. Based on the random effects model, the pooled incidence of ATLI was 11.50% (95% CI: 10.10%-12.97%) and showed an upward trend over time (P < 0.001). Patients who received first-line anti-TB drugs, patients in South America, and patients with hepatitis B and C virus coinfection had a higher incidence of ATLI (13.66%, 18.16%, and 39.19%, respectively). Sensitivity analyses also confirmed this robust incidence after the exclusion of some studies. The metaregression showed that different anti-TB regimens and geographical regions were important explanatory factors of the heterogeneity between studies. Conclusion
Water soluble copper sulfide (CuS) nanoparticles were synthesized using L-cysteine as the ligand. Multiple biotins were conjugated to the antibody of rabbit IgG, and the streptavidin was attached to the CuS nanopartic...
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Water soluble copper sulfide (CuS) nanoparticles were synthesized using L-cysteine as the ligand. Multiple biotins were conjugated to the antibody of rabbit IgG, and the streptavidin was attached to the CuS nanoparticles. The Cu2+ ions enclosed in the nanoparticles were used as the electron spin resonance (ESR) probes and detected with ESR spectrometer. The immunoassay reaction was resulted in the formation of the coating antibody attached to the microplate well, the detecting antibody labeled with the biotins, and the streptavidin attached to the CuS nanoparticles. After the immunoassay reaction was performed, large amount of Cu2+/Cu+ ions inside the nanoparticles were released with the help of diethyldithiocarbamate (DDC) and the Cu2+-DDC complex formed. The Cu2+-DDC complex was extracted into n-butanol, which was used as the analytical sample. Both ESR and UV?vis signals were collected for the analytical sample. The double logarithm standard curve was well simulated with a linear regression equation. The limits of detection of the rabbit IgG were 1.76 pg/mL and 2.36 pg/mL by ESR and UV?vis method, respectively. The detection range using ESR as the detector was from 8.8 pg/mL to 500 ng/mL, covering almost 5 magnitude orders of the rabbit IgG concentrations. The rabbit serum was analyzed and the rabbit IgG concentration was found to be 7.76 mg/mL. The reproducibility of the present method was good enough with the intra-assay error within 3.4 % and the inter-assay error within 11.2 %. The spiked serum samples were analyzed and the experimental results indicated that the recoveries were from 108.2 to 113.7%.
Thioredoxin interacting protein (TXNIP) is a potential target for type 2 diabetes mellitus (T2DM) treatment. A series of quinazoline derivatives were designed, synthesized and evaluated as TXNIP inhibitor to protect p...
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Recently, the graph convolutional networks based methods have achieved remarkable performance in skeleton-based action recognition. However, current methods do not make full use of the topology of the skeleton graph, ...
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ISBN:
(纸本)9781538662496
Recently, the graph convolutional networks based methods have achieved remarkable performance in skeleton-based action recognition. However, current methods do not make full use of the topology of the skeleton graph, because they set and fixed the relation of skeleton joints or adjacency matrix for all input samples manually. In order to solve the problem, we design an end-to-end architecture consisting of joints relation inference network (JRIN) and skeleton graph convolutional network (SGCN). JRIN can aggregate spatial-temporal feature of every two joints globally, then infer the optimal relation between every two joints automatically. These relations of joints is quantified as the optimal adjacency matrices. Then SGCN will take the optimal matrices to do the final action recognition. During training, special initialization and alternate training strategies are proposed to optimize the model. Our method achieves very competitive performance on widely used NTU-RGB+D and Kinetics datasets.
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