Effectiveness and Safety of Chinese Medicine Decoctions for Behcet's Disease: A Systematic Review and Meta-Analysis

Integrated ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry-based components analysis and network pharmacology strategy of Gancao Xiexin Decoction in treating gastric ulcer

Gancao Xiexin Decoction (GCXXD) is a traditional Chinese decoction that is often used in treating gastric ulcers. However, the substance basis and mechanism of action remain unclear. In this study, in vivo and in vitro components of GCXXD were analyzed by ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry. The compound Discover platform was used to ultimately enable rapid identification of compounds. Acquire X intelligent data acquisition technology software was innovatively adopted. In the process of collecting drug-containing plasma, all components detected in blank plasma samples were excluded to eliminate the interference and influence of endogenous components in plasma, making the analysis results more accurate and reliable. At the same time, the possibility of selecting precursor parent ions with low concentration levels within the chromatographic peak can be increased, improving the coverage and integrality of the detection of components in vivo. Also, the targeted network pharmacology strategy combined with molecular docking was established to explore the mechanism of GCXXD in treating gastric ulcers. As a result, 113 components were identified, 41 of which could enter the bloodstream and exert therapeutic effects in vivo. The main effective components are glycyrrhizic acid, 6-gingerol, jatrorrhizine, wogonin, palmatine, and liquiritigenin, main targets in vivo were related to ALB, IL6, and VEGF, which play an important role in anti-inflammatory and promoting angiogenesis. In summary, this study adopted a comprehensive analysis strategy to reveal the pharmacodynamic material basis and mechanism of GCXXD against gastric ulcers, providing a scientific basis for its clinical application.

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

The understanding of how different cell types contribute to amyotrophic lateral sclerosis (ALS) pathogenesis is limited. Here we generated a single-nucleus transcriptomic and epigenomic atlas of the frontal cortex of ALS cases with C9orf72 (C9) hexanucleotide repeat expansions and sporadic ALS (sALS). Our findings reveal shared pathways in C9-ALS and sALS, characterized by synaptic dysfunction in excitatory neurons and a disease-associated state in microglia. The disease subtypes diverge with loss of astrocyte homeostasis in C9-ALS, and a more substantial disturbance of inhibitory neurons in sALS. Leveraging high depth 3'-end sequencing, we found a widespread switch towards distal polyadenylation (PA) site usage across ALS subtypes relative to controls. To explore this differential alternative PA (APA), we developed APA-Net, a deep neural network model that uses transcript sequence and expression levels of RNA-binding proteins (RBPs) to predict cell-type specific APA usage and RBP interactions likely to regulate APA across disease subtypes.

Exploring the pharmacological mechanism of Tripterygium wilfordii hook for treatment of Behcet's disease using network pharmacology and molecular docking

Tripterygium wilfordii hook (TWH) has been used to treat Behcet's disease (BD) but its underlying mechanism remains unclear. This study aims to explore the mechanism of TWH on BD using network pharmacology and molecular docking. The bioactive constituents of TWH and their corresponding target genes were extracted from the Traditional Chinese Medicine systems pharmacology database and analysis platform. BD target genes were obtained by searching the DisGeNet and GeneCards databases. Gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment analysis were conducted to elucidate the function of overlapping genes between TWH and BD target genes. A protein-protein interaction network was constructed using Cytoscape and STRING platforms, and the core target genes were identified from the overlapping genes. Finally, molecular docking was used to assess the binding affinity between the core targets and TWH bioactive constituents. We identified 25 intersection genes related to both TWH and BD and 27 bioactive ingredients of TWH. Through analysis of protein-protein interaction network, 6 core targets (TNF, IFNG, prostaglandin-endoperoxide synthase 2, NOS2, VCAM-1, and interleukin-2) were screened out. Enrichment analysis demonstrated that the antioxidant properties of TWH constituents might play a significant role in their therapeutic effects. Molecular docking revealed high binding affinity between the bioactive constituents of TWH, such as kaempferol, triptolide, 5, 8-Dihydroxy-7-(4-hydroxy-5-methyl-coumarin-3)-coumarin, and their corresponding target genes, suggesting the potential of TWH to treat BD. Our investigation clarified the active components, therapeutic targets of BD in the treatment of TWH and provided a theoretical foundation for further researches.

Acute and subacute oral toxicity assessment of Gancao Xiexin decoction in Sprague-Dawley rats

Gancao Xiexin decoction (GCXXD), a well-known classic traditional Chinese medicine prescription, is used to treat various oral ulcers, Behcet disease, gastrointestinal ulcers, etc. However, there is very little information on its safety. This study aimed to investigate the acute and subacute oral toxicity of GCXXD in Sprague-Dawley rats. In the acute toxicity study, rats were orally administered 10 g/kg GCXXD three times a day. Clinical signs of abnormality and mortality were observed daily for 14 days. In the subacute toxicity study, rats were orally administered 0, 1.47, 3.83, or 10 g/kg GCXXD for 28 days. The rats' clinical signs, body weight, food consumption, hematological and biochemical parameters, bone marrow smear, organ index, and pathological morphology were analyzed. The acute toxicity study showed that GCXXD is safe in rats without any obvious toxicity via an oral dose of 30 g/kg/day (3 × 10 g/kg). After 28 days of administration, slightly decreased RBC, HGB, and HCT were observed in female rats at 10 g/kg, suggesting that repeated doses of high-dose GCXXD may cause mild anemia in female rats. The no-observed-adverse-effect level (NOAEL) and lowest-observed-adverse-effect level (LOAEL) of oral administration of GCXXD for 28 days in rats are considered to be 3.83 g/kg and 10 g/kg, respectively. Long-term toxicity studies are recommended to strengthen the findings.

Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images

Background and aim

A pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical treatment. The model was designed using ensemble deep learning (DL).

Methods

A total of 172 anterior segment images of pterygia were obtained from the Jiangxi Provincial People's Hospital (China) between 2017 and 2022. They were divided by a senior ophthalmologist into the non-surgery group and the surgery group. An artificial intelligence model was then developed based on ensemble DL, which was integrated with four benchmark models: the Resnet18, Alexnet, Googlenet, and Vgg11 model, for detecting the pterygium that requires surgical treatment, and Grad-CAM was used to visualize the DL process. Finally, the performance of the ensemble DL model was compared with the classical Resnet18 model, Alexnet model, Googlenet model, and Vgg11 model.

Results

The accuracy and area under the curve (AUC) of the ensemble DL model was higher than all of the other models. In the training set, the accuracy and AUC of the ensemble model was 94.20% and 0.978, respectively. In the testing set, the accuracy and AUC of the ensemble model was 94.12% and 0.980, respectively.

Conclusion

This study indicates that this ensemble DL model, coupled with the anterior segment images in our study, might be an automated and cost-saving alternative for detection of the pterygia that require surgery.