Mechanism of Si Ni San Combined with Astragalus in Treating Hepatic Fibrosis: A Network Pharmacology and Molecular Docking Study.

Si Ni San combined with Astragalus (SNSQ) has demonstrated significant efficacy in the treatment of hepatic fibrosis (HF), as confirmed by clinical practice. However, its pharmacological mechanism remains unclear. This study employs network pharmacology to identify key targets and proteins for molecular docking. Additionally, animal experiments were conducted to validate the network pharmacology results, providing further insights into the mechanism of SNSQ in treating HF. Effective compounds of SNSQ were screened from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Encyclopedia of Traditional Chinese Medicine (ETCM) databases. Molecular formula structures of these effective compounds were obtained from the PubChem database. Partial target proteins with a probability greater than 0.6 were sourced from the SWISS database. Uniprot IDs corresponding to these target proteins were retrieved from the SUPERPRED database. The remaining target proteins of the compounds were obtained from the Uniprot database based on the Uniprot IDs. The drug target proteins were then summarized. Target points related to HF were selected from the GeneCards and OMIM databases. Common target points were identified in the Venn diagram and imported into Cytoscape 3.9.1 software to construct the "SNSQ-effective compound-target pathway-HF" network. AutoDock software was used for molecular docking of compounds and target proteins with high-degree values. The common target points underwent GO function enrichment and KEGG pathway enrichment analysis using the DAVID database. An HF rat model was established, and serum AST and ALT activities were measured. The Hyp assay kit was utilized to detect the Hyp content in liver tissue. To the transcription levels of pro-inflammatory factors (IL-1ß, TNF-α, IL-6) and anti-inflammatory factors (IL-10, TGF-ß1, IL-4) in rat serum and liver.IL-1ß, TNF-α, IL-10, and TGF-ß1 were chosen for validation through ELISA. Western blotting and qRT-PCR were used to assess the expression of related proteins, namely NFKB1, NF-κBp65, NF-κBp50, α-SMA, and Col-1 in liver tissue. qRT-PCR was also employed to study the expression of ECM synthesis and proliferation-related genes, including Cyclin D1, TIMP1, COL1A1 in HSC-T6 cells and rat liver tissue, as well as the inhibition of the ECM-related gene MMP13 in HSC-T6 cells and rat liver tissue. A total of 16 valid compounds were predicted, with kaempferol, sitosterol, and isorhamnetin exhibiting high-degree values. KEGG enrichment analysis revealed that the target genes of SNSQ were enriched in multiple pathological pathways, with the NF-Kappa B signaling pathway being predominant. Molecular docking simulations indicated strong affinities between SNSQ's primary components-kaempferol, sitosterol, isorhamnetin-and NFKB1. Experimental results demonstrated significant reductions in AST, ALT, and Hyp levels in the SNSQ group. Pro-inflammatory factors (IL-1ß, TNF-ɑ) were markedly reduced, while anti-inflammatory factors (IL-10, TGF-ß1) were substantially increased. The protein expression and transcription levels of α-SMA and Col-1 were significantly decreased, whereas those of NFKB1, NF-κBp65, and NF-κBp50 were notably elevated. mRNA expression levels of Cyclin D1, TIMP1, COL1A1 in HSC-T6 cells and rat liver tissue were significantly decreased, whereas MMP13 mRNA expression level was significantly increased. Treatment of HF with SNSQ involves multiple targets and pathways, with a close association with the overexpression of NFKB1 and activation of the NF-Kappa B signaling pathway. Its mechanism is closely linked to the activation of inflammatory responses, HSC activation, and proliferation.

CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention.

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.

A tamper-resistant timed secure data transmission protocol based on smart contract.

Many time-sensitive scenarios need to decrypt data at a specified time. The timed-release encryption (TRE) primitive can meet this requirement. However, in the single-time server TRE model, there is a single point of failure problem. Therefore, we propose a tamper-resistant timed secure data transmission protocol based on smart contracts. Firstly, by decomposing the ciphertext into ciphertext fragments, the amount of deposit that a single middleman needs to submit is reduced. Secondly, it provides the system with security redundancy that changes with the decomposition mode. Thirdly, the sender is required to submit the hash value of each ciphertext fragment to the blockchain network at the same time as sending data, so that the receiver can quickly verify the authenticity of the ciphertext to resist substitution attack. Security analysis shows that the proposed protocol model can resist interruption attacks, release-ahead attacks and replacement attacks. Finally, we conduct a monetary cost test on the Ethereum's Rinkeby test network. The results show that our running cost is almost double compared with the existing similar scheme, but it is still very low and almost negligible compared with the value of the content and the expected profits it brings.