[Mechanism of Qilongtian Capsules in treatment of acute lung injury based on network pharmacology prediction and experimental validation].

This study aimed to explore the mechanism of Qilongtian Capsules in treating acute lung injury(ALI) based on network pharmacology prediction and in vitro experimental validation. Firstly, UPLC-Q-TOF-MS/MS was used to analyze the main chemical components of Qilongtian Capsules, and related databases were used to obtain its action targets and ALI disease targets. STRING database was used to build a protein-protein interaction(PPI) network. Metascape database was used to conduct enrichment analysis of Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG). AutoDock software was used to perform molecular docking verification on the main active components and key targets. Then, the RAW264.7 cells were stimulated with lipopolysaccharide(LPS) for in vitro experiments. Cell viability was measured by MTT and ROS level was measured by DCFH-DA. NO content was measured by Griess assay, and IL-1ß, IL-6, and TNF-α mRNA expression was detected by RT-PCR. The predicted targets were preliminarily verified by investigating the effect of Qilongtian Capsules on downstream cytokines. Eighty-four compounds were identified by UPLC-Q-TOF-MS/MS. Through database retrieval, 44 active components with 589 target genes were screened out. There were 560 ALI disease targets, and 65 intersection targets. PPI network topology analysis revealed 10 core targets related to ALI, including STAT3, JUN, VEGFA, CASP3, and MMP9. KEGG enrichment analysis showed that Qilongtian Capsules mainly exerted an anti-ALI effect by regulating cancer pathway, AGE-RAGE, MAPK, and JAK-STAT signaling pathways. The results of molecular docking showed that the main active components in Qilongtian Capsules, including crenulatin, ginsenoside F_1, ginsenoside Rb_1, ginsenoside Rd, ginsenoside Rg_1, ginsenoside Rg_3, notoginsenoside Fe, notoginsenoside G, notoginsenoside R_1, notoginsenoside R_2, and notoginsenoside R_3, had good binding affinities with the corresponding protein targets STAT3, JUN, VEGFA, CASP3, and MMP9. Cellular experiments showed that Qilongtian Capsules at 0.1, 0.25, and 0.5 mg·mL~(-1) reduced the release of NO, while Qilongtian Capsules at 0.25 and 0.5 mg·mL~(-1) reduced ROS production, down-regulated mRNA expression of IL-1ß, IL-6, TNF-α, and inhibited the inflammatory cascade. In summary, Qilongtian Capsules may exert therapeutic effects on ALI through multiple components and targets.

Transferrin-Modified Triptolide Liposome Targeting Enhances Anti-Hepatocellular Carcinoma Effects.

Triptolide (TP) is an epoxy diterpene lactone compound isolated and purified from the traditional Chinese medicinal plant Tripterygium wilfordii Hook. f., which has been shown to inhibit the proliferation of hepatocellular carcinoma. However, due to problems with solubility, bioavailability, and adverse effects, the use and effectiveness of the drug are limited. In this study, a transferrin-modified TP liposome (TF-TP@LIP) was constructed for the delivery of TP. The thin-film hydration method was used to prepare TF-TP@LIP. The physicochemical properties, drug loading, particle size, polydispersity coefficient, and zeta potential of the liposomes were examined. The inhibitory effects of TF-TP@LIP on tumor cells in vitro were assessed using the HepG2 cell line. The biodistribution of TF-TP@LIP and its anti-tumor effects were investigated in tumor-bearing nude mice. The results showed that TF-TP@LIP was spherical, had a particle size of 130.33 ± 1.89 nm and zeta potential of -23.20 ± 0.90 mV, and was electronegative. Encapsulation and drug loading were 85.33 ± 0.41% and 9.96 ± 0.21%, respectively. The preparation was stable in serum over 24 h and showed biocompatibility and slow release of the drug. Flow cytometry and fluorescence microscopy showed that uptake of TF-TP@LIP was significantly higher than that of TP@LIP (p < 0.05), while MTT assays indicated mean median inhibition concentrations (IC50) of TP, TP@LIP, and TF-TP@ of 90.6 nM, 56.1 nM, and 42.3 nM, respectively, in HepG2 cell treated for 48 h. Real-time fluorescence imaging indicated a significant accumulation of DiR-labeled TF-TP@LIPs at tumor sites in nude mice, in contrast to DiR-only or DiR-labeled, indicating that modification with transferrin enhanced drug targeting to the tumor tissues. Compared with the TP and TP@LIP groups, the TF-TP@LIP group had a significant inhibitory effect on tumor growth. H&E staining results showed that TF-TP@LIP inhibited tumor growth and did not induce any significant pathological changes in the heart, liver, spleen, and kidneys of nude mice, with all liver and kidney indices within the normal range, with no significant differences compared with the control group, indicating the safety of the preparation. The findings indicated that modification by transferrin significantly enhanced the tumor-targeting ability of the liposomes and improved their anti-tumor effects in vivo. Reducing its distribution in normal tissues and decreasing its toxic effects suggest that the potential of TF-TP@LIP warrants further investigation for its clinical application.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

LTSP: long-term slice propagation for accurate airway segmentation.

PURPOSE: Bronchoscopic intervention is a widely used clinical technique for pulmonary diseases, which requires an accurate and topological complete airway map for its localization and guidance. The airway map could be extracted from chest computed tomography (CT) scans automatically by airway segmentation methods. Due to the complex tree-like structure of the airway, preserving its topology completeness while maintaining the segmentation accuracy is a challenging task. METHODS: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans. We also design a two-stage end-to-end segmentation framework utilizing the LTSP method in the decoding process. Stage 1 is used to generate a coarse feature map by an encoder-decoder architecture. Stage 2 is to adopt the proposed LTSP method for exploiting the continuity information and enhancing the weak airway features in the coarse feature map. The final segmentation result is predicted from the refined feature map. RESULTS: Extensive experiments were conducted to evaluate the performance of the proposed method on 70 clinical CT scans. The results demonstrate the considerable improvements of the proposed method compared to some state-of-the-art methods as most breakages are eliminated and more tiny bronchi are detected. The ablation studies further confirm the effectiveness of the constituents of the proposed method and the efficacy of the framework design. CONCLUSION: Slice continuity information is beneficial to accurate airway segmentation. Furthermore, by propagating the long-term slice feature, the airway topology connectivity is preserved with overall segmentation accuracy maintained.

The effect of nuclear factor of activated T-cells (NFAT) in kidney I/R mediated by C5a/C5aR.

To investigate the relationship between NFAT and C5a/C5aR in C5a/C5aR-mediated kidney Ischemia/reperfusion (I/R) injury, the rats' NRK-52E cell line was used in this study and was distributed into 4 groups, I: the normal control (NC), II: the ischemia/reperfusion (I/R) injury cell model (MG), III: the ischemia/reperfusion (I/R) injury cell model treated with C5a (50 nmol/l) (MG + C5a), IV: the ischemia/reperfusion (I/R) injury cell model treated with C5aR antagonist (2.5 µmol/l) (MG + anti-C5aR). Reverse transcription polymerase chain reaction (RT-PCR), western blot, immunofluorescence and flow cytometry were performed. Nuclear Factor Activated T Cell (NFAT), tumor necrosis factor-α (TNF-α) and interleukin (IL-6) were detected in this study. The results of immunofluorescence showed that NFAT had a nuclear translocation phenomenon during the study. The RT-PCR and WB data indicated that the expression of TNF-α and IL-6 in group III were higher than any other groups. Apoptosis in group III was much serious than other groups. All the results in this study showed that NFAT plays an important role in ischemia/reperfusion injury, it can be induced to up-regulate the inflammatory factor TNF-α and IL-6 by the complement system member C5a/C5aR.