Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS: In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study.

Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features derived from hematoxylin and eosin (H&E) images can evaluate muscle atrophy has not been studied. Therefore, this study aims to establish a new biomarker based on H&E images using radiomics methods to quantitatively analyze H&E images, which is crucial for improving the accuracy of muscle atrophy assessment. Firstly, a weightless muscle atrophy model was established by laying macaques in bed, and H&E images of the shank muscle fibers of the control and bed rest (BR) macaques were collected. Muscle fibers were accurately segmented by designing a semi-supervised segmentation framework based on contrastive learning. Then, 77 radiomics features were extracted from the segmented muscle fibers, and a stable subset of features was selected through the LASSO method. Finally, the correlation between radiomics features and muscle atrophy was analyzed using a support vector machine (SVM) classifier. The semi-supervised segmentation results show that the proposed method had an average Spearman's and intra-class correlation coefficient (ICC) of 88% and 86% compared to manually extracted features, respectively. Radiomics analysis showed that the AUC of the muscle atrophy evaluation model based on H&E images was 96.87%. For individual features, GLSZM_SZE outperformed other features in terms of AUC (91.5%) and ACC (84.4%). In summary, the feature extraction based on the semi-supervised segmentation method is feasible and reliable for subsequent radiomics research. Texture features have greater advantages in evaluating muscle atrophy compared to other features. This study provides important biomarkers for accurate diagnosis of muscle atrophy.

MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network.

Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate identification and segmentation of muscle fiber boundary is an important prerequisite for the evaluation of skeletal muscle fiber atrophy. However, there are many challenges to segment muscle fiber from immunofluorescence images, including the presence of low contrast in fiber boundaries in immunofluorescence images and the influence of background noise. Due to the limitations of traditional convolutional neural network-based segmentation methods in capturing global information, they cannot achieve ideal segmentation results. In this paper, we propose a muscle fiber segmentation network (MF-Net) method for effective segmentation of macaque muscle fibers in immunofluorescence images. The network adopts a dual encoder branch composed of convolutional neural networks and transformer to effectively capture local and global feature information in the immunofluorescence image, highlight foreground features, and suppress irrelevant background noise. In addition, a low-level feature decoder module is proposed to capture more global context information by combining different image scales to supplement the missing detail pixels. In this study, a comprehensive experiment was carried out on the immunofluorescence datasets of six macaques' weightlessness models and compared with the state-of-the-art deep learning model. It is proved from five segmentation indices that the proposed automatic segmentation method can be accurately and effectively applied to muscle fiber segmentation in shank immunofluorescence images.

Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.

Gastric cancer is one of the most common malignant tumors. Although some progress has been made in chemotherapy and surgery, it is still one of the highest mortalities in the world. Therefore, early detection, diagnosis and treatment are very important to improve the prognosis of patients. In recent years, with the proposal of the concept of radiomics, it has been gradually applied to histopathological grading, differential diagnosis, therapeutic efficacy and prognosis evaluation of gastric cancer, whose advantage is to comprehensively quantify the tumor phenotype using a large number of quantitative image features, so as to predict and diagnose the lesion area of gastric cancer early. The purpose of this review is to evaluate the research status and progress of radiomics in gastric cancer, and reviewed the workflow and clinical application of radiomics. The 27 original studies on the application of radiomics in gastric cancer were included from web of science database search results from 2017 to 2021, the number of patients included ranged from 30 to 1680, and the models used were based on the combination of radiomics signature and clinical factors. Most of these studies showed positive results, the median radiomics quality score (RQS) for all studies was 36.1%, and the development prospect and challenges of radiomics development were prospected. In general, radiomics has great potential in improving the early prediction and diagnosis of gastric cancer, and provides an unprecedented opportunity for clinical practice to improve the decision support of gastric cancer treatment at a low cost.

Muscle Atrophy Evaluation via Radiomics Analysis Using Ultrasound Images: A Cohort Data Study.

OBJECTIVE: Existing methods for muscle atrophy evaluation based on muscle size measures from ultrasound images are inadequate in precision. Radiomics has been widely used in various medical studies, but its validity for the evaluation of muscle atrophy has not been fully explored. METHODS: This study presents a radiomics analysis for muscle atrophy evaluation using ultrasound images. The hindlimb unloading rat model was developed to simulate weightlessness muscle atrophy and ultrasound images of the hind limbs were acquired for both the hindlimb unloaded (HU) and control groups during a 21-day HU period. A total of 368 radiomics features were extracted and the stable and informative features were selected through a two-stage feature selection procedure. The feature change trajectory of the stable features was analyzed using the hierarchical clustering method. Finally, an adaptive longitudinal feature selection and grading network, ALNet, was developed to evaluate muscle atrophy. RESULTS: The clustering trajectories of ultrasound image features showed similar trends to the changes in muscle atrophy at the molecular level. The best grading accuracy achieved by the ALNet was 79.5% for the Soleus (Sol) muscle and 82.6% for the Gastrocnemius (Gas) muscle. CONCLUSION: The test-retest is essential in performing radiomics analysis on ultrasound images. The longitudinal feature selection is important for muscle atrophy grading. The ultrasound image features of the Gas muscle have better discrimination ability than that of the Sol muscle. This study proves for the first time the capability of ultrasound image features for muscle atrophy evaluation.

Bioluminescence Imaging-Based Assessment of the Anti-Triple-Negative Breast Cancer and NF-Kappa B Pathway Inhibition Activity of Britanin.

Britanin has been reported to have therapeutic effects on neurodegenerative and inflammation-based diseases. However, whether it is involved in the regulation of triple-negative breast cancer development has not been elucidated. In this study, we investigated the anti-tumor activity against triple-negative breast cancer tumor of Britanin by bioluminescence imaging in vivo using athymic (nu/nu) mice implanted with MDA-MB-231 and SUM-159 cells expressing a luciferase reporter gene, and explored the anti-tumor mechanism of Britanin. The results showed that Britanin treatment inhibited triple-negative breast cancer cell proliferation in vivo, and Cell Counting Kit-8 (IC50 values are 4.27 and 5.05 µM) and colony formation tests (P < 0.001) confirmed this result. Transwell assays were conducted to verify that Britanin treatment inhibited cell migration and invasion (P < 0.001). Apoptosis was determined by TdT-mediated dUTP nick-end labeling method. Western blot and qRT-PCR analysis showed that Britanin treatment caused a decrease in the member expression of NF-kappa B signaling pathway. Computational modeling showed that Britanin could directly bind to a p-65 core region composed of Cys38, Cys120, and Gln128 residues. The results showed that the inhibitory mechanisms of Britanin on cancer cells may be by ways of inhibiting the NF-kappa B pathway. In addition, bioluminescence imaging screening system is useful for accelerating the application of Britanin in the antitumor field, and provides a useful tool for evaluating the phytochemicals efficacy in inhibiting cancer cell proliferation in animal models.

Construction of Biocompatible Dual-Drug Loaded Complicated Nanoparticles for in vivo Improvement of Synergistic Chemotherapy in Esophageal Cancer.

Combination chemotherapy is a routine treatment for esophageal cancer, but some shortcomings, such as drug toxicity and side effects, greatly limit the clinical application of combination therapy. To overcome these shortcomings, we have developed a mesoporous silica nanoparticle system that was used to load doxorubicin and ß-elemene. ß-elemene was encapsulated in the pore of mesoporous silica nanoparticle and doxorubicin was electrostatically adsorbed on the surface of mesoporous silica nanoparticle by hyaluronic acid to construct dual drugs synergistic nanoparticles (bMED NPs, ~77.15 nm). In vitro studies demonstrated that bMED NPs had a good treatment effect in esophageal cancer cell lines. In vivo fluorescence imaging results demonstrated that bMED NPs could accumulate in tumor sites and achieve in vivo long-term circulation and continuous drug release. In addition, bMED NPs exhibited significant antitumor effects in the esophageal cancer mouse model, which may provide a great platform for esophageal cancer chemotherapy.

The Antitumor Effects of Britanin on Hepatocellular Carcinoma Cells and its Real-Time Evaluation by In Vivo Bioluminescence Imaging.

BACKGROUND: Hepatocellular carcinoma is cancer with many new cases and the highest mortality rate. Chemotherapy is the most commonly used method for the clinical treatment of hepatocellular carcinoma. Natural products have become clinically important chemotherapeutic drugs due to their great potential for pharmacological development. Many sesquiterpene lactone compounds have been proven to have antitumor effects on hepatocellular carcinoma. OBJECTIVE: Britanin is a sesquiterpene lactone compound that can be considered for the treatment of hepatocellular carcinoma. The present study aimed to investigate the antitumor effect of britanin. METHODS: BEL 7402 and HepG2 cells were used to study the cytotoxicity and antitumor effects of britanin. Preliminary studies on the nuclear factor kappa B pathway were conducted by western blot analysis. A BEL 7402-luc subcutaneous tumor model was established for the in vivo antitumor studies of britanin. In vivo bioluminescence imaging was conducted to monitor changes in tumor size. RESULTS: The results of the cytotoxicity analysis showed that the IC50 values for britanin in BEL 7402 and HepG2 cells were 2.702µM and 6.006µM, respectively. The results of the colony formation demonstrated that the number of cells in a colony was reduced significantly after britanin treatment. And the results of transwell migration assays showed that the migration ability of tumor cells was significantly weakened after treatment with britanin. Tumor size measurements and staining results showed that tumor size was inhibited after britanin treatment. The western blot analysis results showed the inhibition of p65 protein expression and reduced the ratio of Bcl-2/Bax after treatment. CONCLUSION: A series of in vitro and in vivo experiments demonstrated that britanin had good antitumor effects and provided an option for hepatocellular carcinoma treatment.

In vivo antitumour activity of Britanin against gastric cancer through nuclear factor-κB-mediated immune response.

OBJECTIVES: Britanin was explored for the antitumour effect on gastric cancer, which is a sesquiterpene lactone (SL) extracted from Inula japonica. METHODS: In the present study, cell viability assays were performed to evaluate the antiproliferation effect of Britanin on gastric cancer cells. Tumour development in BGC-823 cell-bearing nude mice was monitored in real-time after Britanin treatment via a bioluminescent imaging method. Western blotting analysis and enzyme-linked immunosorbent assays detected proteins associated with the nuclear factor (NF)-κB signalling pathway. KEY FINDINGS: Britanin can suppress the proliferation of gastric cancer cells in vitro and the growth of tumours in vivo. In the treatment group, decreased levels of p65 and phosphorylated (p)-p65 were observed. This indicated that NF-κB plays an important role in the antitumour effect of Britanin. Furthermore, considering the additional role of NF-κB in the immune system, the levels of the downstream molecules interleukin (IL)-2 and the cytokine IL-10 were subsequently determined in vivo. An increase in the IL-2 level and a decrease in the IL-10 level indicated that Britanin elicited an enhanced immune response. CONCLUSIONS: Britanin may be a promising candidate for gastric cancer chemotherapy, and its anticancer effect likely depends on an NF-κB-mediated immune response.

GMBP1-conjugated manganese oxide nanoplates for in vivo monitoring of gastric cancer MDR using magnetic resonance imaging.

Multidrug resistance (MDR) is a huge challenge for gastric cancer chemotherapy. Therefore, MDR accurate monitoring is of great significance for the treatment of gastric cancer. GMBP1, an extracellular internalization peptide, can target MDR gastric cancer cells through specific binding to GRP78, which is an MDR-related protein that is overexpressed in gastric cancer cells. Herein, we constructed GMBP1 conjugated Mn3O4 nanoplates (Mn3O4@PEG-GMBP1 NPs) for in vivo monitoring of MDR gastric cancer through magnetic resonance imaging (MRI). The generated Mn3O4@PEG-GMBP1 NPs had a size of about 11 nm and exhibited a good colloidal stability in PBS and in 10% FBS medium. Serial in vivo MRI studies in mice demonstrated that the magnetic resonance signal intensity, at the tumor site, reached a peak at 3 h after tail vein injection of Mn3O4@PEG-GMBP1 NPs. The specific targeting ability of MDR gastric cancer cells (SGC7901/ADR) by Mn3O4@PEG-GMBP1 NPs was authenticated in vitro, in vivo and by immunofluorescence analysis experiments. The systematic safety evaluation indicated that the toxicity of Mn3O4@PEG-GMBP1 NPs in mice was negligible. Therefore, the GMBP1 conjugated Mn3O4 nanoplates can be clinically used for accurate imaging and monitoring of MDR gastric cancer.