Loureirin A Promotes Cell Differentiation and Suppresses Migration and Invasion of Melanoma Cells via WNT and AKT/mTOR Signaling Pathways.

Resina Draconis is a traditional Chinese medicine, with the in-depth research, its medicinal value in anti-tumor has been revealed. Loureirin A is extracted from Resina Draconis, however, research on the anti-tumor efficacy of Loureirin A is rare. Herein, we investigated the function of Loureirin A in melanoma. Our research demonstrated that Loureirin A inhibited the proliferation of and caused G0/G1 cell cycle arrest in melanoma cells in a concentration-dependent manner. Further study showed that the melanin content and tyrosinase activity was enhanced after Loureirin A treatment, demonstrated that Loureirin A promoted melanoma cell differentiation, which was accompanied with the reduce of WNT signaling pathway. Meanwhile, we found that Loureirin A suppressed the migration and invasion of melanoma cells through the protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway. Taken together, this study demonstrated for the first time the anti-tumor effects of Loureirin A in melanoma cells, which provided a novel therapeutic strategy against melanoma.

Four uncommon cycloamphilectane-type diterpenoids with antibacterial activity from the South China Sea soft coral Sinularia brassica.

The chemical investigation on the soft coral Sinularia brassica collected off Xuwen Country, Guangdong Province, China, has resulted in the isolation and characterization of three uncommon cycloamphilectane-type diterpenoids, namely sinucycloamtin A-C (1-3), along with two known analogues (5 and 6). In addition, compounds 2 and 3 were hydrolyzed and their hydrolytic derivative sinucycloamtin D (4) was obtained. The structures of these previously undescribed compounds were established on the basis of extensive spectroscopic analysis, X-ray diffraction analysis, chemical conversion, as well as the comparison with the literature reported data. Compounds 1-3 represented the first examples of benzene-containing cycloamphilectane-type diterpenoids isolated from soft coral of genus Sinularia. In the in vitro bioassays, all the isolated and derived diterpenoids exhibited significant antibacterial activities against the fish pathogenic bacteria Phoyobacterium damselae FP2244 and Streptococcus parauberis SPOF3K with MIC90 values ranging from 3.7 to 9.1 µM.

Lobosteroids A-F: Six New Highly Oxidized Steroids from the Chinese Soft Coral Lobophytum sp.

To explore the steroidal constituents of the soft coral Lobophytum sp. at the coast of Xuwen County, Guangdong Province, China, a chemical investigation of the above-mentioned soft coral was carried out. After repeated column chromatography over silica gel, Sephadex LH-20, and reversed-phase HPLC, six new steroids, namely lobosteroids A-F (1-6), along with four known compounds 7-10, were obtained. Their structures were determined by extensive spectroscopic analysis and comparison with the spectral data reported in the literature. Among them, the absolute configuration of 1 was determined by X-ray diffraction analysis using Cu Kα radiation. These steroids were characterized by either the presence of an α,ß-α',ß'-unsaturated carbonyl, or an α,ß-unsaturated carbonyl moiety in ring A, or the existence of a 5α,8α-epidioxy system in ring B, as well as diverse oxidation of side chains. The antibacterial bioassays showed that all isolated steroids exhibited significant inhibitory activities against the fish pathogenic bacteria Streptococcus parauberis FP KSP28, Phoyobacterium damselae FP2244, and Streptococcus parauberis SPOF3K, with IC90 values ranging from 0.1 to 11.0 µM. Meanwhile, compounds 2 and 6-10 displayed potent inhibitory effects against the vancomycin-resistant Enterococcus faecium bacterium G7 with IC90 values ranging from 4.4 to 18.3 µM. Therefore, ten highly oxidized steroids with strong antibacterial activities were isolated from the Chinese soft coral Lobophytum sp., which could be developed as new chemotypes of antibacterial drug leads.

Gamabufotalin Induces Apoptosis and Cytoprotective Autophagy through the mTOR Signaling Pathway in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor with a high rate of recurrence and a poor prognosis. Here, we investigated the effect and the potential antitumor mechanism of Gamabufotalin (CS-6) against HCC. Our results show that CS-6 strikingly reduced cell viability, inhibited colony formation, and promoted apoptosis in Hep3B and Huh7 cells. In vivo, CS-6 inhibited HCC xenograft tumor growth with no toxicity to normal tissues. Mechanistically, we found that CS-6 could induce cytoprotective autophagy through the mTOR-ULK1 signaling pathway through downregulation of p62 and upregulation of LC3 II/LC3 I. Meanwhile, CS-6 activated caspase-3 and PARP mediated apoptosis, and the caspase inhibitor Z-VAD-FMK blocked the CS-6-induced cell death in HCC cells. Moreover, autophagy and apoptosis were found to have antagonistic effects in Hep3B and Huh7 cells. Both the autophagy inhibitor chloroquine (CQ) and the mTOR activator MHY1485 blocked autophagy and further enhanced CS-6-induced apoptosis. Taken together, we demonstrated for the first time that CS-6 promotes apoptosis and cytoprotective autophagy through the mTOR signaling pathway in HCC, which proposes a novel strategy for HCC therapy.

Assessment of kidney function in chronic kidney disease by combining diffusion tensor imaging and total kidney volume.

OBJECTIVE: This study aimed to investigate the value and feasibility of combining fractional anisotropy (FA) values from diffusion tensor imaging (DTI) and total kidney volume (TKV) for the assessment of kidney function in chronic kidney disease (CKD). MATERIALS AND METHODS: Fifty-one patients were included in this study. All MRI examinations were performed with a 3.0 T scanner. DTI was used to measure FA values, and TKV was obtained from DTI and T2-weighted imaging (T2WI). Patients were divided into three groups (mild, moderate, severe) according to eGFR, which was calculated with serum creatinine. Differences in the FA values of the cortex and medulla were analysed among the three groups, and the relationships of FA values, TKV, and the product of the FA values and TKV with eGFR were analysed. Receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficiency of the FA values, TKV, and the product of the FA values and TKV for kidney function in different CKD stages. RESULTS: Medullary FA values (m-FA), TKV, and the product of the m-FA values and TKV (m-FA-TKV) were significantly correlated with eGFR (r = 0.653, 0.685, and 0.797, respectively; all P < 0.001). ROC curve analysis showed that m-FA-TKV exhibited better diagnostic performance than m-FA values (P = 0.022). CONCLUSION: m-FA-TKV obtained by DTI significantly improves the accuracy of kidney function assessment in CKD patients.

Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network.

OBJECTIVE: To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists. MATERIALS AND METHODS: A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I). RESULTS: The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time. CONCLUSIONS: Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.

Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network.

OBJECTIVE: To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). MATERIALS: In this retrospective study, CT images and clinical information (age, sex and medical history) from 1020 participants were collected and divided into a single-centre training set (n = 760; age: 55.8 ± 13.4 years; men: 500), a single-centre testing set (n = 134; age: 53.1 ± 14.3 years; men: 90), and two independent multicentre testing sets from two different hospitals (n = 62, age: 57.97 ± 11.88, men: 41; n = 64, age: 57.40 ± 13.36, men: 35). A Faster Region-based CNN (Faster R-CNN) model was applied to integrate CT images and clinical information. Then, a result merging technique was used to convert 2D inferences into 3D lesion results. The diagnostic performance was assessed on the basis of the receiver operating characteristic (ROC) curve, free-response ROC (fROC) curve, precision, recall (sensitivity), F1-score, and diagnosis time. The classification performance was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity. RESULTS: The CNN model showed improved performance on fresh, healing, and old fractures and yielded good classification performance for all three categories when both clinical information and CT images were used compared to the use of CT images alone. Compared with experienced radiologists, the CNN model achieved higher sensitivity (mean sensitivity: 0.95 > 0.77, 0.89 > 0.61 and 0.80 > 0.55), comparable precision (mean precision: 0.91 > 0.87, 0.84 > 0.77, and 0.95 > 0.70), and a shorter diagnosis time (average reduction of 126.15 s). CONCLUSIONS: A CNN model combining CT images and clinical information can automatically detect and classify rib fractures with good performance and feasibility in actual clinical practice. KEY POINTS: • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.

Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility.

OBJECTIVE: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. MATERIALS AND METHODS: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. RESULTS: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 × 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. CONCLUSION: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.