Lei Hua

To predict the 1p/19q molecular status of Lower-grade glioma (LGG) patients nondestructively, this study developed a deep learning (DL) approach using radiomic to provide a potential decision aid for clinical determination of molecular stratification of LGG. The study retrospectively collected images and clinical data of 218 patients diagnosed with LGG between July 2018 and July 2022, including 155 cases from The Cancer Imaging Archive (TCIA) database and 63 cases from a regional medical centre. Patients' clinical data and MRI images were collected, including contrast-enhanced T1-weighted images and T2-weighted images. After pre-processing the image data, tumour regions of interest (ROI) were segmented by two senior neurosurgeons. In this study, an Ensemble Convolutional Neural Network (ECNN) was proposed to predict the 1p/19q status. This method, consisting of Variational Autoencoder (VAE), Information Gain (IG) and Convolutional Neural Network (CNN), is compared with four machine learning algorithms (Random Forest, Decision Tree, K-Nearest Neighbour, Gaussian Neff Bayes). Fivefold cross-validation was used to evaluate and calibrate the model. Precision, recall, accuracy, F1 score and area under the curve (AUC) were calculated to assess model performance. Our cohort comprises 118 patients diagnosed with 1p/19q codeletion and 100 patients diagnosed with 1p/19q non-codeletion. The study findings indicate that the ECNN method demonstrates excellent predictive performance on the validation dataset. Our model achieved an average precision of 0.981, average recall of 0.980, average F1-score of 0.981, and average accuracy of 0.981. The average area under the curve (AUC) for our model is 0.994, surpassing that of the other four traditional machine learning algorithms (AUC: 0.523–0.702). This suggests that the model based on the ECNN algorithm performs well in distinguishing the 1p/19q molecular status of LGG patients. The deep learning model based on conventional MRI radiomic integrates VAE and IG methods. Compared with traditional machine learning algorithms, it shows the best performance in the prediction of 1p/19q molecular co-deletion status. It may become a potentially effective tool for non-invasively and effectively identifying molecular features of lower-grade glioma in the future, providing an important reference for clinicians to formulate individualized diagnosis and treatment plans.

In recent years, more and more metal oxides have been finding critical tribo-catalytic applications. Presently, we have explored the tribo-catalytic conversion of H₂O and CO₂ using Co₃O₄ nanoparticles and obtained some surprising results. In an as-received 150 mL glass reactor enclosed with 10 mL H₂O, 0.10 g Co₃O₄ nanoparticles, 1 atm CO₂, and a Teflon magnetic rotary disk, we observed the production of as much as 57.41 µmol/L of H₂, and 0.15 µmol/L of CH₄, 0.21 µmol/L of CO after 5 h magnetic stirring. Metallic coatings of Cu, Ni, SUS316, Ti, Nb, Mo, and W were further introduced on reactor bottoms separately. For those coatings of Ni, SUS316, Ti, and Nb, the reduction of CO₂ was dramatically enhanced and C₂₊ products of C₂H₆ and C₂H₄ were observed. Especially for Ti coating, the amounts of H₂ and CH₄ were increased by 2 and 26 times from those for the glass bottom, respectively, and the amounts of C₂H₆ and C₂H₄ were very impressive. Co₃O₄ nanoparticles were proven chemically stable under magnetic stirring in water, and hydroxyl radicals and superoxide radicals have been detected for Co₃O₄ nanoparticles under magnetic stirring through fluorescence spectroscopy and electron paramagnetic resonance spectroscopy analyses. These findings not only reveal an outstanding capability of Co₃O₄ to generate multicarbon products from H₂O and CO₂ through tribo-catalysis but also highlight a promising potential of tribo-catalysis as a whole to harness mechanical energy for addressing energy shortages and environmental pollution.

Ovarian cancer is one of the most lethal gynecologic malignancies worldwide, with the 5-year survival is less than 50%. Although some clinical achievements have been achieved, the overall survival rate has remained unchanged over the past 20 years. Therefore, it is necessary and urgent to develop the potential modifiers and therapeutic approach to improve the overall survival rate in ovarian cancer patients. RBCK1 is an RING protein E3 ubiquitin ligase, which was revealed to involve in the progression of several cancers through its ubiquitination function. In this research, we report that RBCK1 expression is significantly elevated in human ovarian cancer and strongly associated with poor patients’ prognosis. RBCK1 deficiency induces cell apoptosis and inhibits cell proliferation and migration in ovarian cancer cells. In terms of molecular mechanism, we report that RBCK1 interacts with PTEN and promotes PTEN degradation in K48-linked ubiquitination. Our study suggests a new and interesting regulatory mechanism that RBCK1 facilitates PTEN degradation, which could be a new potential therapeutic target for ovarian cancer treatment.

Four new (1–4) and two known compounds (5, 6) were isolated from the aerial parts of Araucaria cunninghamii. By analysis of UV, IR, MS, and NMR, the new compounds were identified as ent-19-(Z)-coumaroyloxylabda-8(17),13(16),14-triene (1), ent-19-(E)-coumaroyloxylabda-8(17),13(16),14-triene (2), shikimic acid n-butyl ester (3), and 5-(Z)-coumaroyloxyquinic acid n-butyl ester (4). Compounds 1 and 6 exhibited inhibitory activity against Escherichia coli, and compound 1 also exhibited moderate activity against human leukemia cells (HL-60) and human hepatoma cells (SMMC-7721) with IC₅₀ values of 8.90 and 11.53 µM, respectively.