Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Urchin-Like Structured MoO2 /Mo3 P/Mo2 C Triple-Interface Heterojunction Encapsulated within Nitrogen-Doped Carbon for Enhanced Hydrogen Evolution Reaction.

The development of highly efficient and cost-effective hydrogen evolution reaction (HER) catalysts is highly desirable to efficiently promote the HER process, especially under alkaline condition. Herein, a polyoxometalates-organic-complex-induced carbonization method is developed to construct MoO2 /Mo3 P/Mo2 C triple-interface heterojunction encapsulated into nitrogen-doped carbon with urchin-like structure using ammonium phosphomolybdate and dopamine. Furthermore, the mass ratio of dopamine and ammonium phosphomolybdate is found critical for the successful formation of such triple-interface heterojunction. Theoretical calculation results demonstrate that such triple-interface heterojunctions possess thermodynamically favorable water dissociation Gibbs free energy (ΔGH2O ) of -1.28 eV and hydrogen adsorption Gibbs free energy (ΔGH* ) of -0.41 eV due to the synergistic effect of Mo2 C and Mo3 P as water dissociation site and H* adsorption/desorption sites during the HER process in comparison to the corresponding single components. Notably, the optimal heterostructures exhibit the highest HER activity with the low overpotential of 69 mV at the current density of 10 mA cm-2 and a small Tafel slope of 60.4 mV dec-1 as well as good long-term stability for 125 h. Such remarkable results have been theoretically and experimentally proven to be due to the synergistic effect between the unique heterostructures and the encapsulated nitrogen-doped carbon.

Water-activated formation of porous graphitic carbon nitride with carbon vacancies to boost photocatalytic hydrogen peroxide production.

A facile water-activated method is developed for preparing porous graphitic carbon nitride with carbon vacancies by co-pyrolysis of melamine and water at a relatively low temperature under an Ar atmosphere, resulting in an increased specific surface area and the efficient separation of photo-generated electrons and holes. As expected, the optimal catalyst exhibited a high H2O2 yield of 180 µM within 4 h and good cycling stability. The reported template-free method may provide a reference for the preparation of high-performance photocatalysts in a facile way.

Identification of skin-expressed genes possibly associated with wool growth regulation of Aohan fine wool sheep.

BACKGROUND: Sheep are valuable resources for the animal fibre industry. Therefore, identifying genes which regulate wool growth would offer strategies for improving the quality of fine wool. In this study, we employed Agilent sheep gene expression microarray and proteomic technology to compare the gene expression patterns of the body side (hair-rich) and groin (hairless) skins of Aohan fine wool sheep (a Chinese indigenous breed). RESULTS: Comparing the body side to the groin skins (S/G) of Aohan fine wool sheep, the microarray study revealed that 1494 probes were differentially expressed, including 602 more highly expressed and 892 less highly expressed probes. The microarray results were verified by means of quantitative PCR. Cluster analysis could distinguish the body side skin and the groin skin. Based on the Database for Annotation, Visualization and Integrated Discovery (DAVID), 38 of the differentially expressed genes were classified into four categories, namely regulation of receptor binding, multicellular organismal process, protein binding and macromolecular complex. Proteomic study revealed that 187 protein spots showed significant (p < 0.05) differences in their respective expression levels. Among them, 46 protein entries were further identified by MALDI-TOF/MS analyses. CONCLUSIONS: Microarray analysis revealed thousands of differentially expressed genes, many of which were possibly associated with wool growth. Several potential gene families might participate in hair growth regulation. Proteomic analysis also indentified hundreds of differentially expressed proteins.