Development of a Clinicopathological-Radiomics Model for Predicting Progression and Recurrence in Meningioma Patients.

RATIONALE AND OBJECTIVES: Tumor progression and recurrence（P/R）after surgical resection are common in meningioma patients and can indicate poor prognosis. This study aimed to investigate the values of clinicopathological information and preoperative magnetic resonance imaging (MRI) radiomics in predicting P/R and progression-free survival (PFS) in meningioma patients. METHODS AND MATERIALS: A total of 169 patients with pathologically confirmed meningioma were included in this study, 54 of whom experienced P/R. Clinicopathological information, including age, gender, Simpson grading, World Health Organization (WHO) grading, Ki-67 index, and radiotherapy history, as well as preoperative traditional radiographic findings and radiomics features for each MRI modality (T1-weighted, T2-weighted, and enhanced T1-weighted images) were initially extracted. After feature selection, the optimal performance was estimated among the models established using different feature sets. Finally, Cox survival analysis was further used to predict PFS. RESULTS: Ki-67 index, Simpson grading, WHO grading, and radiotherapy history were found to be independent predictors for P/R in the multivariate regression analysis. This clinicopathological model had an area under the curve (AUC) of 0.865 and 0.817 in the training and testing sets, respectively. The performance of the combined radiomics model reached 0.85 and 0.84, respectively. A clinicopathological-radiomics model was then established, which significantly improved the prediction of meningioma P/R (AUC = 0.93 and 0.88, respectively). Finally, the risk ratio was estimated for each selected feature, and the C-index of 0.749 was obtained. CONCLUSION: Radiomics signatures of preoperative MRI have the ability to predict meningioma at the risk of P/R. By integrating clinicopathological information, the best performance was achieved.

Next-generation intelligent laboratories for materials design and manufacturing.

The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.

NDC80 Enhances Cisplatin-resistance in Triple-negative Breast Cancer.

BACKGROUNDS: Chemotherapy is a standard systemic treatment option for triple-negative breast cancer (TNBC). Cisplatin has been used to treat TNBC, but frequently leads to cisplatin resistance in patients. The aim of our study was to investigate cisplatin-resistant mechanism in TNBC. MATERIALS AND METHODS: To identify the potential genes and pathways relative to cisplatin resistance, GSE103115 data were analyzed by the Limma package and Gene set enrichment analysis (GSEA). TNBC data from TCGA, GSE76250 and GSE115275 datasets were used to calculate NDC80 expression. Immunohistochemistry detected NDC80 protein expression in TNBC tissues from patients before and after cisplatin treatment. After expose to cisplatin treatment, the viability and proliferation of TNBC cells were measured by CCK-8 and colony formation assays, respectively. RESULTS: NDC80 was regarded as a cisplatin-resistant gene because after cisplatin treatment NDC80 was downregulated in cisplatin-sensitive cells but was upregulated in cisplatin-resistant cells. NDC80 was over-expressed in TNBC tissues compared to normal tissues. Furthermore, NDC80 expression in TNBC patients was increased after cisplatin treatment. Cisplatin-sensitive TNBC patients showed lower NDC80 expression than cisplatin-resistant patients. Additionally, NDC80 expression was correlated with clinical stages, tumor size and chemotherapy of TNBC patients. Moreover, NDC80 overexpression promoted the viability and proliferation of TNBC cells and enhanced the cells resistance to cisplatin. The potential pathways relative to cisplatin resistance were obtained, such as p53 signaling pathway and Oxidative phosphorylation. CONCLUSION: These findings provide new insights for understanding the mechanism of cisplatin resistance in TNBC, and NDC80 may be a potential therapeutic target for TNBC treatment.

Photocatalyst-enzyme hybrid systems for light-driven biotransformation.

Enzymes catalyse target reactions under mild conditions with high efficiency, as well as excellent regional-, stereo-, and enantiomeric selectivity. Photocatalysis utilises sustainable and environment-friendly light power to realise efficient chemical conversion. By combining the interdisciplinary advantages of photo- and enzymatic catalysis, the photocatalyst-enzyme hybrid systems have proceeded various light-driven biotransformation with high efficiency under environmentally benign conditions, thus, attracting unparalleled focus during the last decades. It has also been regarded as a promising pathway towards green chemistry utilising ubiquitous solar energy. This systematic review gives insight into this research field by classifying the existing photocatalyst-enzyme hybrid systems into three sections based on different hybridizing modes between photo- and enzymatic catalysis. Furthermore, existing challenges and proposed strategies are discussed within this context. The first system summarised is the cofactor-mediated hybrid system, in which natural/artificial cofactors act as reducing equivalents that connect photocatalysts with enzymes for light-driven enzymatic biotransformation. Second, the direct contact-based photocatalyst-enzyme hybrid systems are described, including two different kinds of electron exchange sites on the enzyme molecules. Third, some cases where photocatalysts and enzymes are integrated into a reaction cascade with specific intermediates will be discussed in the following chapter. Finally, we provide perspective concerning the future of this field.