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In the traditional surgical intervention procedure, residual tumor cells may potentially cause tumor recurrence. In addition, large bone defects caused by surgery are difficult to self-repair. Thus, it is necessary to design a bioactive scaffold that can not only kill residual tumor cells but also promote bone defect regeneration simultaneously. Here, we successfully developed Cu-containing mesoporous silica nanosphere-modified β-tricalcium phosphate (Cu-MSN-TCP) scaffolds, with uniform and dense nanolayers with spherical morphology via 3D printing and spin coating. The scaffolds exhibited coating time- and laser power density-dependent photothermal performance, which favored the effective killing of tumor cells under near-infrared laser irradiation. Furthermore, the prepared scaffolds favored the proliferation and attachment of rabbit bone marrow-derived mesenchymal stem cells and stimulated the gene expression of osteogenic markers. Overall, Cu-MSN-TCP scaffolds can be considered for complete eradication of residual bone tumor cells and simultaneous healing of large bone defects, which may provide a novel and effective strategy for bone tumor therapy. In the future, such Cu-MSN-TCP scaffolds may function as carriers of anti-cancer drugs or immune checkpoint inhibitors in chemo-/photothermal or immune-/photothermal therapy of bone tumors, favoring for effective treatment.

Analyze performance of unsupervised embedding algorithms in sentiment analysis of knowledge-rich data sets. We apply state-of-the-art embedding algorithms Word2Vec and Doc2Vec as the learning techniques. The algorithms build word and document embeddings in an unsupervised manner. To assess the algorithms’ performance, we define sentiment metrics and use a semantic lexicon SentiWordNet (SWN) to establish the benchmark measures. Our empirical results are obtained on the Obesity data set from i2b2 clinical discharge summaries and the Reuters Science dataset. We use the Welch’s test to analyze the obtained sentiment evaluation. On the Obesity data, the Welch’s test found significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, the Word2Vec results support the SWN results, whereas the Doc2Vec results partially correspond to the Word2Vec and the SWN results. On the Reuters data, the Welch’s test did not find significant difference between the SWN evaluation of the most positive and most negative texts. On the same data, Word2Vec and Doc2Vec results only in part correspond to the SWN results. In unsupervised sentiment analysis of medical and scientific texts, the Word2Vec sentiment analysis has been more consistent with the SentiWordNet sentiment assessment than the Doc2Vec sentiment analysis. The Welch’s test of the SentiWordNet results has been a strong indicator of future correspondence between Word2Vec and SentiWordNet results.

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