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BackgroundSCL/TAL1 interrupting locus (STIL) is associated with the progression of several tumors; however, the biological role of STIL in osteosarcoma remains poorly understood.MethodsIn this study, the clinical significance of STIL in osteosarcoma was analyzed by gene chip data recorded in public databases. STIL expression was silenced in osteosarcoma cell lines to observe the effects on proliferation, apoptosis, invasion, and migration. Differentially expressed genes (DEGs) in the osteosarcoma chip were analyzed using The Limma package, and STIL co-expressed genes were obtained via the Pearson correlation coefficient. The potential molecular mechanism of STIL in osteosarcoma was further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.ResultsMeta-analysis revealed that STIL was highly expressed in osteosarcoma, and the standardized mean difference (SMD) was 1.52. STIL also had a good ability to distinguish osteosarcoma from non-osteosarcoma samples (area under the curve [AUC] = 0.96). After silencing STIL, osteosarcoma cell proliferation decreased, apoptosis increased, and the migratory and invasion ability decreased. A total of 294 STIL differentially co-expressed genes were screened, and a bioinformatics analysis found that differentially co-expressed genes were primarily enriched in the cell signaling pathways. The protein-protein interaction (PPI) network indicated that the hub differentially co-expressed genes of STIL were CDK1, CCNB2, CDC20, CCNA2, BUB1, and AURKB.Conclusions STIL is associated with osteosarcoma proliferation and invasion, and may represent a novel target for its diagnosis and treatment.

Background SCL/TAL1 interrupting locus (STIL) is associated with the progression of several tumors; however, the biological role of STIL in osteosarcoma remains poorly understood. Methods In this study, the clinical significance of STIL in osteosarcoma was analyzed by gene chip data recorded in public databases. STIL expression was silenced in osteosarcoma cell lines to observe the effects on proliferation, apoptosis, invasion, and migration. Differentially expressed genes (DEGs) in the osteosarcoma chip were analyzed using The Limma package, and STIL co-expressed genes were obtained via the Pearson correlation coefficient. The potential molecular mechanism of STIL in osteosarcoma was further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results Osteosarcoma was associated with higher STIL expression compared to the control samples, and the standardized mean difference (SMD) was 1.52. STIL also had a good ability to distinguish osteosarcoma from non-osteosarcoma samples [area under the curve (AUC) = 0.96]. After silencing STIL, osteosarcoma cell proliferation decreased, apoptosis increased, and the migratory and invasion ability decreased. A total of 294 STIL differentially co-expressed genes were screened, and a bioinformatics analysis found that differentially co-expressed genes were primarily enriched in the cell signaling pathways. The protein-protein interaction (PPI) network indicated that the hub differentially co-expressed genes of STIL were CDK1, CCNB2, CDC20, CCNA2, BUB1, and AURKB. Conclusions STIL is associated with osteosarcoma proliferation and invasion, and may be promote the progression of osteosarcoma by regulating the expression of CDK1, CCNB2, CDC20, CCNA2, BUB1 and AURKB.

Medical adherence is often higher in clinical trials than in real world practice. The aim of this study was to investigate the effects of traditional Chinese medicine (TCM) on medical adherence to hormonal therapy (HT) and survival outcome in ER (+) breast cancer patients in Taiwan.Using a nationwide longitudinal population-based database, we enrolled patients with newly diagnosed ER-positive breast cancer who had received HT, and followed for up to 5 years (N = 872). Medication adherence in terms of medication possession ratios (MPR) and patient outcome were evaluated with or without TCM exposure. We applied logistic regression and Cox proportional hazards (PH) analysis to identify factors, including TCM exposure, associated with adherence to HT and mortality.MPR to HT in general decreased over the 5-year period post breast cancer diagnosis. Both TCM and MPR to HT ≥ 80% were significantly associated with reduced risk of breast cancer-associated mortality. Subgroup analysis revealed that TCM annual visits ≥ 3 times with CHP prescription 1~90 days per year affected mortality reduction most significantly (HR: 0.26; 95% CI = 0.08-0.83; p 0.05) compared to other TCM use. In contrast, using TCM (either short-term or long-term) was not associated with MPR in HT.Our results supported the potential advantage of TCM on breast cancer-associated mortality, whereas TCM use does not compromise medical adherence to HT. This study offers important insights in integrative therapy for HT in patients with estrogen receptor (+) breast cancer.

This paper mainly introduces the application of 3D scanning modeling technology and 3D printing technology in the reproduction of fine bronze cultural relics. We apply 3D scanning, point cloud data processing, 3D mod-eling, 3D printing and other key technologies in the field of fine cultural relics reproduction. At the same time, reconstruction technology can be used to reconstruct and restore the damaged parts of excavated cultural relics in three dimensions. We will output the 3D solid model of the copied cultural relic, input the 3D printer to print the high-quality reproduction mold consistent with the original cultural relic, and finally complete the overall casting of the printing mold.

In recent years, with the completion of the new library of Qinghai University, the collection of books in the library has greatly increased. The library has a total collection of 880,000 volumes, covering a dozen disciplines such as science, engineering, agriculture, literature, history, economics, philosophy, law, education, management and medicine. It is difficult for users to find the books they are interested in among the numerous materials. Based on the actual situation of the library of Qinghai University, the differences of different professional users and their personal interests, this paper chooses the item-based collaborative filtering algorithm to realize personalized recommendation. First of all, in the calculation of book similarity, the traditional user score data is not chosen to calculate the similarity, but to calculate the similarity between books and books according to the feature vector of book name. Secondly, in order to avoid the problem of cold start, the system recommends the users who have no borrowing record, but the most borrowed books in their department. The combination of the two realized the personalized recommendation of books. By comparing with other traditional recommendation algorithms, it is found that the algorithm adopted in this paper has better recommendation effect.