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[C11H10O5], monoclinic, P21/n, a = 7.041(3) Å, b = 9.339(4) Å, c = 15.614(6) Å, β = 103.03°, V = 1000.2(7) Å3, Z = 4, Rgt(F) = 0.0337, wRref(F2) = 0.0994, T = 296(2) K.

C11H12O5, orthorhombic, P212121 (no. 19), a = 5.5523(7) Å, b = 12.7610(17) Å, c = 15.374(2) Å, V = 1089.3(2) Å3, Z = 4, R gt(F) = 0.0273, wR ref(F 2) = 0.0787, T = 296(2) K.

Background and purpose: Breast cancer is a major global public health problem. Bone is the most common site of distant metastasis of breast cancer, accounting for about 70% of all metastatic cases. Bone metastasis of breast cancer can cause a series of complications, including severe pain, pathological fracture, hypercalcemia, spinal cord compression, etc., which bring great inconvenience to patients' physical activities and affect their quality of life. Metastatic recurrence is the leading cause of death in breast cancer patients. Therefore, there is an urgent need to build a diagnostic model of bone metastasis in breast cancer to identify patients with a high risk of bone metastasis. The aim of this study was to develop a predictive model based on machine learning to predict the probability of breast cancer developing bone metastasis. Methods: Data of breast cancer patients diagnosed between 2010 and 2015 were extracted from The Surveillance, Epidemiology, and End Results (SEER) database. The variables were screened by least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate logistic regression analysis, and statistically significant risk factors were included to build a prediction model. In this study, nine machine learning algorithms, including decision tree, elastic network, K-nearest neighbor, lightweight gradient elevator, logistic regression, neural network, random forest, support vector machine and limit gradient lifting, were used to adjust the model hyperparameters through random search and 5x cross-validation to build a breast cancer bone metastasis prediction model. The area under the receiver operating characteristic (ROC) curve, calibration curve and decision curve were used to evaluate the model, the optimal model was obtained, and the importance of variables was analyzed based on the optimal model. Finally, a network calculator for predicting the risk of bone metastasis of breast cancer was established using the optimal model. Results: The study included 10 106 patients with breast cancer, 7 073 patients in the training set, and 3 033 patients in the validation set. We found that 4 494 (63.5%) patients in the training set and 1 927 (63.5%) patients in the validation set developed bone metastases, respectively. Race, pathologic grade, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, N stage, lung metastasis, radiotherapy, chemotherapy and surgery were independent predictors of bone metastasis. The training set and verification set were used to verify the model, and the limit gradient lifting algorithm was superior to other machine learning algorithms by integrating the evaluation indexes such as the area under the ROC curve, calibration curve and decision curve. Finally, we used limit gradient algorithm to build network calculator for prediction of breast cancer bone metastases (https://bcbm.shinyapps.io/DynNomapp/). Conclusion: This study developed a predictive model based on machine learning to predict the probability of bone metastases in breast cancer patients, hoping to help clinicians make more rational treatment decisions.

Despite the positive potential of the online social networking platforms, the longitudinal studies regarding how children participate in digital storytelling communities is still scant in the literature. This study investigated how 26 elementary students participated in a 2-year digital storytelling community mediated by a social network application. By analyzing the students' weekly responses, social networks, and their language learning performance, the results suggested that their oral reading proficiency improved along with their participation in the long-term activity. It was also found that the community features impacted the students' sense of engagement in the activity. The feature of the learning community that allows students to team up with partners to complete a story improved their engagement and motivation. The social network analysis found that multiple literacies including multimedia processing, language, and collaboration were all considered by students as important criteria for the partnership to complete the collaborative digital storytelling work. However, such a prolonged collaborative digital storytelling activity also introduced new challenges for those students demonstrating unfavorable collaboration profiles as they needed to learn how to collaborate in order to team up with others. The implications of the findings for educational practice are discussed.

This study used monoethanolamine (MEA) as an amine-based solvent, which was blended with secondary amines (DIPA), tertiary amines, stereo amines, and piperazine (PZ) to prepare mixed amines at the required concentrations, which were used as the test solvents. To search for the best-mixed amines, a continuous bubble-column scrubber was adopted to explore the performance of mixed solvents presented in this study. The solvent regeneration test was also carried out at different temperatures. The selected factors included the type of mixed amine (A), the ratio of mixed amines (B), the liquid feed flow (C), the gas flow rate (D), the concentration of mixed amines (E), and the liquid temperature (F), each having five levels. Using the Taguchi experimental design, the conventional experimental number could be reduced from 15,625 to 25, saving much time and cost. The absorption efficiency (EF), absorption rate (RA), overall mass-transfer coefficient (KGa), and absorption factor (ϕ) were estimated as the indicators. After the Taguchi analysis, E, D, and C were found to play important roles in the capture of CO2 gas. Verifications of optimum conditions were found to be 100%, 19.96 × 10−4 mole/s·L, 1.2312 1/s, and 0.6891 mol-CO2/L·mol-solvent for EF, RA, KGa, and ϕ, respectively. The evaluated indexes suggested that MEA + PZ was the best-mixed amine, followed by MEA and MEA + DIPA. The solvent regeneration tests for the scrubbed solutions performed at different optimum conditions showed that the heat of the regeneration sequence was in the order of MEA > MEA + PZ > MEA + DIPA with minimum energy required at 110 °C. The individual energy required was also analyzed here. [ABSTRACT FROM AUTHOR]

Breast cancer was the most common cancer and had the highest mortality rate in women worldwide.[[1], [3]] Yuanhuacine (YHC), a daphnane-type diterpenoid as the main active ingredients, inhibited breast cancer cell growth, but the detailed mechanism had not yet been described.[[5]] This study aimed to investigate the target of YHC that induced breast cancer cells death and explored the underlying mechanisms. ***p < .001 versus control group. gl The rate-limiting enzyme PKM2 played an important role in tumour glycolysis.[9] Thus, after treatment with YHC, breast cancer cells consumed less glucose and produced less lactate (Figure S7A). YHC-induced glycolysis was synergistically increased in PKM2 siRNA-treated cells compared to YHC-treated cells (Figures S9E-G, K and L). [Extracted from the article]

The metabolites from the endophytic fungus Talaromyces malicola hosted in the gastrointestinal tract of the arthropods Armadillidium vulgare were investigated, and six undescribed fusicoccane diterpenoids, talarmalnoids A–F (1 – 6), along with three known analogs were isolated. Talarmalnoid C (3) was an unprecedented fusicoccane diterpenoid with two sugar units, i.e., 6- O -methyl- α - d -glucose and α - d -glucose. Talarmalnoids D and E (4 and 5) were fusicoccane-type diterpenoids with unusual seven-membered rings attached at the C-3′ and C-4′ positions. Their structures and absolute configurations were identified by NMR, HRESIMS analyses, X-ray single crystal diffraction, GC–MS, ECD analyses, 13C NMR calculations, and DP4+ probability analyses. Bioassay results showed that talarmalnoid B (2) exhibited significant inhibitory activities against the LPS-inducted production of NO in RAW 264.7 cells, with IC 50 value of 0.83 μM. [Display omitted] [ABSTRACT FROM AUTHOR]