A Gram-stain-negative and rod-shaped bacterial strain, 12-OD1T, with rock phosphate solubilizing ability was isolated from agricultural soil in Hailun, Heilongjiang, PR China. The isolate was affiliated to the genus Massilia , based on 16S rRNA gene sequence alignments, having the highest similarities with Massilia putida 6 NM-7T (98.67 %), Massilia kyonggiensis TSA1T (98.28 %), and Massilia norwichensis NS9T (98.07 %), respectively. The DNA G+C content was 67.72 mol% and DNA–DNA hybridization showed low relatedness values (less than 47 %) between strain 12-OD1T and other phylogenetically related species of the genus Massilia . The predominant isoprenoid quinone was Q-8 and the polar lipid profile comprised diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine. The major fatty acids were C17 : 0 cyclo (25.4 %), C16 : 0 (23.4 %) and summed feature 3 (C16 : 1ω7c and/or C16 : 1 ω6c) (22.5 %), which differentiates it from close relatives within the genus Massilia . Combined genetic, physiological and biochemical properties indicate that strain 12-OD1T is a novel species of the genus Massilia , for which the name Massilia phosphatilytica sp. nov., is proposed, with the type strain 12-OD1T (=CCTCC AB 2016251T=LMG 29956T=KCTC 52513T).

Background: The overall survival (OS) of pancreatic cancer (PC) patients with bone metastasis (BM) is extremely low, and it is pretty hard to treat bone metastasis. However, there are currently no effective nomograms to predict the diagnosis and prognosis of pancreatic cancer with bone metastasis (PCBM). Therefore, it is of great significance to establish effective predictive models to guide clinical practice. Methods: We screened patients from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2016. The independent risk factors of PCBM were identified from univariable and multivariable logistic regression analyses, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors affecting the prognosis of PCBM. In addition, two nomograms were constructed to predict the risk and prognosis of PCBM. We used the area under the curve (AUC), C-index and calibration curve to determine the predictive accuracy and discriminability of nomograms. The decision curve analysis (DCA) and Kaplan-Meier(K-M) survival curves were employed to further confirm the clinical effectiveness of the nomogram. Results: Multivariable logistic regression analyses revealed that risk factors of PCBM included age, primary site, histological subtype, N stage, radiotherapy, surgery, brain metastasis, lung metastasis, and liver metastasis. Using Cox regression analyses, we found that independent prognostic factors of PCBM were age, race, grade, histological subtype, surgery, chemotherapy, and lung metastasis. We utilized nomograms to visually express data analysis results. The C-index of training cohort was 0.795 (95%CI: 0.758-0.832), whereas that of internal validation cohort was 0.800 (95%CI: 0.739-0.862), and the external validation cohort was 0.787 (95%CI: 0.746-0.828). Based on AUC of receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance. Conclusion: Nomogram is sufficiently accurate to predict the risk and prognostic factors of PCBM, allowing for individualized clinical decisions for future clinical work.

The rapid development of tissue engineering makes it an effective strategy for repairing cartilage defects. The significant advantages of injectable hydrogels for cartilage injury include the properties of natural extracellular matrix (ECM), good biocompatibility, and strong plasticity to adapt to irregular cartilage defect surfaces. These inherent properties make injectable hydrogels a promising tool for cartilage tissue engineering. This paper reviews the research progress on advanced injectable hydrogels. The cross-linking method and structure of injectable hydrogels are thoroughly discussed. Furthermore, polymers, cells, and stimulators commonly used in the preparation of injectable hydrogels are thoroughly reviewed. Finally, we summarize the research progress of the latest advanced hydrogels for cartilage repair and the future challenges for injectable hydrogels.