Development of hydroxyapatite-based nanomaterials to enhance biological response of osteoblast cells for clinical application

Purpose: To develop a novel chitosan/gelatin-hydroxyapatite (CGHaP) microspheres for evaluating the biological response of pre-osteoblast cells.Methods: The microsphere was prepared by water-in-oil emulsion method. Cell proliferation was studied using AlamarBlue colorimetric assay and DAPI staining while alkaline phosphatase assay was carried out by colorimetric assay method. Chitosan microspheres as well as chitosan-hydroxyapatite microspheres was prepared and tested for biological response from MC3T3-E1 cell line.Results: The results showed that CGHaP promotes MC3T3-E1 cell proliferation and spread on the surface of microspheres. The cells were clustered with more actin filaments and well-linked with neighbouring cells or adjacent cells when cultured in CGHaP microspheres whereas fewer cells were spread on chitosan (CH) microspheres. CGHaP microspheres significantly (p < 0.05) promoted cell attachment, proliferation and extracellular matrix mineralization. CGHaP microspheres presented significantly (p < 0.02) higher calcium deposition (0.5 ng) than CH microspheres (0.28 ng). Specifically,  CGHaP microspheres exhibited high ALP activity (8 units; 2-fold) compared to CH with 3 units, after 7 days of incubation. The results suggest that CGHaP possesses a great ability to facilitate bone ingrowth formation and possibility of good osteointegration in vivo.Conclusion: The nanomaterial enhances the proliferation of pre-osteoblast cells in tissue engineering microspheres. The outcome of this study may have a major impact on the development of novel nanomaterials for bone tissue engineering.Keywords: Chitosan/gelatin-hydroxyapatite, Osteoblast, Osteoinduction, Bone tissue engineering, Nanomaterials, Microspheres, Calcium nodule formatio

Tetraethyl 2,2′-(2,3,5,6-tetrafluoro-p-phenylenedimethylene)dipropanoate1

In the mol­ecule of the title compound, C22H26F4O8, a crystallographic inversion centre is located at the centroid of the benzene ring. C—H⋯F and C—H⋯O intra­molecular hydrogen bonds are observed as well as an inter­molecular C—H⋯O inter­action

Thermal properties of carbon black aqueous nanofluids for solar absorption

In this article, carbon black nanofluids were prepared by dispersing the pretreated carbon black powder into distilled water. The size and morphology of the nanoparticles were explored. The photothermal properties, optical properties, rheological behaviors, and thermal conductivities of the nanofluids were also investigated. The results showed that the nanofluids of high-volume fraction had better photothermal properties. Both carbon black powder and nanofluids had good absorption in the whole wavelength ranging from 200 to 2,500 nm. The nanofluids exhibited a shear thinning behavior. The shear viscosity increased with the increasing volume fraction and decreased with the increasing temperature at the same shear rate. The thermal conductivity of carbon black nanofluids increased with the increase of volume fraction and temperature. Carbon black nanofluids had good absorption ability of solar energy and can effectively enhance the solar absorption efficiency

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

1,2-Diphenyl­ethane-1,2-diyl diiso­nico­tinate monohydrate1

In the novel title compound, C26H20N2O4·H2O, the two phenyl rings make a dihedral angle of 45.3 (1)° with each other, and the dihedral angle between the two pyridyl planes is 69.8 (1)°

Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2

Previous work found that the co-occurring mutations R203K/G204R on the SARS-CoV-2 nucleocapsid (N) protein are increasing in frequency among emerging variants of concern or interest. Through a combination of in silico analyses, this study demonstrates that R203K/G204R are adaptive, while large-scale phylogenetic analyses indicate that R203K/G204R associate with the emergence of the high-transmissibility SARS-CoV-2 lineage B.1.1.7. Competition experiments suggest that the 203K/204R variants possess a replication advantage over the preceding R203/G204 variants, possibly related to ribonucleocapsid (RNP) assembly. Moreover, the 203K/204R virus shows increased infectivity in human lung cells and hamsters. Accordingly, we observe a positive association between increased COVID-19 severity and sample frequency of 203K/204R. Our work suggests that the 203K/204R mutations contribute to the increased transmission and virulence of select SARS-CoV-2 variants. In addition to mutations in the spike protein, mutations in the nucleocapsid protein are important for viral spreading during the pandemic