Asymmetric zinc phthalocyanines with alkyl chain substituents formed highly efficient light-harvesting layers on a TiO2 surface. Dye-sensitized solar cells using PcS20 exhibited a record efficiency of 6.4% under one-sun irradiation.

Molecular Engineering of Zinc Phthalocyanine Sensitizers for Efficient Dye-Sensitized Solar Cell

circRNA_0016624 could sponge miR-98 to regulate BMP2 expression in postmenopausal osteoporosis.

Bone mesenchymal stem cells (BMSCs) can be a useful cell resource for developing biological treatment strategies for bone repair and regeneration, and their therapeutic applications hinge on an understanding of their physiological characteristics. N⁶-methyl-adenosine (m⁶A) is the most prevalent internal chemical modification of mRNAs and has recently been reported to play important roles in cell lineage differentiation and development. However, little is known about the role of m⁶A modification in the cell differentiation of BMSCs. To address this issue, we investigated the expression of N⁶-adenosine methyltransferases (Mettl3 and Mettl14) and demethylases (Fto and Alkbh5) and found that Mettl3 was upregulated in BMSCs undergoing osteogenic induction. Furthermore, we knocked down Mettl3 and demonstrated that Mettl3 knockdown decreased the expression of bone formation-related genes, such as Runx2 and Osterix. The alkaline phosphatase (ALP) activity and the formation of mineralized nodules also decreased after Mettl3 knockdown. RNA sequencing analysis revealed that a vast number of genes affected by Mettl3 knockdown were associated with osteogenic differentiation and bone mineralization. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed that the phosphatidylinositol 3-kinase/AKT (PI3K-Akt) signaling pathway appeared to be one of the most enriched pathways, and Western blotting results showed that Akt phosphorylation was significantly reduced after Mettl3 knockdown. Mettl3 has been reported to play an important role in regulating alternative splicing of mRNA in previous research. In this study, we found that Mettl3 knockdown not only reduced the expression of Vegfa but also decreased the level of its splice variants, vegfa-164 and vegfa-188, in Mettl3-deficient BMSCs. These findings might contribute to novel progress in understanding the role of epitranscriptomic regulation in the osteogenic differentiation of BMSCs and provide a promising perspective for new therapeutic strategies for bone regeneration.

https://www.mdpi.com/1422-0067/20/3/551/pdf

Mettl3 Regulates Osteogenic Differentiation and Alternative Splicing of Vegfa in Bone Marrow Mesenchymal Stem Cells

High-performance porous PLLA-based scaffolds for bone tissue engineering: Preparation, characterization, and in vitro and in vivo evaluation

Sequential growth factor releasing double cryogel system for enhanced bone regeneration

Biodegradable PLA-PEG-PLA block copolymers were synthesized with desired backbone structures and molecular weights using PEG20000. Rectangular scaffolds were prepared by freeze drying with or without using NaCl particles. Bone morphogenetic protein (BMP)-2 was loaded to the matrix after the scaffold formation for sustained release while vascular endothelial growth factor (VEGF) was loaded within the pores with gelatin solution. VEGF release was quite fast and almost 60% of it was released in 2 d. However, sequential - sustained released was observed for BMP-2 in the following few months. Corporation of VEGF/BMP-2 couple into the scaffolds increased the cell adhesion and proliferation. Neither significant cytotoxicity nor apoptosis/necrosis were observed.

https://www.tandfonline.com/doi/pdf/10.3109/21691401.2016.1147454

Sequential VEGF and BMP-2 releasing PLA-PEG-PLA scaffolds for bone tissue engineering: I. Design and in vitro tests

Microalgae are single-celled organisms that have been extensively utilized in biotechnology, pharmacology and foodstuff in recent years. The description and classification of many existing microalgae groups are carried out with classical methods in a long time and with a remarkably qualified labor force. Deep learning methods have achieved success in many fields are applied to the classification of microalga groups. In this study, Cyanobacteria and Chlorophyta microalga groups images are captured by using an inverted microscope. Data augmentation process has been carried out to increase the classification success in Convolutional Neural Network (CNN) models. The collected images are classified by employing two different methods. For the first method, classification is performed with seven different CNN models. In the second method, the Support Vector Machine (SVM) is used to increase the classification success of the AlexNet model with the lowest accuracy. For this, deep features which are extracted from the AlexNet model are classified with SVM. Four different kernel functions are used in the SVM classification process. The highest accuracy is found to be 99.66% among the different CNN models. AlexNet, which has the lowest accuracy with 98%, has reached 99.66% accuracy as a result of its application with SVM.

Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

The dramatic increase in the atmospheric carbon dioxide levels and the apprehensive situation of energy supply are leading to the importance of renewable energy technology studies. Because solar energy is a sustainable energy source, developments in photovoltaic technologies are continuing rapidly. This section describes the biologically improved materials for photovoltaics application technology in detail. Biomaterials and natural compounds contribute to the sustainability of organic electronics because of the advantages of being environmentally friendly and naturally available. Despite these advantages, very few biomaterials have been included in organic-based solar cell production studies. For photovoltaic technologies, improvements or syntheses should be made by considering many physical properties such as hardness, elasticity, elongation coefficient, thermal strength point, optical properties, and long-term strength of candidate biomaterials. Therefore design, development or selection of special biomaterials can only be achieved with the collaboration of scientists in the materials science and bioengineering fields. While polymeric biomaterials used in the photovoltaic technology are generally polypeptide structured, applications with green technology such as microorganisms, pigments, and other biological systems (Photosystem I and Photosystem II) are rapidly growing. In this chapter, the processes of obtaining biomaterials and ways to improve productivity and stability to be used in photovoltaic technology by natural and/or synthetic biology approaches, will be discussed.

Sustainable biomaterials for solar energy technologies

Deep learning-based classification of microalgae using light and scanning electron microscopy images.

This study is the first report on the separation and reusability of ApoUnaG protein, indicating excellent fluorescence response with high affinity and specificity toward unconjugated bilirubin (UC-BR) molecules, from the UnaG-UC-BR complex structure. The fluorescence properties of the UnaG-UC-BR complex (holo-UnaG) are studied by addition of different metal ions to perform possible interactions with holo-UnaG through absorbance and emission spectra. After addition of metal ions, some changes with respect to the type of metal ions are observed in fluorescence intensity of the holo-UnaG. When compared to metal ions, an excellent quenching response is sighted in the presence of Cu2+ ions by binding with UC-BR in the UnaG-UC-BR complex structure. Obtained non-fluorescence holo-UnaG-Cu2+ complex mixture is passed through Ni-NTA agarose to remove the ingredients such as Cu2+, UC-BR and Cu2+-UC-BR coordination complex from holo-UnaG. From the obtained experiments, it is concluded that Cu2+ ion can be used as an agent for the recovery of ApoUnaG protein via binding with UC-BR molecules. Graphical Abstract Recovery and Reusability of ApoUnaG Fluorescence Protein from the Unconjugated Bilirubin Complex Structure.

Numan Eczacioglu

Papers

Sequential VEGF and BMP-2 releasing PLA-PEG-PLA scaffolds for bone tissue engineering: I. Design and in vitro tests

Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

Sustainable biomaterials for solar energy technologies

Deep learning-based classification of microalgae using light and scanning electron microscopy images.

Recovery and Reusability of ApoUnaG Fluorescence Protein from the Unconjugated Bilirubin Complex Structure.