Retinal Proteome Analysis Reveals a Region-Specific Change in the Rabbit Myopia Model

Figure S1: Gene ontology analysis of up-regulated and down-regulated differentially expressed proteins in each retinal region.; Table S1: List of differentially expressed proteins in each retinal region after myopia induction

Retinal Proteome Analysis Reveals a Region-Specific Change in the Rabbit Myopia Model

Uncovering region-specific changes in the myopic retina can provide clues to the pathogenesis of myopia progression. After imposing form deprivation myopia in the right eye of 6-week-old rabbits, we investigated the proteome profile of each retinal region (central, mid-periphery, and far-periphery retina), using accurate high-resolution mass spectrometry. Protein expression was analyzed using gene ontology and network analysis compared with that of the control, the left eyes. Among 2065 proteins detected from whole retinal samples, 249 differentially expressed proteins (DEPs) were identified: 164 DEPs in the far-periphery, 39 in the mid-periphery, and 83 in the central retina. In network analysis, the far-periphery retina showed the most significant connectivity between DEPs. The regulation of coagulation was the most significant biological process in upregulated DEPs in the far-periphery retina. Proteasome was the most significant Kyoto Encyclopedia of Genes and Genomes pathway in downregulated DEPs in the central retina. Antithrombin-III, fibrinogen gamma chain, and fibrinogen beta chain were identified as hub proteins for myopia progression, which were upregulated in the far-periphery retina. Proteomic analysis in this study suggested that oxidative stress can be the primary pathogenesis of myopia progression and that the far-periphery retina plays a role as the key responder

Accelerated Design of High-Efficiency Lead-Free Tin Perovskite Solar Cells via Machine Learning

Tin (Sn) perovskite solar cells (PSCs) are the most promising alternatives to lead (Pb) PSCs, which pose a theoretical limitation on efficiency and an environmental threat. However, Sn PSCs are still in the early stage of development in comparison with the conventional Pb PSCs, and still require a considerable amount of time and effort to obtain an optimum structure via manual trial-and-error methods. Herein, we propose a machine learning (ML) approach to accelerate the design of the optimized structure of Sn PSCs with high efficiency. The proposed method uses K-fold cross-validation-based deep neural networks, thus maximizing the prediction and recommendation accuracy with a limited amount of experimental data recorded for the Sn PSCs. Our approach establishes a new appropriate Sn-PSC design based on an ML recommendation algorithm. The validation experiment reveals a three times higher efficiency of the ML-designed Sn PSCs (5.57%) than that of those designed through unguided fabrication trials (avg. 1.72%)

Accelerated Design of High-Efficiency Lead-Free Tin Perovskite Solar Cells via Machine Learning

Tin (Sn) perovskite solar cells (PSCs) are the most promising alternatives to lead (Pb) PSCs, which pose a theoretical limitation on efficiency and an environmental threat. However, Sn PSCs are still in the early stage of development in comparison with the conventional Pb PSCs, and still require a considerable amount of time and effort to obtain an optimum structure via manual trial-and-error methods. Herein, we propose a machine learning (ML) approach to accelerate the design of the optimized structure of Sn PSCs with high efficiency. The proposed method uses K-fold cross-validation-based deep neural networks, thus maximizing the prediction and recommendation accuracy with a limited amount of experimental data recorded for the Sn PSCs. Our approach establishes a new appropriate Sn-PSC design based on an ML recommendation algorithm. The validation experiment reveals a three times higher efficiency of the ML-designed Sn PSCs (5.57%) than that of those designed through unguided fabrication trials (avg. 1.72%)

Tissue extracellular matrix hydrogels as alternatives to Matrigel for culturing gastrointestinal organoids

© 2022, The Author(s).Matrigel, a mouse tumor extracellular matrix protein mixture, is an indispensable component of most organoid tissue culture. However, it has limited the utility of organoids for drug development and regenerative medicine due to its tumor-derived origin, batch-to-batch variation, high cost, and safety issues. Here, we demonstrate that gastrointestinal tissue-derived extracellular matrix hydrogels are suitable substitutes for Matrigel in gastrointestinal organoid culture. We found that the development and function of gastric or intestinal organoids grown in tissue extracellular matrix hydrogels are comparable or often superior to those in Matrigel. In addition, gastrointestinal extracellular matrix hydrogels enabled long-term subculture and transplantation of organoids by providing gastrointestinal tissue-mimetic microenvironments. Tissue-specific and age-related extracellular matrix profiles that affect organoid development were also elucidated through proteomic analysis. Together, our results suggest that extracellular matrix hydrogels derived from decellularized gastrointestinal tissues are effective alternatives to the current gold standard, Matrigel, and produce organoids suitable for gastrointestinal disease modeling, drug development, and tissue regeneration.11Nsciescopu

Upcycled synthesis and extraction of carbon-encapsulated iron carbide nanoparticles for gap Plasmon applications in perovskite solar cells

Funding Information: This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) of the Korean government (NRF‐2021R1C1C1009200, NRF‐2021M3H4A6A01045764). This research was supported by Sungkyunkwan University (SKKU) and the BK21 FOUR (Graduate School Innovation) funded by the Ministry of Education (MOE, Korea) and NRF. This work was supported by the Academy of Finland (ANCED project). We thank Kyungshin Holdings Co. Ltd. for financial support. Publisher Copyright: © 2023 The Authors. EcoMat published by The Hong Kong Polytechnic University and John Wiley & Sons Australia, Ltd.An effective method for obtaining large amounts of metal nanoparticles (NPs) encapsulated by carbon layers through upcycling from floating-catalyst aerosol chemical vapor-deposited carbon nanotubes is demonstrated. NPs with diameters of less than 20 μm are selectively extracted from the synthesized carbon assortments through sonication, centrifugation, and filtration. The particles show an aggregation behavior owing to the π–π interaction between the graphitic carbon shells surrounding the iron carbides. By controlling the degree of the aggregation and arrangement, the light scattering by the gap-surface plasmon effect in perovskite solar cells is maximized. Application of the NPs to the devices increased the power conversion efficiency from 19.71% to 21.15%. The short-circuit current density (JSC) trend over the particle aggregation time accounts for the plasmonic effect. The devices show high stability analogue to the control devices, confirming that no metal-ion migration took place thanks to the encapsulation. (Figure presented.).Peer reviewe

Liquid-State Dithiocarbonate-Based Polymeric Additives with Monodispersity Rendering Perovskite Solar Cells with Exceptionally High Certified Photocurrent and Fill Factor

Dithiocarbonate-based non-hygroscopic polymers with a glass transition temperature (T-g) and polydispersity index (PDI) of approximate to 4 degrees C and 1, respectively, are synthesized through living cationic ring-opening polymerization. These liquid-state polymers are characterized by monodispersity based on the low T-g and PDI, rendering remarkable miscibility with the perovskite precursors without aggregation. Accordingly, these polymers are added to perovskite solar cells (PSCs) to enhance their power conversion efficiency (PCE). The PCE of reference PSCs increases from 19.70% to 23.52% after direct addition of the synthesized polymer. This efficiency improvement is attributed to the considerable increases in short-circuit current density (J(SC)) and fill factor (FF), resulting from the augmented size and defect passivation of perovskite crystals induced by added polymers. In fact, the PCE and J(SC) of the devices measured in the laboratory and the certification center are the highest among the reported polymer-added PSCs, thanks to the great miscibility of the new polymers leading to the large amount addition which enables more thorough passivation among the grain boundaries. The improvement in open-circuit voltage falls short as compared to that in J(SC) and FF, ascribed to the relatively moderate interaction strength between perovskite materials and dithiocarbonate groups