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Efficient delivery of therapeutic agents to the lesion site or specific cells is an important way to achieve "toxicity reduction and efficacy enhancement". Macrocycles have always provided many novel ideas for drug or gene loading and delivery processes. Specifically, macrocycles represented by crown ethers, cyclodextrins, cucurbit[n]urils, calix[n]arenes, and pillar[n]arenes have unique properties, which are different cavity structures, good biocompatibility, and good stability. Benefited from these diverse properties, a variety of supramolecular drug delivery systems can be designed and constructed to effectively improve the physical and chemical properties of guest molecules as needed. This review provides an outlook on the current application status and main limitations of macrocycles in supramolecular drug delivery systems. [ABSTRACT FROM AUTHOR]

Fludarabine (FA) is still considered as a first-line chemotherapy drug for hematological tumors related to B lymphocytes. However, it is worth noting that the non-specific distribution and non-different cytotoxicity of FA may lead to irreversible consequences such as central nervous system damage such as blindness, coma, and even death. Therefore, it is very important to develop a system to targeting delivery FA. In preliminary studies, it was found that B lymphoma cells would specific highly expressing the sialic acid-binding immunoglobulin-like lectin 2 (known as CD22). Inspired by the specific recognition of sialic acid residues and CD22, we have developed a supramolecular prodrug based on polysialic acid, an endogenous biomacromolecule, achieving targeted-therapy of B-cell non-Hodgkin's lymphoma (B-NHL). Specifically, the prepared hydrophobic reactive oxygen species-responsive FA dimeric prodrug (F2A) interacts with the TPSA, which polysialic acid were modified by the thymidine derivatives, through non-covalent intermolecular interactions similar to "Watson–Crick" base pairing, resulting in the formation of nanoscale supramolecular prodrug (F@TPSA). Cell experiments have confirmed that F@TPSA can be endocytosed by CD22+ B lymphoma cells including Raji and Ramos cells, and there is a significant difference of endocytosis in other leukocytes. Furthermore, in B-NHL mouse model, compared with FA, F@TPSA is determined to have a stronger tumor targeting and inhibitory effect. More importantly, the distribution of F@TPSA in vivo tends to be enriched in lymphoma tissue rather than nonspecific, thus reducing the leukopenia of FA. The targeted delivery system based on PSA provides a new prodrug modification strategy for targeted treatment of B-NHL. [ABSTRACT FROM AUTHOR]

Perovskite quantum dots (QDs) possess remarkable luminescent properties, making them highly promising for optoelectronic devices. However, their industrial applications are limited due to their poor stability in various environmental conditions. In this study, we present a novel approach to fabricate stabilized CsPbBr3 QDs by incorporating them into a three-dimensional layered yttrium hydroxide (3D-LYH) matrix using a thermal injection method. The resulting 3D-LYH-CsPbBr3 composites exhibit an impressive solid photoluminescence quantum yield (PLQY) of 84.34%. Additionally, it demonstrates excellent stability, retaining 65.64% of its initial PLQY even after 120 days of storage in a humid environment. Moreover, the composites exhibit a marginal decrease of only 20% in PL strength after undergoing four heating–cooling cycles ranging from 25 to 100 °C. This enhanced stability is attributed to the unique flower-like structure of the 3D-LYH matrix, which effectively stabilizes the CsPbBr3 QDs. Furthermore, we successfully obtained white light-emitting diodes by spin-coating the 3D-LYH-CsPbBr3 composites. The resulting white light achieves color coordinates of (0.310, 0.321) and a correlated color temperature of 6750 K. These advancements highlight the potential of LYH-based composite luminescent materials for applications in display and lighting devices. [ABSTRACT FROM AUTHOR]

Terrain features are an important basis for realizing high‐precision landform classification, and feature selection is a key step of machine learning and knowledge mining. However, the feature selection process is facing challenges due to the multidimensionality and correlation of multisource terrain feature datasets and factors. Traditional feature selection methods lack enough consideration for the interpretability and transparency of feature factors, but the transparent decision‐making process of feature selection precisely determines the modelling effect and the reliability of model application results. Current research urgently needs to work out the black holes of visual representation during feature selection. In the process of intelligent landform classification, multiple and effective terrain feature is an essential factor in enhancing the performance and generalisation ability of the network. Therefore, we initially selected 40 terrain feature parameters, including basic terrain factors and digital elevation model (DEM) terrain textures, to calculate the feature contribution degree and sort the parameter importance based on the SHapley Additive exPlanations (SHAP) method, then reserved 10%, 20%, 30%, 40% and 50% terrain features in turn for constructing the landform classification dataset. Because the traditional UNet network cannot completely capture abrupt landform features, the convolutional block attention module (CBAM) was integrated into the UNet, and a deep learning model was established for the fine‐grained classification of regional landforms. Considering the calculation rate, even though there are large regional spatial differences and genetic mechanisms, it is appropriate to retain 20% of terrain features for intelligent landform classification. The classification accuracy of typical regions, namely, the Hanzhong Basin, North China Plain, Yunnan–Guizhou Plateau and Tibetan Plateau, reached 98.76%, 97.36%, 96.3% and 92.78%, respectively, and what's more, some accuracies went up to a higher level under other feature combinations. Meanwhile, given the different feature combinations corresponding to regional landform types, the combinative stability and spatial orderliness characteristics were explored to explain the accuracy variation trend. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides an effective technical means for the precise extraction of information on aquacultural areas, which is of great significance in realizing the scientific supervision of the aquaculture industry. Existing optical remote sensing methods for the extraction of aquacultural area information mostly focus on the use of image spatial features and research on classification methods of single aquaculture patterns. Accordingly, the comprehensive utilization of a combination of spectral information and deep learning automatic recognition technology in the feature expression and discriminant extraction of aquaculture areas needs to be further explored. In this study, using Sentinel-2 remote sensing images, a method for the accurate extraction of different algae aquaculture zones combined with spectral information and deep learning technology was proposed for the characteristics of small samples, multidimensions, and complex water components in marine aquacultural areas. First, the feature expression ability of the aquaculture area target was enhanced through the calculation of the normalized difference aquaculture water index (NDAWI). Second, on this basis, the improved deep convolution generative adversarial network (DCGAN) algorithm was used to amplify the samples and create the NDAWI dataset. Finally, three semantic segmentation methods (UNet, DeepLabv3, and SegNet) were used to design models for classifying the algal aquaculture zones based on the sample amplified time series dataset and comprehensively compare the accuracy of the model classifications for achieving accurate extraction of different algal aquaculture information within the seawater aquaculture zones. The results show that the improved DCGAN amplification exhibited a better effect than the generative adversarial networks (GANs) and DCGAN under the indexes of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The UNet classification model constructed on the basis of the improved DCGAN-amplified NDAWI dataset achieved better classification results (Lvshunkou: OA = 94.56%, kappa = 0.905; Jinzhou: OA = 94.68%, kappa = 0.913). The algorithmic model in this study provides a new method for the fine classification of marine aquaculture area information under small sample conditions. [ABSTRACT FROM AUTHOR]

CsPbBr 3 perovskite quantum dots (QDs) have attracted much attention in the optical field because of their low band gap, wide absorption spectrum and high color purity. However, their poor stability in extreme environments such as water, light and heat severely limits their application in optical fields. Here, we prepared m-SiO 2 /CsPbBr 3 composite luminescent material using an aqueous phase method combined with high temperature calcination. The material can retain 87% of the initial photoluminescence quantum efficiency after 60 days of storage under ambient conditions (humidity ∼80%; temperature ∼25 °C), its photoluminescence intensity only decreased by 33% after immersion in water for 90 min. This indicates that the material retains good stability under a high humidity environment. Finally, PMMA@m-SiO 2 /CsPbBr 3 flexible films were prepared by aqueous solution polymerization. The flexible film has excellent green light emission properties and exhibits (0.092, 0.625) CIE coordinates., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)