Bioreducible Peptide-Dendrimeric Nanogels with Abundant Expanded Voids for Efficient Drug Entrapment and Delivery

Dendrimer-based nanoplatforms have exhibited wide prospects in the field of nanomedicine for drug delivery, without great success due to many predicaments of cytotoxicity, high cost, and low yield. In this work, we report a feasible strategy on dynamic cross-linkings of low-generation peptide dendrimers into bioreducible nanogels for efficient drug controlled release. With a facile fabrication, the disulfide cross-linking of biocompatible peptide dendrimers successfully possess well-defined and stable nanostructures with abundant expanded voids for efficient molecular encapsulation. More importantly, high reducing condition is capable of triggering the cleavage of disulfide bonds, the disintegration of peptide-dendrimeric nanogels, and stimuli-responsive release of guest molecules. The bioreducible nanogels improve antitumor drug internalization, contribute to endosomal escape, and realize intracellular drug controlled release. The doxorubicin-loaded nanogels afford high antitumor efficiency and reduce the side effects to BALB/c mice bearing 4T1 tumor. Therefore, dynamic cross-linkings of low-generation dendrimers into smart nanogels will be an alternative and promising strategy to resolve the dilemmas of current dendrimer-based nanocarriers as well as develop innovative nanoplatforms

Table_1_Detection of citrus diseases in complex backgrounds based on image–text multimodal fusion and knowledge assistance.docx

Diseases pose a significant threat to the citrus industry, and the accurate detection of these diseases represent key factors for their early diagnosis and precise control. Existing diagnostic methods primarily rely on image models trained on vast datasets and limited their applicability due to singular backgrounds. To devise a more accurate, robust, and versatile model for citrus disease classification, this study focused on data diversity, knowledge assistance, and modal fusion. Leaves from healthy plants and plants infected with 10 prevalent diseases (citrus greening, citrus canker, anthracnose, scab, greasy spot, melanose, sooty mold, nitrogen deficiency, magnesium deficiency, and iron deficiency) were used as materials. Initially, three datasets with white, natural, and mixed backgrounds were constructed to analyze their effects on the training accuracy, test generalization ability, and classification balance. This diversification of data significantly improved the model’s adaptability to natural settings. Subsequently, by leveraging agricultural domain knowledge, a structured citrus disease features glossary was developed to enhance the efficiency of data preparation and the credibility of identification results. To address the underutilization of multimodal data in existing models, this study explored semantic embedding methods for disease images and structured descriptive texts. Convolutional networks with different depths (VGG16, ResNet50, MobileNetV2, and ShuffleNetV2) were used to extract the visual features of leaves. Concurrently, TextCNN and fastText were used to extract textual features and semantic relationships. By integrating the complementary nature of the image and text information, a joint learning model for citrus disease features was achieved. ShuffleNetV2 + TextCNN, the optimal multimodal model, achieved a classification accuracy of 98.33% on the mixed dataset, which represented improvements of 9.78% and 21.11% over the single-image and single-text models, respectively. This model also exhibited faster convergence, superior classification balance, and enhanced generalization capability, compared with the other methods. The image-text multimodal feature fusion network proposed in this study, which integrates text and image features with domain knowledge, can identify and classify citrus diseases in scenarios with limited samples and multiple background noise. The proposed model provides a more reliable decision-making basis for the precise application of biological and chemical control strategies for citrus production.</p