Protein nanoparticles as drug delivery systems for cancer theranostics.

Protein-based nanoparticles have garnered significant attention in theranostic applications due to their superior biocompatibility, exceptional biodegradability and ease of functionality. Compared to other nanocarriers, protein-based nanoparticles offer additional advantages, including biofunctionality and precise molecular recognition abilities, which make them highly effective in navigating complex biological environments. Moreover, proteins can serve as powerful tools with self-assembling structures and reagents that enhance cell penetration. And their derivation from abundant renewable sources and ability to degrade into harmless amino acids further enhance their suitability for biomedical applications. However, protein-based nanoparticles have so far not realized their full potential. In this review, we summarize recent advances in the use of protein nanoparticles in tumor diagnosis and treatment and outline typical methods for preparing protein nanoparticles. The review of protein nanoparticles may provide useful new insights into the development of biomaterial fabrication.

'Multiple and short-range' cross-linking of dialdehyde carboxymethyl cellulose contributes to regulating the physicochemical property of collagen fibril.

Collagen fibril hydrogel (CH), with controllable micro-structure, sufficient modifying sites and excellent biocompatibility, has received widely attention in the regulation of biomacromolecules. Herein, dialdehyde carboxymethyl cellulose (DCMC) in different -CHO contents and molecular weights demonstrated two types of cross-linking behaviors to CH, 'limited and long-range' or 'multiple and short range' cross-linking, corresponding to -CHO content ranged from 0 to 53 % and 53 to 90 %, respectively. In regard of structure, non-destroying effect of DCMC on collagen was supported by FT-IR and XRD analysis. CH cross-linked by DCMC (CH-DC) showed declining porosity and aggregating fibrils as -CHO content of DCMC rising. In regard of physicochemical properties, DCMC with >53 % -CHO strengthened the hydrophilicity, thermal stability and degradation resistance of CH-DC. Also, there was 110 % growth on gel strength, 86 Pa enhancements on storage modulus, and 4.6 times decrease on the swelling ratio of CH-DC. Results indicated that DCMC with 79 % -CHO remarkably improved the physicochemical properties of CH via developing sufficient Schiff-base bonds with collagen fibril in a short distance. This study distinguished two patterns of DCMC cross-linking from physicochemical view. In other words, DCMC is potential to meet the requirement of protein-based materials with different expectations by adjusting its -CHO content and molecular weight.

Triple attention learning for classification of 14 thoracic diseases using chest radiography.

Chest X-ray is the most common radiology examinations for the diagnosis of thoracic diseases. However, due to the complexity of pathological abnormalities and lack of detailed annotation of those abnormalities, computer-aided diagnosis (CAD) of thoracic diseases remains challenging. In this paper, we propose the triple-attention learning (A 3 Net) model for this CAD task. This model uses the pre-trained DenseNet-121 as the backbone network for feature extraction, and integrates three attention modules in a unified framework for channel-wise, element-wise, and scale-wise attention learning. Specifically, the channel-wise attention prompts the deep model to emphasize the discriminative channels of feature maps; the element-wise attention enables the deep model to focus on the regions of pathological abnormalities; the scale-wise attention facilitates the deep model to recalibrate the feature maps at different scales. The proposed model has been evaluated on 112,120images in the ChestX-ray14 dataset with the official patient-level data split. Compared to state-of-the-art deep learning models, our model achieves the highest per-class AUC in classifying 13 out of 14 thoracic diseases and the highest average per-class AUC of 0.826 over 14 thoracic diseases.