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.

'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.

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 Net) model for this CAD task.