Precise patterning of biomaterials has widespread applications, including drug release, degradable implants, tissue engineering, and regenerative medicine. Patterning of protein‐based microstructures using UV‐photolithography has been demonstrated using protein as the resist material. The Achilles heel of existing protein‐based biophotoresists is the inevitable wide molecular weight distribution during the protein extraction/regeneration process, hindering their practical uses in the semiconductor industry where reliability and repeatability are paramount. A wafer‐scale high resolution patterning of bio‐microstructures using well‐defined silk fibroin light chain as the resist material is presented showing unprecedent performances. The lithographic and etching performance of silk fibroin light chain resists are evaluated systematically and the underlying mechanisms are thoroughly discussed. The micropatterned silk structures are tested as cellular substrates for the successful spatial guidance of fetal neural stems cells seeded on the patterned substrates. The enhanced patterning resolution, the improved etch resistance, and the inherent biocompatibility of such protein‐based photoresist provide new opportunities in fabricating large scale biocompatible functional microstructures.

The prospective applications of chitosan-based hydrogels (CBHs), a category of biocompatible and biodegradable materials, in biomedical disciplines such as tissue engineering, wound healing, drug delivery, and biosensing have garnered great interest. The synthesis and characterization processes used to create CBHs play a significant role in determining their characteristics and effectiveness. The qualities of CBHs might be greatly influenced by tailoring the manufacturing method to get certain traits, including porosity, swelling, mechanical strength, and bioactivity. Additionally, characterization methods aid in gaining access to the microstructures and properties of CBHs. Herein, this review provides a comprehensive assessment of the state-of-the-art with a focus on the affiliation between particular properties and domains in biomedicine. Moreover, this review highlights the beneficial properties and wide application of stimuli-responsive CBHs. The main obstacles and prospects for the future of CBH development for biomedical applications are also covered in this review.

Sericin, a fascinating natural biomaterial derived from silkworms, has received increasing interest in recent years for its unique bioactivity and high compatibility. Silkworms can be divided into wild-type or silk fibroin-deficient mutants according to whether they synthesize and secrete silk fibroin. Silk fibroin-deficient mutant silkworms and their cocoons are convenient for us to obtain diverse and high-quality sericin, which has been applicated in various fields such as cell culture, tissue engineering, drug delivery, and cosmetics. Here, we present an overview of our silkworm varieties resources, especially silk fibroin-deficient mutant silkworms. We optimized various extraction methods of sericin and summarized the characteristics and advantages of sericin. Finally, we developed and discussed a series of sericin-based biomaterials for promising applications for a diverse set of needs.

Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set ( ${\mathrm{R}}_{\mathrm{C}}^2$ ), R-square of the cross-validation set ( ${\mathrm{R}}_{\mathrm{C}\mathrm{V}}^2$ ) and R-square of the prediction set ( ${\mathrm{R}}_{\mathrm{P}}^2$ ) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The ${\mathrm{R}}_{\mathrm{C}}^2$ , ${\mathrm{R}}_{\mathrm{C}\mathrm{V}}^2$ and ${\mathrm{R}}_{\mathrm{P}}^2$ of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has ${\mathrm{R}}_{\mathrm{C}}^2$ , ${\mathrm{R}}_{\mathrm{C}\mathrm{V}}^2$ , and ${\mathrm{R}}_{\mathrm{P}}^2$ of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.