Gelatin-based anticancer drug delivery nanosystems: A mini review

Drug delivery nanosystems (DDnS) is widely developed recently. Gelatin is a high-potential biomaterial originated from natural resources for anticancer DDnS, which can effectively improve the utilization of anticancer drugs and reduce side effects. The hydrophilic, amphoteric behavior and sol-gel transition of gelatin can be used to fulfill various requirements of anticancer DDnS. Additionally, the high number of multifunctional groups on the surface of gelatin provides the possibility of crosslinking and further modifications. In this review, we focus on the properties of gelatin and briefly elaborate the correlation between the properties and anticancer DDnS. Furthermore, we discuss the applications of gelatin-based DDnS in various cancer treatments. Overall, we have summarized the excellent properties of gelatin and correlated with DDnS to provide a manual for the design of gelatin-based materials for DDnS

Effects of feeding different levels of dietary corn silage on growth performance, rumen fermentation and bacterial community of post-weaning dairy calves

Objective The objective of this study was to evaluate the growth performance, rumen fermentation parameters and bacterial community of post-weaning dairy calves in response to five diets varying in corn silage (CS) inclusion. Methods A total of forty Holstein weaned bull calves (80±3 days of age;128.2±5.03 kg at study initiation) were randomized into five groups (8 calves/group) with each receiving one of five dietary treatments offered as total mixed ration in a 123-d feeding study. Dietary treatments were control diet (CON; 0% CS dry matter [DM]); Treatment 1 (T1; 27.2% CS DM); Treatment 2 (T2; 46.5% CS DM); Treatment 3 (T3; 54.8% CS DM); and Treatment 4 (T4; 67.2% CS DM) with all diets balanced for similar protein and energy concentration. Results Results showed that calves offered CS had greater average daily gain, body length and chest depth growth, meanwhile altered rumen fermentation indicated by decreased rumen acetate concentrations. Principal coordinate analysis showed the rumen bacterial community structure was affected by varying CS inclusion diets. Bacteroidetes and Firmicutes were the predominant bacterial phyla in the calf rumens across all treatments. At the genus level, the abundance of Bacteroidales_RF16_group was increased, whereas Unclassified_Lachnospiraceae was decreased for calves fed CS. Furthermore, Spearman’s correlation test between the rumen bacteria and rumen fermentation parameters indicated that Bacteroidales_RF16_group and Unclassified Lachnospiraceae were positively correlated with propionate and acetate, respectively. Conclusion The results of the current study suggested that diet CS inclusion was beneficial for post-weaning dairy calf growth, with 27.2% to 46.5% CS of diet DM recommended to achieve improved growth performance. Bacteroidales_RF16_group and Unclassified Lachnospiraceae play an important role in the rumen fermentation pattern for post-weaning calves fed CS

Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from hematoxylin and eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.Comment: 13 pages, 11 figures, 2table