Niosomes in cancer treatment: A focus on curcumin encapsulation.

Curcumin is widely used as a therapeutic drug for cancer treatment. However, its limited absorption and rapid excretion are the major therapeutic limitations to its clinical use. Using niosomes as a curcumin delivery system is a cheap, easy, and less toxic strategy for enhancing the absorption of curcumin by cells and delaying its excretion. Thus, there is a vital need to explore curcumin niosomes to configure the curcumin to suitably serve and aid current pharmacokinetics in treatments for cancer. To date, no comprehensive review has focused on the cytotoxic effects of curcumin niosomes on malignant cells. Thus, this review provides a critical analysis of the curcumin niosomes in cancer treatment, formulations of curcumin niosomes, characterizations of curcumin niosomes, and factors influencing their performance. The findings from this review article can strongly accelerate the understanding of curcumin niosomes and pave a brighter direction towards advances in the pharmaceutical, biotechnology, and medical industries.

Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator's Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems to help acne patients better understand their conditions and support doctors in acne diagnosis.

Endothelial cell behavior inside myoblast sheets with different thickness.

Using a cell sheet stacking method, we developed an in vitro culture system in which green fluorescent protein expressing human umbilical vein endothelial cells (GFP-HUVECs) were cultured under human skeletal muscle myoblast (HSMM) sheets with different layer numbers. Our aim in developing this system was to examine the different endothelial behaviors in the cell sheet. During 96 h of incubation, in monolayer HSMM sheet, HUVECs quickly reached the top of the cell sheet and detached. In three-layered HSMM sheet, HUVECs also migrated to the top layer and formed island-shaped aggregates. In five-layered HSMM sheet, HUVECs migrated into the middle of the cell sheet and formed net-shaped aggregates. In seven-layered HSMM sheet, HUVECs migrated in the basal of the cell sheet and formed sparse net-shaped aggregates. The thickness of the HSMM sheet, which can be controlled by the layer number of the cell sheet, is therefore an important parameter that affects the migration time, encounters, localization, and morphology of HUVECs inside the HSMM sheet.

Network formation through active migration of human vascular endothelial cells in a multilayered skeletal myoblast sheet.

Autologous transplantation of myoblast sheet has attracted attention as a new technique for curing myocardial infarction. Myoblast sheet has the ability to secret cytokines that improve heart function via the facilitation of angiogenesis on affected part. To mimic the in vivo angiogenesis in the myoblast sheet after transplantation, a five-layered cell sheet of human skeletal muscle myoblasts (HSMMs) was overlaid on human umbilical vein endothelial cells (HUVECs) which enables evaluation of dynamic HUVEC behavior. HUVECs existing initially at the bottom of the sheet changed to be a stretched shape and migrated upward compared with the surrounding HSMMs in the sheet. Prolonged incubation resulted in network formation of HUVECs in the middle of the sheet, although non-networked HUVECs continued to migrate to the top of the sheet, which meant the spatial habitation of HUVECs in the cell sheet. Image processing was performed to determine the variation in the extent of network formation at different HUVEC densities. It was found that the extent of formed network depended on the frequency of encounters among HUVECs in the middle of the sheet. The present system, which can evaluate network formation, is considered to be a promising in vitro angiogenesis model.

Evaluation of vertical cell fluidity in a multilayered sheet of skeletal myoblasts.

The procedure for fabricating a multilayered cell sheet has been developed by combining multiple sheets using a thermo-responsive surface and stamp system. Confocal laser scanning microscopy revealed that the fluidity of a multilayered sheet of skeletal myoblasts could be estimated as vertical diffusivity and changed upon addition of dermal fibroblasts.