Lipid nanoparticles (LNPs) hold transformative potential for nucleic acid delivery, with applications ranging from clinical use, particularly in COVID‐19 vaccines, to gene therapy and cancer immunotherapy. However, a major limitation lies in their preferential accumulation in the liver following intravenous administration, making most targets hard‐to‐reach. In this study, a novel platform called endogenous targeting lipid nanoparticles (ENDO), containing cholecalciferol (vitamin D3) as a fifth component is reported, that selectively delivers mRNA to the pancreas – a target previously inaccessible through intravenous administration. The top formulation, C‐CholF3, demonstrates an unprecedented 99% pancreas selectivity with robust and sustained protein expression for up to 3 days in a dose‐dependent manner with minimal toxicity that makes it suitable for repeat administration. This organ‐specific delivery is proposed to be mediated by an endogenous targeting mechanism involving the Vitamin D receptor (VDR). C‐CholF3 also enables selective pancreatic delivery of plasmid DNA and circular mRNA, underscoring its versatility and therapeutic potential. Furthermore, C‐CholF3 exhibits pancreas‐specific gene editing in the Ai14 transgenic mouse model, showing high expression of tdTomato in the β cells. These findings highlight its potential for translational applications in protein replacement and CRISPR/Cas9‐mediated gene editing for currently incurable pancreatic diseases, including pancreatic cancer and diabetes.

Melanoma is the most invasive and deadly skin cancer, which causes most of the deaths from skin cancer. It has been demonstrated that the mechanical properties of tumor tissue are significantly altered. However, data about characterizing the mechanical properties of in vivo melanoma tissue are extremely scarce. In addition, the viscoelastic or viscous properties of melanoma tissue are rarely reported. In this study, we measured and quantitated the viscoelastic properties of human melanoma tissues based on the stress relaxation test, using the indentation-based mechanical analyzer that we developed previously. The melanoma tissues from eight patients of different ages (57–95), genders (male and female patients), races (White and Asian), and sites (nose, arm, shoulder, and chest) were excised and tested. The results showed that the elastic property (i.e., shear modulus) of melanoma tissue was elevated compared to normal tissue, while the viscous property (i.e., relaxation time) was reduced. Moreover, the tissue thickness had a significant impact on the viscoelastic properties, probably due to the amount of the adipose layer. Our findings provide new insights into the role of the viscous and elastic properties of melanoma cell mechanics, which may be implicated in the disease state and progression.

Recent advances in microphysiological systems (MPS), also known as organs‐on‐a‐chip (OoC), enable the recapitulation of more complex organ and tissue functions on a smaller scale in vitro. MPS therefore provide the potential to better understand human diseases and physiology. To date, numerous MPS platforms have been developed for various tissues and organs, including the heart, liver, kidney, blood vessels, muscle, and adipose tissue. However, only a few studies have explored using MPS platforms to unravel the effects of aging on human physiology and the pathogenesis of age‐related diseases. Age is one of the risk factors for many diseases, and enormous interest has been devoted to aging research. As such, a human MPS aging model could provide a more predictive tool to understand the molecular and cellular mechanisms underlying human aging and age‐related diseases. These models can also be used to evaluate preclinical drugs for age‐related diseases and translate them into clinical settings. Here, we provide a review on the application of MPS in aging research. First, we offer an overview of the molecular, cellular, and physiological changes with age in several tissues or organs. Next, we discuss previous aging models and the current state of MPS for studying human aging and age‐related conditions. Lastly, we address the limitations of current MPS and present future directions on the potential of MPS platforms for human aging research.

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.