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Abstract Messenger RNA (mRNA) cancer vaccines are a new class of immunotherapies that can activate the immune system to recognize and destroy cancer cells. However, their effectiveness in treating colorectal cancer located on the mucosal surface of the gut is limited due to the insufficient activation of mucosal immune response and inadequate infiltration of cytotoxic T cells into tumors. To address this issue, a new mRNA cancer vaccine is developed that can stimulate mucosal immune responses in the gut by co‐delivering all‐trans‐retinoic acid (ATRA) and mRNA using lipid nanoparticle (LNP). The incorporation of ATRA has not only improved the mRNA transfection efficiency of LNP but also induced high expression of gut‐homing receptors on vaccine‐activated T cells. Additionally, the use of LNP improves the aqueous solubility of ATRA, eliminating the need for toxic solvents to administer ATRA. Upon intramuscular injections, ATRA‐adjuvanted mRNA‐LNP significantly increase the infiltration of antigen‐specific, cytotoxic T cells in the lamina propria of the intestine, mesenteric lymph nodes, and orthotopic colorectal tumors, resulting in significantly improved tumor inhibition and prolonged animal survival compared to conventional mRNA‐LNP without ATRA. Overall, this study provides a promising approach for improving the therapeutic efficacy of mRNA cancer vaccines against colorectal cancer.

The color filter array of the camera is an effective fingerprint for digital forensics. Most previous color filter array (CFA)-based forgery localization methods perform under the assumption that the interpolation algorithm is linear. However, interpolation algorithms commonly used in digital cameras are nonlinear, and their coefficients vary with content to enhance edge information. To avoid the impact of this impractical assumption, a CFA-based forgery localization method independent of linear assumption is proposed. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90.