Novel mRNA adjuvant ImmunER enhances prostate cancer tumor-associated antigen mRNA therapy via augmenting T cell activity.

Prostate cancer (PCa) is characterized as a "cold tumor" with limited immune responses, rendering the tumor resistant to immune checkpoint inhibitors (ICI). Therapeutic messenger RNA (mRNA) vaccines have emerged as a promising strategy to overcome this challenge by enhancing immune reactivity and significantly boosting anti-tumor efficacy. In our study, we synthesized Tetra, an mRNA vaccine mixed with multiple tumor-associated antigens, and ImmunER, an immune-enhancing adjuvant, aiming to induce potent anti-tumor immunity. ImmunER exhibited the capacity to promote dendritic cells (DCs) maturation, enhance DCs migration, and improve antigen presentation at both cellular and animal levels. Moreover, Tetra, in combination with ImmunER, induced a transformation of bone marrow-derived dendritic cells (BMDCs) to cDC1-CCL22 and up-regulated the JAK-STAT1 pathway, promoting the release of IL-12, TNF-α, and other cytokines. This cascade led to enhanced proliferation and activation of T cells, resulting in effective killing of tumor cells. In vivo experiments further revealed that Tetra + ImmunER increased CD8+T cell infiltration and activation in RM-1-PSMA tumor tissues. In summary, our findings underscore the promising potential of the integrated Tetra and ImmunER mRNA-LNP therapy for robust anti-tumor immunity in PCa.

Mpox virus mRNA-lipid nanoparticle vaccine candidates evoke antibody responses and drive protection against the Vaccinia virus challenge in mice.

In response to the human Mpox (hMPX) epidemic that began in 2022, there is an urgent need for a monkeypox vaccine. Here, we have developed a series of mRNA-lipid nanoparticle (mRNA-LNP)-based vaccine candidates that encode a collection of four highly conserved Mpox virus (MPXV) surface proteins involved in virus attachment, entry, and transmission, namely A29L, A35R, B6R, and M1R, which are homologs to Vaccinia virus (VACV) A27, A33, B5, and L1, respectively. Despite possible differences in immunogenicity among the four antigenic mRNA-LNPs, administering these antigenic mRNA-LNPs individually (5 µg each) or an average mixture of these mRNA-LNPs at a low dose (0.5 µg each) twice elicited MPXV-specific IgG antibodies and potent VACV-specific neutralizing antibodies. Furthermore, two doses of 5 µg of A27, B5, and L1 mRNA-LNPs or a 2 µg average mixture of the four antigenic mRNA-LNPs protected mice against weight loss and death after the VACV challenge. Overall, our data suggest that these antigenic mRNA-LNP vaccine candidates are both safe and efficacious against MPXV, as well as diseases caused by other orthopoxviruses.

Application of deep machine learning for the radiographic diagnosis of periodontitis.

OBJECTIVE: Successful application of deep machine learning could reduce time-consuming and labor-intensive clinical work of calculating the amount of radiographic bone loss (RBL) in diagnosing and treatment planning for periodontitis. This study aimed to test the accuracy of RBL classification by machine learning. MATERIALS AND METHODS: A total of 236 patients with standardized full mouth radiographs were included. Each tooth from the periapical films was evaluated by three calibrated periodontists for categorization of RBL and radiographic defect morphology. Each image was pre-processed and augmented to ensure proper data balancing without data pollution, then a novel multitasking InceptionV3 model was applied. RESULTS: The model demonstrated an average accuracy of 0.87 ± 0.01 in the categorization of mild (< 15%) or severe (≥ 15%) bone loss with fivefold cross-validation. Sensitivity, specificity, positive predictive, and negative predictive values of the model were 0.86 ± 0.03, 0.88 ± 0.03, 0.88 ± 0.03, and 0.86 ± 0.02, respectively. CONCLUSIONS: Application of deep machine learning for the detection of alveolar bone loss yielded promising results in this study. Additional data would be beneficial to enhance model construction and enable better machine learning performance for clinical implementation. CLINICAL RELEVANCE: Higher accuracy of radiographic bone loss classification by machine learning can be achieved with more clinical data and proper model construction for valuable clinical application.