Background/Objectives: Messenger RNA (mRNA) vaccine technology has shown great potential in the prevention of infectious diseases and treatment of cancers, but its full potential is limited by non-specific delivery mediated by the current lipid nanoparticle (LNP) platform. Methods: Here, we developed a dendritic cell (DC)-targeting LNP incorporated with an ultra-high-affinity CLEC9A-specific nanobody that facilitates enhanced DC uptake but reduced liver accumulation. We assessed the therapeutic efficacy of nanobody-functionalized lipid nanoparticles (Nb-LNPs) in a mouse Lewis lung carcinoma (LLC) model, alongside an evaluation of T cell-mediated immune responses and dendritic cell activation, facilitated by the delivery of mRNA-based neoantigen vaccines. Results: Compared with the use of an unfunctionalized LNP, personalized mRNA cancer vaccines encapsulated with this Nb-LNP demonstrated not only superior anti-tumor effects but also a favorable bio-safety profile in a mouse Lewis lung carcinoma model. The mRNA Nb-LNP neoantigen vaccines also induced substantially higher levels of DC maturation and more potent antigen-specific T cell responses, in particular CD4⁺ T cell responses, which are critical for initiation of anti-tumor immunity and immune memory. Conclusions: Taken together, these results suggest that precision-engineered LNPs conjugated with a CLEC9A-specific antibody or nanobody could be a promising platform for delivering mRNA vaccines specifically to dendritic cells, improving their prophylactic or therapeutic effects.

Batch inconsistency is a major problem when applying LC-MS based untargeted metabolomics in real-time analysis situation such as clinical diagnosis or health monitoring. And inefficiency of collecting MS2 is a major problem for metabolite identification. Here, we developed a reference-feature based quantification and identification strategy (RFQI). In RFQI, samples are individually profiled using a pre-fixed reference feature table. Quantification results show that RFQI improves features’ overlap rate and reduce variance across batches significantly in real-time-analysis mode, and can find more than 4-fold numbers of features. Besides, RFQI collects MS2 from consecutive increasing samples for metabolite identification of pre-fixed features, thus it can effectively compensate for the poor efficiency of MS2 collection in data-dependent acquisition mode. In summary, RFQI can make full advantage of consecutive increasing samples in real-time analysis situation, both for quantification and identification.

At present, there are three main implementations of Nonparametrics Bayes model in machine learning: Dirichlet Process and CRP model, Beta Process and Beta Bernouilli Process model, Gamma Process and Gamma Poisson Process model. Aiming at the infinite sampling process constructed by Gamma Process Stick Breaking proposed by Anirban Roychowdhury, this paper discusses the problem of exact inference based on a finite number of observation samples, analyzes the exact probability distribution function of Gamma Process Stick Breaking construction, and takes this distribution function as a priori, and applies the corresponding results to the inference of Gamma Poisson process.

AI-driven data analysis has emerged as an increasingly influential and rapidly evolving field in recent years, reshaping conventional approaches in data processing and statistical modeling. However, current AI agents still face challenges in scientific research, including limited reproducibility, potential data security risks and high token costs. To address these limitations, we propose Npp, which combines a visual node-based workflow canvas with a sufficient node library with the support of manageable AI agents. Illustrative workflows demonstrate Npp's efficiency in data preprocessing and data analysis. By integrating AI agents with modular, reproducible, and extensible workflows, Npp provides an extensive framework for advanced data science and supporting collaborative scientific research.