Sicong Yu

MicroRNAs (miRNAs) are small non-coding RNAs (18-26 nucleotides) that regulate gene expression by interacting with target mRNAs, affecting various physiological and pathological processes. miRTarBase, a database of experimentally validated miRNA-target interactions (MTIs), now features over 3 817 550 validated MTIs from 13 690 articles, significantly expanding its previous version. The updated database includes miRNA interactions with therapeutic agents, revealing roles in drug resistance and therapeutic strategies. It also highlights miRNAs as predictive, safety and monitoring biomarkers for toxicity assessment, clinical treatment guidance and therapeutic optimization. The expansion of miRNA-mRNA and miRNA-miRNA networks allows the identification of key regulatory genes and co-regulatory miRNAs, providing deeper insights into miRNA functions and critical target genes. Information on oxidized miRNA sequences has been added, shedding light on how oxidative modifications influence miRNA targeting and regulation. The integration of the LLAMA3 model into the NLP pipeline, alongside prompt engineering, enables the efficient identification of MTIs and miRNA-disease associations without large training datasets. An updated data integration and a redesigned user interface enhance accessibility, reinforcing miRTarBase as an essential resource for molecular oncology, drug development and related fields. The updated miRTarBase is available at https://mirtarbase.cuhk.edu.cn/∼miRTarBase/miRTarBase_2025.

In Positron Emission Tomography (PET), the coincident emission of gamma photon pairs constitutes the useful signals that should be detected and processed to reconstruct the desired PET images of the studied objects. However, along with the useful signal, noise is also generated and added to the detected signals that are sorted with respect to their line-ofresponse and arranged as a sinogram for each two-dimensional slice. In this paper, the type and properties of noise in PET sinogram data will be evaluated. Furthermore, the effect of the used linear and non-linear image denoising and reconstruction procedures on the type of noise will be analyzed. For this purpose, the Gaussian filter, the Median filter, the Patch Confidence k-Nearest Neighbor filter (PCkNN) and the Block Matching 3D filter (BM3D) were used to denoise PET image data, as well as the maximum likelihood expectation maximization algorithm (MLEM) and the Filtered Back Projection algorithm (FBP) to reconstruct the PET images.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high mortality rate in humans, which is caused by Dabie bandavirus (DBV), formerly known as SFTS virus. Clinical manifestations of SFTS are characterized by high fever, thrombocytopenia, leukopenia, hemorrhage, gastrointestinal symptoms, myalgia and local lymph node enlargement with up to 30% case fatality rates in human. Macrophage depletion in secondary lymphoid organs have important roles in the pathogenic process of fatal SFTS, but its exact cell death mechanism remains largely unknown. Here, we showed for the first time that DBV infection induced macrophagic pyroptosis, as evidenced by swollen cells, pore-forming structures, accumulation of gasdermin D N-terminal (GSDMD-NT) as well as the release of lactate dehydrogenase (LDH) and IL-1β in human macrophages. In addition to the upregulation of pyronecrosis genes, the expressions of pyroptosis-related proteins (GSDMD, caspase-1 and IL-1β) were also elevated. To be noted, platelets were found to play a protective role in DBV-derived pyroptosis. Transcriptome analysis and in vitro studies demonstrated that platelets significantly reduced the gene expressions and protein production of pro-pyroptotic markers and inflammatory cytokines in macrophages, whereas platelets conferred a propagation advantage for DBV. Collectively, this study demonstrates a novel mechanism by which DBV invasion triggers pyroptosis as a host defense to remove replication niches in human macrophages and platelets provide an additional layer to reduce cellular death. These findings may have important implications to the pathogenesis of lethal DBV, and provide new ideas for developing novel therapeutics to combat its infection.

With the increasing status of Unmanned Aerial Vehicle (UAV) in the modern battlefield, the problem of its trajectory planning has become a research hotspot in the academic world, and the Artificial Bee Colony Algorithm(ABC) is simple to operate, fewer parameters, easy to implement, and robust. Strong, it has certain advantages in solving combined optimization problems such as UAV trajectory planning. This article mainly summarizes the current applied research status of the ABC algorithm in UAV trajectory planning, proposes the following solution to the limitations of the algorithm, and provides a reference for future UAV trajectory planning problems.