University Of Jinan

Influenza A virus (IAV) exhibits notable genetic diversity and cross-species transmission capacity, posing a continuous challenge to public health. Elucidating host immune regulatory mechanisms is crucial for identifying new antiviral targets that can overcome viral resistance. Here, using human A549 lung epithelial cells as the primary model, we identify fibroblast growth factor 8 (FGF8) as a crucial host factor whose expression is significantly elevated during infection by various IAV subtypes (including H1N1, H13N2, H9N2, and PR8). Through gain- and loss-of-function assays, we demonstrate that FGF8 specifically enhances viral replication at the post-entry stage by suppressing interferon-beta (IFN-β) and interferon-stimulated genes (ISGs) expression. Mechanistically, FGF8 reduces retinoic acid-inducible gene I (RIG-I) protein stability via K48-linked polyubiquitination without affecting its mRNA levels. Ubiquitination identifies Lysine 258 (K258) on RIG-I as the essential modification site; notably, a K258R mutation prevents RIG-I degradation and restores IFN-β induction. Furthermore, TurboID-based proximity labeling captures the close spatial association of FGF8 with both tripartite motif containing 16 (TRIM16) and RIG-I, revealing that FGF8 acts as a molecular scaffold to recruit the E3 ligase TRIM16 to RIG-I. Consistently, TRIM16 silencing replicates the antiviral effects of FGF8 knockdown. Collectively, our findings demonstrate that FGF8 recruits TRIM16 to degrade RIG-I, thereby facilitating viral immune evasion. Disrupting this interaction offers a potential avenue for anti-influenza A virus intervention.

Okadaic acid (OA), a potent diarrhetic shellfish toxin produced by marine dinoflagellates, accumulates in shellfish and poses a significant threat to human health by causing severe gastrointestinal illness. Consequently, the highly sensitive detection of OA is of crucial importance. In this study, an electrochemiluminescence (ECL) biosensor for OA detection was developed based on a Tb(Ⅲ)-Cbatpy-MOGs complex and a nucleic acid amplification strategy. The sensor integrates a cyclic DNA walker with the enzymatic assistance of Exonuclease III for accurate target quantification. The ECL intensity showed a strong linear relationship with the logarithm of OA concentration over a wide range, achieving a detection limit as low as 0.16 pg/mL, attesting to its high sensitivity. Furthermore, the biosensor exhibited excellent selectivity, reproducibility and stability, confirming its reliability for practical applications. This work provides a robust and highly sensitive method for OA detection and holds great promise for the practical monitoring of marine toxins.

Detecting the component contents of raw meal is an important step in the quality control of the cement industry. Near infrared spectroscopy (NIRS) has demonstrated a significant application potential in the quality control of cement raw meal. This study proposes an interval selective ensemble learning (ISEL) method for quantitative analysis of CaO, SiO₂, Al₂O₃ and Fe₂O₃ in cement raw meal using NIRS. Firstly, the spectral data are divided into several sub-intervals to train individual learners. Then the K-means algorithm is used to cluster and select dominant individual learners from trained individual learners. The stacking integration strategy is used to combine the outputs of the advantageous individual learners. The cross-validation outputs of dominant individual learners are combined to construct a meat-training set, which is used to train a multilayer perceptron (MLP) for nonlinear integration of dominant individual learners. ISEL is validated on two NIRS datasets obtained from different cement plants and measured with different types of spectrometers. Experimental results show that the proposed ISEL outperforms widely used chemometric methods as well as ensemble and selective ensemble approaches. For dataset A, R²_p are 0.9721, 0.9476, 0.9383 and 0.9255, with RMSEP of 0.181%, 0.2015%, 0.0698% and 0.056%. For dataset B, the R²_p and RMSEP are 0.9247, 0.8544, 0.8474, 0.8363 and 0.1868%, 0.2238%, 0.0781%, 0.0328%, respectively. This study demonstrates that NIRS couples with the ISEL is a novel and reliable approach for the quantitative analysis of cement raw meal.