Tongliao Academy of Agricultural Sciences

High-quality silage enhances palatability and feed intake; however, the effects of co-fermentation with flavouring agents and lactic acid bacteria (LAB) on its flavour quality, core microbiota, and taste-active amino acids remain unclear. This study investigated the effects of fermentation using Lactiplantibacillus plantarum (LP) alone or in combination with phenethyl acetate (LPP) on the flavour profile of alfalfa silage and its subsequent influence on feed intake. Both LP and LPP significantly improved fermentation quality versus control (CK), with markedly higher feed intake-LP > CK and LPP > LP. Key flavour compounds, including dimethyl trisulfide, 4-ethylphenol and β-damascenone, were significantly increased in the LP alone group. Contrarily, essential taste-related amino acids including aspartic acid, alanine, proline, histidine, isoleucine, and valine were decreased, except for arginine. These metabolic shifts collectively contributed to enhanced feed intake. The addition of LPP increased phenylethyl alcohol, benzyl alcohol and hexanal, and decreased arginine, contributing to enhanced palatability. Aryl alcohol dehydrogenase, proline aminopeptidase, histidine dehydrogenase, and branched-chain amino acid transaminase from LP played a crucial role in the formation of phenylethyl alcohol, proline, histidine and isoleucine during fermentation. These results provide insights into how LAB and flavouring agents jointly regulate flavour development in high-quality alfalfa silage.

The bovine mammary gland, the exclusive site of milk synthesis, is a structurally specialized tissue that houses distinct cellular subsets, yet it remains highly susceptible to major mastitis pathogens, including Staphylococcus aureus, Streptococcus agalactiae, and Escherichia coli. Infection disrupts redox homeostasis, leading to excessive accumulation of reactive oxygen species (ROS) and rapid activation of antioxidant pathways. In this study, we integrated 16S DNA sequencing, histopathology (hematoxylin and eosin), and immunohistochemistry to map the mastitis-associated microbiota and visualize oxidative-damage foci in mammary tissues challenged by Staphylococcus aureus, Streptococcus agalactiae, or Escherichia coli. Quantitative reverse transcription polymerase chain reaction and Western blot analyses were subsequently performed on the same samples to measure the kinetic response of six oxidative-stress-related signalling nodes: adenosine 5'-monophosphate-activated protein kinase, cytochrome P450 1A1, heme oxygenase 1, nitric oxide synthase, mammalian target of rapamycin, and superoxide dismutase. By correlating the temporal expression patterns of these genes/proteins with ROS accumulation and histological severity, this study delineates the molecular cascade linking oxidative imbalance to mastitis pathology, providing data-driven targets for future preventive and therapeutic strategies.

Efficient antigen delivery to B cells and dendritic cells (DC) is critical for enhancing vaccine immunogenicity. Here, we develop a dimeric vaccine strategy by fusing antigens to the N-terminal of heat shock protein GP96. This platform generates compact, nanoscale particles that fully exposed antigenic sites. We validate the vaccine strategy using the SARS-CoV-2 receptor-binding domain (RBD) antigen in a viral challenge model with hACE2 mice and the human papillomavirus (HPV) E7 protein in a HeLa xenograft model with nude mice. The GP96 moiety directly bound its receptor, LRP1, thereby enhancing antigen accumulation on follicular DCs and prolonging lymph node retention, ultimately amplifying germinal center B cell responses. Furthermore, GP96-LRP1 interaction on DCs promotes antigen endocytosis, underpinning epitope presentation and robust cross-conserved T cell activation. Consequently, this design induces potent, durable humoral immunity, cross-conserved T cell responses, and pulmonary mucosal immunity, underscoring its promise as a versatile and effective vaccination strategy.

Production of nano-sized solid-dosage drugs is useful for pharmaceutical industry owing to high solubility and efficacy of the drugs for patients, which can also reduce the drugs side effects. For the solid-dosage oral formulations, the nanomedicine can be prepared via either top-down or bottom-up approach to enhance the drug solubility which in turns enhances the drug bioavailability. A novel methodology for simulation and prediction of medicine solubility in supercritical solvent was developed based on supervised learning algorithms for classification of the data. The data for the simulations were collected on solubility of a model drug in supercritical carbon dioxide. The supercritical-based processing is usually used for preparation of nanomedicine with enhanced bioavailability, and the developed simulation method can help design and optimize the process for industrial applications. The data was obtained with temperature and pressure as the input parameters, whereas the drug solubility is considered as sole estimated output in the model. The validation outputs indicated that great agreement was obtained between the measured data and the simulated values with acceptable regression coefficient for the whole simulations. The simulation results revealed that the supervised learning algorithm is robust and rigorous for prediction of drug solubility data in supercritical conditions and can be used for process optimization and understanding the effects of process parameters. This study is innovative as it methodically assesses diverse machine learning methodologies, encompassing polynomial regression at different complexity tiers and the Gaussian Process Regressor for predicting pharmaceutical solubility. This comparative framework illustrates the bias-variance tradeoff and offers pragmatic guidance for choosing suitable models according to dataset attributes. The methodology presents a time-efficient and cost-effective alternative to conventional thermodynamic modelling for supercritical pharmaceutical processing.

Background: The entomopathogenic fungus Metarhizium brunneum (M. brunneum) is an effective biocontrol agent against various vector arthropods such as ticks, mosquitoes, and flies. However, its virulence mechanisms remain poorly understood, which hinders its broader application. This study aims to establish an integrative framework for prioritizing virulence-related pathways in M. brunneum to aid in the development of more effective biocontrol strategies. Methods: A multidimensional virulence pathway scoring framework was developed using publicly available protein annotation data of M. brunneum. This approach integrates protein pathway enrichment, Gene Ontology (GO) functional analysis, PHI-base virulence factor mapping, and literature-derived evidence. A total of 20 pathways were evaluated, and a scoring system was applied based on protein coverage, Gene Ontology Biological Process (GO-BP) support, PHI-base hits, and literature support. Results: Among the 20 pathways evaluated, five pathways, including MAPK signaling, apoptosis, endocytosis, carbon metabolism, and biosynthesis of secondary metabolites received the highest priority scores. These pathways were identified as key virulence-related candidates, supported by both functional annotation and existing biological evidence. Conclusions: The proposed framework provides a reliable and scalable strategy for prioritizing virulence pathways in entomopathogenic fungi. It offers a solid foundation for subsequent transcriptomic validation, target screening, and functional characterization. This framework can also be applied to other fungi, contributing to the development of optimized biocontrol formulations.