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Icariin (ICA), a natural flavonoid glucoside from traditional Chinese medicine, possesses various pharmacological properties such as anti-inflammatory, anti-aging, and neuroprotective effects. Recent studies suggest its potential in treating Alzheimer’s disease (AD). However, the exact mechanisms of how ICA modulates neuroinflammation in AD remain unclear. In this study, oral ICA administration improved cognitive function in mice, decreasing escape latency in behavioral tests and altering protein levels related to AD pathology, including boosting acetylcholine and reducing p-tau/tau and acetylcholinesterase. Additionally, in 3 × Tg-AD mice, ICA therapy inhibited microglia and astrocyte activation and reduced inflammatory cytokines (IL-1β, TNF-α, IL-6) at the protein level. RNA-seq analysis revealed decreased expression of Nrxn3, Meg3, and Malat1 genes in 3 × Tg-AD animals treated with ICA. Furthermore, ICA activated the Akt/GSK-3β signaling pathway, known for its role in neuroinflammation, suggesting a potential mechanism by which ICA suppresses inflammation. This study proposes Meg3 and Malat1 lncRNA as therapeutic targets against AD, offering a novel approach for combating neuroinflammation in AD through the inhibition of the Akt/GSK-3β pathway.

Nowadays, Megalobrama hoffmanni is a typical cultured fish in south China due to its resource decline in the Pearl River. Meanwhile, since antibiotics had been banned internationally, Chinese medical herbal plant serving as alternative to antibiotics has been adopted in aquaculture. In the present study, to ensure the health growth of M. hoffmanni, extract of traditional medical herbal plant Ampelopsis grossedentata was dietary supplemented and a series experiments were performed, including growth performance determination, physiological/biochemical detection, nutrition analysis, histology analysis, and 16S rRNA amplicon sequencing. Growth performance enhancement was determined since the weight gain rate (WGR), specific growth rate (SGR), and condition factor (CF) of M. hoffmanni increased as feeding inclusion A. grossedentata extract. Interestingly, the total content of muscle fatty acids ascended via supplementing A. grossedentata extract at middle level, in which group the activities of superoxide dismutase (SOD) and catalase (CAT) significantly increased and thus retarded the lipid peroxidation process (manifesting as malondialdehyde (MDA) content rising). Additionally, immune response and inflammatory reaction was stimulated in low and high level A. grossedentata extract added groups, indicating a suitable dosage of A. grossedentata extract benefited in safety production. Moreover, gut microbiota community varied hugely as daily supplementation A. grossedentata extract and the keystone species were tightly related to lipid transformation, which ultimately led to fatty acids composition variation. Our results confirmed that dietary supplementation A. grossedentata extract at the middle level (0.5‰, w/w) is suitable for serving as feed additive in healthful aquaculture of M. hoffmanni.

The epigenetic modifier N6-methyladenosine (m6A), recognized as the most prevalent internal modification in messenger RNA (mRNA), has recently emerged as a pivotal player in immune regulation. Its dysregulation has been implicated in the pathogenesis of various autoimmune conditions. However, the implications of m6A modification within the immune microenvironment of Sjögren's syndrome (SS), a chronic autoimmune disorder characterized by exocrine gland dysfunction, remain unexplored. Herein, we leverage an integrative analysis combining public database resources and novel sequencing data to investigate the expression profiles of m6A regulatory genes in SS. Our cohort comprised 220 patients diagnosed with SS and 62 healthy individuals, enabling a comprehensive evaluation of peripheral blood at the transcriptomic level. We report a significant association between SS and altered expression of key m6A regulators, with these changes closely tied to the activation of CD4+ T cells. Employing a random forest (RF) algorithm, we identified crucial genes contributing to the disease phenotype, which facilitated the development of a robust diagnostic model via multivariate logistic regression analysis. Further, unsupervised clustering revealed two distinct m6A modification patterns, which were significantly associated with variations in immunocyte infiltration, immune response activity, and biological function enrichment in SS. Subsequently, we proceeded with a screening process aimed at identifying genes that were differentially expressed (DEGs) between the two groups distinguished by m6A modification. Leveraging these DEGs, we employed weight gene co-expression network analysis (WGCNA) to uncover sets of genes that exhibited strong co-variance and hub genes that were closely linked to m6A modification. Through rigorous analysis, we identified three critical m6A regulators - METTL3, ALKBH5, and YTHDF1 - alongside two m6A-related hub genes, COMMD8 and SRP9. These elements collectively underscore a complex but discernible pattern of m6A modification that appears to be integrally linked with SS's pathogenesis. Our findings not only illuminate the significant correlation between m6A modification and the immune microenvironment in SS but also lay the groundwork for a deeper understanding of m6A regulatory mechanisms. More importantly, the identification of these key regulators and hub genes opens new avenues for the diagnosis and treatment of SS, presenting potential targets for therapeutic intervention.

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.

Background/purpose: Metagenomic next-generation sequencing (mNGS) has been widely used for the detection of pathogens causing infectious diseases. This study aimed to evaluate the potential ability of mNGS to detect pathogens causing oral and maxillofacial space infection (OMSI) and compare the results with those of the traditional diagnostic microbial culture method. Materials and methods: We retrospectively reviewed the data of 218 patients diagnosed with OMSI who underwent microbial culture and mNGS at the Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, from July 2020 to January 2022. Results: The positivity rate of mNGS (216 cases) was significantly higher than that of microbial culture (123 cases). The most frequently detected bacteria were different between these two detection methods. Streptococcus constellatus (16.05%, 35), Streptococcus anginosus (15.69%, 34) and Klebsiella pneumoniae (6.88%, 15) were the most commonly isolated bacteria by culture. However, Peptostreptococcus stomatis (61.47%, 134), Parvimonas micra (68.35%, 149) and Streptococcus constellatus (57.34%, 125) were the most commonly detected bacteria by mNGS. mNGS also has advantages in diagnosing viral infections. The optimal numbers of diagnostic reads were 1162 and 588 for the diagnosis of Streptococcus anginosus and Streptococcus constellatus infections, respectively. Read numbers were significantly correlated with C-reactive protein (CRP), procalcitonin (PCT), and blood glucose levels and neutrophil percentage (NEUT%). Conclusion: For pathogens causing OMSI, mNGS had a higher rate of microbial pathogen detection and remarkable advantages in identifying coinfections involving viruses and fungi. The read numbers for mNGS are important for diagnostic accuracy and disease severity evaluation.

Salmonella screening is a key to ensure food safety in poultry supply chains. Currently available Salmonella detection methods including culture, polymerase chain reaction and enzyme-linked immuno-sorbent assay could not achieve rapid, sensitive, and in-field detection. In this study, different strategies for separation and detection of Salmonella were proposed, compared, and improved based on our previous studies on immunomagnetic separation and impedance biosensor. First, the coaxial capillary for immunomagnetic separation of target bacteria was improved with less contamination, and 3 strategies based on the improved capillary and immunomagnetic nanoparticles were compared to separate the target bacteria from sample and form the magnetic bacteria. The experimental results showed that the strategy of capture in tube and separation in capillary was the most suitable with separation efficiency of approximately 88%. Then, the immune gold nanoparticles coated with urease were used to label the magnetic bacteria, resulting in the formation of enzymatic bacteria, which were injected into the capillary. After the urea was catalyzed by the urease on the enzymatic bacteria in the capillary, different electrodes were compared to measure the impedance of the catalysate and the screen-printed electrode with higher sensitivity and better stability was the most suitable. This impedance biosensor-based bacterial detection strategy was able to detect Salmonella as low as 102 CFU/mL in 2 h without complex operations. Compared to the gold standard culture method for practical screening of Salmonella in poultry supply chains, this proposed strategy had an accuracy of approximately 90% for 75 real poultry samples.