The widespread use of food additives, such as butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), and tert-butylhydroquinone (TBHQ), has raised concerns about their potential toxicity, especially their hepatotoxicity, nephrotoxicity, and neurotoxicity. This study explores the targets and mechanisms of food additive-induced toxicity using network toxicology. Toxicity predictions of BHA, BHT, and TBHQ were performed using the ProTox-3.0, ADMETlab 3.0, and Xundrug databases, and potential targets were identified using the SwissTargetPrediction, Batman-TCM, SuperPred, and SEA databases. These were integrated with GeneCards—The Human Gene Database (GeneCards) and the Online Mendelian Inheritance in Man (OMIM) database to extract toxicity-related targets for subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Core-acting genes were further screened through protein–protein interactions (PPIs), and molecular docking was performed to verify the binding free energy between BHA, BHT, and TBHQ and their core targets. Additionally, the mRNA-miRNA-lnRNA interaction regulatory networks of the core targets and potential carcinogenic mechanisms were analyzed. The targets of BHA, BHT, and TBHQ were as follows: ACE, HIF1A, NR1H4, NFKB1, TNF, IL6, IFNG, IL1B, and ESR1 for hepatotoxicity; APP, NFKB1, ACE, FOS, IL10, IL1B, IL6, TNF, and ALB for nephrotoxicity; and GRIN2B, IL1B, and TNF for neurotoxicity. These interactions primarily involved pathways such as interleukin-17 (IL-17) and Janus kinase-signal transducer and activator of transcription (JAK-STAT), as well as various pathways related to non-alcoholic fatty liver disease (NAFLD). This study highlights the potential toxicity of BHA, BHT, and TBHQ to the liver, kidneys, and nerves, providing insights for better safety evaluations.

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the deposition of senile plaques, neurofibrillary tangles, and neuronal dysfunction, resulting in severe cognitive and memory decline. The root of the Scrophulariaceae plant Rehmannia glutinosa (Gaertn.) DC. (Rehmanniae radix; RR) and its product Rehmanniae radix praeparata (RRP) possess high nutritional and medicinal value. Both show therapeutic potential for AD in traditional medical settings. However, the differences in their bioactive components and the mechanisms of action underlying their anti-AD effects remain unclear.Methods: In this study, APP/PS1 mice were used as the animal model of AD. Ultra-high-performance liquid chromatography coupled with Q-Exactive tandem mass spectrometry (MS/MS) (UPLC–QE-MS/MS), network pharmacology, proteomics, molecular docking, and 16S rRNA sequencing were used to investigate the differences in the medicinal components of RR and RRP and their mechanisms of action in the treatment of AD. The mechanisms of action of two identified critical components, ajugol and isoacteoside, were further verified in the D-galactose/AlCl₃-induced Institute of Cancer Research (ICR) mouse model of AD—with cognitive function evaluated using the Morris water maze and open-field tests—and the amyloid-beta (Aβ)-induced BV2 cell model of inflammation.Results: Ajugol and isoacteoside were identified as the key anti-AD bioactive compounds in RR and RRP, respectively, through UPLC–QE-MS/MS. Integrated network pharmacology, proteomics, and 16S rRNA sequencing implicated neuroinflammation, apoptosis, and autophagy as critical pathways for their anti-AD effects. Subsequently, in vivo and in vitro experiments demonstrated that ajugol exerted its effects mainly by modulating the TLR/NF-κB/NLRP3 and BCL-2/BAX/cytochrome C/caspase-3 pathways, while isoacteoside primarily acted via the LC3-Ⅱ/P62/p-mTOR/mTOR pathway. Ajugol and isoacteoside mitigated cognitive impairment in AD models, decreased Aβ plaque accumulation in hippocampal tissues, and attenuated inflammatory injury-induced cytotoxicity in BV2 microglia, thereby suppressing AD progression.Conclusion: In this work, we systematically elucidated the differential mechanisms underlying the anti-AD effects of ajugol and isoacteoside. We found that ajugol primarily acts via the TLR/NF-κB/NLRP3 and BCL-2/BAX/cytochrome C/caspase-3 pathways, while isoacteoside acts via the LC3-II/P62/p-mTOR/mTOR pathway. These findings establish a foundation for developing RRP-based complementary medicines and functional foods.

Parabens (PBs) are associated with an increased risk of breast cancer, yet their underlying molecular mechanisms remain poorly understood. This study aimed

Background: The “five-flavor” ( wu wei ) classification is a core organizing principle in Traditional Chinese Medicine (TCM) pharmacology and quality control, yet its assessment still relies on subjective organoleptic evaluation, limiting standardization and reproducibility. Methods: Eighty-four processed herbal slices were analyzed using an electronic tongue to obtain multichannel sensor response profiles. Based on these data, we developed a dual-strategy ensemble learning framework in which a heterogeneous stacking model was trained for five-flavor identification while a voting ensemble was established in parallel for intensity grading. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP) to quantify feature contributions and to relate prediction outcomes to specific electrochemical response patterns. Results: On the independent test set, the stacking ensemble showed moderate and flavor-dependent classification performance, with a macro-AUC of 0.876 and a balanced accuracy of 0.629. Discriminative performance was higher for Bitter and Sweet than for Sour, Salty, and Pungent. Full-stack SHAP analysis further identified P_13 and P_8 as the most influential features overall, with P_13 contributing mainly to the discrimination of bitter and pungent attributes, whereas P_8 was more strongly associated with sour, salty, and sweet characterization. Conclusions: This study demonstrates a data-driven workflow for digitizing TCM five-flavor assessment. Further validation with expanded sample sizes and chemical corroboration of sensor attributions is needed before operational deployment.

Schizophrenia (SCZ) and major depressive disorder (MDD) are severe psychiatric disorders with substantial shared genetic liability and frequent clinical co-occurrence, yet their shared cell type-specific genetic mechanisms remain unclear. We integrated single-cell expression quantitative trait locus (sc-eQTL) data from eight major brain cell types with large-scale genome-wide association study (GWAS) summary statistics for SCZ and MDD. Cell type-specific genetically regulated gene expression was evaluated using Mendelian randomization (MR) and Bayesian colocalization analyses, followed by validation in independent single-cell RNA sequencing datasets and functional characterization using virtual gene knockout and phenome-wide association study (PheWAS) analyses. Integrative analyses prioritized three shared candidate genes, ZSCAN31, BTN3A2, and YLPM1, all showing strong colocalization support (PP·H4 > 0.8). Single-cell transcriptomic validation supported distinct cell type-specific patterns, with ZSCAN31 enriched in astrocytes and endothelial cells, BTN3A2 in microglia, and YLPM1 in oligodendrocytes. Virtual knockout of YLPM1 suggested perturbation of neurodevelopment-related pathways in SCZ and synaptic signaling pathways in MDD. These findings provide a cell type-resolved view of shared genetic architecture between SCZ and MDD and highlight candidate genes for further mechanistic and translational investigation.