Jining Medical University

As a core compulsory course for Traditional Chinese Medicine (TCM) majors, TCM Diagnostics serves as a critical link connecting basic TCM theories and clinical practice, whose teaching quality directly determines the core competence of TCM talent cultivation. Currently, TCM diagnostics teaching faces practical dilemmas including abstract theoretical knowledge, insufficient practical scenarios, lack of personalized teaching, and rigid evaluation systems, which restrict the quality and efficiency of TCM talent cultivation. With its core advantages in data processing, intelligent simulation, and personalized adaptation, artificial intelligence (AI) technology demonstrates inherent consistency with the core thinking of TCM diagnostics such as "syndrome differentiation and treatment" and "holistic concept", providing a brand-new technological path for solving teaching difficulties and promoting teaching reform. Based on constructivist learning theory, embodied cognition theory and precision teaching theory, this paper systematically analyzes the theoretical foundation and internal logic of AI-empowered TCM diagnostics teaching reform, examines the existing problems in current TCM diagnostics teaching, explores the application paths of AI in theoretical teaching, practical teaching, assessment and evaluation of TCM diagnostics, and proposes targeted safeguard measures, so as to provide theoretical support and practical reference for promoting the digital and intelligent transformation of TCM diagnostics teaching and cultivating high-quality TCM talents who meet the needs of the new era.

Acute myocardial infarction (AMI) is a critical and fatal cardiovascular condition. The role of cuproptosis as an emerging mechanism in the pathogenesis of AMI remains to be fully elucidated. Patient data were acquired from the Gene Expression Omnibus database. Differential expression analysis was performed on cuproptosis-related genes (CRGs). LASSO regression was employed to identify key CRGs and develop a diagnostic classification model for AMI. Weighted gene co-expression network analysis was performed to investigate key modules associated with the disease and its molecular classification. Subsequently, machine learning techniques were used to identify critical genes within these modules. The diagnostic efficacy of these genes for AMI was assessed using receiver operating characteristic analysis. The functions of the identified key genes were ultimately validated at the cellular level. Six characteristic CRGs were selected via LASSO regression. The AMI diagnostic classification model based on these CRGs demonstrated superior performance. Patients were classified into two subtypes related to cuproptosis, revealing significant differences in enrichment pathways between these subtypes. SYTL3, SCML1, and PMAIP1 were identified as key genes for AMI, with area under the curves of 0.813, 0.795, and 0.873, respectively. Knocking down PMAIP1 expression reduced intracellular copper levels in hypoxia-induced HUVECs. This study clarifies the role of cuproptosis in AMI and highlights the potential involvement of PMAIP1, providing a theoretical basis for further investigation into cuproptosis in AMI.

BackgroundTo investigate the mechanisms and intergenerational effects of Toxoplasma gondii infection in parental male mice on the transcriptome of the brain of their offspring.MethodsMale parental mice were infected with the T. gondii strain TgCtwh6 and then mated with healthy female mice to produce offspring F1. Three independent and comparable groups were established: infected male mice (M) versus F1 male generation (F1♂) (M vs. F1♂), healthy female mice (F) versus F1 female generation (F1♀) (F vs. F1♀), and parental generation (P) versus F1 generation (F1) (P vs. F1). RNA was extracted from the brain tissues of both parental and offspring mice for transcriptome sequencing, screening for differentially expressed genes (DEGs) common to all three groups. DEGs were identified and validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and evolutionary genealogy of genes: Nonsupervised Orthologous Groups (eggNOG) classification were performed to reveal the potential functions of DEGs in mice and genes in biological processes, key metabolic or signaling pathways, which provide a molecular basis for further studies on how to affect transcriptional expression in offspring.ResultsAn overlap in gene expression was observed among the M versus F1♂, F versus F1♀, and P versus F1 comparisons. Collectively, these three comparisons identified 66 DEGs that were consistently altered across all groups, comprising 19 upregulated and 47 downregulated genes. GO analysis revealed that these DEGs were predominantly enriched in categories such as identical protein binding, positive regulation of NF-kappa B transcription factor activity, and membrane raft. KEGG analysis further indicated that the majority of enriched pathways were associated with immune responses, including those involved in viral infection pathways. qRT-PCR was employed to validate the expression changes of key genes.ConclusionsT. gondii infection of male parental mice significantly downregulates gene expression in the brain tissue of their offspring and negatively regulates the immune system and signal transduction pathways. This study provides valuable insights into the intergenerational effects of T. gondii infection and highlights the importance of further research in this critical area.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s13071-026-07302-7.

Background: Deficits in visual working memory (vWM) represent a core cognitive impairment in schizophrenia; however, the dynamic spatiotemporal characterization of their underlying neural mechanisms remains unclear. The present study employed multivariate pattern classification (MVPC) and searchlight analysis to investigate neural signaling differences between patients with schizophrenia (PSZ) and healthy control subjects (HCS) during a vWM task. Methods: A total of 46 participants (22 PSZ, 24 HCS) completed a change detection task comprising three conditions: two targets, zero distractors (2T0D); two targets, two distractors (2T2D); and four targets, zero distractors (4T0D). Contralateral delay activity (CDA) was extracted through event-related potential (ERP) analysis. MVPC was applied in the temporal dimension, while a searchlight approach was employed in the spatial dimension to decode memory load (2T0D/2T2D/4T0D) and memory side (left/right) information. Results: CDA amplitude was significantly reduced in the PSZ group, particularly in the 2T2D condition (p = 0.01), indicating that the scope and control of attention elicited comparable CDA amplitudes. MVPC analysis revealed that decoding accuracy in the PSZ group was significantly lower than in the HCS group during the time window of 93–652 ms (pcorrected < 0.05), suggesting diminished efficiency of neural information encoding during the delay period. The searchlight analysis identified the most pronounced decrease in decoding accuracy within the left parietal region in the PSZ group, consistent with the hypothesis of abnormal functional connectivity in the inferior parietal gyrus (IPG). Conclusions: This study reveals the spatiotemporal dynamics of vWM deficits in schizophrenia, characterized by ERP decoding technology. It offers a novel target for the development of neuromarker-based cognitive interventions.