This study explored the potential therapeutic targets and mechanisms of Danggui Shaoyao San(DSS) in the prevention and treatment of Alzheimer's disease(AD) through transcriptomics and metabolomics, combined with animal experiments. Fifty male C57BL/6J mice, aged seven weeks, were randomly divided into the following five groups: control, model, positive drug, low-dose DSS, and high-dose DSS groups. After the intervention, the Morris water maze was used to assess learning and memory abilities of mice, and Nissl staining and hematoxylin-eosin(HE) staining were performed to observe pathological changes in the hippocampal tissue. Transcriptomics and metabolomics were employed to sequence brain tissue and identify differential metabolites, analyzing key genes and metabolites related to disease progression. Reverse transcription-quantitative polymerase chain reaction(RT-qPCR) was employed to validate the expression of key genes. The Morris water maze results indicated that DSS significantly improved learning and cognitive function in scopolamine(SCOP)-induced model mice, with the high-dose DSS group showing the best results. Pathological staining showed that DSS effectively reduced hippocampal neuronal damage, increased Nissl body numbers, and reduced nuclear pyknosis and neuronal loss. Transcriptomics identified seven key genes, including neurexin 1(Nrxn1) and sodium voltage-gated channel α subunit 1(Scn1a), and metabolomics revealed 113 differential metabolites, all of which were closely associated with synaptic function, oxidative stress, and metabolic regulation. RT-qPCR experiments confirmed that the expression of these seven key genes was consistent with the transcriptomics results. This study suggests that DSS significantly improves learning and memory in SCOP model mice and alleviates hippocampal neuronal pathological damage. The mechanisms likely involve the modulation of synaptic function, reduction of oxidative stress, and metabolic balance, with these seven key genes serving as important targets for DSS in the treatment of AD.
Animals ; Alzheimer Disease/genetics* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Mice, Inbred C57BL ; Metabolomics ; Transcriptome/drug effects* ; Maze Learning/drug effects* ; Hippocampus/metabolism* ; Humans ; Disease Models, Animal ; Memory/drug effects*

With the growing awareness of public health, the value and importance of traditional Chinese medicine(TCM) resources have become increasingly prominent. Despite the undeniable significance of TCM in medical treatment and healthcare, the protection, development, and utilization of TCM resources still face numerous challenges. Under the traditional model, the development and utilization of TCM resources heavily rely on manual labor and empirical decision-making, which not only leads to inefficiencies and high costs but also causes serious issues such as unstable drug quality and imbalances in market supply and demand. In the current era of rapid advancements in artificial intelligence(AI) and technology, AI has emerged as a new engine to address many challenges and difficulties throughout the entire TCM resource industry chain. By leveraging AI technology, intelligent management, precise production, and optimized utilization of TCM resources can be achieved, thereby improving efficiency, reducing costs, ensuring stable quality, and balancing market supply and demand. This article primarily explores the application of AI technology in the entire TCM resource industry chain from different perspectives and provides an in-depth analysis of the future development of AI in the TCM industry. It holds significant importance and value in promoting the intelligent development of the TCM sector and facilitating the healthy development of the entire TCM resource industry chain.
Artificial Intelligence ; Medicine, Chinese Traditional/economics* ; Humans ; Drugs, Chinese Herbal/economics* ; Drug Industry

Increasing evidence showed that histone deacetylase 6 (HDAC6) dysfunction is directly associated with the onset and progression of various diseases, especially cancers, making the development of HDAC6-targeted anti-tumor agents a research hotspot. In this study, artificial intelligence (AI) technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline, which combined Voting strategy based on compound-protein interaction (CPI) prediction models, cascade molecular docking, and molecular dynamic (MD) simulations. The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays. Among the identified compounds, Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity (IC₅₀ = 5.41 nM) than that of tubastatin A (TubA) (IC₅₀ = 15.11 nM), along with a favorable subtype selectivity profile (selectivity index ≈ 117.23 for HDAC1), which was further verified by the Western blot analysis. Additionally, Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells, exerting desirable antiproliferative activity (IC₅₀ = 2.59 μM). Furthermore, based on long-term MD simulation trajectory, the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis, thereby elucidating its binding mechanism. Moreover, the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation, thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.

ObjectiveTo investigate the effects of Dihuang Yinzi on the cognitive function in the mouse model of learning and memory impairments induced by scopolamine （SCOP） and explore the treatment mechanism. MethodsA mouse model of learning and memory impairment was induced by intraperitoneal injection of SCOP. Sixty male C57BL/6J mice were randomized into six groups： control （0.9% NaCl， n=10）， model （SCOP 1 mg·kg^-1·d^-1， n=10）， low-， medium-， and high-dose Dihuang Yinzi （SCOP 1 mg·kg^-1·d^-1 + Dihuang Yinzi 5.5， 11.0， and 22.0 g·kg^-1·d^-1， n=10）， and donepezil （SCOP 1 mg·kg^-1·d^-1 + donepezil 0.84 mg·kg^-1·d^-1， n=10）. Mice were administrated with corresponding drugs for 6 weeks. Modeling started in the 4th week， and mice in other groups except the control group were injected with SCOP intraperitoneally 40 min after daily gavage. Behavioral testing began in the 5th week， 30 min after modeling each day. The Morris water maze and novel object recognition tests were carried out to evaluate the spatial learning and memory function of mice. Nissl staining was employed to observe the survival of neurons and Nissl bodies in the hippocampal CA1 region. Western blot was employed to determine the protein levels of silent information regulator 2 （SIRT2）， α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid （AMPA） receptor 1 （GluA1）， protein kinase A （PKA）， cAMP response element-binding protein （CREB）， phosphorylated-CREB （p-CREB）， postsynaptic density protein 95 （PSD95）， growth-associated protein-43 （GAP-43）， and synaptophysin （SYN） in the hippocampus. Immunofluorescence was used to detect the expression of doublecortin （DCX） in the hippocampal dentate gyrus （DG） region. ResultsCompared with the control group， the model group showed impaired learning and memory （P<0.01）， obvious neuronal damage in the hippocampal CA1 region， a reduction in neuron survival （P<0.01）， a decrease in DCX expression in the hippocampal DG region （P<0.01）， down-regulated proteins levels of GluA1， PKA， p-CREB/CREB， PSD95， SYN， and GAP-43 in the hippocampal tissue （P<0.05， P<0.01）， and an up-regulated protein level of SIRT2 （P<0.01）. Compared with the model group， the medium- and high-dose Dihuang Yinzi groups and the donepezil group showed improvements in learning and memory （P<0.05， P<0.01）， while the low-， medium-， and high-dose Dihuang Yinzi groups and the donepezil group had increased neuron survival （P<0.05， P<0.01）. The medium-dose Dihuang Yinzi group and the donepezil group showed increased DCX expression （P<0.05， P<0.01）. The medium- and high-dose Dihuang Yinzi groups and the donepezil group showed up-regulation in the protein levels of GluA1， PKA， p-CREB/CREB， PSD95， SYN， and GAP-43 （P<0.05， P<0.01） and down-regulation in the protein level of SIRT2 （P<0.01）. ConclusionDihuang Yinzi can improve the cognitive function in the mouse model of learning and memory impairments induced by SCOP by inhibiting the upregulation of SIRT2， activating the PKA/CREB signaling pathway， improving synaptic plasticity， and reducing hippocampal neuronal damage.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

[Objective] To investigate the differences in metabolomics between apheresis platelets stored at 4℃ and at 22℃ with agitation, aiming to provide a theoretical basis for the cold storage of apheresis platelets. [Methods] Samples were collected at four time points (d1, d5, d10, d15) for platelets stored at 4℃ (experimental group) and two time points (d1, d5) for platelets stored at 22℃ with agitation (control group). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology was used to detect changes in platelet metabolome levels under different storage conditions. Platelet functional activity was assessed by thromboelastography (TEG) for maximum amplitude (MA) values and flow cytometry for CD62P activation rates. [Results] Metabolites in the glycolytic pathway, key metabolites in the tricarboxylic acid cycle (citrate, α-ketoglutarate), metabolites in the purine metabolism pathway (adenine, inosine monophosphate, guanine, etc.) and amino acid metabolites significantly decreased by d5 in the control group, whereas they remained stable in the experimental group. The content of fatty acid metabolites, such as prostaglandin G2, 13(S)-HOTrE, and linoleic acid, significantly increased in the control group. Statistically significant differences in MA values were observed between the two groups at d1 and d5 (P<0.05). However, in the experimental group, as the storage time extended, the MA values at d10 and d15 showed no significant difference compared to the control group at d5 (P>0.05). The CD62P activation rate between the two groups was statistically significant (P<0.05). Additionally, the CD62P activation rate of platelets in the 22℃ group increased rapidly from d1, while it rose gradually in the 4 ℃ group. [Conclusion] Platelets stored at 4 ℃ exhibit more stable metabolic activity and slower functional deterioration, which is beneficial for extending the effective storage period of platelets.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.