OBJECTIVE To construct specialized file management and risk warning system for narcotic drugs， and promote the refined management of narcotic drugs in medical institutions. METHODS Through the rational medication management system， patient-indexed narcotic drug medication record information was integrated， pain assessment and rapid dose titration modules were constructed， and a prescription pre-review risk warning model was established. Statistical analysis was conducted on the number of drugs returned by patients， interventions for adverse events related to narcotic drug abuse， patient and physician satisfaction， and patient medication adherence before the system was constructed （January-July 2023） and after its stable operation （January-July 2024）. RESULTS After the construction of the system， the number of narcotic drugs returned by patients decreased， except for Morphine tablets. The number of interventions for adverse events related to narcotic drug abuse increased from 0 to 5 cases. The patients’ satisfaction with pain control increased from 46.25% to 67.50%， the proportion of patients with poor adherence decreased from 23.75% to 7.50%， and the overall satisfaction of physicians was 72.41%. CONCLUSIONS The construction of a specialized file management and risk warning system for narcotic drugs enables refined management of narcotic drugs， provides alerts for abnormal prescriptions， reduces patient stockpiling of narcotic drugs， allows timely detection and intervention of adverse events related to narcotic drug abuse， improves physician treatment efficiency and precision， and enhances patient pain control satisfaction and medication adherence.

OBJECTIVE To construct a predictive model for evaluating the efficacy of a triple antiemetic regimen （neurokinin- 1 receptor antagonist+5-hydroxytryptamine 3 receptor antagonist+dexamethasone） for preventing nausea and vomiting induced by highly emetogenic chemotherapy （HEC） based on interpretable deep learning algorithms. METHODS Clinical data of cancer patients who received HEC and were treated with the standard triple antiemetic regimen in the oncology department of Tianjin Medical University General Hospital from January 2018 to December 2022 were collected retrospectively. Demographic， clinical and metabolism-related variables were integrated. After data pre-processing， two deep learning algorithms （deep random forest and dense neural network） and four machine learning algorithms （support vector machine， categorical boosting， random forest and decision tree） were used to build predictive models. Subsequently， model performance evaluation and model interpretability analysis were conducted. RESULTS Among the six candidate models， the deep random forest model demonstrated the best predictive performance on the test set， with an area under the receiver operating characteristic curve of 0.850， an accuracy of 0.911， a precision of 0.805， a recall of 0.783， an F1 score of 0.793， and a Brier score of 0.075. Interpretability analysis revealed that creatinine clearance rate （Ccr） was the key predictive factor， and low Ccr levels， female gender， younger age， highly emetogenic drugs （particularly cisplatin-containing chemotherapy regimens）， and anticipatory nausea and vomiting were positively correlated with the risk of HEC-related nausea and vomiting. CONCLUSIONS The deep random forest model exhibits the best performance in predicting the efficacy of triple antiemetic regimen for preventing HEC-related nausea and vomiting. The key predictors in this model primarily include Ccr，anticipatory nausea and vomiting， gender， age， and highly emetogenic drugs.

ObjectiveTo investigate the mechanisms by which Renshentang treats atherosclerosis （AS） in mice， focusing on the regulation of endothelial inflammatory responses mediated by transient receptor potential vanilloid subtype 1 （TRPV1）. MethodsAn AS model was established in apolipoprotein E knockout （ApoE^-/-） mice fed a high-fat diet. The mice were randomly divided into a simvastatin group （0.02 g·kg^-1·d^-1） and low-， medium-， and high-dose Renshentang groups （1.77， 3.54， 7.08 g·kg^-1·d^-1）， with 12 mice in each group. ApoE^-/- mice were fed a high-fat diet and treated simultaneously. C57BL/6J mice fed a normal diet served as the normal group （n=9）. After continuous administration for 12 weeks， mice were anesthetized and the aortas were collected. Oil Red O staining was used to observe lipid plaque formation in the aorta. Hematoxylin-eosin （HE） staining was performed to examine pathological changes in the aortic root. Immunohistochemistry was used to analyze the levels of pro-inflammatory factors tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）， as well as the expression of TRPV1， phosphorylated phosphoinositide 3-kinase （p-PI3K）， and phosphorylated protein kinase B （p-Akt） in the aortic root. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect endothelial nitric oxide synthase （eNOS） mRNA expression in the aorta， and Western blot was used to detect TRPV1 protein expression. ResultsCompared with the normal group， the model group showed a significant increase in aortic plaque formation （P<0.01） and significantly elevated levels of TNF-α and IL-1β in the aortic root （P<0.01）. The expression levels of TRPV1， p-PI3K， and p-Akt were decreased （P<0.05， P<0.01）， and eNOS mRNA expression was reduced （P<0.05， P<0.01）. Compared with the model group， all Renshentang groups significantly reduced aortic plaque formation （P<0.01）， significantly decreased TNF-α and IL-1β levels （P<0.01）， and markedly increased the expression levels of TRPV1， p-PI3K， p-Akt， and eNOS mRNA （P<0.05， P<0.01）. ConclusionRenshentang may inhibit endothelial inflammation and suppress the formation of AS by increasing TRPV1 protein expression and up-regulating the PI3K/Akt/eNOS signaling pathway， which may be one of the molecular mechanisms underlying its therapeutic effect against AS.

OBJECTIVE To provide a scientific basis for the quality evaluation and clinical application of Qingwen hufei granules. METHODS Fourteen batches of Qingwen hufei granules were used as samples to establish high-performance liquid chromatography （HPLC） fingerprints using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 Edition）. The chromatographic peaks were identified and the similarity was evaluated. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were used to conduct chemical pattern recognition analysis on the 14 batches of samples. Meanwhile， the contents of neochlorogenic acid （NGA）， chlorogenic acid （CHA）， cryptochlorogenic acid （CGA）， forsythoside A （FTA）， 3，5-O-dicaffeoylquinic acid （3，5-O- DA）， 4，5-O-dicaffeoylquinic acid （4，5-O-DA）， and angoroside C （AGC） in the samples were determined by HPLC. RESULTS The methodological investigation results of both the fingerprint and the content determination complied with the relevant requirements. Fourteen common peaks were indicated in the HPLC fingerprints of the 14 batches of samples， and 7 of them were identified [NGA （peak 2）， CHA （peak 3）， CGA （peak 5）， FTA （peak 11）， 3，5-O-DA （peak 12）， 4，5-O-DA （peak 13）， and AGC （peak 14）]； the similarity of each sample was greater than 0.94. The results of CA and PCA showed that the samples could be classified into 3 categories； the results of OPLS-DA indicated that peak 4 （unknown）， peak 11 （FTA）， peak 8 （unknown）， peak 9 （unknown）， and peak 1 （unknown） were the differential components. The content ranges of NGA， CHA， CGA， 3，5-O-DA， FTA， 4，5-O-DA and AGC in the 14 batches of samples were 0.210 4-0.458 7， 0.269 1-0.506 3， 0.228 1-0.461 1， 0.443 9-1.044 6， 0.066 7-0.155 7， 0.062 8-0.143 8， and 0.057 4-0.105 7 mg/g， respectively. CONCLUSIONS The HPLC fingerprint and multi-component content determination methods established in this study are efficient and reliable， and can be used for the quality evaluation of Qingwen hufei granules.

As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

Objective: To develop QingNangTCM, a specialized large language model (LLM) tailored for expert-level traditional Chinese medicine (TCM) question-answering and clinical reasoning, addressing the scarcity of domain-specific corpora and specialized alignment. Methods: We constructed QnTCM_Dataset, a corpus of 100 000 entries, by integrating data from ShenNong_TCM_Dataset and SymMap v2.0, and synthesizing additional samples via retrieval-augmented generation (RAG) and persona-driven generation. The dataset comprehensively covers diagnostic inquiries, prescriptions, and herbal knowledge. Utilizing P-Tuning v2, we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM. A multi-dimensional evaluation framework, assessing accuracy, coverage, consistency, safety, professionalism, and fluency, was established using metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), and LLM-as-a-Judge with expert review. Qualitative analysis was conducted across four simulated clinical scenarios: symptom analysis, disease treatment, herb inquiry, and failure cases. Baseline models included GLM-4-9B-Chat, DeepSeek-V2, HuatuoGPT-II (7B), and GLM-4-9B-Chat (freeze-tuning). Results: QingNangTCM achieved the highest scores in BLEU-1/2/3/4 (0.425/0.298/0.137/0.064), ROUGE-1/2 (0.368/0.157), and METEOR (0.218), demonstrating a balanced and superior normalized performance profile of 0.900 across the dimensions of accuracy, coverage, and consistency. Although its ROUGE-L score (0.299) was lower than that of HuatuoGPT-II (7B) (0.351), it significantly outperformed domain-specific models in expert-validated win rates for professionalism (86%) and safety (73%). Qualitative analysis confirmed that the model strictly adheres to the “symptom-syndrome-pathogenesis-treatment” reasoning chain, though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes. Conclusion Combining domain-specific corpus construction with parameter-efficient prompt tuning enhances the reasoning behavior and domain adaptation of LLMs for TCM-related tasks. This work provides a technical framework for the digital organization and intelligent utilization of TCM knowledge, with potential value for supporting diagnostic reasoning and medical education.

ObjectiveTo investigate the mechanism of action of Kaixinsan in the treatment of Alzheimer's disease （AD） based on network pharmacology， molecular docking， and animal experimental validation. MethodsThe Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform（TCMSP） and the Encyclopedia of Traditional Chinese Medicine（ETCM） databases were used to obtain the active ingredients and targets of Kaixinsan. GeneCards， Online Mendelian Inheritance in Man（OMIM）， TTD， PharmGKB， and DrugBank databases were used to obtain the relevant targets of AD. The intersection （common targets） of the active ingredient targets of Kaixinsan and the relevant targets of AD was taken， and the network interaction analysis of the common targets was carried out in the STRING database to construct a protein-protein interaction（PPI） network. The CytoNCA plugin within Cytoscape was used to screen out the core targets， and the Metascape platform was used to perform gene ontology（GO） functional enrichment analysis and Kyoto encyclopedia of genes and genomes（KEGG） pathway enrichment analysis. The “drug-active ingredient-target” interaction network was constructed with the help of Cytoscape 3.8.2， and AutoDock Vina was used for molecular docking. Scopolamine （SCOP） was utilized for modeling and injected intraperitoneally once daily. Thirty-two male C57/BL6 mice were randomly divided into blank control （CON） group （0.9% NaCl， n=8）， model （SCOP） group （3 mg·kg^-1·d^-1， n=8）， positive control group （3 mg·kg^-1·d^-1 of SCOP+3 mg·kg^-1·d^-1 of Donepezil， n=8）， and Kaixinsan group （3 mg·kg^-1·d^-1 of SCOP+6.5 g·kg^-1·d^-1 of Kaixinsan， n=8）. Mice in each group were administered with 0.9% NaCl， Kaixinsan， or Donepezil by gavage twice a day for 14 days. Morris water maze experiment was used to observe the learning memory ability of mice. Hematoxylin-eosin （HE） staining method was used to observe the pathological changes in the CA1 area of the mouse hippocampus. Enzyme linked immunosorbent assay（ELISA） was used to determine the serum acetylcholine （ACh） and acetylcholinesterase （AChE） contents of mice. Western blot method was used to detect the protein expression levels of signal transducer and activator of transcription 3（STAT3） and nuclear transcription factor（NF）-κB p65 in the hippocampus of mice. ResultsA total of 73 active ingredients of Kaixinsan were obtained， and 578 potential targets （common targets） of Kaixinsan for the treatment of AD were screened out. Key active ingredients included kaempferol， gijugliflozin， etc.. Potential core targets were STAT3， NF-κB p65， et al. GO functional enrichment analysis obtained 3 124 biological functions， 254 cellular building blocks， and 461 molecular functions. KEGG pathway enrichment obtained 248 pathways， mainly involving cancer-related pathways， TRP pathway， cyclic adenosine monophosphate（cAMP） pathway， and NF-κB pathway. Molecular docking showed that the binding of the key active ingredients to the target targets was more stable. Morris water maze experiment indicated that Kaixinsan could improve the learning memory ability of SCOP-induced mice. HE staining and ELISA results showed that Kaixinsan had an ameliorating effect on central nerve injury in mice. Western blot test indicated that Kaixinsan had a down-regulating effect on the levels of NF-κB p65 phosphorylation and STAT3 phosphorylation in the hippocampal tissue of mice in the SCOP model. ConclusionKaixinsan can improve the cognitive impairment function in SCOP model mice and may reduce hippocampal neuronal damage and thus play a therapeutic role in the treatment of AD by regulating NF-κB p65， STAT3， and other targets involved in the NF-κB signaling pathway.

Chronic heart failure （CHF） is the final stage of cardiovascular diseases. It is a complex syndrome， with dyspnea and edema as the main clinical manifestations， and it is characterized by complex disease conditions， difficult cure， and high mortality. Ferroptosis， a new type of programmed cell death， is different from other types of programmed cell death. Ferroptosis is iron-dependent， accompanied by lipid peroxide accumulation and mitochondrial shrinkage， becoming a hot research topic. Studies have confirmed that ferroptosis plays a key role in the occurrence and development of CHF. The regulation of ferroptosis may become a potential target for the treatment of CHF in the future. The theory of Qi deficiency and stagnation refers to the pathological state of original Qi deficiency and abnormal transportation and distribution of Qi， blood， and body fluid， which has guiding significance for revealing the pathogenesis evolution of some chronic diseases. We believe that Qi deficiency and stagnation is a summary of the pathogenesis of ferroptosis in CHF. Deficiency of Qi （heart Qi） is the root cause of CHF， and stagnation （phlegm turbidity and blood stasis） is the branch of this disease. The two influence each other in a vicious circle to promote the development of this disease. Traditional Chinese medicine （TCM） plays an important role in the treatment of CHF， improving the prognosis and quality of life of CHF patients. This paper explores the correlation between the theory of Qi deficiency and stagnation and the mechanism of ferroptosis in CHF. Furthermore， this paper reviews the mechanism of Chinese medicines and compound prescriptions in preventing and treating CHF by regulating ferroptosis according to the principles of replenishing Qi and dredging to remove stagnation， aiming to provide new ideas and methods for the treatment of CHF with TCM.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.