Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

As China accelerates its efforts in deep space exploration and long-duration space missions, including the operationalization of the Tiangong Space Station and the development of manned lunar missions, safeguarding astronauts’ physiological and cognitive functions under extreme space conditions becomes a pressing scientific imperative. Among the multifactorial stressors of spaceflight, microgravity emerges as a particularly potent disruptor of neurobehavioral homeostasis. Dopamine (DA) plays a central role in regulating behavior under space microgravity by influencing reward processing, motivation, executive function and sensorimotor integration. Changes in gravity disrupt dopaminergic signaling at multiple levels, leading to impairments in motor coordination, cognitive flexibility, and emotional stability. Microgravity exposure induces a cascade of neurobiological changes that challenge dopaminergic stability at multiple levels: from the transcriptional regulation of DA synthesis enzymes and the excitability of DA neurons, to receptor distribution dynamics and the efficiency of downstream signaling pathways. These changes involve downregulation of tyrosine hydroxylase in the substantia nigra, reduced phosphorylation of DA receptors, and alterations in vesicular monoamine transporter expression, all of which compromise synaptic DA availability. Experimental findings from space analog studies and simulated microgravity models suggest that gravitational unloading alters striatal and mesocorticolimbic DA circuitry, resulting in diminished motor coordination, impaired vestibular compensation, and decreased cognitive flexibility. These alterations not only compromise astronauts’ operational performance but also elevate the risk of mood disturbances and motivational deficits during prolonged missions. The review systematically synthesizes current findings across multiple domains: molecular neurobiology, behavioral neuroscience, and gravitational physiology. It highlights that maintaining DA homeostasis is pivotal in preserving neuroplasticity, particularly within brain regions critical to adaptation, such as the basal ganglia, prefrontal cortex, and cerebellum. The paper also discusses the dual-edged nature of DA plasticity: while adaptive remodeling of synapses and receptor sensitivity can serve as compensatory mechanisms under stress, chronic dopaminergic imbalance may lead to maladaptive outcomes, such as cognitive rigidity and motor dysregulation. Furthermore, we propose a conceptual framework that integrates homeostatic neuroregulation with the demands of space environmental adaptation. By drawing from interdisciplinary research, the review underscores the potential of multiple intervention strategies including pharmacological treatment, nutritional support, neural stimulation techniques, and most importantly, structured physical exercise. Recent rodent studies demonstrate that treadmill exercise upregulates DA transporter expression in the dorsal striatum, enhances tyrosine hydroxylase activity, and increases DA release during cognitive tasks, indicating both protective and restorative effects on dopaminergic networks. Thus, exercise is highlighted as a key approach because of its sustained effects on DA production, receptor function, and brain plasticity, making it a strong candidate for developing effective measures to support astronauts in maintaining cognitive and emotional stability during space missions. In conclusion, the paper not only underscores the centrality of DA homeostasis in space neuroscience but also reflects the authors’ broader academic viewpoint: understanding the neurochemical substrates of behavior under microgravity is fundamental to both space health and terrestrial neuroscience. By bridging basic neurobiology with applied space medicine, this work contributes to the emerging field of gravitational neurobiology and provides a foundation for future research into individualized performance optimization in extreme environments.

Non-steroidal anti-inflammatory drugs(NSAIDs),commonly utilized analgesics,are extensively employed for managing pain associated with musculoskeletal disorders or injuries.Recent clinical studies have demonstrated a heightened risk of bone stress injuries(BSI)in soldiers and athletes,particularly during high-intensity training,due to NSAID usage.Furthermore,the impact of NSAIDs on fracture healing is well-documented;however,the precise mechanism by which their use during training contributes to an increased incidence of stress bone injuries remains unclear.This article aims to summarize potential mechanisms through an extensive review of domestic and international literature in order to standardize the utilization and clinical management of NSAIDs,optimize pain management strategies,and prevent stress bone injuries or fractures in specific populations such as soldiers and elite athletes.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective:To compare the degranulation levels of basophils in peripheral blood mononuclear cell (PBMC) and granulocyte populations between healthy subjects and patients with sepsis, and to explore the underlying mechanisms. Additionally, plasma cytokine levels were measured in these volunteers.Methods:Peripheral blood samples were collected from both healthy individuals and sepsis patients. The degranulation level of basophils in sepsis patients and its potential mechanisms were examined. Plasma levels of IL-1β, IL-9, and IL-10 were detected, and Pearson correlation analysis was performed to assess the relationship between degranulated basophils in the granulocyte population and IL-9 levels.Results:Compared with healthy subjects, sepsis patients showed a reduction in basophil percentages within PBMC and granulocyte populations by 94.8% and 37.9%, respectively ( Z = -6.441, P < 0.05; Z = -2.681, P < 0.05). In contrast, both the proportion and number of degranulated basophils in the granulocyte population were increased (both P < 0.05). Plasma levels of IL-1β, IL-9, and IL-10 were significantly elevated in sepsis patients--by 80.6%, 36.7%, and 11.9-fold, respectively ( Z = -4.258, P < 0.05; Z = -3.606, P < 0.05; Z = -4.814, P < 0.05). Moreover, plasma IL-9 levels were highly correlated with both the percentage and count of degranulated basophils in the granulocyte population (both P < 0.05). GO and KEGG enrichment analyses revealed cytological changes and potential mechanisms involving basophils in the PBMC of sepsis patients. Conclusions:The decreased total count of basophils in sepsis patients may elevate the risk of secondary infection. Degranulated basophils in the granulocyte population may contribute to excessive inflammatory responses through IL-9 secretion.

AIM To investigate the effects of Qifu Yixin Granules on cardiac inflammation in a rat model of heart failure.METHODS The rats were induced into chronic heart failure(CHF)models by 6-week intraperitoneal injection of doxorubicin followed by the random assignment of the successful rat models into the model group,the captopril group(22.5 mg/kg),and the low-dose,medium-dose,and high-dose Qifu Yixin Granules groups(2.84,5.67,11.34 g/kg),in contrast to the normal rats of the blank group.The rats had their body weight monitored;their cardiac function assessed by echocardiography;their serum levels of NT-proBNP,TNF-α,IL-6,IL-1 and CRP measured by ELISA;their cardiac morphological alterations observed by HE and Masson staining;their cardiac protein expressions of TLR4,MyD88 and NF-κB detected by immunohistochemistry and Western blot;and their cardiac mRNA expressions of TLR4,MyD88 and NF-κB measured by RT-qPCR.RESULTS Compared to the blank group,the model group exhibited significantly reduced body weight,LVEF and LVFS(P＜0.01),alongside significantly elevated LVEDD,LVESD,and serum concentrations of NT-proBNP,TNF-α,IL-6,IL-1 and CRP(P＜0.01).Additionally,the model group displayed greater myocardial inflammatory cell aggregation,increased collagen deposition(P＜0.01);and upregulated myocardial protein and mRNA expressions of TLR4,MyD88 and NF-κB(P＜0.01).Compared to the model group,the groups intervened with captopril or medium/high dose Qifu Yixin Granules demonstrated significantly increased body weight,LVEF and LVFS(P＜0.05,P＜0.01);significantly reduced LVEDD,LVESD,and serum levels of the aforementioned indicators(P＜0.05,P＜0.01);mitigated inflammation and collagen deposition(P＜0.05,P＜0.01);and downregulated myocardial protein and mRNA expressions of TLR4,MyD88 and NF-κB(P＜0.05,P＜0.01).CONCLUSION Qifu Yixin Granules attenuate cardiac inflammation and improve cardiac function in doxorubicin-induced CHF rats;this therapeutic effect is mediated by inhibiting the activation of the TLR4/MyD88/NF-κB signaling pathway.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

OBJECTIVE: To explore the pain-location interaction between pressure-sensitive points on the body surface and traditional acupoints in patients with chronic non-specific low back pain (CNLBP) under different disease courses, using Euclidean distance and multivariate statistical analysis. METHODS: A pressure-sensitive point detection was performed on 30 CNLBP patients with varying disease courses. A constant pressure was applied using an FDK20 algometer within a designated lumbar area, a total of 50 points were tested, and the tested points were numbered; the visual analogue scale (VAS) pain score was recorded simultaneously. MatlabR2022a9.12. software was used to extract the positions of pressure-sensitive points, and preprocessing and normalization of point location and VAS scores data were conducted. Under constraint conditions (VAS≥8.0 ∩ Euclidean distance to acupoint≤0.5), the proportion of pressure-sensitive points within the Euclidean distance threshold to each acupoint (PVD_acupoint) was calculated, followed by multivariate statistical analysis. RESULTS: ①Constrained analysis of PVD_acupoint showed that PVD_{Qihaishu (BL24)} and PVD_{Dachangshu (BL25)} were positively correlated with disease course (r=0.55, P<0.01). ②Factor analysis and silhouette analysis revealed that PVD_{Shenshu (BL23)} and PVD_{Dachangshu (BL25)} exhibited trends consistent with disease course progression (P>0.05), with different degree (P<0.01). CONCLUSION The PVD_acupoint value based on Euclidean distance can characterize the pressure sensitivity features of traditional acupoints associated with disease. Multivariate statistical analysis of PVD_acupoint confirms that selecting the acupoint combination of Shenshu (BL23) and Dachangshu (BL25) for CNLBP is associated with the distribution of surrounding pressure-sensitive points and the pathological characteristics of the condition.
Humans ; Acupuncture Points ; Low Back Pain/physiopathology* ; Male ; Female ; Middle Aged ; Adult ; Aged ; Acupuncture Therapy ; Young Adult ; Pressure