OBJECTIVE To preliminarily investigate the antiasthmatic effect and mechanism of Ephedrae Herba-Armeniacae Semen Amarum herb pair on the respiratory center. METHODS Male SD rats were randomly divided into blank group， model group， dexamethasone group （positive control）， and Ephedrae Herba-Armeniacae Semen Amarum 2∶1， 1∶1 and 1∶2 groups. Rats in each group were administered different ratios of the herb pair decoction [all at 18 g （crude drug）/kg]， dexamethasone suspension （0.5 mg/kg）， or normal saline intragastrically twice daily for seven consecutive days. Forty minutes after the last administration， medicated cerebrospinal fluid was collected to determine the content of effective components entering the brain. One and a half hours after the last administration， the nucleus tractus solitarius （NTS） was located using a stereotaxic apparatus. Histamine phosphate （1 μL） was injected into the NTS region at a constant rate of 1 μL/min using a 10 μL microsyringe to induce excitation of the respiratory center in rats； the blank group was injected with normal saline. The contents of neurotransmitters [nerve growth factor （NGF）， substance P （SP）， norepinephrine （NA）， 5-hydroxytryptamine （5-HT） and acetylcholine （Ach）] in the medulla oblongata brain tissue were detected. The mRNA expressions of neurokinin-1 receptor （NK-1R）， p38 mitogen-activated protein kinase （MAPK）， and c-fos in the medulla oblongata， as well as the protein expressions of NK-1R， p38 MAPK， and c-fos in the NTS region， were determined. RESULTS The main active components of Ephedrae Herba-Armeniacae Semen Amarum herb pair entering the brain were ephedrine， pseudoephedrine， and methylephedrine. Compared with blank group， the contents of NGF， SP， NA， 5-HT and Ach， and the relative expression levels of NK-1R， p38 MAPK， and c-fos mRNA and protein were significantly increased in the model group （ P ＜0.01）. Compared with model group， Ephedrae Herba-Armeniacae Semen Amarum herb pair groups with different ratios significantly reduced the neurotransmitter contents and the relative expression levels of NK-1R， p38 MAPK， and c-fos mRNA and protein （ P ＜0.01）， with the 2∶1 Ephedrae Herba-Armeniacae Semen Amarum herb pair and 1∶1 mass ratios showing relatively better effects. CONCLUSIONS Ephedrae Herba alkaloids are the main active components in affecting the function of the respiratory center. The herb pair groups with a larger proportion of Ephedrae Herba exhibit stronger effects. Ephedrae Herba-Armeniacae Semen Amarum herb pair can reduce the excitability of the respiratory center by down-regulating the expression of the NK-1R/MAPK/c-fos pathway in the NTS and decreasing the abnormal release of neurotransmitters such as NGF and SP.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Based on the pharmacokinetics theory, this study investigated the pharmacokinetic characteristics of albiflorin, paeoniflorin, wogonoside, and wogonin in normal and febrile rats and summarized absorption and elimination rules of Dayuanyin in them to provide reference for further development and clinical application of Dayuanyin. Blood samples were taken from the fundus venous plexus of normal and model rats after intragastric administration of Dayuanyin at different time points. The concentration of each substance in blood was determined by ultra performance liquid chromatography-triple quadrupole mass spectrometry(UPLC-MS/MS) technique at different time points. DAS 2.0, a piece of pharmacokinetics software, was used to calculate the pharmacokinetic parameters of each component. The results show that the 4 components had good linear relationship in their respective ranges, and the results of methodological investigation met the requirements. The pharmacokinetic parameters of C_(max), T_(max), t_(1/2), AUC_(0-t), AUC_(0-∞), and MRT_(0-t) were calculated by the DAS 2.0 non-compartmental model. Compared with those in the normal group, C_(max) and AUC_(0-t) of the 4 components in the model group were significantly increased. There were significant differences in the pharmacokinetic characteristics between the normal and model groups, suggesting that the absorption and elimination of Dayuanyin may be affected by the changes of internal environment of the body in different physiological states.
Animals ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Fever/metabolism* ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid ; Glucosides/pharmacokinetics* ; Monoterpenes

Objective To analyze the predictive value of patient-reported outcomes(PRO)com-bined with hematological indicators for moderate-to-severe endoscopic activity in patients with ulcera-tive colitis(UC).Methods Clinical data of UC patients were retrospectively collected,including PRO,hematological parameters and endoscopic findings.Based on the Mayo endoscopic score,pa-tients were divided into remission and mild activity group(＜2 points)and moderate-to-severe activity group(≥ 2 points).Independent influencing factors for moderate-to-severe endoscopic activity were screened through multivariate Logistic regression analysis,and a binary Logistic regression model was constructed.The receiver operating characteristic(ROC)curve was used to evaluate the discriminato-ry ability of the predictive model.The calibration of the model was assessed using calibration curves and the Spiegelhalter Z-test,and the model's performance was further validated in an external valida-tion cohort.Results Rectal bleeding(RB),C-reactive protein/albumin(CAR)and erythrocyte sed-imentation rate(ESR)were independent influencing factors for moderate-to-severe endoscopic activity(P＜0.05).The area under the curve(AUC)of the nomogram predictive model constructed based on the logistic regression analysis results was 0.848(95％CI,0.798 to 0.900),with sensitivity of 76.6％and specificity of 79.6％.In the external validation cohort,the model's AUC was 0.778(95％CI,0.699 to 0.857),and there was no statistically significant difference compared with the AUC of the training cohort model(P＞0.05).In both the training and validation cohorts,the Spiegelhalter Z-test results indicated that the model had good goodness-of-fit(P＞0.05).Conclu-sion RB,CAR and ESR are independent influencing factors for moderate-to-severe endoscopic ac-tivity in UC patients.The combination of RB,CAR and ESR has high predictive value for moderate-to-severe endoscopic activity in UC patients,with good discriminatory and calibration abilities.

ObjectiveThe study explores the influence of voluntary wheel running on the behavioral abnormalities and the activation state of the hypothalamic-pituitary-adrenal (HPA) axis in autism spectrum disorder (ASD) rats through gut microbiota. MethodsSD female rats were selected and administered either400 mg/kg of valproic acid (VPA) solution or an equivalent volume of saline via intraperitoneal injection on day 12.5 of pregnancy. The resulting offspring were divided into 2 groups: the ASD model group (PASD, n=35) and the normal control group (PCON, n=16). Behavioral assessments, including the three-chamber social test, open field test, and Morris water maze, were conducted on postnatal day 23. After behavioral testing, 8 rats from each group (PCON, PASD) were randomly selected for serum analysis using enzyme-linked immunosorbent assay (ELISA) to measure corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) concentration, to evaluate the functional state of the HPA axis in rats. On postnatal day 28, the remaining 8 rats in the PCON group were designated as the control group (CON, n=8), and the remaining 27 rats in the PASD group were randomly divided into 4 groups: ASD non-intervention group (ASD, n=6), ASD exercise group (ASDE, n=8), ASD fecal microbiota transplantation group (FMT, n=8), and ASD sham fecal microbiota transplantation group (sFMT, n=5). The rats in the ASD group and the CON group were kept under standard conditions, while the rats in the ASDE group performed 6 weeks of voluntary wheel running intervention starting on postnatal day 28. The rats in the FMT group were gavaged daily from postnatal day 42 with 1 ml/100 g fresh fecal suspension from ASDE rats which had undergone exercise for 2 weeks, 5 d per week, continuing for 4 weeks. The sFMT group received an equivalent volume of saline. After the interventions were completed, behavioral assessments and HPA axis markers were measured for all groups. ResultsBefore the intervention, the ASD model group exhibited significantly reduced social ability, social novelty preference, spontaneous activity, and exploratory interest, as well as impaired spatial learning, memory, and navigation abilities compared to the normal control group (P<0.05). Serum concentration of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) in the PASD group were significantly higher than those in the PCON group (P<0.05). Following 6 weeks of voluntary wheel running, the ASDE group showed significant improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, spatial learning, memory, and navigation skills compared to the ASD group (P<0.05), with a significant decrease in serum CORT concentration (P<0.05), and a downward trend in CRH and ACTH concentration. After 4 weeks of fecal microbiota transplantation in the exercise group, the FMT group showed marked improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, as well as spatial learning, memory, and navigation abilities compared to both the ASD and sFMT groups (P<0.05). In addition, serum ACTH and CORT concentration were significantly reduced (P<0.05), and CRH concentration also showed a decreasing trend. ConclusionExercise may improve ASD-related behaviors by suppressing the activation of the HPA axis, with the gut microbiota likely playing a crucial role in this process.

Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*