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.

ObjectiveTo evaluate the levels of physical exercise, mental health and social response in children with autism spectrum disorder (ASD), and explore the mediating effect of social response on physical exercise and mental health. MethodsFrom September, 2019 to April, 2024, 211 children with ASD from three special education schools in Haidian District and Shijingshan District of Beijing were selected. They were assessed with general data questionnaire, Physical Activity Rating Scale (PARS-3), Chinese version of Psycho-Educational Profile (C-PEP) and Social Response Scale-Short Form (SRS-SF). The correlation among physical exercise, mental health and social response was analyzed. The mediating effect of social response on physical exercise and mental health was explored. ResultsThe average physical exercise level was (58.72±3.34), the average mental health level was (14.85±1.67), and the average social response level was (24.98±3.79). Physical exercise was positively correlated with mental health (r = 0.546, P < 0.05) and negatively correlated with social response (r = -0.298, P < 0.05). Mental health was negatively correlated with social response (r = -0.397, P < 0.05). Average monthly family income, parental relationship, repeated transcranial magnetic stimulation therapy, physical exercise, social response were the influencing factors of mental health (P < 0.05). Social response was intermediary between physical exercise and mental health, accounting for 14.56%. ConclusionThe mental health level of children with ASD is poor, and there are many influencing factors. Physical exercise can directly affect the mental health of children with ASD, and can also play an indirect role through social response.

Background/Aims: Reflux symptoms frequently present in patients diagnosed with functional dyspepsia (FD). This investigation sought to elucidate the contribution of gastroesophageal reflux in the overlap relationship. Methods: Consecutive patients presenting with reflux symptoms and/or FD symptoms were prospectively included. Comprehensive assessments, including symptoms evaluation, endoscopy, esophageal functional examinations (high-resolution manometry and reflux monitoring), and proton pump inhibitor (PPI) treatment efficacy evaluation, were conducted in these patients. Results: The study enrolled 315 patients, 43.2% of which had concurrent FD symptoms and overlapping reflux symptoms. Notably, a mere 28.7% of patients in the overlap symptoms group had objective gastroesophageal reflux disease evidences (the grade of esophagitis≥ B or the acid exposure time ≥ 4.2%). Functional heartburn was demonstrated to be the main cause of overlapping reflux symptoms(55.1%). Reflux parameters analysis revealed that the reflux burden in the overlap symptoms group paralleled that of the FD symptoms group, with both registering lower levels than the reflux symptoms group (P < 0.05). Furthermore, PPI response rates were notably diminished in the overlap symptoms group (P < 0.001), even for those with objective gastroesophageal reflux disease evidences. Conclusions The study illuminated that overlapping reflux symptoms in FD was common. Strikingly, these symptoms primarily diverged from reflux etiology and exhibited suboptimal responses to PPI intervention. These findings challenge prevailing paradigms and accentuate the imperative for nuanced therapeutic approaches tailored to the distinctive characteristics of overlapping reflux symptoms in the context of FD.

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*

OBJECTIVE: To reveal the effects and potential mechanisms by which synaptic vesicle glycoprotein 2A (SV2A) influences the distribution of amyloid precursor protein (APP) in the trans-Golgi network (TGN), endolysosomal system, and cell membranes and to reveal the effects of SV2A on APP amyloid degradation. METHODS: Colocalization analysis of APP with specific tagged proteins in the TGN, ensolysosomal system, and cell membrane was performed to explore the effects of SV2A on the intracellular transport of APP. APP, β-site amyloid precursor protein cleaving enzyme 1 (BACE1) expressions, and APP cleavage products levels were investigated to observe the effects of SV2A on APP amyloidogenic processing. RESULTS: APP localization was reduced in the TGN, early endosomes, late endosomes, and lysosomes, whereas it was increased in the recycling endosomes and cell membrane of SV2A-overexpressed neurons. Moreover, Arl5b (ADP-ribosylation factor 5b), a protein responsible for transporting APP from the TGN to early endosomes, was upregulated by SV2A. SV2A overexpression also decreased APP transport from the cell membrane to early endosomes by downregulating APP endocytosis. In addition, products of APP amyloid degradation, including sAPPβ, Aβ _1-42, and Aβ _1-40, were decreased in SV2A-overexpressed cells. CONCLUSION These results demonstrated that SV2A promotes APP transport from the TGN to early endosomes by upregulating Arl5b and promoting APP transport from early endosomes to recycling endosomes-cell membrane pathway, which slows APP amyloid degradation.
Amyloid beta-Protein Precursor/genetics* ; Membrane Glycoproteins/genetics* ; Animals ; Protein Transport ; Nerve Tissue Proteins/genetics* ; Humans ; Mice ; Endosomes/metabolism* ; trans-Golgi Network/metabolism*