This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

Objective:To explore the clinical characteristics, diagnosis and treatment strategies, and prognosis of pulmonary embolism (PE) complicating childhood rheumatic diseases.Methods:A retrospective case series study was performed on the demographic data, laboratory indicators, imaging features, treatment regimens, and follow-up data of 8 children with rheumatic diseases complicated by PE who were admitted to the Department of Rheumatology and Immunology, Capital Center for Children′s Health, Capital Medical University from January 2014 to October 2023.Results:Among the 8 children, there were 4 boys and 4 girls, with an age of 12.0 (7.5, 13.0) years. Among the primary diseases, there were 3 cases of systemic lupus erythematosus, 2 cases of Beh?et′s disease, 2 cases of Takayasu arteritis, and 1 case of antiphospholipid syndrome. All children developed PE during the active phase of the primary disease. PE was detected at the onset of the primary disease in 3 cases, and the median time from the diagnosis of the primary disease to the development of PE was 10.0 (6.0, 25.0) months in the remaining 5 cases. Fever was present in all 8 children, 4 cases were accompanied by chest tightness, dyspnea, etc., and 2 cases only presented with fever. Laboratory examinations revealed the following results: erythrocyte sedimentation rate was 42.0 (17.0, 78.0) mm/1 h, high-sensitivity C-reactive protein was 12.7 (2.6, 78.7) mg/L, white blood cell count was 9.6 (7.2, 18.7)×10 9/L; D-dimer was 2.3 (0.9, 6.2) mg/L; and hemoglobin was (109±16) g/L.Imaging examinations revealed that 5 cases had involvement of the bilateral lower pulmonary arteries, 5 cases had peripheral embolism, and 3 cases had central PE. Complications included 3 cases of deep vein thrombosis, 2 cases of intracranial venous sinus thrombosis, and 1 case of mild pulmonary hypertension.In terms of treatment, 7 cases received anticoagulation with heparin followed by warfarin. Immunomodulation was mainly based on glucocorticoids combined with immunosuppressants, and 4 cases were combined with biological agents. The follow-up time of 4.17 (1.75, 7.17) years, the time for complete absorption of PE was 10.5 (6.0, 18.0) months; all 8 children had no target events, with no recurrence or chronic thromboembolic pulmonary hypertension, and the pulmonary artery remodeling was good. Conclusions:PE complicating childhood rheumatic diseases is closely related to the activity of the primary disease. The clinical manifestations are insidious, with fever as the main symptom. Imaging examination is the key to diagnosis.Early adoption of heparin followed by warfarin anticoagulation and glucocorticoids combined with immunosuppressants and (or) biological agents to control the primary disease can achieve a favorable prognosis.

Objective:To explore the clinical characteristics, diagnosis and treatment strategies, and prognosis of pulmonary embolism (PE) complicating childhood rheumatic diseases.Methods:A retrospective case series study was performed on the demographic data, laboratory indicators, imaging features, treatment regimens, and follow-up data of 8 children with rheumatic diseases complicated by PE who were admitted to the Department of Rheumatology and Immunology, Capital Center for Children′s Health, Capital Medical University from January 2014 to October 2023.Results:Among the 8 children, there were 4 boys and 4 girls, with an age of 12.0 (7.5, 13.0) years. Among the primary diseases, there were 3 cases of systemic lupus erythematosus, 2 cases of Beh?et′s disease, 2 cases of Takayasu arteritis, and 1 case of antiphospholipid syndrome. All children developed PE during the active phase of the primary disease. PE was detected at the onset of the primary disease in 3 cases, and the median time from the diagnosis of the primary disease to the development of PE was 10.0 (6.0, 25.0) months in the remaining 5 cases. Fever was present in all 8 children, 4 cases were accompanied by chest tightness, dyspnea, etc., and 2 cases only presented with fever. Laboratory examinations revealed the following results: erythrocyte sedimentation rate was 42.0 (17.0, 78.0) mm/1 h, high-sensitivity C-reactive protein was 12.7 (2.6, 78.7) mg/L, white blood cell count was 9.6 (7.2, 18.7)×10 9/L; D-dimer was 2.3 (0.9, 6.2) mg/L; and hemoglobin was (109±16) g/L.Imaging examinations revealed that 5 cases had involvement of the bilateral lower pulmonary arteries, 5 cases had peripheral embolism, and 3 cases had central PE. Complications included 3 cases of deep vein thrombosis, 2 cases of intracranial venous sinus thrombosis, and 1 case of mild pulmonary hypertension.In terms of treatment, 7 cases received anticoagulation with heparin followed by warfarin. Immunomodulation was mainly based on glucocorticoids combined with immunosuppressants, and 4 cases were combined with biological agents. The follow-up time of 4.17 (1.75, 7.17) years, the time for complete absorption of PE was 10.5 (6.0, 18.0) months; all 8 children had no target events, with no recurrence or chronic thromboembolic pulmonary hypertension, and the pulmonary artery remodeling was good. Conclusions:PE complicating childhood rheumatic diseases is closely related to the activity of the primary disease. The clinical manifestations are insidious, with fever as the main symptom. Imaging examination is the key to diagnosis.Early adoption of heparin followed by warfarin anticoagulation and glucocorticoids combined with immunosuppressants and (or) biological agents to control the primary disease can achieve a favorable prognosis.

This article aims to study the processing methods by exploring the main chemical constituents of Aconiti Lateralis Radix Praeparata and the toxicity-attenuating mechanisms. The relevant articles were retrieved from multiple databases with the time interval of 1960-2024, and the chemical constituents of Aconiti Lateralis Radix Praeparata and the toxicity-attenuating mechanisms of its processing methods were summarized. The review revealed that the chemical constituents of Aconiti Lateralis Radix Praeparata included 32 diester-type alkaloids, 36 monoester-type alkaloids, 43 alkanolamine-type alkaloids, and 8 lipid-type alkaloids. At the same time, other chemical constituents such as water-soluble alkaloids were also studied, and their pharmacological activities were summarized. The toxicity-attenuating mechanisms of the processing methods included constituent loss, hydrolysis, ester exchange, and ion-pair action. The processing methods of Aconiti Lateralis Radix Praeparata have developed from being traditional to modern, with simplified operation and increased retention amounts of active constituents, which have improved the efficacy of processed Aconiti Lateralis Radix Praeparata products and have facilitated the industrial production. However, the existing processing methods of Aconiti Lateralis Radix Praeparata cannot completely solve the problem of possible reduction in efficacy during toxicity attenuation. More toxicity-attenuating mechanisms and lipid-type alkaloids of Aconiti Lateralis Radix Praeparata should be explored, which is expected to reduce its toxicity while retaining its efficacy.
Aconitum/toxicity* ; Drugs, Chinese Herbal/isolation & purification* ; Alkaloids/chemistry* ; Animals ; Humans

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

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer