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 identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

The seed kernel of Momordica cochinchinensis, i.e., Momordicae Semen, is used for medicinal purposes, but to date, no research has been reported on its chemical constituents. In this study, the chemical constituents of Momordicae Semen were investigated for the first time using silica gel column chromatography, semi-preparative HPLC, HR-MS, and NMR. As a result, eight compounds were isolated and identified as: p-hydroxybenzoic acid-7-O-trehaloside(mubeside A, 1), 2,6-dimethoxyphenol-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside B, 2), 1-O-p-methoxybenzoyl-1,4-benzenediol-4-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside C, 3), 1-O-p-hydroxybenzoyl-1,4-benzenediol-4-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside D, 4), gypsogenin-3-O-β-D-galactosyl-(1→2)-β-D-glucuronoside(5), quillaic acid-3-O-β-D-galactosyl-(1→2)-β-D-glucuronoside(6), violanthin(7), and kaempferitrin(8). Compounds 1-4 are new compounds, while compounds 5-8 were isolated from Momordicae Semen for the first time.
Glycosides/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Molecular Structure ; Magnetic Resonance Spectroscopy ; Chromatography, High Pressure Liquid

Bawei Chenxiang Powder(BCP), first documented in the Tibetan medical work Four Medical Classics, has been widely applied in clinical practices in Tibetan and Mongolian medicines since its development. It has the effect of clearing the heart heat, calming the mind, and inducing resuscitation. On the basis of BCP, multiple types of formulae have been developed, such as Bawei Yiheyi Chenxiang Powder, Bawei Rang Chenxiang Powder, and Bawei Pingchuan Chenxiang Powder, which are widely used for treating cardiovascular and respiratory diseases. Current pharmacological research has revealed the pharmacological effects of BCP and its related formulae against myocardial ischemia, cerebral ischemia, renal ischemia, and anti-hypoxia. BCP and its related formulae introduced more treatment options for related clinical diseases and provided insights for fully comprehending the essence and pharmacological components of the formulae. This paper systematically reviewed the clinical and pharmacological research on BCP and its related formulae, analyzing the formulation principles and potential key flavors and active ingredients. This lays a fundamental scientific basis for the clinical use, quality evaluation, and subsequent development and application of BCP and its related formulae, providing references for studying traditional Chinese medicine formulae in a thorough and systematic manner.
Drugs, Chinese Herbal/chemistry* ; Humans ; Powders/chemistry* ; Animals ; Medicine, Chinese Traditional

The medicinal materials of Bawei Chenxiang Pills(BCPs) were extracted via three methods: reflux extraction by water, reflux extraction by 70% ethanol, and extraction by pure water following reflux extraction by 70% ethanol, yielding three extracts of ST, CT, and CST. The efficacy of ST(760 mg·kg~(-1)), CT(620 mg·kg~(-1)), and CST(1 040 mg·kg~(-1)) were evaluated by acute myocardial ischemia(AMI) and p-chlorophenylalanine(PCPA)-induced insomnia in mice, respectively. Western blot was further utilized to investigate their hypnosis mechanisms. The main chemical components of different extracts were identified by the UPLC-Q-Exactive-MS technique. The results showed that CT and CST significantly increased the ejection fraction(EF) and fractional shortening(FS) of myocardial infarction mice, reduced left ventricular internal dimension at end-diastole(LVIDd) and left ventricular internal dimension at end-systole(LVIDs). In contrast, ST did not exhibit significant effects on these parameters. In the insomnia model, CT significantly reduced sleep latency and prolonged sleep duration, whereas ST only prolonged sleep duration without shortening sleep latency. CST showed no significant effects on either sleep latency or sleep duration. Additionally, both CT and ST upregulated glutamic acid decarboxylase 67(GAD67) protein expression in brain tissue. A total of 15 main chemical components were identified from CT, including 2-(2-phenylethyl) chromone and 6-methoxy-2-(2-phenylethyl) chromone. Six chemical components including chebulidic acid were identified from ST. The results suggested that chromones and terpenes were potential anti-myocardial ischemia drugs of BCPs, and tannin and phenolic acids were potential hypnosis drugs. This study enriches the pharmacological and chemical research of BCPs, providing a basis and reference for their secondary development, quality standard improvement, and clinical application.
Animals ; Drugs, Chinese Herbal/isolation & purification* ; Mice ; Male ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Humans ; Myocardial Infarction/drug therapy* ; Myocardial Ischemia/drug therapy*

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: Baicalein has been reported to have wide therapeutic effects that act through its anti-inflammatory activity. This study examines the effect and mechanism of baicalein on sepsis-induced cardiomyopathy (SIC). METHODS: A thorough screening of a small library of natural products, comprising 100 diverse compounds, was conducted to identify the most effective drug against lipopolysaccharide (LPS)-treated H9C2 cardiomyocytes. The core target proteins and their associated signaling pathways involved in baicalein's efficacy against LPS-induced myocardial injury were predicted by network pharmacology. RESULTS: Baicalein was identified as the most potent protective agent in LPS-exposed H9C2 cardiomyocytes. It exhibited a dose-dependent inhibitory effect on cell injury and inflammation. In the LPS-induced septic mouse model, baicalein demonstrated a significant capacity to mitigate LPS-triggered myocardial deficits, inflammatory responses, and ferroptosis. Network pharmacological analysis and experimental confirmation suggested that hypoxia-inducible factor 1 subunit α (HIF1-α) is likely to be the crucial factor in mediating the impact of baicalein against LPS-induced myocardial ferroptosis and injury. By combining microRNA (miRNA) screening in LPS-treated myocardium with miRNA prediction targeting HIF1-α, we found that miR-299b-5p may serve as a regulator of HIF1-α. The reduction in miR-299b-5p levels in LPS-treated myocardium, compared to the control group, was reversed by baicalein treatment. The reverse transcription quantitative polymerase chain reaction, Western blotting, and dual-luciferase reporter gene analyses together identified HIF1-α as the target of miR-299b-5p in cardiomyocytes. CONCLUSION Baicalein mitigates SIC at the miRNA level, suggesting the therapeutic potential of it in treating SIC through the regulation of miR-299b-5p/HIF1-α/ferroptosis pathway. Please cite this article as: Zhou WY, Du JK, Liu HH, Deng L, Ma K, Xiao J, Zhang S, Wang CN. Baicalein attenuates lipopolysaccharide-induced myocardial injury by inhibiting ferroptosis via miR-299b-5p/HIF1-α pathway. J Integr Med. 2025; 23(5):560-575.
Flavanones/pharmacology* ; Animals ; MicroRNAs/genetics* ; Lipopolysaccharides ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Ferroptosis/drug effects* ; Mice ; Myocytes, Cardiac/metabolism* ; Signal Transduction/drug effects* ; Rats ; Male ; Mice, Inbred C57BL ; Cardiomyopathies/etiology* ; Cell Line ; Sepsis/complications*