Chronic heart failure （CHF） is a major global public health problem， and myocardial infarction is one of its main causes. The mouse model of heart failure induced by coronary artery ligation is widely used in the study of CHF， while the TCM syndrome attributes of this model have not yet been clarified. According to the theory of correspondence between prescriptions and syndromes， the method of syndrome identification by prescription efficacy is an important means of current syndrome research of animal models. This method deduces the syndrome characteristics of animal models through prescription efficacy. Taking the four basic syndrome elements of Qi， blood， Yin and Yang as the classification reference， this study used coronary artery ligation to construct a mouse model of CHF and treated the model with four representative TCM injections with the effects of replenishing Qi， warming Yang， nourishing Yin， and activating blood and enalapril. Echocardiography， tongue color parameters， histopathology， serum N-terminal pro-brain natriuretic peptide （NT-proBNP） and cardiac troponin Ⅰ （cTnⅠ） levels， and systematically explored the TCM syndrome attributes of this model. The results showed that the coronary ligation model presented an obvious cardiac function decline， myocardial fibrosis， infarct size expansion， and purple dark tongue， which were consistent with the basic syndrome characteristics of blood stasis in CHF. Danhong injection had significant effects of improving the cardiac function， alleviating myocardial fibrosis， and reducing serum NT-proBNP and cTnⅠ levels. Huangqi Injection and Shenfu injection can improve the cardiac function and tongue color parameters， with limited effects. The effect of Shenmai injection group was not obvious. This study verifies that the established model conforms to blood stasis syndrome through the method of syndrome identification by prescription efficacy， which provides an experimental basis for the study of TCM syndrome mechanism of CHF.

ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

OBJECTIVE To analyze the specific risks and long-term toxicities of four BCR-ABL1 tyrosine kinase inhibitors （TKIs）（imatinib， dasatinib， nilotinib， and bosutinib） in pediatric patients with hematological malignancies. METHODS Adverse drug event （ADE） reports submitted to the the United States FDA Adverse Event Reporting System （FAERS） from January 2012 to December 2024， with imatinib， dasatinib， nilotinib， and bosutinib as the primary suspect drugs， were collected. Data mining was performed using the reporting odds ratio method and proportional reporting ratio method. ADE terms were classified and summarized by system organ class （SOC） and preferred term （PT） according to the Medical Dictionary for Drug Regulatory Activities （MedDRA， version 26.0）. Meanwhile， the ADE reports were divided by age into the adult group （≥18 years） and the pediatric group （＜18 years） to compare the differences in ADE between the two groups. RESULTS A total of 1 512 pediatric ADE reports were included： 993 for imatinib， 391 for dasatinib， 112 for nilotinib， and 16 for bosutinib. Among the reported ADEs， the patients were mainly aged 12-＜18 years； the reports mainly originated from the United States， France， and Japan； and the primary indications were chronic myeloid leukemia and acute lymphoblastic leukemia. A total of 5 256 ADE signals were mined， among which 235 were positive signals， involving 1 103 PT across 27 SOC. The top five PT ranked by the number of positive signals were nausea， febrile neutropenia， abdominal pain， neutropenia， and anemia. The top two SOC were general disorders and administration site conditions， and gastrointestinal disorders. Compared with the adult group， the pediatric group had relatively higher proportions of events related to infections and infestations as well as blood and lymphatic system disorders. Pediatric long-term toxicity signals primarily included growth retardation， accompanied by signals related to endocrine system abnormalities and bone metabolism abnormalities. Specific signals included imatinib-associated septic shock， dasatinib-associated chylothorax， and nilotinib-associated electrocardiographic QT interval prolongation. CONCLUSIONS When pediatric patients use BCR-ABL1 TKIs， priority monitoring of infection risk and hematologic parameters is required， along with long-term follow-up of height， endocrine， and bone metabolism parameters. Targeted screening and management of drug-specific signals should be performed to ensure the long-term safety of pediatric medication.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Coptis chinensis Franch. and Panax ginseng C. A. Mey. are traditional herbal medicines with millennia of documented use and broad therapeutic applications, including anti-diabetic properties. However, the synergistic effect of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng on type 2 diabetes mellitus (T2DM) and its underlying mechanism remain unclear. The research demonstrated that the optimal ratio of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng was 4∶1, exhibiting maximal efficacy in improving insulin resistance and gluconeogenesis in primary mouse hepatocytes. This combination demonstrated significant synergistic effects in improving glucose tolerance, reducing fasting blood glucose (FBG), the weight ratio of epididymal white adipose tissue (eWAT), and the homeostasis model assessment of insulin resistance (HOMA-IR) in leptin receptor-deficient (db/db) mice. Subsequently, a T2DM liver-specific network was constructed based on RNA sequencing (RNA-seq) experiments and public databases by integrating transcriptional properties of disease-associated proteins and protein-protein interactions (PPIs). The network recovery index (NRI) score of the combined treatment group with a 4∶1 ratio exceeded that of groups treated with individual components. The research identified that activated adenosine 5'-monophosphate-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling in the liver played a crucial role in the synergistic treatment of T2DM, as verified by western blot experiment in db/db mice. These findings demonstrate that the 4∶1 combination of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng significantly improves insulin resistance and glucose and lipid metabolism disorders in db/db mice, surpassing the efficacy of individual treatments. The synergistic mechanism correlates with enhanced AMPK/ACC signaling pathway activity.
Animals ; Panax/chemistry* ; Ginsenosides/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Mice ; Male ; Alkaloids/pharmacology* ; Coptis/chemistry* ; Drug Synergism ; Insulin Resistance ; Mice, Inbred C57BL ; Humans ; Transcriptome/drug effects* ; Blood Glucose/metabolism* ; Hypoglycemic Agents/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Hepatocytes/metabolism*

Acute lung injury (ALI) is a severe disease caused by viral infection that triggers an uncontrolled inflammatory response. This study investigated the capacity of jasurolignoside (JO), a natural compound, to bind to Toll-like receptor 4 (TLR4) and treat ALI. The anti-inflammatory properties of JO were evaluated in vitro through Western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence staining, and co-immunoprecipitation. The investigation utilized a lipopolysaccharide (LPS)-induced ALI animal model to examine the therapeutic efficacy and mechanism of JO in vivo. JO attenuated inflammatory symptoms in infected cells and tissues by modulating the NOD-like receptor family pyrin domain containing protein 3 (NLRP3) inflammasome and the nuclear factor κB (NF-κB)/mitogen-activated protein kinase (MAPK) pathway. Molecular docking simulations revealed JO binding to TLR4 active sites, confirmed by cellular thermal shift assay. Surface plasmon resonance (SPR) demonstrated direct interaction between JO and TLR4 with a Kd value of 35.1 μmol·L^-1. Moreover, JO inhibited tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and IL-6 secretion and reduced leukocyte, neutrophil, lymphocyte, and macrophage infiltration in ALI-affected mice. JO also enhanced lung function and reduced ALI-related mortality. Immunohistochemical staining demonstrated JO's ability to suppress TLR4 expression in ALI-affected mouse lung tissue. This study establishes that JO can bind to TLR4 and effectively treat ALI, indicating its potential as a therapeutic agent for clinical applications.
Toll-Like Receptor 4/chemistry* ; Animals ; Acute Lung Injury/chemically induced* ; Mice ; Humans ; Ilex/chemistry* ; Molecular Docking Simulation ; Male ; NF-kappa B/immunology* ; Mice, Inbred C57BL ; NLR Family, Pyrin Domain-Containing 3 Protein/immunology* ; Tumor Necrosis Factor-alpha/genetics* ; Interleukin-1beta/genetics* ; RAW 264.7 Cells ; Disease Models, Animal

The accurate prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties represents a crucial step in early drug development for reducing failure risk. Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks. This research proposes molecular properties prediction with parallel-view and collaborative learning (MolP-PC), a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints (MFs), 2D molecular graphs, and 3D geometric representations, incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions. Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks, with its multi-task learning (MTL) mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks. Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization. A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC's effective generalization in predicting key pharmacokinetic parameters such as half-life (T0.5) and clearance (CL), indicating its practical utility in drug modeling. However, the model exhibits a tendency to underestimate volume of distribution (VD), indicating potential for improvement in analyzing compounds with high tissue distribution. This study presents an efficient and interpretable approach for ADMET property prediction, establishing a novel framework for molecular optimization and risk assessment in drug development.
Deep Learning