OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

OBJECTIVES: A ventricular assist device (VAD) is an electromechanical device used to assist cardiac blood circulation, which can be employed for the treatment of end-stage heart failure and is most commonly placed in the left ventricle. Despite enhancing perfusion performance, the implantation of left ventricular assist device (LVAD) transforms the local intraventricular flow and thus may increase the risk of thrombogenesis. This study aims to investigate fluid-particle interactions and thromboembolic risk under different LVAD configurations using three-dimensional (3D) reconstruction models, focusing on the effects of outflow tract orientation and blood flow rates. METHODS: A patient-specific end-diastolic 3D reconstruction model was initially constructed in stereo lithography (STL) format using Mimics software based on CT images. Transient numerical simulations were performed to analyze fluid-particle interactions and thromboembolic risks for LVAD with varying outflow tract orientations under 2 flow rates (4 L/min and 5 L/min), using particles of uniform size (2 mm), and a blood flow rate optimization protocol was implemented for this patient. RESULTS: When the LVAD flow rate was 5 L/min, helicity and flow stagnation of the blood flow increased the particle residence time (RT) and the risk of thrombogenesis of the aortic root. The percentage of particles traveling toward the brachiocephalic trunk was up to 20.33%. When the LVAD flow rate was 4 L/min, blood turbulence in the aorta was reduced, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 10.54%. When the LVAD blood flow rate was 5 L/min and the direction of the outflow pipe was optimal, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 11.22%. A 18-month follow-up observation of the patient revealed that the LVAD was in good working order and the patient had no complications related to the implantation of LVAD. CONCLUSIONS Implantation of LVAD results in a higher risk of cerebral infarction; When implanting LVAD with the same outflow tract direction, optimizing flow velocity and outflow tract can reduce the risk of cerebral infarction occurrence.
Heart-Assist Devices/adverse effects* ; Humans ; Heart Failure/physiopathology* ; Blood Flow Velocity ; Thromboembolism/prevention & control* ; Models, Cardiovascular ; Heart Ventricles/physiopathology* ; Imaging, Three-Dimensional

OBJECTIVES: Multiple myeloma (MM) is a hematologically malignant clonal plasma cell disease. This study aims to explore the association between immunophenotypes and prognosis in patients with MM, to determine whether the expression of CD45 and CD200 is related to the prognosis of newly diagnosed MM (NDMM) patients, and to evaluate the significance of the combined expression of CD45 and CD200 in NDMM. METHODS: A total of 123 NDMM patients admitted to Shengjing Hospital of China Medical University from July 2015 to August 2019 were enrolled. Five key immunophenotypic markers (including CD38, CD138, CD45, CD56, and CD200) were screened through flow cytometry and identified using random forest analysis and univariate Cox regression analysis. Patients were divided into 3 groups: Group A, CD45 and CD200 double-positive; Group B, CD45 or CD200 single-positive; Group C, CD45 and CD200 double-negative. Kaplan-Meier curves were used to analyze overall survival (OS) and progression-free survival (PFS) across groups. Multivariate Cox regression was performed to evaluate prognostic factors, and a nomogram was constructed based on these results. RESULTS: The OS and PFS of single-positive groups for CD38, CD138, CD45, CD56, and CD200 were all shorter than those of their respective single-negative groups (all P<0.05). Significant differences were observed in OS (P<0.001) and PFS (P=0.001) among Groups A, B, and C. Group A had shorter OS and PFS (all P=0.001) compared to the Group B+C (cases from Group B and Group C were combined). CD45 and CD200 double-positive was an independent prognostic factor for NDMM [hazard ratio (HR)=2.178, 95% confidence interval (CI) 1.048 to 4.529; P=0.037]. The nomogram and calibration curves constructed from multivariate Cox regression analysis demonstrated good concordance (concordance index=0.706; 95% CI 0.661 to 0.751). CONCLUSIONS NDMM patients with double-positive expression of CD45 and CD200 have significantly shorter OS and PFS. Compared with the use of either marker alone, the combined assessment of CD45 and CD200 may provide better prognostic stratification for MM patients.
Humans ; Multiple Myeloma/metabolism* ; Male ; Female ; Middle Aged ; Antigens, CD/metabolism* ; Prognosis ; Leukocyte Common Antigens/metabolism* ; Aged ; Adult ; Immunophenotyping ; Nomograms ; Biomarkers, Tumor ; Clinical Relevance

Cardiac arrest (CA) is a critical condition in the field of cardiovascular medicine. Despite successful resuscitation, patients continue to have a high mortality rate, largely due to post CA syndrome (PCAS). However, the injury and pathophysiological mechanisms underlying PCAS remain unclear. Experimental animal models are valuable tools for exploring the etiology, pathogenesis, and potential interventions for CA and PCAS. Current CA animal models include electrical induction of ventricular fibrillation (VF), myocardial infarction, high potassium, asphyxia, and hemorrhagic shock. Although these models do not fully replicate the complexity of clinical CA, the mechanistic insights they provide remain highly relevant, including post-CA brain injury (PCABI), post-CA myocardial dysfunction (PAMD), systemic ischaemia/reperfusion injury (IRI), and the persistent precipitating pathology. Summarizing the methods of establishing CA models, the challenges encountered in the modeling process, and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.
Animals ; Disease Models, Animal ; Post-Cardiac Arrest Syndrome/physiopathology* ; Heart Arrest/physiopathology* ; Humans ; Ventricular Fibrillation/complications*

OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units

OBJECTIVES: The purinergic receptor P2X2 (P2RX2) encodes an ATP-gated ion channel permeable to Na⁺, K⁺, and especially Ca²⁺. Loss-of-function mutations in P2RX2 are known to cause autosomal dominant nonsyndromic deafness 41 (DFNA41), which manifests as high-frequency hearing loss, accelerated presbycusis, and increased susceptibility to noise-induced damage. Zebrafish, owing to their small size, rapid development, high fecundity, transparent embryos, and high gene conservation with humans, provide an ideal model for studying human diseases and developmental mechanisms. This study aims to generate a p2rx2 knockout zebrafish model using CRISPR/Cas9 gene editing system to investigate the effect of p2rx2 deficiency on the auditory system, providing a basis for understanding P2RX2-related hearing loss and developing gene therapy strategies. METHODS: Two CRISPR targets (sgRNA1 and sgRNA2) spaced 47 bp apart were designed within the zebrafish p2rx2 gene. Synthesized sgRNAs and Cas9 protein were microinjected into single-cell stage Tübingen (TU)-strain zebrafish embryos. PCR and gel electrophoresis verified editing efficiency at 36 hours post-fertilization (hpf). Surviving embryos were raised to adulthood (F0), tail-clipped, genotyped, and screened for positive mosaics. F1 heterozygotes were generated by outcrossing, and F2 homozygous mutants were obtained by intercrossing. Polymerase chain reaction (PCR) combined with sequencing verified mutation type and heritability. At 5 days post-fertilization (dpf), YO-PRO-1 staining was used to examine hair cell morphology and count in lateral line neuromasts and the otolith region. Auditory evoked potential (AEP) thresholds at 600, 800, 1 000, and 2 000 Hz were measured in nine 4-month-old wild type and mutant zebrafish per group. RESULTS: A stable p2rx2 knockout zebrafish line was successfully established. Sequencing revealed a 66 bp insertion at the first target site introducing a premature stop codon (TAA), leading to early termination of protein translation and loss of function. Embryos developed normally with no gross malformations. At 5 dpf, mutants exhibited significantly reduced hair cell density in the otolith region compared with wild type, although lateral line neuromasts were unaffected. AEP testing showed significantly elevated auditory thresholds at all 4 frequencies in homozygous mutants compared with wild type (all P<0.001), indicating reduced hearing sensitivity. CONCLUSIONS We successfully generated a p2rx2 loss-of-function zebrafish model using CRISPR/Cas9 technology. p2rx2 deficiency caused hair cell defects in the otolith region and increased auditory thresholds across frequencies, indicating its key role in maintaining zebrafish auditory hair cell function and hearing perception. The phenotype's restriction to the otolith region suggests tissue-specific roles of p2rx2 in sensory organs. This model provides a valuable tool for elucidating the molecular mechanisms of P2RX2-related hearing loss and for screening otoprotective drugs and developing gene therapies.
Animals ; Zebrafish/genetics* ; Receptors, Purinergic P2X2/deficiency* ; CRISPR-Cas Systems/genetics* ; Gene Knockout Techniques ; Phenotype ; Zebrafish Proteins/genetics* ; Disease Models, Animal

OBJECTIVES: Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability. METHODS: This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI). RESULTS: Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58). CONCLUSIONS Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
Humans ; Sepsis/complications* ; Machine Learning ; Nomograms ; Venous Thrombosis/complications* ; Retrospective Studies ; Hospital Mortality ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Prognosis ; Bayes Theorem

Chronic obstructive pulmonary disease (COPD) currently lacks effective treatments to halt disease progression, making the search for preventive and therapeutic drugs a pressing issue. Natural products, with their accessibility, affordability, and low toxicity, offer promising avenues. Investigating the pharmacological effects and related signaling mechanisms of active components from natural products on COPD animal models induced by various triggers has become an important focus. In animal models induced by cigarette smoke, cigarette smoke combined with lipopolysaccharide (LPS), air pollution, elastase, bacterial or viral infections, the active compounds of natural products, such as flavonoids, terpenoids, and phenolics, can exert anti-inflammatory, antioxidant, mucus-regulating, and airway remodeling-inhibiting effects through key signaling pathways including nuclear factor-erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and mitogen-activated protein kinase (MAPK). These findings not only provide a theoretical basis for the clinical diagnosis and treatment of COPD but also point to new directions for future scientific research.
Pulmonary Disease, Chronic Obstructive/etiology* ; Animals ; Disease Models, Animal ; Biological Products/pharmacology* ; Humans ; NF-kappa B/metabolism* ; Flavonoids/pharmacology* ; Signal Transduction/drug effects* ; Anti-Inflammatory Agents/pharmacology* ; Heme Oxygenase-1/metabolism* ; Terpenes/pharmacology* ; Antioxidants/pharmacology* ; NF-E2-Related Factor 2/metabolism* ; Smoke/adverse effects* ; Phenols/therapeutic use*

OBJECTIVES: Cardiovascular disease (CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals. METHODS: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set (n=4 524) and a validation set (n=1 131) in an 8꞉2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA). RESULTS: The mean age of participants was 52 years old. There were 3 400 males (60.12%). The overall detection rate of vascular plaque in the screening population was 49.87% (2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets (all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals (all P<0.05). The area under the curve (AUC) of the nomogram model for predicting vascular plaque risk were 0.778 (95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760 (95% CI 0.732 to 0.787, P<0.001) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated good model calibration (training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99. CONCLUSIONS The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.
Humans ; Male ; Middle Aged ; Female ; Nomograms ; Retrospective Studies ; Risk Factors ; Plaque, Atherosclerotic/diagnosis* ; Aged ; Adult ; Physical Examination ; Logistic Models ; Cardiovascular Diseases/epidemiology* ; ROC Curve

Ulcerative colitis (UC) is an inflammatory condition of the intestine, resulting from an increase in oxidative stress and pro-inflammatory mediators. In this study, the extract of endophytic bacterium Rhizobium aegyptiacum was prepared for the first time using liquid chromatography-mass spectrometry (LC-MS). In addition, also for the first time, the protective potential of R. aegyptiacum was revealed using an in vivo rat model of UC. The animals were grouped into four categories: normal control (group I), R. aegyptiacum (group II), acetic acid (AA)‍-induced UC (group III), and R. aegyptiacum-treated AA-induced UC (group IV). In group IV, R. aegyptiacum was administered at 0.2 mg/kg daily for one week before and two weeks after the induction of UC. After sacrificing the rats on the last day of the experiment, colon tissues were collected and subjected to histological, immunohistochemical, and biochemical investigations. There was a remarkable improvement in the histological findings of the colon tissues in group IV, as revealed by hematoxylin and eosin (H&E) staining, Masson's trichrome staining, and periodic acid-Schiff (PAS) staining. Normal mucosal surfaces covered with a straight, intact, and thin brush border were revealed. Goblet cells appeared magenta in color, and there was a significant decrease in the distribution of collagen fibers in the mucosa and submucosal connective tissues. All these findings were comparable to the respective characteristics of the control group. Regarding cyclooxygenase-2 (COX-2) immunostaining, a weak immune reaction was shown in most cells. Moreover, the colon tissues were examined using a scanning electron microscope, which confirmed the results of histological assessment. A regular polygonal unit pattern was seen with crypt orifices of different sizes and numerous goblet cells. Furthermore, the levels of catalase (CAT), myeloperoxidase (MPO), nitric oxide (NO), interleukin-6 (IL-6), and interlukin-1β (IL-1β) were determined in the colonic tissues of the different groups using colorimetric assay and enzyme-linked immunosorbent assay (ELISA). In comparison with group III, group IV exhibited a significant rise (P<0.05) in the CAT level but a substantial decline (P<0.05) in the NO, MPO, and inflammatory cytokine (IL-6 and IL-1β) levels. Based on reverse transcription-quantitative polymerase chain reaction (RT-qPCR), the tumor necrosis factor-‍α (TNF-‍α) gene expression was upregulated in group III, which was significantly downregulated (P<0.05) by treatment with R. aegyptiacum in group IV. On the contrary, the heme oxygenase-1 (HO-1) gene was substantially upregulated in group IV. Our findings imply that the oral consumption of R. aegyptiacum ameliorates AA-induced UC in rats by restoring and reestablishing the mucosal integrity, in addition to its anti-oxidant and anti-inflammatory effects. Accordingly, R. aegyptiacum is potentially effective and beneficial in human UC therapy, which needs to be further investigated in future work.
Animals ; Colitis, Ulcerative/prevention & control* ; Rats ; Male ; Rhizobium ; Disease Models, Animal ; Colon/pathology* ; Rats, Sprague-Dawley ; Oxidative Stress ; Cyclooxygenase 2/metabolism*