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: This study investigates independent risk factors for postoperative internal fixation device infection in patients with maxillofacial fractures and proposes an early warning model based on the synthetic minority over-sampling technique (SMOTE) algorithm. METHODS: A total of 1 104 patients who underwent surgical treatment for maxillofacial fractures at Oral and Maxillofacial Surgery Department, Affiliated Hospital of Nantong University from January 2021 to December 2024 were retrospectively analyzed. The patients were divided into two groups based on the presence of postoperative internal fixation device infection: the infection group (27 cases) and non-infection group (1 077 cases). Clinical data from both groups were collected and subjected to statistical analysis. Univariate and binary Logistic regression analysis were used to identify risk factors for postoperative internal fixation device infection in maxillofacial fractures. Subsequently, a Logistic regression model was established, and the dataset was improved based on the SMOTE algorithm to construct an early warning model with the improved dataset. The prediction performance of the models was compared and validated. RESULTS: Among the 1 104 patients who underwent surgical treatment for maxillofacial fractures, 27 cases of postoperative internal fixation device infections were identified, corresponding to an infection rate of 2.45% (27/1 104). Age, diabetes history, fracture severity, and oral hygiene status were all identified as risk factors for postoperative internal fixation device infections in maxillofacial fractures (all P<0.05). The prediction model based on the original data (P1). The prediction model based on the SMOTE algorithm (P2). Receiver operating characteristic (ROC) curve analysis shows that the area under curve (AUC) for the P2 model was 0.882, the P1 model was 0.861, indicating the superior predictive performance of the P2 model. The DeLong test results show that the difference in AUC between the two models was statistically significant (P<0.05). CONCLUSIONS Age, diabetes history, postoperative fracture severity, and oral hygiene status are all risk factors for infections associated with internal fixation devices after maxillofacial fracture surgery. The proposed early warning model demonstrated good predictive performance. Medical professionals can utilize this model to effectively intervene and anticipate infections related to internal fixation devices after maxillofacial fracture surgery.
Humans ; Algorithms ; Retrospective Studies ; Male ; Female ; Fracture Fixation, Internal/instrumentation* ; Risk Factors ; Middle Aged ; Adult ; Logistic Models ; Surgical Wound Infection/epidemiology* ; Aged ; Internal Fixators/adverse effects* ; Maxillofacial Injuries/surgery* ; Adolescent

OBJECTIVES: This study aimed to use machine learning algorithms to build a prediction model of the first permanent molar caries of 9-year-old children in Suzhou and screen out risk factors. METHODS: Random stratified whole group sampling was applied to randomly select 9-year-old students from 38 primary schools in 14 townships and streets in Wuzhong District for oral examination and questionnaire survey. Multifactor Logistics regression was used to analyze the risk factors of tooth decay. The data set was randomly divided into training sets and verification sets according to 8∶2, and R 4.3.1 was used to build five machine learning algorithms: random forest, decision tree, extreme gradient boosting (XGBoost), Logistics regression, and lightweight gradient enhancement (LightGBM). The predictive effect of these five models was evaluated using the area under the characteristic curve (AUC). The marginal contribution of quantitative characteristics to the caries prediction model was determined through Shapley additive explanations (SHAP). RESULTS: This study included 7 225 samples that met the standard. The caries rate of the first permanent molar was 54.96%. Multifactor Logistic regression analysis showed that sweet drinks, dessert and candy, snack frequency, and snacks before going to bed after brushing teeth were correlated with the occurrence of first permanent molar caries (P<0.05). The AUC values of decision tree, Logistic regression, LightGBM, random forest, and XGBoost were 75.5%, 83.9%, 88.6%, 88.9%, and 90.1%, respectively. Compared with the variables after single heat coding, the SHAP value of high-frequency sweets (such as dessert candy ≥2 times a day, mother's sugary diet ≥2 times a day) and bad oral hygiene habits (such as frequent snacks before going to bed after brushing teeth and irregular brushing teeth) exhibited the highest positive. CONCLUSIONS XGBoost algorithm has a good prediction effect for first permanent molar caries in 9-year-old children. High-frequency sweet factors and bad oral hygiene habits have a strong positive impact on the risk of first permanent molar caries and are key drivers that can be used in the formulation of targeted interventions.
Humans ; Dental Caries/epidemiology* ; Child ; Machine Learning ; China/epidemiology* ; Molar ; Risk Factors ; Female ; Logistic Models ; Male ; Decision Trees ; Algorithms

Biomanufacturing harnesses engineered cells for the large-scale production of biochemicals, biopharmaceuticals, biofuels, and biomaterials, playing a vital role in mitigating global environmental crises, achieving carbon peaking and neutrality, and driving the green transformation of the economy and society. The effective design and construction of these engineered cells require precise and comprehensive computational models. Recent technological breakthroughs including high-throughput sequencing, mass spectrometry, spectroscopy, and microfluidic devices, coupled with advances in data science, artificial intelligence, and automation, have enabled the rapid acquisition of large-scale biological datasets, thereby facilitating a deeper understanding of cellular dynamics and the construction of mechanism-based models with enhanced accuracy. This review systematically summarises the mathematical frameworks employed in cellular modelling. It begins by evaluating prevalent mathematical paradigms, such as network topology analyses, stochastic processes, and kinetic equations, critically assessing their applicability across various contexts. The discussion then categorises modelling strategies for specific cellular processes, including cellular growth and division, morphogenesis, DNA replication, transcriptional regulation, metabolism, signal transduction, and quorum sensing. We also examine the recent progress in developing whole-cell models through the integration of diverse cellular processes. The review concludes by addressing key challenges such as data scarcity, unknown mechanisms, multi-dimensional data integration, and exponentially escalating computational complexity. Overall, this work consolidates the mathematical models for the precise simulation of cellular processes, thereby enhancing our understanding of the molecular mechanisms governing cellular functions and contributing to the future design and optimisation of engineered organisms.
Models, Biological ; Cell Physiological Phenomena ; Cell Engineering/methods* ; Humans

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.
Metabolic Networks and Pathways/physiology* ; Models, Biological ; Artificial Intelligence ; Systems Biology ; Kinetics ; Cell Physiological Phenomena ; Computational Biology ; Synthetic Biology ; Humans

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.
Fermentation ; Genomics/methods* ; Biotechnology/methods* ; Proteomics/methods* ; Models, Biological ; Metabolomics/methods* ; Bioreactors ; Multiomics

Objective To explore the application value of multi-model adaptive statistical iterative reconstruction-Veo (ASiR-V) in ultra-low dose chest CT examination of children in the plateau area. Methods The children who underwent chest CT examination in Xizang Autonomous Region People's Hospital were enrolled in this study and assigned into two groups according to the scanning conditions.Group A underwent scanning at a tube voltage of 100 kV and ASiR-V 50% reconstruction,and group B underwent scanning at a tube voltage of 80 kV and ASiR-V 0 (Group B1) and ASiR-V 50% (Group B2) reconstruction.The image quality of each group was evaluated objectively and subjectively.The radiation dose and image quality were compared between groups. Results Groups A and B showed the volume CT dose indexes of (2.33±0.62) mGy and (0.86±0.01) mGy and the dose length products of (65.01±25.12) mGy·cm and (23.55±3.38) mGy·cm,respectively,which presented differences between groups (both P<0.001).The image noise in the bilateral upper and middle lung areas in group B2 was lower than that in group B1 but higher than that in group A (all P<0.001).There was no significant difference in image quality score of the lung window among groups (all P>0.05).Groups A,B1,and B2 had no significant differences in ascending aorta (P=0.538) or liver CT value (P=0.175) in the mediastinal window.The signal-to-noise ratios and contrast-to-noise ratios of ascending aorta and liver in group B2 were higher than those in group B1 (all P<0.001) and lower than those in group A (all P<0.05).The image quality score of the mediastinal window followed a descending order of group A>group B2>group B1 (all P<0.001)。Conclusion ASiR-V combined with low tube voltage can effectively reduce the radiation dose and guarantee the image quality of chest CT of children in the plateau area.
Humans ; Radiation Dosage ; Tomography, X-Ray Computed/methods* ; Child ; Male ; Female ; Child, Preschool ; Radiography, Thoracic/methods* ; Infant ; Models, Statistical ; Image Processing, Computer-Assisted/methods*

Objective To explore the effect of rehmanniae radix extract(RRE)on chondrocyte apoptosis in the rabbit model of knee osteoarthritis(KOA)by regulating the miR-485-5p/heat shock protein 90 beta family member 1(Hsp90b1)axis.Methods New Zealand rabbits were randomly assigned into control,KOA,low-dose RRE,medium-dose RRE,high-dose RRE,celecoxib,high-dose RRE+antagonist control,and high-dose RRE+miR-485-5p antagonist groups,with 12 rabbits in each group.Rabbits in other groups except the control group were modeled for KOA with the improved Hulth method.After modeling for 8 weeks,the rabbits were administrated with corresponding agents for 4 weeks.The changes in the activity rating of rabbits were recorded.ELISA was employed to measure the levels of tumor necrosis factor-α(TNF-α)and interleukin(IL)-6 in the serum.Safranine O-fast green staining was conducted to reveal the pathological changes in the cartilage tissue and Mankin scoring was performed.TUNEL was employed to detect chondrocyte apoptosis.Real-time fluorescence quantitative PCR was performed to determine the expression of miR-485-5p in the cartilage tissue.Western blot was employed to determine the protein levels of Hsp90b1,cleaved cysteinyl aspartate-specific proteinase-3(Caspase-3),and Bcl2-associated-X(Bax)in the cartilage tissue.The dual-luciferase reporter assay was employed to examine the relationship between miR-485-5p and Hsp90b1.Results Compared with the control group,the KOA group showed down-regulated expression of miR-485-5p,elevated levels of TNF-α and IL-6 in the serum,cartilage erosion and losses,and increases in activity rating,Mankin score,chondrocyte apoptosis rate,and protein levels of Hsp90b1,cleaved Caspase-3,and Bax(all P<0.001).Compared with the KOA group,RRE at low,medium,and high doses,and celecoxib up-regulated the expression of miR-485-5p,lowered the levels of TNF-α and IL-6 in the serum,alleviated the pathological damage to the cartilage tissue,and decreased the activity rating,Mankin score,chondrocyte apoptosis rate,and protein levels of Hsp90b1,cleaved Caspase-3,and Bax(all P<0.05).Compared with the high-dose RRE group and the high-dose RRE+antagonist control group,high-dose RRE+miR-485-5p antagonist down-regulated the expression of miR-485-5p,elevated the levels of TNF-α and IL-6 in the serum,exacerbated the pathological damage to the cartilage tissue,and increased the activity rating,Mankin score,chondrocyte apoptosis rate,and protein levels of Hsp90b1,cleaved Caspase-3,and Bax(all P<0.05).The results indicated that there was a targeted regulatory relationship between miR-485-5p and Hsp90b1.Conclusion RRE may inhibit the expression of Hsp90b1 by up-regulating miR-485-5p,thereby inhibiting chondrocyte apoptosis in the rabbit model of KOA.
Animals ; Rabbits ; Apoptosis/drug effects* ; Chondrocytes/pathology* ; Osteoarthritis, Knee/drug therapy* ; MicroRNAs/metabolism* ; Rehmannia/chemistry* ; Disease Models, Animal ; Tumor Necrosis Factor-alpha/blood* ; Plant Extracts/pharmacology* ; Interleukin-6/blood* ; HSP90 Heat-Shock Proteins/metabolism* ; Male ; Drugs, Chinese Herbal/pharmacology*

Objective To investigate the role and mechanism of afzelin(AFZ)in treating Crohn's disease-like colitis.Methods A mouse model of 2,4,6-trinitrobenzene sulfonic acid-induced colitis was established to assess the effect of AFZ on experimental colitis in vivo.A Caco-2 cell model of tumor necrosis factor(TNF)-α-induced inflammation was established to evaluate the effects of AFZ on the intestinal barrier function,intestinal epithelial cell apoptosis,and mitochondrial function in vitro.The animal and cell experiments were performed to validate the regulatory role of the adenosine monophosphate-activated protein kinase(AMPK)/silent information regulater 1(SIRT1)/peroxisome proliferator-activated receptor gamma coactivator(PGC)-1α pathway in the treatment of colitis with AFZ.Results AFZ reduced the disease activity index(P=0.003),weight loss(P<0.001),colon shortening(P<0.001),inflammation score(P=0.002),pro-inflammatory cytokine release(interleukin-6:P<0.001;TNF-α:P=0.010),and intestinal barrier permeability(fluorescein isothiocyanate dextran 4:P<0.001;intestinal-type fatty acid-binding protein:P=0.013).Meanwhile,AFZ increased the colonic transepithelial electric resistance(P=0.001),reduced bacterial translocation(P<0.001),and promoted the localization and up-regulated the expression of tight junction proteins [zonula occluden-1(P=0.005) and Claudin-1(P=0.024)].AFZ exerted a protective effect on the Caco-2 cells exposed to TNF-α in terms of intestinal epithelial cell permeability(P=0.017),transepithelial electric resistance(P=0.014),and tight junction protein[zonula occluden-1(P=0.014) and Claudin-1(P=0.006)] localization and expression.Furthermore,the cell and animal experiments confirmed that AFZ reduced the percentage of apoptosis(P<0.001,P=0.013)and the expression of cleaved-caspase 3(P=0.028,P=0.004)and Bax(P=0.004,P=0.020),and upregulated the Bcl2(P=0.020,P=0.006)level in intestinal epithelial cells.Additionally,AFZ increased the number of mitochondria,mitochondrial membrane potential,and copy number of mitochondrial DNA(P=0.007)in intestinal epithelial cells,while enhancing the activities of mitochondrial respiratory chain complex Ⅰ(P=0.005)and complex Ⅳ(P=0.001).The activation of the AMPK/SIRT1/PGC-1α pathway was involved in the protective effects of AFZ on mitochondrial function and apoptosis in intestinal epithelial cells.Conclusion AFZ alleviates mitochondrial dysfunction and apoptosis in intestinal epithelial cells by activating the AMPK/SIRT1/PGC-1α pathway,thereby ameliorating intestinal barrier dysfunction and experimental colitis.
Animals ; Colitis/drug therapy* ; Humans ; Caco-2 Cells ; Mice ; Trinitrobenzenesulfonic Acid ; Apoptosis/drug effects* ; Disease Models, Animal ; AMP-Activated Protein Kinases/metabolism* ; Sirtuin 1/metabolism*

Objective To analyze the risk factors for prolonged length of hospital stay in patients with peritoneal dialysis associated peritonitis(PDAP)and construct a nomogram based on Logistic regression model.Methods A retrospective study was conducted on patients with PDAP who were hospitalized at the Affiliated Hospital of Jiangsu University from January 2013 to December 2023.Using the 75th percentile of hospitalization time as the cutoff(>21 days),the patients were divided into prolonged length of hospital stay group and normal length of hospital stay group.Clinical data were compared between the two groups.Logistic regression analysis was used to analyze the risk factors for prolonged hospital stay in PDAP patients and to construct a nomogram.Results A total of 131 PDAP patients were included in this study,including 40 cases in prolonged length of hospital stay group and 91 cases in normal length of hospital stay group.Multivariate Logistic regression analysis showed that Gram-negative bacteria detected in ascites(OR=6.012,95% CI=1.878-19.248,P=0.003)and elevated platelet count(OR=1.010,95% CI=1.005-1.015,P<0.001)were independent risk factors for prolonged length of hospital stay,while elevated serum chloride(OR=0.885,95% CI=0.802-0.978,P=0.016)was a protective factor.Based on the above three indicators,a nomogram was constructed.The multivariate Logistic regression model showed an area under the receiver operating characteristic curve(AUC)of 0.755,with an internal validation AUC of 0.727 using the Bootstrap method.The calibration curve indicated that the predicted probability was consistent with the actual probability.The decision curve showed that the model was clinically applicable when the threshold probabilities were 9%-10%,13% and 18%-92%.Conclusion A nomogram,based on the detection of gram-negative bacteria in ascites,platelet count and serum chloride,was helpful for clinical screening PADP patients at risk for prolonged length of hospital stay,and can provide a basis for optimizing clinical decision-making.
Humans ; Nomograms ; Risk Factors ; Peritoneal Dialysis/adverse effects* ; Retrospective Studies ; Length of Stay ; Peritonitis/etiology* ; Logistic Models ; Male ; Female ; Middle Aged ; Aged