Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective To identify renal function and other readily accessible indicators influencing the prognosis of renal cell carcinoma (RCC) patients after nephrectomy, and to construct a prognostic prediction model based on them. Methods The clinical and pathological data of 624 RCC patients undergoing radical or partial nephrectomy in the First Hospital of Shanxi Medical University during Jan. 2013 and Dec. 2021 were retrospectively analyzed. The independent prognostic factors affecting postoperative overall survival (OS) were identified with LASSO algorithm combined with Cox regression analysis. Based on these factors, a nomogram was constructed to predict the 1-, 3-, and 5-year survival rates. The performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and the area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Finally, the cutoff value of the nomogram was calculated to stratify patients into risk groups. Kaplan-Meier survival analysis with log-rank testing was performed to compare outcomes between the risk groups. Results Nine independent prognostic factors were identified, including age, smoking history, diabetes mellitus history, maximum tumor diameter, tumor subtype, N stage, Fuhrman grade, preoperative serum creatinine and cystatin C (P＜0.05). The nomogram achieved a C-index of 0.891, with time-dependent AUC of 0.920, 0.903, and 0.867 for 1-, 3-, and 5-year OS, respectively. Calibration curves showed excellent agreement with actual observations, and DCA demonstrated significant clinical net benefits across a wide threshold probability range. Using a cutoff score of 110.4, Kaplan-Meier analysis revealed statistically significant survival differences between risk groups (P＜0.001). Conclusion The proposed nomogram, incorporating renal function and other readily accessible clinical indicators, exhibits strong predictive accuracy for postoperative survival in RCC patients and may serve as a practical tool for individualized clinical decision-making.

Health management is highly complex due to interactions across multiple levels and factors. System dynamics model (SDM) offers a holistic perspective and a dynamic analytical framework for understanding such complex systems. It has been applied across various domains of health management, including psychological interventions, chronic disease management, rehabilitation, optimization of medical services, and health policy development. By identifying key factors and pathways influencing health behaviors, determining critical targets for interventions, conducting cost-benefit analyses and process optimization, and simulating the long-term effects of health policies, SDM provides quantitative support for decision-making from individual-level interventions to macro-level policies. This article reviews the application of SDM in these four major areas within health management, discusses its advantages and limitations, and serves as a reference for researchers and practitioners aiming to utilize SDM in future studies. The goal is to advance health management toward greater personalization and precision, thereby offering stronger support for health interventions and policy development.
Humans ; Health Policy ; Models, Theoretical

[Purpose]To analyze the trends of incidence and age at onset of bone malignant tumors in cancer registration areas of Jiangsu Province from 2009 to 2019.[Methods]Incidence data of bone malignant tumors from 2009 to 2019 were collected from 16 consecutive and quality-con-trolled cancer registries in Jiangsu Province.The incidence rates,average age at onset,and inci-dence composition of bone malignant tumors were calculated.A birth cohort model was constructed to analyze the changes in the incidence of bone malignant tumors in the population born from 1929 to 2019.Joinpoint regression models were used to analyze the average annual percentage change(AAPC)in the incidence rates and the incidence composition of bone malignant tumors for each year in those aged 60 years old and above.A general linear regression model was used to ana-lyze the trend of the average age of onset.[Results]The crude incidence rate of bone malignant tumors in women in Jiangsu cancer registration areas decreased from 2009 to 2019,with an AAPC of-2.62％(P=0.025).After adjusting the population composition,except for urban areas,the incidence of bone malignant tumors in the whole province,men,women and rural areas all decreased significantly,with AAPC of-3.15％,-2.49％,-4.31％and-2.23％,respectively.The average age at onset of bone malignant tumors in the whole province,men and urban areas de-creased significantly yearly,with an average annual decrease of 0.365,0.504 and 0.469 years old,respectively.In the same period,the incidence of malignant bone tumors in the whole province,men,women and urban areas of age groups of 50～59,60～69 and 70～79 years old showed a decreasing trend,the AAPC ranged from-9.06％to-4.14％(all P＜0.05),and the inci-dence decreased gradually with the year of birth.The incidence of malignant bone tumors in men＜30 years old increased yearly with an AAPC of 4.30％(P＜0.05).Compared with 2009,the com-position of incidence in men aged 15～39 years old and in urban population increased in 2019,while the incidence of bone malignant tumors in the age group of 60～79 years old in the province generally decreased.After age structure adjustment,the incidence of bone malignant tumors in people over 60 years old in urban areas decreased with an AAPC of-1.42％(P＜0.05).[Conclu-sion]The incidence of bone malignant tumors in Jiangsu Province is decreasing and the age at on-set is moving forward,indicating that the prevention and control measures of bone malignant tu-mors should be adjusted accordingly.

Objective:To analyze the correlation between caregiver characteristics and clinical manifestations of patients with Alzheimer′s disease (AD) dementia.Methods:It was a cross-sectional study. A total of 167 patients with AD dementia and 167 matched caregivers from Chinese Imaging, Biomarkers, and Lifestyle study of Alzheimer′s Disease (CIBL) study between January 1 and December 31, 2023 were consecutively selected as the study subjects. The data such as gender, age, body max index (BMI), waist to hip ratio, educational years, combined chronic diseases, disease course and the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Neuropsychiatric Inventory (NPI), Activities of Daily Living (ADL), Mini-Nutritional Assessment (MNA), and Dietary Diversity Score (DDS) of the patients were collected. The data such as gender, educational years, the relationship with patients, and the Caregiver Burden Inventory (CBI) score in the caregivers were also evaluated. According to the gender of caregivers, the patients were divided into female caregiver group (108 cases) and male caregiver group (59 cases). The Spearman correlation and multiple linear regression analyses were used to assess the impact of the caregiver characteristics on the cognitive function, NPS, activities of daily living, and nutritional status of the patients with AD dementia.Results:Among the 167 patients with AD dementia, 80 cases were men, and 87 cases were women, with a mean age of (68.43±7.51) years. Among the 167 matched caregivers, 59 were spouse, 98 were offspring, and 10 were others (nanny or other relatives). The AD patients in the male caregiver group had lower BMI and MNA scores than those in female caregivers′ group [(23.35±3.70) vs (24.80±3.33) kg/m 2, 22.00 (20.00, 24.50) vs 24.00 (21.00, 26.00) points] (both P<0.05). The educational years of the caregivers was negatively related to the ADL score ( r=-0.196), and was positively related to the MNA score of the patients ( r=0.180) (both P<0.05); while the CBI score of the caregiver was positively related to the age ( r=0.180), NPI score ( r=0.568) and ADL scores ( r=0.702) in the patients, and it was negatively related to the BMI ( r=-0.163), MMSE score ( r=-0.499), MoCA score ( r=-0.491) and MNA scores ( r=-0.387) in the patients (all P<0.05). The shorter educational years and elevated CBI score were independent risk factors for the decline in the MMSE score ( β=0.310, 95% CI: 0.021-0.598; β=-0.225, 95% CI:-0.297--0.154) and MNA score ( β=0.204, 95% CI: 0.067-0.340; β=-0.082, 95% CI:-0.116--0.049), as well as the increase in the NPI score ( β=-0.628, 95% CI:-1.208--0.047; β=0.575, 95% CI: 0.431-0.718) and ADL score ( β=-0.519, 95% CI:-0.860--0.179; β=0.361, 95% CI: 0.277-0.445) in the patients with AD dementia (all P<0.001). Conclusions:A low educational level of caregivers and a heavy caregiver burden are associated with decreased cognitive function, worsening NPS, reduced ADL, and declining nutritional status in patients with AD dementia.

Background: Developing and validating a modified parameter, the SYS-G angle (the angle between the lower endplate of the C2 and the upper endplate of C7 vertebrae), as a feasible substitute for the C2–C7 Cobb method in assessing cervical sagittal alignment and exploring its reference range through a large-scale retrospective study. Methods: The visibility of the C6, C7 upper, and C7 lower endplates was graded and compared. Baseline data such as height, weight, body mass index (BMI), age, and sex were analyzed for their impact on the visibility of the C7 lower endplate. Values of C2-6 Cobb angle, SYS-G angle, and C2-7 Cobb angle were measured. The intra- and interobserver reliability, differences, and efficacy of evaluation on cervical lordosis of the parameters were compared, and the correlations among the parameters were analyzed. Furthermore, reference ranges for the SYS-G angle were established based on lateral cervical spine x-rays of 825 asymptomatic Chinese adults across different age groups and sexes. Results: The visibility of the C7 lower endplates was significantly reduced compared to the C6 lower and C7 upper endplates.Age, weight, BMI, and male sex were identified as factors negatively influencing the visibility of the C7 lower endplate. Both intraobserver and interobserver reliability demonstrated excellence for all tested parameters. The linear regression model unveiled a stronger association of the SYS-G angle with the C2-7 Cobb angle compared to the C2-6 Cobb angle. Furthermore, the SYS-G angle exhibited excellent efficacy in evaluating cervical lordosis. Age displayed a positive correlation with the SYS-G angle, and across every age bracket from 20 to 69 years, men exhibited a higher mean SYS-G angle compared to women. Conclusions The visibility of the C7 lower endplate diminishes with increasing age, weight, BMI, and male sex. In cases where the C7 lower endplate is unclear, the SYS-G angle emerges as a reliable method for estimating cervical sagittal morphology. Reference ranges for the SYS-G angle were established across various age groups and sexes among asymptomatic Chinese adults, offering a valuable resource to guide therapeutic interventions for cervical spine disorders and deformities.

Background: Developing and validating a modified parameter, the SYS-G angle (the angle between the lower endplate of the C2 and the upper endplate of C7 vertebrae), as a feasible substitute for the C2–C7 Cobb method in assessing cervical sagittal alignment and exploring its reference range through a large-scale retrospective study. Methods: The visibility of the C6, C7 upper, and C7 lower endplates was graded and compared. Baseline data such as height, weight, body mass index (BMI), age, and sex were analyzed for their impact on the visibility of the C7 lower endplate. Values of C2-6 Cobb angle, SYS-G angle, and C2-7 Cobb angle were measured. The intra- and interobserver reliability, differences, and efficacy of evaluation on cervical lordosis of the parameters were compared, and the correlations among the parameters were analyzed. Furthermore, reference ranges for the SYS-G angle were established based on lateral cervical spine x-rays of 825 asymptomatic Chinese adults across different age groups and sexes. Results: The visibility of the C7 lower endplates was significantly reduced compared to the C6 lower and C7 upper endplates.Age, weight, BMI, and male sex were identified as factors negatively influencing the visibility of the C7 lower endplate. Both intraobserver and interobserver reliability demonstrated excellence for all tested parameters. The linear regression model unveiled a stronger association of the SYS-G angle with the C2-7 Cobb angle compared to the C2-6 Cobb angle. Furthermore, the SYS-G angle exhibited excellent efficacy in evaluating cervical lordosis. Age displayed a positive correlation with the SYS-G angle, and across every age bracket from 20 to 69 years, men exhibited a higher mean SYS-G angle compared to women. Conclusions The visibility of the C7 lower endplate diminishes with increasing age, weight, BMI, and male sex. In cases where the C7 lower endplate is unclear, the SYS-G angle emerges as a reliable method for estimating cervical sagittal morphology. Reference ranges for the SYS-G angle were established across various age groups and sexes among asymptomatic Chinese adults, offering a valuable resource to guide therapeutic interventions for cervical spine disorders and deformities.

Background: Developing and validating a modified parameter, the SYS-G angle (the angle between the lower endplate of the C2 and the upper endplate of C7 vertebrae), as a feasible substitute for the C2–C7 Cobb method in assessing cervical sagittal alignment and exploring its reference range through a large-scale retrospective study. Methods: The visibility of the C6, C7 upper, and C7 lower endplates was graded and compared. Baseline data such as height, weight, body mass index (BMI), age, and sex were analyzed for their impact on the visibility of the C7 lower endplate. Values of C2-6 Cobb angle, SYS-G angle, and C2-7 Cobb angle were measured. The intra- and interobserver reliability, differences, and efficacy of evaluation on cervical lordosis of the parameters were compared, and the correlations among the parameters were analyzed. Furthermore, reference ranges for the SYS-G angle were established based on lateral cervical spine x-rays of 825 asymptomatic Chinese adults across different age groups and sexes. Results: The visibility of the C7 lower endplates was significantly reduced compared to the C6 lower and C7 upper endplates.Age, weight, BMI, and male sex were identified as factors negatively influencing the visibility of the C7 lower endplate. Both intraobserver and interobserver reliability demonstrated excellence for all tested parameters. The linear regression model unveiled a stronger association of the SYS-G angle with the C2-7 Cobb angle compared to the C2-6 Cobb angle. Furthermore, the SYS-G angle exhibited excellent efficacy in evaluating cervical lordosis. Age displayed a positive correlation with the SYS-G angle, and across every age bracket from 20 to 69 years, men exhibited a higher mean SYS-G angle compared to women. Conclusions The visibility of the C7 lower endplate diminishes with increasing age, weight, BMI, and male sex. In cases where the C7 lower endplate is unclear, the SYS-G angle emerges as a reliable method for estimating cervical sagittal morphology. Reference ranges for the SYS-G angle were established across various age groups and sexes among asymptomatic Chinese adults, offering a valuable resource to guide therapeutic interventions for cervical spine disorders and deformities.

Objective: To explore the influencing factors of recurrence in postoperative patients with renal cell carcinoma，construct machine learning prediction models and evaluate their performance. Methods: Clinical data of 915 patients with renal cell carcinoma treated in our hospital during 2013 and 2021 were retrospectively collected.The data were randomly divided into a training set (n=510) and a validation set (n=218) in a 7∶3 ratio.In the training set，LASSO regression algorithm was used to screen important variables，and machine learning prediction models were constructed to predict the recurrence risk.In the validation set，the effectiveness of the models was compared combined with the area under receiver operating characteristic curve (AUC)，accuracy rate，F1 value and other indicators. Results: LASSO regression screened out the risk factors，including smoking history，tumor size，N stage，Fuhrman grade，thrombin time and fibrinogen，based on which，the logistic model，decision tree model，random forest model，and Bayes model were constructed.In the validation set，the AUC of the above 4 models was 0.862，0.792，0.843 and 0.861，respectively; the accuracy was 0.917，0.908，0.904 and 0.927，respectively; F1 value was 0.357，0.286，0.323 and 0.600，respectively.The Bayes model had the most stable performance and best differentiation. Conclusion: In this data set，the prediction model based on Bayes algorithm has a good performance and can provide reference for clinical decision making.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.