Background: s/Aims: Increasing evidence indicates that metabolites play a significant role in the pathogenesis of liver cancer and have potential as biomarkers for early detection. This review summarizes the current literature on the utility of metabolomic profiling as a screening strategy for early diagnosis of liver cancer. Methods: We searched PubMed, Embase, and Web of Science for studies published between 2004 and 2024 that examined metabolite alterations in liver cancer. The metabolites differentially expressed in liver cancer versus healthy controls, cirrhosis, and hepatic B virus cases are summarized. The diagnostic performance of the metabolite-based models was also evaluated, highlighting their potential as early detection biomarkers for liver cancer. Results: A total of 96 studies were included in this review, encompassing case-only, case-control, nested case-control, and cohort designs. The analysis identified taurine and taurochenodeoxycholic acid to be consistently associated with an increased risk of liver cancer, supported by findings from both the discovery and validation cohorts. Notably, a diagnostic model incorporating 10 metabolites including taurine and taurochenodeoxycholic acid, achieved an area under the receiver operating characteristic curve of 0.86 (95% confidence interval, 0.82-0.88), indicating strong discriminatory power for early liver cancer detection. Nevertheless, heterogeneity across studies was observed, largely owing to differences in biological sample types and metabolomic platforms. Conclusions This review highlights the significant roles of taurine and taurochenodeoxycholic acid in liver cancer development. Future research should prioritize the standardization of analytical methodologies, increased sample sizes, and integration of metabolomics with other omics layers to enhance our understanding of liver cancer biology and improve biomarker accuracy and clinical utility.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To provide a reference for the establishment, development and application of the adverse drug reaction （ADR） automated monitoring system, through verifying and quantifying the research hotspots and advantages of the system by CiteSpace software and systematic review. Methods Literature on ADR automated monitoring up to December 2023 were retrieved and screened from CNKI and web of science databases. CiteSpace 6.4.R1 software was used to conduct co-occurrence, clustering and emergence analysis, and to visualize and comparatively analyze the research hotspots, rules and distribution in the field of automated monitoring of ADR at home and abroad. In compliance with the preferred reporting items for systematic reviews and Meta-analyses （PRISMA）, literature covering publications in English and Chinese including detection rates of ADR collected using Incident Reporting Systems （IRSs） and/or automated monitoring systems were retrieved and screened. The advantages and disadvantages of automated monitoring systems were analyzed by comparing the differences between these two systems in terms of the number of ADR reports and the types of positive signals. Results A total of 56 articles in English and 80 articles in Chinese were indexed by CiteSpace. The research hotspots in recent years included data mining, deep learning, text classification techniques, machine learning and so on. A total of seven studies compiled with the inclusion criteria for the systematic evaluation, all of which were completed between 1991 and 2021 in hospitals in four countries. 150 526 medical records were reviewed from 15 institutions. A total of 194 ADR reports were collected by IRSs. A total of 2 090 ADR reports were collected by the automated monitoring system over the same period, indicating a 977% increase in the number of ADR reports （P=0.0156） compared with the IRSs. Conclusion The ADR automatic monitoring system had significantly improved the level of drug risk identification and reduced costs, but it was necessary to optimize the algorithm, expand the data source and carry out standardization construction to overcome the current limitations.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

In response to the demand for portable and rapid measurement in normalized nutritional metabolism monitoring,a low-power and multifunctional measurement method and system scheme based on indirect calorimetry is proposed. Firstly,a combination of Kalman and the 5th order Bessel filtering algorithm is used to preprocess the real-time data of oxygen,carbon dioxide,flow rate and electrocardiogram,and the cumulative amount of gas per unit time is obtained. Secondly,a rapid nutrient metabolism measurement method that considers resting analyses of electrocardiogram and respiratory waves is designed,and a calculation model for energy metabolism parameters is constructed. Then,a dedicated collection and analysis circuit system is developed to transmit data to the upper computer or cloud through wireless networks,achieving intelligent and networked design. Finally,the resting state of the subjects is determined by analyzing electrocardiogram and respiratory parameters,and the test of monitoring nutrient metabolism is completed. The results show that the system can effectively detect metabolism related physiological signals with high precision,and achieve accurate measurement of human fat and sugar consumptions on metabolism,providing an effective means for measuring human metabolic energy.

As the use of nursing information systems (NIS) in clinical nursing practice has proliferated, NIS education has received increased attention. This paper introduces the background and research form of NIS in nursing education at home and abroad, and summarizes the deficiencies in the application and puts forward suggestions, in order to provide references for the subsequent development of a high-quality system and the development of courses that fit the actual situation of nursing students in China.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To investigate the features of peripheral blood lymphocyte subsets in children with sepsis and evaluate the value of these cells in combination with multiple indicators in the diagnosis of pediatric sepsis.Methods:A retrospective study was conducted on 86 sepsis children and 83 children with local infection admitted to Hebei Children′s Hospital from October 2022 to October 2024. Baseline clinical data, peripheral blood lymphocyte subsets, and other laboratory indicators were compared between the two groups. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to evaluate the independent risk factors correlated with the initiation of sepsis. The receiver operating characteristic (ROC) curve was plotted to evaluate the value of each independent risk factor for diagnosing sepsis.Results:The thrombin time, the absolute counts of NK, CD3 + T, CD4 + T, and CD8 + T cells, the levels of IgG and IgM, and the counts of lymphocytes and platelets were lower in the sepsis children than in the children with local infection. However, the prothrombin time (PT), activated partial thromboplastin time, the levels of fibrinogen, direct bilirubin, and C-reactive protein (CRP) were higher in the sepsis group (all P<0.05). The variables screened by LASSO regression were analyzed by the multivariate logistic regression, and the results showed that PT, absolute CD4 + T cell count, and the levels of IgM and CRP were independent risk factors for sepsis. The ROC analysis indicated that the area under the ROC curve (AUC) for PT, absolute CD4 + T cell count, and IgM and CRP levels when used individually in diagnosing sepsis was 0.729, 0.593, 0.605, and 0.795, respectively. The AUC for the four indexes when used in combination for diagnosing sepsis reached 0.822, showing greater superiority over that of the single index. Conclusions:The combined measurement of PT, absolute CD4 + T cell count, and the levels of IgM and CRP can improve the diagnostic efficacy for sepsis in children. Early monitoring of these indexes facilitates the assessment of the condition in children suffering from sepsis.

The adaptive motion control method for bio-inspired hexapod robot is explored based on bionics principles,as well as the limb structure and behavioral characteristics of tarantulas.Firstly,a bionic kinematic model for a single leg of the robot is established using the Denavit-Hartenberg method combined with mechanical parameters.The forward and inverse kinematic expressions are derived,and the triangular gait of the hexapod robot is implemented through composite cycloid trajectory planning.Subsequently,inspired by biological tactile sense and biological vestibular balance mechanisms,force sensors and inertial measurement unit are employed to acquire force control and posture information of the hexapod robot,followed by fusion processing,which enables the robot to perceive environmental changes and adjust its trajectory in real time.A motion control model integrating hybrid gaits is proposed to enhance the robot's environmental perception and adaptability.Finally,a semi-physical simulation experimental platform is constructed to verify the effectiveness and reliability of the proposed model.Experimental results demonstrate that this model can significantly improve the motion stability and traversal efficiency of the robot in complex and rugged terrains,providing robust support for the application of bio-inspired adaptive control methods in multi-legged robots.