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.

Traumatic brain injury (TBI), as a significant central nervous system damage disease with high frequency in the world, leads to a huge number of patients with impaired health and lower quality of life every year. Lung injury is a common and dangerous consequence, which dramatically raises the mortality of patients. Discovering the pathophysiology of lung injury after TBI and discovering viable therapeutic targets has become an important need for clinical diagnosis and therapy. Neurotransmitters, as the fundamental chemical agents of the nervous system for signal transmission, not only govern neuronal activity and apoptosis in TBI but also significantly influence the pathophysiological mechanisms of lung injury subsequent to TBI. The imbalance is intricately linked to the onset and progression of lung damage. This paper systematically reviews the clinical characteristics and predominant pathogenesis of lung injury following TBI, emphasizing the role of key neurotransmitters, including glutamate (Glu), γ-aminobutyric acid (GABA), norepinephrine (NE), dopamine (DA), and acetylcholine (ACh), in lung injury post-TBI. It examines their influence on inflammatory response, vascular permeability, and pulmonary circulation function. Additionally, the paper evaluates the research advancements and potential applications of targeted therapeutic strategies for various neurotransmitter systems, such as receptor antagonists, transporter inhibitors, and neurotransmitter analogues. This research aims to offer a theoretical framework for clarifying the neural regulatory mechanisms of lung injury following TBI and to establish a basis for the development of novel therapeutic strategies and enhancement of the prognosis of the patients.
Humans ; Brain Injuries, Traumatic/metabolism* ; Neurotransmitter Agents/metabolism* ; Lung Injury/metabolism* ; gamma-Aminobutyric Acid/metabolism* ; Glutamic Acid/metabolism* ; Norepinephrine/metabolism* ; Dopamine/metabolism* ; Acetylcholine/metabolism*

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.

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.

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.