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.

ObjectiveTo explore the changes in volatile components， total polysaccharides， enzyme activity， and chromaticity value of Myristicae Semen Koji（MSK） during the fermentation process， and conduct correlation analysis. MethodsBased on gas chromatography-mass spectrometry（GC-MS）， the changes of volatile components in MSK at different fermentation times were identified. The phenol sulfuric acid method， dinitrosalicylic acid method（DNS）， and carboxymethyl cellulose sodium salt method（CMC-Na） were used to investigate the total polysaccharide content， amylase activity， and cellulase activity during the fermentation process. Visual analysis technology was used to explore the changes in chromaticity values， revealing the fermentation process of MSK and the dynamic changes of various measurement indicators， partial least squares-discriminant analysis（PLS-DA） was used to explore the differential compounds of MSK at different fermentation degrees， and Pearson correlation analysis was used to explore the correlation between volatile components of MSK and total polysaccharides， enzyme activity， and chromaticity values. ResultsA total of 60 volatile compounds were identified from MSK， the relative contents of components such as （+）-α-pinene， β-phellandrene， β-pinene， （+）-limonene， and p-cymene obviously increased， while the relative contents of components such as safrole， methyl isoeugenol， methyleugenol， myristicin， and elemicin significantly decreased. During the fermentation process， the total polysaccharide content showed an upward trend， while the activities of amylase and cellulase showed an initial increase followed by a decrease， and reached their maximum value at 40 h. the overall brightness（L^*） and total color difference（ΔE^*） gradually increased， while the changes in red-green value（a^*） and yellow-blue value（b^*） were not obvious. PLS-DA results showed that MSK could be clearly distinguished at different fermentation times， and 13 differential biomarkers were screened out. Pearson correlation analysis results showed that the contents of α-terpinene， β-phellandrene， methyleugenol， β-cubebene and myristic acid had an obvious correlation with chromaticity values. ConclusionAfter fermentation， the volatile components， total polysaccharides， amylase activity， and cellulase activity of MSK undergo significant changes， and there is a clear correlation between them and chromaticity values， which reveals the dynamic changes in the fermentation process and related indicators of MSK， laying a foundation for the quality control.

Cerebral small vessel disease（CSVD） refers to a series of clinical， imaging， and pathological syndromes caused by various etiologies affecting arterioles，capillaries， and venules in the brain， and the main clinical manifestations of CSVD include cognitive impairment， gait and balance disorders， urinary incontinence， and mental and behavioral disorders such as depression， anxiety， apathy， and personality changes. At present， the pathophysiological mechanism of CSVD remains unclear. As one of the most important trace elements in the human body， zinc ions play an important role in the development of the nervous system， and the change in zinc ion concentration will affect a variety of nervous system diseases；therefore， the research on the association between zinc ion homeostasis and the development and progression of cSVD has gradually become a hot topic. This article reviews the mechanism of the development and progression of CSVD， the role of zinc ions in the nervous system， the association between zinc ions and the development and progression of CSVD， and the latest research advances.

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*

Objective:To explore the clinical and biochemical characteristics of patients with hyperuricemia according to different pathophysiological subtypes. This may facilitate rapid identification of each subtype in clinical settings and provide evidence for personalized urate-lowering treatment.Methods:Patients diagnosed with hyperuricemia at the Department of Endocrinology and Metabolism, Tenth People′s Hospital of Tongji University between October 2015 and January 2024 were included. Based on 24-h urinary uric acid excretion(UUE) and the fractional excretion of uric acid(FEUA), patients were classified into four subtypes: renal uric acid underexcretion type(RUE), renal uric acid overload type(ROL), combined type and renal normal type. Clinical and biochemical variables-including sex, age, BMI, smoking history, comorbidities, blood glucose, and serum uric acid-were analyzed. Binary logistic regression was used to identify factors associated with each subtype.Results:Among 2 073 patients with hyperuricemia, 55.8% were RUE type, 6.9% were ROL type, 31.3% were combined type and 6.0% were renal normal type. RUE type had lower blood glucose levels and fewer cases of diabetes [ OR=0.685(95% CI 0.478-0.980), P<0.05]. ROL type showed a higher incidence of tophi, positively correlated with smoking history [ OR=1.672(95% CI 1.009-2.771), P<0.05], and negatively correlated with serum uric acid levels [ OR=0.994(95% CI 0.990-0.998), P=0.001]. Combined type had the youngest onset age, shortest disease duration, and the fewest comorbidities, and was associated with higher BMI [ OR=1.035(95% CI 1.001-1.070), P<0.05]. Renal normal type had the oldest age of onset, the highest proportion of female patients and comorbidities, and was associated with lower serum uric acid levels[ OR=0.994(95% CI 0.989-0.998), P=0.007], higher BMI[ OR=1.064(95% CI 1.003-1.129), P<0.05], and increased tophi incidence[ OR=2.261(95% CI 1.206-4.237), P=0.011]. Conclusion:Each pathophysiological subtype of hyperuricemia exhibits distinct clinical and biochemical characteristics, which may serve as useful references for subtype identification and personalized management in clinical practice.