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.

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.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional“one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.

Objective:To investigate the effects of pulsed electric field (PEF) combined with gemcitabine (GEM) on the viability and stemness of HCCC-9810 cholangiocarcinoma stem cells.Methods:HCCC-9810 cholangiocarcinoma stem cells were established in serum-free, cytokine-rich medium and divided into four groups: the control group, GEM group, PEF group, and the pulsed electric field combined with gemcitabine (PEF+ GEM) group. Cell proliferation was detected using the Cell Counting Kit-8 (CCK8) assay. Cell viability and apoptosis rate were measured by flow cytometry. Cell invasion ability was assessed using the Transwell assay. The expression of stemness marker proteins CD133 and Octamer-binding transcription factor 4 (OCT4), as well as the expression of β-catenin, was detected by Western blotting.Results:Regarding cell viability, the GEM, PEF, and PEF+ GEM groups showed significantly lower cell viability and higher apoptosis rate than the control group at 24 h, 48 h, and 72 h (all P<0.05). At 48 h and 72 h, the PEF+ GEM group showed significantly lower cell viability (7.2%±0.3% and 5.9%±0.8%, respectively) than the GEM group (50.7%±0.6% and 31.0%±1.2%, respectively) and the PEF group (12.2%±0.2% and 12.8%±0.2%, respectively) (all P<0.05). Regarding stemness inhibition, the PEF+ GEM groups showed significantly lower expression levels of CD133 and OCT4 at 24 h, 48 h, and 72 h compared with the control group (all P<0.05). Notably, at 48 h, the PEF+ GEM group showed a significantly lower expression level of the OCT4 (0.61±0.02) than the GEM group (0.87±0.08) and the PEF group (1.00±0.10) ( P<0.01). Furthermore, at 24 h and 48 h, the GEM, PEF, and PEF+ GEM groups showed significantly lower expression levels of β-catenin compared with the control group (all P<0.05). Conclusion:Pulsed electric field combined with gemcitabine therapy demonstrated more effective anti-proliferation and cancer stemness inhibition effects on HCCC-9810 cholangiocarcinoma stem cells compared with either monotherapy.

Objective: To investigate the knowledge of hepatitis C prevention and treatment and related factors among two groups of female sex workers (FSW) and men who have sex with men (MSM) in the Xizang Autonomous Region (Xizang) to provide a basis for the subsequent development of Hepatitis C publicity and education strategies. Methods: From August to September 2021, a special survey was conducted among 1 244 FSW and 234 MSM in 5 districts (counties) of 4 regions in Xizang. A one-on-one face-to-face questionnaire survey was adopted, and the χ² test and logistic regression were used to analyze the related factors of awareness of hepatitis C prevention and treatment among FSW and MSM. Results: The awareness rates of hepatitis C prevention and treatment knowledge among FSW and MSM were 35.0% (436/1 244) and 11.1% (26/234), respectively. Multivariate logistic regression analysis revealed that the positive related factors of FSW' awareness of hepatitis C prevention and treatment knowledge among those who had high school or technical secondary school education (aOR=4.72, 95%CI: 3.30-6.74) and college education or above (aOR=2.24, 95%CI: 1.58-3.18), those who experienced self-perceived risk of HCV infection (aOR=1.87, 95%CI: 1.43-2.45), negative related factors included married or cohabiting (aOR=0.58, 95%CI: 0.35-0.95), divorce or windowless (aOR=0.44, 95%CI: 0.27-0.72), no condom was used in the most recent commercial sexual activity (aOR=0.54, 95%CI: 0.43-0.69). The positive related factors of MSM's awareness of hepatitis C prevention and treatment knowledge were over 40 years old (aOR=8.65, 95%CI: 3.19-23.42) and having a self-perceived risk of HCV infection (aOR=6.25, 95%CI: 2.50-15.61). Conclusions: The awareness rate of hepatitis C prevention and treatment among FSW and MSM was relatively low in Xizang in 2021 and urgently needs to be improved. It is necessary to formulate targeted publicity strategies based on the characteristics of these two groups of people, increase publicity efforts, and expand the coverage of knowledge publicity to popularize key points about the clinical manifestations, treatment options, and transmission routes of hepatitis C, and carry out necessary warnings and education.
Humans ; Male ; Hepatitis C/therapy* ; Health Knowledge, Attitudes, Practice ; Surveys and Questionnaires ; Sex Workers/psychology* ; Homosexuality, Male ; Female ; Adult ; China ; Young Adult ; Middle Aged ; Logistic Models

Objective: To evaluate the application value of a digital technology-based multiparameter vital signs monitoring system in perioperative comprehensive full-cycle surveillance. Methods: A comprehensive multidimensional vital signs monitoring system was developed through the integration of medical-grade wireless wearable devices, incorporating patch-type ambulatory electrocardiographic monitor, continuous glucose monitoring sensor, pulse oximeter, wireless digital thermometer, smart wristband, and bioelectrical impedance analyzer. This system facilitates continuous real-time acquisition of multiple physiological parameters including electrocardiogram, blood glucose, oxygen saturation, body temperature, physical activity, and body composition indices. The acquired data were systematically integrated and analyzed through a four-level digital architecture consisting of nurse mobile interfaces, bedside patient terminals, centralized ward monitoring displays, and hospital management information systems. One patient with gastric cancer complicated by diabetes mellitus was selected for full-cycle digital monitoring from preoperative evaluation to hospital discharge. The technical performance of the monitoring system was assessed in terms of data acquisition continuity and timeliness of abnormal event alerts. Results: The monitoring system effectively identified early postoperative abnormalities, such as decreased oxygen saturation and blood glucose fluctuations, providing timely guidance for clinical intervention. The built-in algorithm enabled visualization of perioperative stress levels through heart rate variability indices and continuous glucose monitoring data. The patient demonstrated good compliance with early postoperative mobilization, and the satisfaction score for monitoring management was 4 points based on the Likert 5-point scale. Conclusions: The multiparameter vital signs monitoring system enhanced the precision of perioperative management through continuous and dynamic physiological status assessment. Its modular design aligns with the principles of enhanced recovery after surgery, offering a novel technological solution for intelligent perioperative management.
Humans ; Stomach Neoplasms/physiopathology* ; Vital Signs ; Monitoring, Physiologic/instrumentation* ; Diabetes Mellitus ; Wearable Electronic Devices ; Perioperative Period