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.

OBJECTIVE To establish a quantitative fingerprint analysis method for sugar components in Yuanhu Zhitong oral liquid using high performance liquid chromatography-charged aerosol detection(HPLC-CAD). METHODS Chromatographic column was NH2P-50 4E(4.6 mm×250 mm, 5 μm) column. Water(A) and acetonitrile(B) were used as the mobile phase in the gradient elute mode. The column temperature was 30 ℃. The injection volume was 10 μL. The flow rate was 0.6 mL·min−1. The evaporation temperature of CAD was 35 ℃. The acquisition frequency was 10 Hz. The power function value was 1.0. RESULTS The linear relationship of the quantitative component was good within the quantitative range, with R2>0.999. The relative standard deviations(RSDs) of instrument precision, intermediate precision and method repeatability were all <3%. The test solution was stable within 24 h. The average recoveries at low, medium and high concentration levels ranged 97.15%−101.13%. There were 5 common peaks in the fingerprint. The RSDs of instrument precision, method repeatability and sample stability were all <4%. CONCLUSION The established analytical method is stable, accurate and reproducible. It can be used to detect sugar excipients in the preparations.

ObjectiveTo improve the quality standard of Yuanhu Zhitong oral liquid in order to strengthen the quality control of this oral liquid. MethodThin layer chromatography（TLC） was used for the qualitative identification of Corydalis Rhizoma and Angelicae Dahuricae Radix in Yuanhu Zhitong oral liquid by taking tetrahydropalmatine， corydaline reference substances and Corydalis Rhizoma reference medicinal materials as reference， and cyclohexane-trichloromethane-methanol（5∶3∶0.5） as developing solvent， Corydalis Rhizoma was identified using GF254 glass thin layer plate under ultraviolet light（365 nm）. And taking petroleum ether（60-90 ℃） -ether-formic acid（10∶10∶1） as developing solvent， Angelicae Dahuricae Radix was identified using a silica gel G TLC plate under ultraviolet light（305 nm）. High performance liquid chromatography（HPLC） was performed on a Waters XSelect HSS T3 column（4.6 mm×250 mm， 5 μm） with acetonitrile（A）-0.1% glacial acetic acid solution（adjusted pH to 6.1 by triethylamine）（B） as the mobile phase for gradient elution（0-10 min， 20%-30%A； 10-25 min， 30%-40%A； 25-40 min， 40%-50%A； 40-60 min， 50%-60%A）， the detection wavelength was set at 280 nm， then the fingerprint of Yuanhu Zhitong oral liquid was established， and the contents of tetrahydropalmatine and corydaline were determined. ResultIn the thin layer chromatograms， the corresponding spots of Yuanhu Zhitong oral liquid， the reference substances and reference medicinal materials were clear， with good separation and strong specificity. A total of 12 common peaks were identified in 10 batches of Yuanhu Zhitong oral liquid samples， and the peaks of berberine hydrochloride， dehydrocorydaline， glaucine， tetrahydropalmatine and corydaline. The similarities between the 10 batches of samples and the control fingerprint were all >0.90. The results of determination showed that the concentrations of corydaline and tetrahydropalmatine had good linearity with paek area in the range of 0.038 6-0.193 0， 0.034 0-0.170 0 g·L^-1， respectively. The methodological investigation was qualified， and the contents of corydaline and tetrahydropalmatine in 10 batches of Yuanhu Zhitong oral liquid samples were 0.077 5-0.142 9、0.126 1-0.178 2 g·L^-1， respectively. ConclusionThe established TLC， fingerprint and determination are simple， specific and reproducible， which can be used to improve the quality control standard of Yuanhu Zhitong oral liquid.

The hemodynamics of children with congenital heart disease (CHD) often changes during the perioperative period. Unfortunately, the evaluation of cardiac function in children with CHD is mainly focused on the left ventricle. With the further understanding of cardiac hemodynamics, the monitoring of RV function has gradually become an important part of cardiac intensive care department. We totally searched five databases including Pubmed, Embase, Cochrane, Wanfang Med, as well as China National Knowledge Infrastructure (CNKI) and reviewed the clinical research progress of the application of TAPSE in the evaluation of RV systolic function in children with CHD to provide a theoretical basic for the monitoring of RV function before and after operation in children with CHD.

The emergence of SARS-CoV-2 variants of concern and repeated outbreaks of coronavirus epidemics in the past two decades emphasize the need for next-generation pan-coronaviral therapeutics. Drugging the multi-functional papain-like protease (PLpro) domain of the viral nsp3 holds promise. However, none of the known coronavirus PLpro inhibitors has been shown to be in vivo active. Herein, we screened a structurally diverse library of 50,080 compounds for potential coronavirus PLpro inhibitors and identified a noncovalent lead inhibitor F0213 that has broad-spectrum anti-coronaviral activity, including against the Sarbecoviruses (SARS-CoV-1 and SARS-CoV-2), Merbecovirus (MERS-CoV), as well as the Alphacoronavirus (hCoV-229E and hCoV-OC43). Importantly, F0213 confers protection in both SARS-CoV-2-infected hamsters and MERS-CoV-infected human DPP4-knockin mice. F0213 possesses a dual therapeutic functionality that suppresses coronavirus replication via blocking viral polyprotein cleavage, as well as promoting antiviral immunity by antagonizing the PLpro deubiquitinase activity. Despite the significant difference of substrate recognition, mode of inhibition studies suggest that F0213 is a competitive inhibitor against SARS2-PLpro via binding with the 157K amino acid residue, whereas an allosteric inhibitor of MERS-PLpro interacting with its 271E position. Our proof-of-concept findings demonstrated that PLpro is a valid target for the development of broad-spectrum anti-coronavirus agents. The orally administered F0213 may serve as a promising lead compound for combating the ongoing COVID-19 pandemic and future coronavirus outbreaks.
Animals ; Coronavirus Papain-Like Proteases/antagonists & inhibitors* ; Cricetinae ; Humans ; Mice ; Pandemics ; SARS-CoV-2/enzymology* ; COVID-19 Drug Treatment

Objective:To explore the knowledge of pediatric nurses about intravenous infusion therapy, find the problems, and provide a reference for managers to provide targeted training.Methods:In this multicenter cross-sectional study, the nurses on duty were selected from 43 hospitals under the medical alliance of Beijing Children's Hospital by convenient sampling from July 24th to 31st, 2018. The self-made questionnaires were used in this study, and data were collected online through wjx.cn. The questionnaires were developed by 4 veteran venous infusion therapists according to the Infusion Therapy Standards of Practice (2016 ) as well as the Technical Standards for Intravenous Therapy Nursing Issued by the National Health Commission of the People's Republic of China in 2014. The questionnaire mainly included three parts. Part I was the basic information of the respondent subjects and hospitals; Part II was the knowledge of the subjects about domestic standards for infusion therapy and Infusion Therapy Standards of Practice; and Part III the knowledge of the subjects about infusion therapy operations and maintenance as well as the current status of infusion therapy in their departments. SPSS 17.0 was used for statistical analysis. Results:A total of 8 405 pediatric nurses were investigated in this study. The average knowledge of infusion therapy was (11.72±2.41), and the pass rate was 54.8%. The survey showed that nurses performed well in selecting infusion tools and puncture sites according to the nature of the drug, evaluating the patient and treatment plan before infusion therapy, the sealing method of peripheral vein indwelling needles, and treatment methods when resistance was encountered or no blood was returned in suction. The univariate analysis revealed that the level of the hospital, whether the hospital had a dosing center and an infusion therapy group, the nurse's education, job title, working years, position, and whether to teach young nurses were the main influencing factors to nurses' knowledge about infusion therapy ( P<0.05) . The results of Logistic regression analysis showed that whether the hospital had a dosing center and a infusion therapy group, and the nurse's education background, title, working years, position, and teaching were the main influencing factors for the nurse's knowledge of infusion therapy ( P<0.05) . Conclusions:Pediatric nurses have insufficient knowledge about infusion therapy. They need to be further trained in the basics of infusion therapy, especially in the correct use of infusion tools, the proper selection of puncture sites, and catheter maintenance.

Objective To establish a rat model of superior mesenteric vein thrombosis by vein ligation and to simulate the pathological process of the disease, and to provide the basis for studies of its pathogenesis and treatment.Methods Ninety-six SPF male SD rats were randomly divided into three groups: Group A (sham operation group), group B (strangulation group) and group C (simple group), 32 rats in each group.Rats in group A were only opened the abdominal cavity but not blocked the blood supply.The rats were sacrificed at 8, 24, 48 and 72 h after operation.The rats in groups B and C were subjected to establish the strangulation and simple models by superior mesenteric vein thrombosis, respectively, and were sacrificed at 8, 24, 48 and 72 h after modeling.Histological changes (H&E staining) in the rat intestinal tissues were evaluated by a pathological scoring system.The levels of intestinal fatty acid binding protein (IFABP) and α-glutathione S-transferase (α-GST) were detected by ELISA.Results The rat model of mesenteric vein thrombosis was successfully established, with a success rate of 100% (96/96).The pathological analysis revealed that compared with the group A, different degrees of blood stasis and injuries were observed in the intestinal tissues of groups B and C, and the injury were gradually increased in the group B, while gradually reduced in the group C.The degrees of blood stasis and injury were positively correlated with the scope of ligation.The result of ELISA showed that the serum levels of IFABP and α-GST of the rats in groups B and C were significantly higher than those in group A (P < 0.05), and the degree of elevation was positively correlated with the scope of ligation.Conclusions In this study, the rat model of superior mesenteric vein thrombosis is successfully established by vein ligation.This model is simple and easy to operate with a high success rate, and can be used in related research.

Yttrium vanadate:europium nanoprobes (YVO4∶Eu NPs) with good fluorescence properties and water solubility were synthesized by solvent thermal method.Due to the overlapping of the excitation spectrum of YVO4∶Eu NPs and the absorption spectrum of tryptophan, fluorescent internal filter effect (IFE) occurred, in which YVO4∶Eu NPs were the fluorophore and tryptophan was the absorber, leading the fluorescence of YVO4∶Eu NPs was quenched.Therefore, a new method for the determination of tryptophan was established by using fluorescent YVO4∶Eu as nanoprobes based on IFE.Some experimental parameters, such as the adding amount of YVO4∶Eu NPs, pH value of the reacting solution, and reacting time, were investigated.Under the optimum reaction conditions, the linear range of the method was 4.0×10-6-4.0×104 mol/L and the detection limit was 1.0×10-6 mol/L (3σ).The content of tryptophan in the soy sauce was determined with the recovery of 95.2% and 97.3%.This method is simple, rapid, sensitive and accurate.