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 This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective To evaluate the efficacy and safety of rabbit anti-human thymocyte immuneglobulin(rATG)induction therapy in kidney transplant recipients from donation after cardiac death in China.Methods This was a prospective,multicenter,single-arm and interventional study conducted in China(NCT03099122).Adult patients who underwent kidney transplantation from donation after cardiac death and received rATG induction therapy(cumulative dose of 5 mg/kg)were included.Univariate and multivariate logistic regression analyses were used to identify factors associated with acute rejection(AR),delayed graft function(DGF),graft failure and patient death.The occurrence of adverse events was also analyzed.Results A total of 115 adult patients were enrolled in the study,of whom 107 were evaluable for efficacy.The incidence of biopsy-proven acute rejection(BPAR)and acute rejection(AR)was 2.8%(95%confidence interval 0.6%-8.0%)and 4.7%(95%confidence interval 1.5%-10.6%),respectively.The incidence of delayed graft function(DGF)was 13.1%(95%confidence interval 7.3%-21.0%).Graft and patient survival rates were 97.2%(95%confidence interval 92.0%-99.4%)and 99.1%(95%confidence interval 94.9%-100%),respectively.Multivariate logistic regression analysis showed that donor serum creatinine and recipient panel reactive antibodies were risk factors for DGF(both P<0.05).Common treatment-emergent adverse events(incidence>5%)included anemia(8.7%),infectious pneumonia(8.7%),and urinary tract infection(8.7%).Conclusions Standard-dose rATG induction therapy demonstrates low incidences of BPAR,AR,and DGF,and good safety in kidney transplant recipients from donation after cardiac death in China.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective To evaluate the efficacy and safety of rabbit anti-human thymocyte immuneglobulin(rATG)induction therapy in kidney transplant recipients from donation after cardiac death in China.Methods This was a prospective,multicenter,single-arm and interventional study conducted in China(NCT03099122).Adult patients who underwent kidney transplantation from donation after cardiac death and received rATG induction therapy(cumulative dose of 5 mg/kg)were included.Univariate and multivariate logistic regression analyses were used to identify factors associated with acute rejection(AR),delayed graft function(DGF),graft failure and patient death.The occurrence of adverse events was also analyzed.Results A total of 115 adult patients were enrolled in the study,of whom 107 were evaluable for efficacy.The incidence of biopsy-proven acute rejection(BPAR)and acute rejection(AR)was 2.8%(95%confidence interval 0.6%-8.0%)and 4.7%(95%confidence interval 1.5%-10.6%),respectively.The incidence of delayed graft function(DGF)was 13.1%(95%confidence interval 7.3%-21.0%).Graft and patient survival rates were 97.2%(95%confidence interval 92.0%-99.4%)and 99.1%(95%confidence interval 94.9%-100%),respectively.Multivariate logistic regression analysis showed that donor serum creatinine and recipient panel reactive antibodies were risk factors for DGF(both P<0.05).Common treatment-emergent adverse events(incidence>5%)included anemia(8.7%),infectious pneumonia(8.7%),and urinary tract infection(8.7%).Conclusions Standard-dose rATG induction therapy demonstrates low incidences of BPAR,AR,and DGF,and good safety in kidney transplant recipients from donation after cardiac death in China.

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.

Objective To analyze the radiomic and clinical features extracted from 2D ultrasound images of thyroid tumors in patients with Hashimoto's thyroiditis(HT)combined with papillary thyroid carcinoma(PTC)using machine learning(ML)models,and to explore the diagnostic performance of the method in making preoperative noninvasive identification of cervical lymph node metastasis(LNM).Methods A total of 528 patients with HT combined with PTC were enrolled and divided into two groups based on their pathological results of the presence or absence of LNM.The groups were subsequently designated the With LNM Group and the Without LNM Group.Three ultrasound doctors independently delineated the regions of interest and extracted radiomic features.Two modes,radiomic features and radiomics-clinical features,were used to construct random forest(RF),support vector machine(SVM),LightGBM,K-nearest neighbor(KNN),and XGBoost models.The performance of these five ML models in the two modes was evaluated by the receiver operating characteristic(ROC)curves on the test dataset,and SHapley Additive exPlanations(SHAP)was used for model visualization.Results All five ML models showed good performance,with area under the ROC curve(AUC)ranging from 0.798 to 0.921.LightGBM and XGBoost demonstrated the best performance,outperforming the other models(P＜0.05).The ML models constructed with radiomics-clinical features performed better than those constructed using only radiomic features(P＜0.05).The SHAP visualization of the best-performing models indicated that the anteroposterior diameter,superoinferior diameter,original_shape_VoxelVolume,age,wavelet-LHL_firstorder_10Percentile,and left-to-right diameter had the most significant effect on the LightGBM model.On the other hand,the superoinferior diameter,anteroposterior diameter,left-to-right diameter,original_shape_VoxelVolume,original_firstorder_InterquartileRange,and age had the most significant effect on the XGBoost model.Conclusion ML models based on radiomics and clinical features can accurately evaluate the cervical lymph node status in patients with HT combined with PTC.Among the 5 ML models,LightGBM and XGBoost demonstrate the best evaluation performance.

Objective:To understand the relationship between the RNA shedding time of SARS-CoV-2 infected persons and the single nucleotide mutation of the virus, the population of infected persons, underlying diseases and other factors, so as to provide more clues for the study of SARS-CoV-2 infection dynamics.Methods:The data of epidemiology, clinical manifestations, and underlying diseases of infected persons in a cluster epidemic of COVID-19 in Jiangsu province from July to September 2021 were collected. Nasopharyngeal swab samples of cases were collected, and the whole genome of the virus was sequenced by second-generation sequencing technology. The online analysis platform was used to judge the virus type and analyze the mutation site, and Cox proportional risk model was used to analyze the relationship between the RNA shedding time of SARS-CoV-2 and various research factors.Results:There were 350 persons who finally obtained the whole genome sequence of the virus in this COVID-19 outbreak, of which 60.3% were female, the median age was 49 years old (interquartile range, IQR: 37-65 years old)), and the median time of virus shedding was 33 days ( IQR, 26-44 days). The whole-genome sequencing analysis showed that compared with the Wuhan reference strain sequence, the infected persons’ sequence had 34~41 nucleotide mutation sites, belonging to VOC/Delta variant (B.1.617.2 evolutionary branch), and C346T, C1060T, T2803C, T7513C, A29681C were the main single nucleotide polymorphisms (SNPs) of this epidemic. Cox regression analysis showed that age, underlying disease, clinical classification, vaccination, SNP T2803C and T7513C had an impact on the RNA shedding time of SARS-CoV-2. The adjusted multivariate Cox regression result showed that age [ HR=0.73, 95% CI (0.55, 0.95)] and T7513C [ HR=0.37, 95% CI (0.18, 0.77)] were still the risk factors for the extension of SARS-CoV-2 RNA shedding time. Conclusions:This study analyzed the effects of the individual factors and viral single nucleotide variations on the time of viral RNA shedding. Those who were older, suffered from hypertension, had more severe clinical symptoms, were not vaccinated or incompletely vaccinated, and had T7513C mutation in the infected virus, had a risk of a long RNA shedding time of SARS-CoV-2, which should be given special attention and follow-up after rehabilitation.