ObjectiveTo explore the mechanism of Sangpi Zhike prescription in treating cough after respiratory syncytial virus （RSV） infection through the "lung-intestine co-treatment" approach using network pharmacology and animal experimental validation. MethodsActive ingredients and targets of Sangpi Zhike prescription were retrieved from the Traditional Chinese Medicine Systems Pharmacology （TCMSP） database. Disease targets were obtained from GeneCards and Online Mendelian Inheritance in Man（OMIM） databases. Protein-protein interaction （PPI） networks and drug-component-target networks were constructed using overlapping targets between drugs and diseases to identify core targets. Gene ontology（GO） and Kyoto encyclopedia of genes and genomes（KEGG） pathway enrichment analyses were performed on the overlapping targets. Sixty mouse models were established： 10 as the normal group， and the remaining mice were infected with RSV via slow nasal drip of RSV suspension， with cough induced using capsaicin solution. After modeling， mice were divided into a model group， a Montelukast Sodium group （1 mg·kg^-1·d^-1）， and low， medium， and high dose groups of Sangpi Zhike prescription （4.875，9.75，and 19.5 g·kg^-1·d^-1）， with 10 mice per group. From day 14 after RSV infection， the normal and model groups received saline via gavage， while other groups received corresponding drug treatments once daily for 5 d. Hematoxylin-eosin（HE） staining was used to observe pathological changes in lung and intestinal tissue. The protein content of extracellular signal-regulated kinase 1/2 （ERK1/2） and phosphorylated （p）-ERK1/2 in the lung and colon tissue of mice was detected by Western blot. Real-time polymerase chain reaction（Real-time PCR） detected ERK1/2 mRNA expression in lung and intestinal tissue. Immunohistochemistry assessed p-MEK1/2， p-ERK1/2， p-c-Fos protein levels， and inflammatory cytokines interleukin（IL）-4 and （TNF）-α in lung and colon tissue. ResultsNetwork pharmacology identified 184 active ingredients and 684 targets in Sangpi Zhike prescription， with 1 344 RSV-related disease targets and 209 overlapping targets. Core targets included TNF， Fos， and Jun. KEGG enrichment revealed 179 pathways， primarily mitogen-activated protein kinase（MAPK）， cancer， TNF， and IL-17 signaling pathways. Animal experiments showed that， compared to those of the normal group， the lung tissue sections of the model group showed typical inflammatory damage， infiltration of inflammatory cells， rupture of alveolar septa， extensive alveolar fusion， and disruption of tight junctions between single-layer columnar epithelial cells in the intestinal tissue. The values of p-ERK1/2 and ERK1/2 in lung and intestinal tissue were significantly increased （P<0.01）， and the expression level of ERK1/2 mRNA was significantly elevated （P<0.01）. The levels of ERK1/2， p-MEK1/2， p-ERK1/2， p-c-Fos， IL-4， and TNF-α along the ERK pathway were significantly increased （P<0.05， P<0.01）. Compared to the model group， Sangpi Zhike prescription groups showed reduced lung and intestinal inflammation， decreased p-ERK1/2/ERK1/2 ratios （P<0.05，P<0.01）， lower ERK1/2 mRNA levels， and downregulated ERK pathway proteins （P<0.05，P<0.01）. ConclusionSangpi Zhike prescription alleviates cough and intestinal symptoms after RSV infection via the "lung-intestine co-treatment" mechanism by suppressing expression levels of ERK1/2， p-MEK1/2， p-ERK1/2， p-c-Fos， IL-4， and TNF-α on ERK pathway components， thereby mitigating lung and intestinal pathological damage.

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 construct a nomogram based on preoperative MRI imaging features for the prediction of muscle-invasive upper urinary tract urothelial carcinoma（UTUC）and evaluate its performance.Methods:This retrospective cohort study analyzed the clinical data of 99 UTUC patients treated at the First Affiliated Hospital of Nanjing Medical University from April 2018 to May 2024. Among them，69（69.7%）were male and 30（30.3%）were female，with a median age of 67.0 years. All patients underwent preoperative MRI and radical nephroureterectomy. According to postoperative pathology，tumors staged ≥ T 2 were assigned to the muscle-invasive group，and those staged ≤ T 1 were assigned to the non-muscle-invasive group. Baseline data，pathological information，and imaging characteristics were collected and compared between the two groups. Logistic regression analysis was performed to identify risk factors for muscle-invasive UTUC，and a nomogram was constructed. The diagnostic performance of the model was assessed using receiver operating characteristic（ROC）curves，calibration curves，and decision curve analysis（DCA）. Results:Among the 99 patients，70（70.7%）were diagnosed with muscle-invasive UTUC，and 29（29.3%）with non-muscle-invasive UTUC. The muscle-invasive group had significantly larger tumor size［4.5（2.8，7.0）cm vs. 3.0（2.3，4.5）cm， P = 0.029］，a higher incidence of multifocal tumors［37.1%（26/70）vs. 3.5%（1/29）， P < 0.001］，patchy tumors［30.0%（21/70）vs. 6.9%（2/29）， P = 0.019］，spiculated tumor margins［52.9%（37/70）vs. 17.2%（5/29）， P = 0.001］，tumor compression on renal parenchyma or periureteral/peripelvic fat［68.6%（48/70）vs. 10.3%（3/29）， P < 0.001］，high-grade pathology［92.9%（65/70）vs. 75.9%（22/29）， P = 0.043］，lymph node metastasis［28.6%（20/70）vs. 0， P = 0.001］，and lymphovascular invasion［42.9%（30/70）vs. 10.3%（3/29）， P=0.002］. The apparent diffusion coefficient（ADC）values［0.9（0.8，1.1）× 10 -3 mm2/s vs. 1.1（1.0，1.4）× 10 -3 mm2/s， P < 0.001］and normalized ADC（NADC）values［0.8（0.7，1.0）vs. 0.9（0.8，1.1）， P = 0.002］were significantly lower in the muscle-invasive group. Univariate logistic regression identified multifocality，patchy tumor patterns，spiculated tumor margins，tumor compression on renal parenchyma or periureteral/peripelvic fat，and low NADC values as risk factors for muscle-invasive UTUC（all P < 0.05）. Multivariate analysis revealed multifocality（ OR = 17.903，95% CI 1.650 - 194.253， P = 0.018），tumor compression on renal parenchyma or perirenal / ureteral fat（ OR = 14.690，95% CI 3.069 - 70.323， P < 0.001），and low NADC value（ OR = 0.016，95% CI 0.001 - 0.471， P = 0.017）as independent risk factors. A nomogram was constructed based on these factors. The area under the ROC curve（AUC）of the model was 0.898（95% CI 0.838 - 0.957），with an optimal cutoff value of 0.639. The model showed an accuracy of 83.8%，sensitivity of 81.4%，and specificity of 89.7%. Calibration curves indicated good calibration，and DCA showed that the model provided substantial clinical net benefit. Conclusions:This study constructed a nomogram based on preoperative MRI features，including tumor multifocality，compression on renal parenchyma or periureteral/peripelvic fat and NADC value，which demonstrates good predictive performances for muscle-invasive UTUC.

Objective:To explore the feasibility of the new auxiliary analysis software in chromosomal aberration analysis of radiation workers during occupational health examinations.Methods:Health examination data of 2 469 radiation workers in Henan province were collected. Manual analysis of chromosomal aberrations in peripheral blood lymphocytes was conducted using the new auxiliary software and the Ikaros software. Then, the chromosomal aberrations detected using both software tools were compared.Results:The new auxiliary software yielded a lower chromosomal aberration rate among radiation workers compared with the Ikaros software [(0.314 ± 0.014)% vs. (0.391 ± 0.022)%, χ2 = 9.24, P = 0.002]. Notably, the new auxiliary software yielded a significantly lower rate of acentric fragments (ace) [(0.136 ± 0.009)% vs. (0.209 ± 0.020)%, χ2= 17.76, P < 0.001]. However, no statistically significant differences were observed between the result of the two software tools in the rates of dicentrics plus rings (dic + r) and translocations ( P > 0.05). According to the GBZ/T 248-2014 standard, the differences in abnormality rates of chromosomal aberrations between the two groups had no statistically significance ( P > 0.05), with both groups showing an abnormality rate of 0 for ace. Furthermore, the new auxiliary software could double the detection efficiency. Among pre-service radiation workers of various occupations, the differences in the chromosomal aberrations detected using the two software tools exhibited statistical significance ( χ2 = 10.26, P = 0.001). In contrast, the differences in the chromosomal aberrations among in-service and post-service radiation workers had no statistically different significance ( P>0.05). The Poisson regression analysis result demonstrated that the rate of chromosomal aberrations excluding ace was affected by age ( z = 2.73, P = 0.006), while gender, analysis method, service status, and occupation had no impact. Conclusions:The two software tools yielded largely consistent result in detecting chromosomal aberrations induced by exposure to ionizing radiation. Notably, the new auxiliary software can significantly improve detection efficiency, indicating the feasibility of applying it to chromosomal aberration analysis among radiation workers.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

Objective:To describe the prevalence of preserved ratio impaired spirometry (PRISm) in participants from the China Kadoorie Biobank (CKB) and explore the influencing factors.Methods:The CKB project conducted the baseline survey, the first and the second resurvey in 2004-2008, 2008, and 2013-2014, respectively. Based on the lung function tests, the participants were categorized into three groups: regular, PRISm, and airflow obstruction. The prevalence of PRISm was reported by gender, age, and region at the baseline survey. The secular trend in the prevalence of PRISm was described during the three surveys. Finally, we used the multiple logistic regression model to examine the factors related to PRISm in the baseline survey.Results:After standardization for gender, age, and region according to the sixth national census data in 2010, the overall prevalence of PRISm and airflow obstruction among the 434 760 participants at baseline was 24.8% and 6.1%, respectively. The prevalence of PRISm was higher in rural (25.4%) than that in urban areas (24.3%). Of the 10 study regions, Gansu had the highest prevalence of PRISm (56.0%), while Henan had the lowest (15.4%). After standardization for gender, age, and region according to the baseline population, the prevalence of PRISm decreased from 24.9% at baseline to 15.7% in the second resurvey, and the prevalence of airflow obstruction increased from 5.9% to 21.4%. Unmarried status, current smoking, using solid fuels for cooking, low body weight, being overweight, obesity, and central obesity were associated with an increased risk of PRISm. In contrast, higher education attainments, increased household income, and maintaining a specific degree of physical activity were associated with a reduced risk of PRISm.Conclusions:The prevalence of PRISm was high in adults aged 40 years and above in China, and it varied by sociodemographic and lifestyle factors.