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.

The medicinal use of Malvae Semen has a long history. In this paper， by consulting the ancient materia medica， prescription， agronomy， literature and other aspects of the classics， the name， origin， evolution of scientific name， quality， harvesting and processing， functions and indications and others of Malvae Semen were systematically sorted out and verified， so as to provide a basis for the development and utilization of famous classical formulas containing this herb. According to the textual research， Shennong Bencaojing began to use Dongkuizi as the correct name， which was used in the past dynasties， and there were also aliases such as Kuicaizi， Huacai， and Kuizi. Through the original research， it can be seen that Kuicai is the mainstream original plant of Malvae Semen， that is， Malva verticillata var. crispa， the Alcea rosea and M. cathayensis are also used. In modern times， the seeds of Abutilon theophrasti have been passed off as Malvae Semen， while the seeds of M. verticillata var. crispa have rarely been used in medicine. And Abutili Semen has been another medicinal material with different efficacy since the collection of Newly Revised Materia Medica in the Tang dynasty. Since the Ming and Qing dynasties， the cultivation of Kuicai has been decreasing， while A. theophrasti is more common and easy to obtain， and Abutili Semen and Malvae Semen are similar in morphology and confused， which should be corrected. In addition， Malvae Fructus is a Mongolian customary medicinal herb， which is different from the traditional use of seeds in traditional Chinese medicine. Kuicai， as an important vegetable in history， was widely cultivated and gradually shrunk after the Song dynasty， it is now mainly produced in southern provinces. The quality evaluation of Malvae Semen is better for those with dry bodies， full grain， grayish brown color， no mud， and no impurities. The harvesting is generally in the autumn and winter. After drying， it is seeded， sieved peel and impurities， mashed， or slightly stir-fried to yellow-white color with gentle fire. It is sweet， cold and slippery in nature and taste， with the main effects of laxation， diuresis， lactation and elimination of swelling. The efficacy of Abutili Semen is clearing heat and removing toxicity， promoting diuresis and removing nebula， the efficacy is quite different from that of Malvae Semen. Based on the results of textual research， it is suggested that M. verticillata var. crispa should be used as the medicinal source of Malvae Semen in the development of famous classical formulas， the corresponding processing methods should be selected according to the requirements of drug processing in the formulas， while the raw products are recommended to be used if the processing is not specified.

The medicinal use of Malvae Semen has a long history. In this paper， by consulting the ancient materia medica， prescription， agronomy， literature and other aspects of the classics， the name， origin， evolution of scientific name， quality， harvesting and processing， functions and indications and others of Malvae Semen were systematically sorted out and verified， so as to provide a basis for the development and utilization of famous classical formulas containing this herb. According to the textual research， Shennong Bencaojing began to use Dongkuizi as the correct name， which was used in the past dynasties， and there were also aliases such as Kuicaizi， Huacai， and Kuizi. Through the original research， it can be seen that Kuicai is the mainstream original plant of Malvae Semen， that is， Malva verticillata var. crispa， the Alcea rosea and M. cathayensis are also used. In modern times， the seeds of Abutilon theophrasti have been passed off as Malvae Semen， while the seeds of M. verticillata var. crispa have rarely been used in medicine. And Abutili Semen has been another medicinal material with different efficacy since the collection of Newly Revised Materia Medica in the Tang dynasty. Since the Ming and Qing dynasties， the cultivation of Kuicai has been decreasing， while A. theophrasti is more common and easy to obtain， and Abutili Semen and Malvae Semen are similar in morphology and confused， which should be corrected. In addition， Malvae Fructus is a Mongolian customary medicinal herb， which is different from the traditional use of seeds in traditional Chinese medicine. Kuicai， as an important vegetable in history， was widely cultivated and gradually shrunk after the Song dynasty， it is now mainly produced in southern provinces. The quality evaluation of Malvae Semen is better for those with dry bodies， full grain， grayish brown color， no mud， and no impurities. The harvesting is generally in the autumn and winter. After drying， it is seeded， sieved peel and impurities， mashed， or slightly stir-fried to yellow-white color with gentle fire. It is sweet， cold and slippery in nature and taste， with the main effects of laxation， diuresis， lactation and elimination of swelling. The efficacy of Abutili Semen is clearing heat and removing toxicity， promoting diuresis and removing nebula， the efficacy is quite different from that of Malvae Semen. Based on the results of textual research， it is suggested that M. verticillata var. crispa should be used as the medicinal source of Malvae Semen in the development of famous classical formulas， the corresponding processing methods should be selected according to the requirements of drug processing in the formulas， while the raw products are recommended to be used if the processing is not specified.

Diabetes of the exocrine pancreas(DEP) refers to a complication of diseases of the exocrine pancreas, associated with an increased risk of pancreatic cancer. Recent evidence has indicated that DEP, after type 2 diabetes, has become the second most common type of adults-onset diabetes, with a higher prevalence than type 1 diabetes. DEP is not a single condition but encompasses various exocrine pancreatic disorders that lead to hyperglycemia through different mechanisms, creating significant diagnostic and therapeutic challenges. This review examines the latest research on the risk factors and pathogenesis of DEP, aiming to provide insights to improve its clinical management strategies.

Objective:To develop an intelligent surveillance system for identifying upper gastrointestinal high-risk patients and assigning surveillance intervals, and to verify its efficacy.Methods:The endoscopic and pathological reports of 23 035 patients undergoing endoscopy at Renmin Hospital of Wuhan University from January to October 2021 were collected retrospectively. A training set of 17 934 patients (January to August) and a test set of 5 101 patients (September to October) were established. Keywords in the endoscopic and pathological reports were extracted by the intelligent surveillance system, and high-risk patients were automatically identified and classified into 7 risk levels. Then the standardized surveillance intervals were assigned based on the guideline. Guideline-based surveillance intervals assigned by expert endoscopists based on endoscopic and pathological reports were used as the golden standard. The accuracy of the intelligent surveillance system was calculated. Of the patients within the test set, 189 were hospitalized and the surveillance intervals given by physicians could be obtained from the electronic health records. The accuracy of the intelligent surveillance system with that of physicians from different departments was compared. Then 67 patients were randomly selected from 189 patients by simple random sampling to evaluate the adjunctive effect of the system in assigning surveillance intervals among 3 endoscopists.Results:The overall accuracy of the intelligent surveillance system in identifying upper gastrointestinal high-risk patients was 99.94% (5 098/5 101), and that of assigning surveillance intervals to correctly included patients was 100.00% (534/534). The intelligent surveillance system achieved significantly higher accuracy compared with all physicians from different departments [98.94% (187/189) VS 35.45% (67/189), χ2=118.01, P<0.001] as well as physicians from department of gastroenterology [100.00% (117/117) VS 24.79% (29/117), χ2=86.01, P<0.001]. With the assistance of the intelligent surveillance system, the endoscopists' accuracy of assigning surveillance intervals to 67 patients was significantly improved [55.22% (111/201) VS 22.39% (45/201), χ2=58.68, P<0.001]. Conclusion:The intelligent surveillance system can accurately identify upper gastrointestinal high-risk patients and assign surveillance intervals according to risk levels, which can alleviate the workload of doctors and improve the follow-up rate of patients.

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.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.