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.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background Pneumoconiosis, a group of lung disease caused by long-term inhalation of occupational dust, features progressive development, irreversibility, and a high incidence of complications. It seriously endangers the health of the occupational population and exacerbates the socioeconomic burden. Objective To understand the development and major research themes of artificial intelligence research concerning pneumoconiosis and its complications. Methods Relevant academic papers before 2024-10-01 were retrieved from China National Knowledge Infrastructure and Web of Science, and analyzed separately according to the author, institutions, and keywords, then visualized with Citespace, the Bibliometrix package in R, and VOSviewer software. Results This study included 1068 articles (208 Chinese and 860 English). The results showed that artificial intelligence application in pneumoconiosis and its complications has seen explosive growth in the past five years. Institutions were mainly concentrated in universities and their affiliated medical units. Identified research hotspots included medical image recognition and analysis, auxiliary diagnosis, and risk prediction. Keywords like artificial intelligence, tuberculosis, deep learning, chronic obstructive pulmonary disease, and machine learning showed high centrality. Conclusion As computer technology advances, the use of artificial intelligence in addressing pneumoconiosis and its related complications is steadily expanding. Its research perspective has gradually expanded from simple disease prediction to diversified fields such as image recognition and health monitoring. Early pneumoconiosis screening, auxiliary differential diagnosis, andhealth monitoring will be the focus of future research.

Due to the poor ionization efficiency and the weak mass spectrometry(MS)intensity of weakly polar substances,direct analysis using the traditional electrospray ionization mass spectrometry(ESI-MS)is a big challenge.In this study,a novel rapid on-site detection method of four prohibited sex hormones in cosmetics was proposed using online derivatization strategy coupled with a miniature mass spectrometer.The target substances in the samples were extracted by a custom-made polyaniline/multi-walled carbon nanotube solid-phase microextraction(SPME)probe.The stirring speed was 200 r/min,the extraction temperature was 40℃,and the extraction time was 2 min.A pulled dual-channel θ borosilicate glass capillary emitter was used as the nano-ESI ion source.The SPME probe was inserted into the channel containing methanol in theθborosilicate glass capillary.When the spray voltage was applied,the four sex hormones were desorbed and formed spray microdroplets,which then collided with the hydroxylamine microdroplets generated from the other channel.The microdroplets of reaction product entered into the miniature mass spectrometer for direct analysis.The limits of detection(LOD)and limits of quantification(LOQ)for the four sex hormones were 10-20 ng/mL and 20-50 ng/mL,respectively.The recoveries were from 84.6%to 107.8%with the relative standard deviations(RSD)from 4.1%to 11.6%.Compared to detection without derivatization,the MS signals of the four target substances were increased by 3 to 15 times.This method was simple,rapid,highly efficient and sensitive,and suitable for on-site rapid analysis of weakly polar sex hormones in cosmetics.

Objective:To explore the expression changes of nerve growth factor (NGF)/tropomyosin receptor kinase A (TrkA) signaling pathway of dorsal root ganglia (DRG) in incisional rat remifentanil-induced hyperalgesia and its effect on the expression of membrane delta opioid receptor (DOR).Methods:A total of 48 SPF male SD rats were randomly divided into 6 groups based on body weight matching, with 8 in each group, which were control group (infusion of 0.9% NaCl solution via the tail vein), incision pain group (incision pain model established using the Brennan method), remifentanil group (infusion of remifentanil via the tail vein), incision pain+ remifentanil model group (incision pain model established using the Brennan method, followed by infusion of remifentanil via the tail vein), NGF group and TrkA inhibitor group(established incision pain+ remifentanil model after intrathecal injection of NGF (0.06 μg/g) or K252a (0.3 μg/g, TrkA inhibitor)). Mechanical paw withdrawal threshold (PWT) was used to assess pain sensitivity in rats. Western blot was employed to measure the expression of NGF, TrkA, and the total DOR(tDOR) and the membrane DOR(mDOR) in DRG tissues. Immunoelectron microscopy was used to detect subcellular DOR expression in DRG. Data were processed using SPSS 24.0 software. Multiple comparisons among groups were conducted by repeated measures ANOVA or one-way ANOVA, and post-hoc comparisons were performed using the Bonferroni test.Results:(1) The results of pain behavior showed that there was a significant interaction effect between time and group in the comparison of PWT among the six groups of rats before and after intervention ( F=345.817, P<0.001). At each time point after intervention, the PWTs of the incision pain+ remifentanil group were lower than those of the incision pain group and remifentanil group, the PWTs of the NGF group were lower than those of the incision pain+ remifentanil group, and the PWTs of the TrkA inhibitor group were higher than those of the incision pain+ remifentanil group and NGF group (all P<0.05). (2)The Western blot results showed that there were statistically significant differences in the relative levels of NGF, TrkA, and mDOR in the DRG tissues of the six groups of rats ( F=156.2, 163.8, 421.2, all P<0.001). The levels of NGF, TrkA, and mDOR proteins in the incision pain+ remifentanil group (1.45±0.07, 1.46±0.04, 3.01±0.20) were higher than those in the incision pain group (1.25±0.05, 1.24±0.04, 1.84±0.05) and remifentanil group (1.24±0.04, 1.26±0.03, 1.84±0.04) (all P<0.05). The levels of NGF, TrkA, and mDOR in the NGF group (1.57±0.03, 1.58±0.07, 3.74±0.25) were higher than those in the incision pain+ remifentanil group (all P<0.05). The relative expression levels of TrkA, and mDOR in the TrkA inhibitor group (1.25±0.04, 1.68±0.07) were lower than those in the incision pain+ remifentanil group and the NGF group (all P<0.05). (3)The results of immunoelectron microscopy showed that there were statistically significant differences in the localization of DOR in the cell membrane, subcellular sites of synthesis pathways, and subcellular localization of degradation pathways among the six groups of rat DRG tissues ( F=140.3, 60.63, 60.28, all P<0.01). The DOR of the synthesis pathway of incision pain+ remifentanil group was higher than that of the incision pain group and remifentanil group, while the DOR of the synthesis pathway of NGF was higher than that of the incision pain+ remifentanil group.The DOR of the synthesis pathway of TrkA inhibitor group was lower than that of the incision pain+ remifentanil group and NGF group (both P<0.05). The DOR of the degradation pathway in the incision pain+ remifentanil group was lower than that in the incision pain group and remifentanil group, the DOR of the degradation pathway in the NGF group was lower than that in the incision pain+ remifentanil group, and the DOR of the degradation pathway in the TrkA inhibitor group was higher than that in the incision pain+ remifentanil group and NGF group (both P<0.05). Conclusion:The NGF/TrkA signaling pathway is involved in rat incisional pain-remifentanil hyperalgesia by upregulating the delta opioid receptor of the dorsal root ganglia.

Aim To investigate the effects of Ixerin Z,a sesquiterpene lactone from Ixeris sonchifolia,on bone-lipid metabolic imbalance in ApoE-/-mice and to elu-cidate its molecular mechanisms.Methods A mouse model of ApoE-/-was induced using a high-fat diet,followed by eight weeks of Ixerin Z administration at doses of 1 and 10 mg·kg-1.Serum markers related to bone-lipid metabolism and inflammatory cytokines were quantified.Bone mineral density,biomechanical prop-erties,bone tissue morphology,and bone microstructure changes were analyzed.Computational molecular doc-king was performed to identify potential target proteins of Ixerin Z,and its regulatory effects on bone-lipid me-tabolism were investigated.Results Treatment with Ixerin Z markedly decreased the serum levels of total cholesterol,triglycerides,TNF-α,and IL-1β in ApoE-/-mice.It significantly improved bone mineral density,enhanced biomechanical strength,restored tra-becular structure,and reduced fat accumulation in bone tissue.Investigations revealed that Ixerin Z activated PPARα,thereby promoting fatty acid β-oxidation in bone tissue,and stimulating the Wnt/β-Catenin signa-ling pathway to facilitate bone formation.Furthermore,Ixerin Z suppressed the OPG/RANKL/NF-κB signaling pathway,leading to reduced bone resorption,independ-ent of PPARα activation.Conclusions Ixerin Z dem-onstrates potent therapeutic effects on bone-lipid meta-bolic imbalance in ApoE-/-mice.The mechanism in-volves activating PPARα to promote fatty acid β-oxida-tion in bone tissue,activating PPARα/Wnt/β-Catenin signaling pathway to promote bone formation,and in-hibiting OPG/RANKL/NF-κB signaling pathway to re-duce bone resorption.