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.

Objective To evaluate cardiac involvement in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) using echocardiography combined with electrocardiography. Methods A retrospective analysis was performed on the detailed medical records of AAV patients treated in Jining First People’s Hospital between January 2020 and December 2024. Eighty patients were enrolled in the AAV group, and the risk of heart disease was compared between the AAV group and a control group with 80 subjects matched for age, sex, and cardiovascular disease risk factors. Results Electrocardiographic abnormalities were observed in 78.75% of patients in the AAV group, while significant electrocardiographic abnormalities only occurred in symptomatic patients in the control group. There were no differences in left atrial enlargement or interventricular septal thickening between the AAV group and the control group. The overall left ventricular systolic function in the AAV group was lower than that in the control group (8.75% vs. 0). The incidence of reduced diastolic function in the AAV group was significantly higher than that in the control group (37.5% vs. 15%). The incidence rates of tricuspid regurgitation, mitral regurgitation, aortic regurgitation, and pericardial effusion in the AAV group were significantly higher than those in the control group. Pericardial thickening, aortic stenosis, pulmonary hypertension, and rare periaortic granulomas were found in the AAV group, but not in the control group. Conclusion Echocardiography and electrocardiography are important examination methods for evaluating cardiac involvement in AAV. These methods have key roles in disease screening, diagnosis and treatment, follow-up, and prognosis judgment.

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.

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit. REGISTRATION No. CRD42023427480; https://www.crd.york.ac.uk/prospero/display_record.php?
Humans ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Dose-Response Relationship, Drug ; Randomized Controlled Trials as Topic

BACKGROUND: Prospective evidence on how offspring number influences morbidity and mortality remains limited. This study investigated the associations between number of offspring and morbidity and mortality risks among 0.5 million Chinese adults. METHODS: By using data from the China Kadoorie Biobank (CKB; n = 512,723, an approximately 12-year follow-up), sex-stratified phenome-wide association study (PheWAS) analyses were conducted to investigate associations between offspring number (without vs . with offspring; more than one vs . one offspring) and risks of ICD10-coded morbidity and mortality. Sex-specific adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were estimated by Cox proportional-hazards models. RESULTS: Among 210,129 men and 302,284 women aged 30-79 years, 1,338,837 incident events were recorded. PheWAS results revealed that offspring number was associated with disease risks across multiple systems. Cox models showed that childless men ( vs . one offspring) had higher risks for nine of 36 diseases, while childless women for five of 37. Each additional offspring was associated with reduced risks of mental and behavioral disorders in men (aHR [95% CI] = 0.93 [0.87-0.98]) and both mental and behavioral disorders (aHR [95% CI] = 0.93 [0.89-0.97]) and breast cancer (aHR [95% CI] = 0.82 [0.78-0.86]) in women. However, each additional offspring was associated with a 4% increase in the risk of cholelithiasis and cholecystitis in women (aHR [95% CI] = 1.04 [1.02-1.07]). Among 282,630 patients, 44,533 deaths were documented. Childless patients had higher mortality risk in both men (aHR [95% CI] = 1.37 [1.28-1.47]) and women (aHR [95% CI] = 1.27 [1.15-1.41]). For men, each additional offspring reduced mortality by 4% (aHR [95% CI] = 0.96 [0.95-0.98]), while for women, the lowest risk was observed among those with three to four offspring ( Pnonlinear <0.0001). CONCLUSIONS Offspring number is closely linked to morbidity and mortality risks. Further research is warranted to verify our findings and clarify the underlying mechanisms involved.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology* ; Morbidity ; Proportional Hazards Models ; Prospective Studies ; Risk Factors ; Family Characteristics ; Mortality ; East Asian People

A primary hallmark of pathological cardiac hypertrophy is excess protein synthesis due to enhanced translational activity. However, regulatory mechanisms at the translational level under cardiac stress remain poorly understood. Here we examined the translational regulations in a mouse cardiac hypertrophy model induced by transaortic constriction (TAC) and explored the conservative networks versus the translatome pattern in human dilated cardiomyopathy (DCM). The results showed that the heart weight to body weight ratio was significantly elevated, and the ejection fraction and fractional shortening significantly decreased 8 weeks after TAC. Puromycin incorporation assay showed that TAC significantly increased protein synthesis rate in the left ventricle. RNA-seq revealed 1,632 differentially expressed genes showing functional enrichment in pathways including extracellular matrix remodeling, metabolic processes, and signaling cascades associated with pathological cardiomyocyte growth. When combined with ribosome profiling analysis, we revealed that translation efficiency (TE) of 1,495 genes was enhanced, while the TE of 933 genes was inhibited following TAC. In DCM patients, 1,354 genes were upregulated versus 1,213 genes were downregulated at the translation level. Although the majority of the genes were not shared between mouse and human, we identified 93 genes, including Nos3, Kcnj8, Adcy4, Itpr1, Fasn, Scd1, etc., with highly conserved translational regulations. These genes were remarkably associated with myocardial function, signal transduction, and energy metabolism, particularly related to cGMP-PKG signaling and fatty acid metabolism. Motif analysis revealed enriched regulatory elements in the 5' untranslated regions (5'UTRs) of transcripts with differential TE, which exhibited strong cross-species sequence conservation. Our study revealed novel regulatory mechanisms at the translational level in cardiac hypertrophy and identified conserved translation-sensitive targets with potential applications to treat cardiac hypertrophy and heart failure in the clinic.
Animals ; Humans ; Cardiomegaly/physiopathology* ; Ribosomes/physiology* ; Protein Biosynthesis/physiology* ; Mice ; Cardiomyopathy, Dilated/genetics* ; Ribosome Profiling