Respiratory Research最新文献

{"title":"Spontaneous pneumomediastinum and/or pneumothorax in anti-MDA5 dermatomyositis: a refined staging system.","authors":"Jiajia Jin, Wenwen Xu, Huijing Wang, Xiaodong Wang, Qiong Fu, Wanlong Wu, Shuang Ye","doi":"10.1186/s12931-025-03347-x","DOIUrl":"https://doi.org/10.1186/s12931-025-03347-x","url":null,"abstract":"","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"281"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional trends and disparities of lung cancer mortality in the United States, 1999-2022: a retrospective cohort study.","authors":"Taylor Billion, Logan Verheyen, Ali Bin Abdul Jabbar, Noureen Asghar, Mohsin Mirza, Muazzam Mirza, Abubakar Tauseef","doi":"10.1186/s12931-025-03344-0","DOIUrl":"https://doi.org/10.1186/s12931-025-03344-0","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer is the leading cause of cancer death in the US and worldwide. This study examines regional and state-level trends in lung cancer mortality in the US from 1999 to 2022.</p><p><strong>Methods: </strong>Center for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) was used to obtain data on lung cancer mortality in people ages 25 years and older. Age-Adjusted Mortality Rate (AAMR) was calculated along with Annual Percent Change (APC) and Average Annual Percent Change (AAPC).</p><p><strong>Results: </strong>In general, the age-adjusted mortality rate (AAMR) for lung cancer declined from 91.4 (95% CI 91.0 to 91.9) in 1999 to 52.0 (95% CI 51.7 to 52.2) in 2022. Throughout the study, the Midwest and South regions consistently exhibited the highest mortality rates. Notably, distinct patterns emerged when analyzing mortality rates by race and gender within each region.</p><p><strong>Conclusion: </strong>This study revealed significant variations in lung cancer mortality at both regional and state levels. Recognizing these disparities is essential for healthcare policymakers and researchers to monitor progress in reducing lung cancer deaths and identifying the populations most vulnerable to this disease.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"280"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in interstitial lung disease and the COVID-19 pandemic in the United States from 1999 to 2022.","authors":"Charles Leary, Christopher Bine, Nikita Baral, Ali Bin Abdul Jabbar, Anum Mirza, Mohsin Mirza, Abubakar Tauseef","doi":"10.1186/s12931-025-03350-2","DOIUrl":"https://doi.org/10.1186/s12931-025-03350-2","url":null,"abstract":"<p><strong>Introduction: </strong>Although the population was aging, interstitial lung disease mortality had remained relatively stable until it rose following the COVID-19 pandemic in the United States. Despite the consistent overall mortality trend prior to the pandemic, significant demographic and geographic disparities persist across the country.</p><p><strong>Methods: </strong>Disparities in ILD-related mortality were analyzed temporally using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiological Research (CDC WONDER) database. Using ICD Codes J84.1, J84.8, and J84.9 ILD-related crude mortality (CMR) and age-adjusted mortality rates (AAMR) were calculated, and the Join-point Regression Program was used to determine mortality trends between 1999 and 2022. ILD-related mortality was identified using the International Classification of Diseases, 10th Revision, and clinical modification codes J84.1 (Other interstitial pulmonary diseases with fibrosis), J84.8 (Other specified interstitial pulmonary diseases), and J84.9 (Interstitial pulmonary disease, unspecified) were used in patients ≥ 45 years.</p><p><strong>Results: </strong>Based on the results of this study, there have been 609,157 deaths due to ILD during this study period, with the highest mortality rates seen in the Non-Hispanic (NH) American Indian or Alaskan Native population, and the lowest mortality rates seen in the NH African American population. Hispanic or Latinos and NH Whites experienced similar mortality. Males had higher mortality rates than females in all racial groups. Regarding region, the Northeast had significantly lower mortality rates than all other census areas throughout the entire study period. The results of this study demonstrate large increases in AAMR at the onset of COVID-19 beginning in 2020, peaking in 2021, and decreasing in 2022.</p><p><strong>Conclusions: </strong>The COVID-19 pandemic posed unique challenges to our society, especially for patients with interstitial lung disease. While further research is needed to better understand these trends, it is concerning that ILD-related mortality increased throughout the study period. Medical professionals should be cognizant of these trends when treating patients diagnosed with ILD.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"277"},"PeriodicalIF":5.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and multi-database validation of interpretable machine learning models for predicting In-Hospital mortality in pneumonia patients: A comprehensive analysis across four healthcare systems.","authors":"Jiahuan Chen, Dongni Hou, Yuanlin Song","doi":"10.1186/s12931-025-03348-w","DOIUrl":"https://doi.org/10.1186/s12931-025-03348-w","url":null,"abstract":"<p><strong>Background: </strong>Existing machine learning studies for pneumonia mortality prediction are limited by small sample sizes, single-center designs, and lack of comprehensive external validation across diverse healthcare systems. No previous study has systematically validated machine learning models across multiple large-scale databases for pneumonia mortality prediction.</p><p><strong>Methods: </strong>This retrospective multicenter study utilized four large-scale databases to develop and validate machine learning models for predicting in-hospital mortality in pneumonia patients. MIMIC-IV served as the primary training dataset (9,410 patients), with external validation on MIMIC-III (2,487 patients), eICU (13,541 patients), and an in-house multicenter prospective cohort from fudan university (345 patients). Five algorithms were implemented: Random Forest, XGBoost, Logistic Regression, LASSO, and Support Vector Machine. Feature selection used the Boruta algorithm across 21 variables. Model interpretability was assessed using SHAP analysis.</p><p><strong>Results: </strong>The cohort comprised 25,783 pneumonia patients with mortality rates of 17.1%-38.3% across databases. Nine consistently important features were identified: age, diastolic blood pressure, heart rate, temperature, respiratory rate, creatinine, blood urea nitrogen, platelet count, and white blood cell count. XGBoost achieved optimal performance with training AUC 0.747 (95% CI: 0.733-0.761) and robust external validation AUCs of 0.672 (MIMIC-IV testing), 0.670 (MIMIC-III), 0.695 (eICU), and 0.653 (FAHZU). SHAP analysis revealed platelet count as the most influential predictor, followed by blood urea nitrogen and age.</p><p><strong>Conclusions: </strong>This study represents the first comprehensive multi-database validation of machine learning models for pneumonia mortality prediction, demonstrating superior performance compared to traditional scoring systems. The XGBoost model with SHAP interpretability provides a robust tool for clinical decision support, with consistent validation across four databases including our in-house prospective cohort.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"279"},"PeriodicalIF":5.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sodium butyrate targets VWF to inhibit cigarette smoke extract-induced endothelial cell-macrophage crosstalk in regulating inflammatory response.","authors":"Xiaolong Ma, Jiaqi Zhou, Shuyou Yang, Yingqing Zhang, Chaoping Zhu, Yuejiao Sun, Jinli Miao, Hongyan Mo","doi":"10.1186/s12931-025-03338-y","DOIUrl":"https://doi.org/10.1186/s12931-025-03338-y","url":null,"abstract":"","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"278"},"PeriodicalIF":5.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward precision medicine in COPD: phenotypes, endotypes, biomarkers, and treatable traits.","authors":"Cong Xie, Kepeng Wang, Kai Yang, Yuanyuan Zhong, Aman Gul, Weihang Luo, Maimaititusun Yalikun, Jiemin He, Wenjing Chen, Weifang Xu, Jingcheng Dong","doi":"10.1186/s12931-025-03356-w","DOIUrl":"10.1186/s12931-025-03356-w","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder characterized by diverse clinical manifestations, pathophysiological mechanisms, and therapeutic responses. This review explores the evolving landscape of precision medicine in COPD management, with particular emphasis on optimizing patient care through the integration of phenotypes, endotypes, biomarkers, and treatable traits. Phenotypic classification based on observable clinical and radiographic features has facilitated the identification of distinct subgroups such as \"emphysema-dominant\" or \"frequent-exacerbator\" subtypes. Emerging research, however, increasingly emphasizes endotypes-disease subcategories defined by unique biological mechanisms including neutrophilic inflammation, eosinophilic airway involvement, or α₁ antitrypsin deficiency-which may demonstrate superior predictive value for therapeutic responses. Biomarkers encompassing blood eosinophil counts, serum C-reactive protein, and sputum transcriptomics are progressively being implemented for patient stratification and guidance of targeted therapies, including inhaled corticosteroids or biologics. Furthermore, the \"treatable traits\" framework enhances personalized management by addressing modifiable factors beyond airflow limitation, such as comorbidities, psychosocial determinants, and exacerbation triggers. Despite these advancements, persistent challenges remain in biomarker validation, standardization of phenotypic definitions, and clinical translation of research findings. Future directions involve early detection of pre-COPD states and treatable traits, integration of multi-omics data, machine learning-driven dynamic phenotyping, and pragmatic clinical trials evaluating precision-guided interventions. By aligning pathobiological mechanisms with targeted therapies, precision medicine holds promise for transforming COPD care from reactive management to proactive, individualized therapeutic paradigms.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"274"},"PeriodicalIF":5.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZNF184-mediated transcriptional activation of SAE1 drives the cell cycle entry and immune evasion in non-small cell lung cancer.","authors":"Feng Shi, Luquan Zhang, Chunli Wang, Qingwei Meng","doi":"10.1186/s12931-025-03354-y","DOIUrl":"10.1186/s12931-025-03354-y","url":null,"abstract":"<p><p>Non-small cell lung cancer (NSCLC) is a highly prevalent subtype of lung cancer with an unsatisfactory prognosis, necessitating new therapeutic targets. The ubiquitin-like SUMO proteins covalently modify substrates and their functional properties. Here we report that SAE1 regulates p53 signaling through its ability to SUMOylate p53 and dissect the upstream modifiers of SAE1. Basal SAE1 levels were elevated in NSCLC cells, especially lung adenocarcinoma A549 and squamous cell carcinoma PC-10 lines. To probe SAE1 function, we utilized lentiviral short hairpin RNAs to stably knock down SAE1 in A549 and PC-10 cells. Knockdown of SAE1 significantly impeded the immune invasion in NSCLC in vitro and in vivo and promoted cell cycle arrest of NSCLC cells in vitro. Mechanistically, SAE1 knockdown suppressed p53 nuclear export and SUMOylation, while zinc finger protein 184 (ZNF184) with abnormally reduced methylation in NSCLC increased SAE1 transcription. Notably, the silencing of p53 rescued the immune evasion impeded by SAE1 knockdown, and SAE1 overexpression also rescued the antitumor benefits of sh-ZNF184. Overall, this study elucidates that abnormally reduced methylation of ZNF184 mediates transcriptional activation of SAE1 and impedes p53 expression through SUMOylation, thereby governing cell cycle arrest and promoting immune evasion in the NSCLC progression.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"275"},"PeriodicalIF":5.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fibulin 5 mediates pulmonary artery smooth muscle cell dysfunction and vascular remodelling in congenital heart disease associated pulmonary arterial hypertension via TGF-β1/PI3K/AKT signalling.","authors":"Jing-Jing Zhou, En Qiao, Zhi-Yu He, Yi-Cheng Yang, Yu-Ling Qian","doi":"10.1186/s12931-025-03361-z","DOIUrl":"10.1186/s12931-025-03361-z","url":null,"abstract":"<p><strong>Background: </strong>The mechanism of pulmonary arterial hypertension (PAH) associated with congenital heart disease (CHD) remains poorly understood. Fibulin 5, a multifunctional extracellular matrix protein, was reinduced in balloon-injured vessels and may contribute to vascular remodelling. This study aimed to investigate the role and molecular mechanism of action of fibulin 5 in the pathogenesis of CHD-PAH.</p><p><strong>Methods & results: </strong>We found elevated fibulin 5 expression in pulmonary artery smooth muscle cells (PASMCs) from CHD-PAH patients and shunt-induced PAH rats. In vivo, downregulation of fibulin-5 expression by intratracheally delivered adeno-associated viral vectors prevents shunt-associated PAH and associated pulmonary artery remodeling. In vitro, fibulin 5 expression in human PASMCs (hPASMCs) was changed through transduction with lentiviruses. We found fibulin 5 overexpression enhanced TGF-β1-induced proliferation and migration, as assessed by EdU, cell count, and transwell assays. Western blotting showed altered expression of contractile and synthetic phenotype markers. These effects were reversed by the PI3K/AKT inhibitor LY294002, suggesting fibulin 5 acts through the TGF-β1/PI3K/AKT pathway.</p><p><strong>Conclusions: </strong>Overall, our data provide new insights into the influence of fibulin 5 on the modification of hPASMC dysfunction and pulmonary artery remodelling in shunt-associated PAH.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"276"},"PeriodicalIF":5.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acute COPD exacerbation despite triple inhaled therapy: a molecular insight - TripleEx study.","authors":"Noriane A Sievi, Felix Schmidt, Kai Fricke, Diego M Baur, Sarah Basler, Jonas Herth, Malcolm Kohler","doi":"10.1186/s12931-025-03352-0","DOIUrl":"10.1186/s12931-025-03352-0","url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disorder characterized by persistent airflow limitation and acute exacerbations (AECOPD), which accelerate disease progression. Although triple inhaled therapy is recommended for patients with severe COPD and frequent AECOPD, some patients continue to experience exacerbations. The mechanisms behind this remain unclear. Exhaled breath analysis has the potential to unravel molecular changes during AECOPD, thereby adding to the understanding of molecular drivers for AECOPD. This study aimed to investigate metabolic changes in exhaled breath during AECOPD compared to stable state.</p><p><strong>Methods: </strong>In COPD patients treated with triple inhaled therapy we conducted real time breath analysis during AECOPD and subsequent stable state. Molecular breath patterns were compared between AECOPD and stable state by pathway enrichment analysis. Minimum description length model was used to build a feature based prediction model differentiating AECOPD from stable state.</p><p><strong>Results: </strong>28 patients (61% male) with a mean (SD) age of 65 (10.2) years with severe AECOPD were included. Metabolic alterations were predominantly detected in aminosugar, linoleate, and butanoate pathways. AECOPD could be discriminated from stable state with high power (AUC = 0.84), and balanced good sensitivity and specificity (86% each).</p><p><strong>Conclusion: </strong>Metabolic analysis of AECOPD revealed disturbances in aminosugar metabolism as a potential driver mechanism and thus may be a therapeutic target for patients with exacerbations despite triple inhaled therapy. Moreover, real-time breath analysis could enable rapid detection of AECOPD, improving diagnostic accuracy and treatment efficiency.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov (NCT04638920), registered on 20.11.2020.</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"273"},"PeriodicalIF":5.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145179732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG-based identification of COPD patients at risk for atrial fibrillation and its impact on adverse clinical outcomes-a subgroup analysis of the prospective multicenter COSYCONET cohort.","authors":"Martin Eichenlaub, Björn Christian Frye, Heiko Lehrmann, Frank Biertz, Amir Sherwan Jadidi, Klaus Kaier, Thomas Melzer, Peter Alter, Henrik Watz, Benjamin Waschki, Barbara Christine Weckler, Franziska Christina Trudzinski, Julia Dorothea Michels-Zetsche, Frederik Trinkmann, Felix Josef-Friedrich Herth, Hans-Ulrich Kauczor, Kathrin Kahnert, Rudolf Jörres, Robert Bals, Dirk Westermann, Thomas Arentz, Claus Franz Vogelmeier, Daiana Stolz, Sebastian Fähndrich","doi":"10.1186/s12931-025-03342-2","DOIUrl":"10.1186/s12931-025-03342-2","url":null,"abstract":"<p><strong>Background: </strong>Atrial fibrillation (AF) frequently occurs in patients with chronic obstructive pulmonary disease (COPD) and is associated with adverse clinical outcomes. We aimed to identify patients at risk for AF using amplified p-wave duration (APWD) analysis on electrocardiogram (ECG) as non-invasive tool to diagnose an atrial cardiomyopathy (AtCM) which is an established risk factor for AF.</p><p><strong>Methods: </strong>This subgroup analysis of the prospective COSYCONET cohort included 2,385 COPD patients from 31 study centers with baseline sinus rhythm ECG and at least one follow-up examination. Of these, 73 patients showed AF during follow-up and were propensity-score matched to controls. APWD was measured at baseline and future major adverse cardiac and cerebrovascular events (MACCE) and health related outcome were assessed.</p><p><strong>Results: </strong>219 COPD patients (70 [64-74] years, 79.5% male) were analyzed during a follow-up of 586 (210-1137) days. APWD was significantly longer in patients with AF occurrence compared to controls (132 [125-141] ms vs. 124 [117-133] ms, p < 0.001) and remained significant in multivariate regression analysis (OR: 1.05 [1.01-1.09], p = 0.03). An APWD ≥ 131 ms was identified as best cut-off for AF prediction (62% sensitivity, 70% specificity, OR: 3.91 [2.58 to 5.95], p < 0.001). Patients with AF had a significantly higher MACCE rate (24.7% versus 8.2%, p = 0.001) and a significantly lower physical activity score (1,074 [264-4,776] vs. 2,706 [975-7,339], p = 0.008).</p><p><strong>Conclusions: </strong>This study demonstrates that ECG-based AtCM diagnosis identifies COPD patients at risk for AF, which was associated with a substantially elevated MACCE rate and a significantly reduced physical activity. This easy, cost-effective and widely available digital biomarker might enable early therapy initiation and prevention of adverse clinical outcomes.</p><p><strong>Trial registration: </strong>NCT01245933 on Clinical-Trials.gov (Registration date: 22.11.2010).</p>","PeriodicalId":49131,"journal":{"name":"Respiratory Research","volume":"26 1","pages":"272"},"PeriodicalIF":5.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}