Biomedical subjects

Jingjing Li

Publications and source records attributed to Jingjing Li.

3 recordsLinked to original sources

PubMed2026

Characteristics and assembly mechanisms of tobacco-associated bacteria in typical tobacco-planting regions across China.

INTRODUCTION: Plant-associated microbiota critically modulates host growth and environmental adaptation, yet assembly mechanisms, niche differentiation, and ecological strategies of bacterial communities inhabiting tobacco microhabitats remain poorly elucidated across geographical gradients. METHODS: Here, we systematically characterized bacterial microbiome assembly across five tobacco-associated niches (bulk soil, rhizosphere soil, root, stem, and leaf) from seven typical tobacco-planting regions using 16S rRNA amplicon sequencing, genome annotation, and niche breadth analysis. The independent and interactive effects of geographical location and host compartment on community structure, and further compared genomic traits, functional profiles, and life-history strategies between specialist and generalist bacterial populations were quantified. RESULTS: The results revealed a deterministic soil-plant continuum stratification of bacterial communities and diversity, with progressively simplified communities and decreasing alpha diversity from bulk soil to above-ground tissues, accompanied by progressive dominance of Proteobacteria. Geographical factors predominantly structured soil microbial communities via divergent edaphic properties, while host filtering acted as a universal dominant driver shaping endophytic microbiome assembly. Niche differentiation analysis demonstrated that niche-specialized bacterial ASVs overwhelmingly dominated all microhabitats and geographical sites, whereas generalist taxa only constituted auxiliary populations. Although specialist and generalist microbes exhibited highly conserved core genomic architectures and overall functional repertoires, they displayed distinct niche-specific functional divergence in metabolic pathways, stress resistance, and secondary metabolism across host compartments. Life-history strategy analysis further revealed that Y-strategist represented the core adaptive bacterial population, especially enriched in above-ground tobacco tissues. DISCUSSION: Our study establishes a hierarchical dual-filtering assembly model for tobacco microbiota, clarifies the ecological differentiation and functional adaptation of specialist and generalist bacteria, and provides fundamental insights into the assembly rules and adaptive mechanisms of crop-associated microbiomes for future microbial resource utilization and agricultural microbiome regulation.

biogeography↗

PubMed2026

A Bibliometric Analysis of Systematic Reviews in the Field of Ankylosing Spondylitis from 2007 to 2025.

BACKGROUND: Ankylosing spondylitis (AS) is an inflammatory autoimmune disease and the most common clinical form of spinal arthritis. Over the past decades, tremendous progress has been made in systematic reviews on AS. This study aimed to conduct a bibliometric analysis of AS-related meta-analyses to visualize the hotspots and trends in the field. METHODS: A comprehensive search was conducted for publications of AS meta-analysis from 2007 to 2025 using the Web of Science Core Collection database. Bibliometric analysis was performed using the Bibliometrix software package, VOSviewer, and CiteSpace. RESULTS: In total, 1073 articles were identified, and the number of relevant publications showed annual growth. China, the USA, and England were the most productive countries. Annals of the Rheumatic Diseases was the most productive journal (54, 10.65%), Pan Faming was the most productive author (23, 4.54%), and Anhui Medical University (45, 8.88%) was the most productive institution. High-frequency keywords were mainly grouped into five themes: complications, biologics, physical therapy exercises, gut microbiota, and analytical methods. DISCUSSION: This first bibliometric analysis of AS-related EBM research showed a 20-year upward trend in AS meta-analyses, consistent with prior studies. CiteSpace revealed that China (top since 2014) and the USA led in publications (53 countries) but had limited collaboration. Pan Faming (23 articles) was the most active author, and Annals of the Rheumatic Diseases published the most articles. Keyword analysis identified five themes (e.g., AS complications, biologics) and research frontiers: pre-2012 genome-AS links, post-2012 multi-center RCTs, and the recent focus on AS patients' HRQoL. Limitations included database and English-language bias; future meta-analyses should adopt standardized outcomes. CONCLUSION: In recent years, there has been a remarkable surge in the number of meta-analyses on AS. This significant increase underscores the importance of this research area. Studies in this field have mainly focused on several key aspects: risk factors, network meta-analysis, and quality- of-life studies. These findings are highly valuable for understanding advancements in ASrelated research and can also encourage researchers and clinicians to focus on both effective medical treatments and the well-being of AS patients.

Spondylitis, Ankylosing↗

PubMed2025

Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study.

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions. METHODS: A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions. RESULTS: The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway. CONCLUSIONS: The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

Humans↗