Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

ObjectiveTo analyze the changes in the degree of coordination of China's major epidemic prevention and control efforts before and after the outbreak of the Corona Virus Disease 2019 (COVID-19), so as to explore the impact of epidemic prevention and control measures on coordination dynamics. MethodsA total of 3 864 policy documents related to epidemic prevention and control from January 2000 to December 2020 across 31 provinces (autonomous regions, and municipalities) in China were systematically collected. Contents specific to collaborative and cooperative efforts were extracted, and the extent of interdepartmental coordination were quantified to assess the effectiveness of epidemic prevention and control efforts. Wilcoxon signed-rank test was adopted to statistically analyze the differences between the indicators before and after the epidemic. ResultsThe average overall coordination level for major epidemic prevention and control in 31 provinces (autonomous regions, and municipalities) increased from 43.06% to 97.62%, and the average coordination levels in the eastern, central, and western China soared from 42.29%, 37.50%, and 47.46%, to 98.81%, 96.20%, and 97.46%, respectively, with statistically significant differences (all P<0.05). In terms of department categorization, coordination levels in the professional departments and the key support departments peaked at 100.00%, while other support departments rose to 95.43%, with an increase of 77.15%, 181.85%, and 139.89%, respectively, exhibiting noteworthy statistically significant differences (all P<0.001). ConclusionThe scope of coordination departments of China’s major epidemic prevention and control exists a remarkable surge following the COVID-19 outbreak, notable heightened coordination is particularly observed among the key support departments. Future endeavors should prioritize the roles played by diverse departments in epidemic prevention and control, enhancing both the clarity of departmental responsibilities and the effectiveness of interdepartmental coordination.

ObjectiveTo systematically evaluate the public health governance capacity of 40 cities in Jiangsu, Zhejiang, and Anhui Provinces, providing a scientific evaluation basis for building a "Healthy Yangtze River Delta". MethodsA comprehensive collection of policy documents, public information reports, and research literature related to public health governance capacity in Jiangsu, Zhejiang, and Anhui Provinces was conducted, totaling 6 920 policy documents, 1 720 information reports, and 1 200 literature pieces. Based on the evaluation standards for an appropriate public health system established by the research team, the basic status of public health governance capacity was assessed to identify the strengths and weaknesses of the 40 cities. ResultsIn 2022, the public health governance capacity score for the 40 cities in Jiangsu, Zhejiang, and Anhui Provinces was (562.5±38.0) points. In terms of specific areas, the emergency response field received the highest score of (791.4±49.7) points, while the chronic disease prevention and control field received the lowest score of (368.2±29.6) points. The Jiangsu-Zhejiang-Anhui region has largely achieved the strategic priority of health, gradually improved public health legal regulations, and established a basic organizational framework with a solid foundation for information and data infrastructure. However, challenges still need to be addressed, such as unstable government funding for public health, unclear departmental responsibilities, and barriers to information interoperability. ConclusionThe public health governance capacity of the 40 cities in Jiangsu, Zhejiang, and Anhui Province has been at a moderate level, but disparities have still existed across regions and fields. In the future, while continuing to deepen existing advantages, it is essential to accurately identify the causes of problems, establish a long-term and stable investment mechanism, enhance information connectivity mechanisms, further clarify departmental responsibilities, and promote the achievement of the "Healthy Yangtze River Delta" goal.

Optical imaging is highly valued for its superior temporal and spatial resolution. This is particularly important in near-infrared II (NIR-II, 1 000-3 000 nm) imaging, which offers advantages such as reduced tissue absorption, minimal scattering, and low autofluorescence. These characteristics make NIR-II imaging especially suitable for deep tissue visualization, where high contrast and minimal background interference are critical for accurate diagnosis and monitoring. Currently, inorganic fluorescent probes—such as carbon nanotubes, rare earth nanoparticles, and quantum dots—offer high brightness and stability. However, they are hindered by ambiguous structures, larger sizes, and potential accumulation toxicity in vivo. In contrast, organic fluorescent probes, including small molecules and polymers, demonstrate higher biocompatibility but are limited by shorter emission wavelengths, lower quantum yields, and reduced stability. Recently, gold clusters have emerged as a promising class of nanomaterials with potential applications in biocatalysis, fluorescence sensing, biological imaging, and more. Water-soluble gold clusters are particularly attractive as fluorescent probes due to their remarkable optical properties, including strong photoluminescence, large Stokes shifts, and excellent photostability. Furthermore, their outstanding biocompatibility—attributed to good aqueous stability, ultra-small hydrodynamic size, and high renal clearance efficiency—makes them especially suitable for biomedical applications. Gold clusters hold significant potential for NIR-II fluorescence imaging. Atomic-precision gold clusters, typically composed of tens to hundreds of gold atoms and measuring only a few nanometers in diameter, possess well-defined three-dimensional structures and clear spatial coordination. This atomic-level precision enables fine-tuned structural regulation, further enhancing their fluorescence properties. Variations in cluster size, surface ligands, and alloying elements can result in distinct physicochemical characteristics. The incorporation of different atoms can modulate the atomic and electronic structures of gold clusters, while diverse ligands can influence surface polarity and steric hindrance. As such, strategies like alloying and ligand engineering are effective in enhancing both fluorescence and catalytic performance, thereby meeting a broader range of clinical needs. In recent years, gold clusters have attracted growing attention in the biomedical field. Their application in NIR-II imaging has led to significant progress in vascular, organ, and tumor imaging. The resulting high-resolution, high signal-to-noise imaging provides powerful tools for clinical diagnostics. Moreover, biologically active gold clusters can aid in drug delivery and disease diagnosis and treatment, offering new opportunities for clinical therapeutics. Despite the notable achievements in fundamental research and clinical translation, further studies are required to address challenges related to the standardized synthesis and complex metabolic behavior of gold clusters. Resolving these issues will help accelerate their clinical adoption and broaden their biomedical applications.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Humans ; Adolescent ; Acne Vulgaris/therapy* ; Skin Care ; Surveys and Questionnaires ; Cognition ; China ; Skin

Hypertension and osteoporosis(OP)are common diseases in middle-aged and elderly people,and the number of patients with both diseases has gradually increased in recent years.Because the onset of the disease is hidden,it is easy to cause fractures and serious complications of heart,brain and kidney in the later stage,which not only seriously damages the quality of life of patients,but also increases the difficulty of clinical treatment.Therefore,it is particularly necessary to strengthen the research on this disease.More and more studies have found that the disorder of intestinal flora will lead to the occurrence of OP,while the intestinal flora of patients with hypertension is obviously out of balance.Therefore,this paper thinks that intestinal flora may be the key influencing factor of hypertension complicated with OP,and the imbalance of intestinal flora will lead to the imbalance of short-chain fatty acid metabolism,immune inflammatory reaction and increased sympathetic nerve activity,thus causing the imbalance of bone homeostasis and promoting the occurrence of OP.Therefore,it is suggested that regulating intestinal flora may be a new way to intervene hypertension complicated with OP.

Objective To investigate the therapeutic effect and mechanism of paeoniflorin(PAE)on thrombosis angiitis obliterans(TAO)in rats.Methods TAO rat model was established by sodium laurate injection.Rats were randomly divided into sham operation group(intraperitoneal injection of 0.9％NaCl),model group(intraperitoneal injection of 0.9％NaCl),experimental-L,-H groups(intraperitoneal injection of PAE 5,20 mg·kg-1·d-1),experimental-H+agonist group(intraperitoneal injection of 20 mg·kg-1·d-1 PAE+caudal vein injection of 10 ng·mL-1·kg 1·d-1 740 Y-P).Thrombin time(TT)was measured by magnetic bead coagulation;the levels of interleukin(IL)-1 β and endothelin 1(ET-1)were detected by enzyme-linked immunosorbent assay kit;the expression levels of phosphatidylinositol 3-kinase(PI3K),phosphorylated-PI3K(p-PI3 K),protein kinase B(AKT),p-AKT,nuclear factor(NF)-κB p65,p-NF-κB p65 were detected by Western blotting.Results The TT of sham operation group,model group,experimental-L,-H groups and experimental-H+agonist group were(14.88±1.32),(10.02±0.95),(12.65±1.22),(14.70±1.36)and(10.64±1.21)s;IL-1β were(154.23±13.45),(356.69±31.17),(268.62±23.58),(199.64±20.87)and(337.48±31.46)pg·mL-1;ET-1 were(6.78±0.68),(14.43±1.14),(11.23±1.07),(8.20±0.81)and(13.33±1.27)pg·mL-1;p-PI3K/PI3K were 0.36±0.04,0.76±0.07,0.59±0.05,0.44±0.04 and 0.69±0.07;p-AKT/AKT were 0.52±0.05,0.90±0.09,0.74±0.08,0.61±0.06 and 0.86±0.08;p-NF-κB p65/NF-κB p65 were 0.28±0.03,0.95±0.04,0.69±0.07,0.35±0.05 and 0.87±0.08,respectively.There were statistically significant differences between model group and sham operation group(all P＜0.05);the above indexes in experimental-L group and experimental-H group were significantly different from those in medel group(all P＜0.05);the above indexes in experimental-H+agonist group were significantly different from those in experimental-H group(all P＜0.05).Conclusion PAE may improve disease progression in TAO rats by inhibiting the PI3K/AKT/NF-κB signaling pathway.

Tranexamic acid is widely used in joint orthopedic surgery.At the same time,it has high safety and few adverse drug reactions.It can effectively improve intraoperative bleeding and promote early functional recovery of patients.This article reviews the mode of administration,safe dose,administration time and adverse drug reactions of tranexamic acid in the perioperative period of joint replacement and arthroscopic surgery,in order to provide reference for the clinical application of tranexamic acid.

Objective:To investigate the correlation between the expression of GLI1 and im-mune invasion and clinical prognosis in gastric cancer.To study the effect of GLI1 expression on drug resistance in gastric cancer.Methods:The expression difference of GLI1 in gastric cancer and normal tissues was analyzed by using TCGA database,and the effect of clinical features and GLI1 gene ex-pression level on prognosis of patients with gastric cancer was analyzed.The correlation between GLI1 gene expression and tumor immune cell infiltration in gastric cancer tissues was analyzed to explore its influence on drug resistance of chemotherapy drugs and targeted drugs.Clinical samples were collect-ed to analyze the difference of GLI1 expression in gastric cancer and paracancer tissues.Results:The expression of GLI1 in gastric cancer tissues was 1.7 times that in normal tissues,and the overall sur-vival and disease-free survival of patients with high expression are shorter than those with low ex-pression(P＜0.05).The interstitial score,immune score and abundance of immunoinfiltrating cells were higher in the high expression of GLI1 in gastric cancer tissues.High expression of GLI1 reduces drug sensitivity and is positively correlated with the expression of immune checkpoint markers PDCD1(P＜0.05).GLI1 expression was significantly increased in patients with subdifferentiated gastric cancer.Conclusions:GLI1 expression is associated with the prognosis and immune infiltration of patients with gastric cancer,and it may lead to poor prognosis of patients by regulating chemotherapy resis-tance,which may be a potential therapeutic target and molecular marker for gastric cancer.