JiangSu Armed Police General Hospital

Gastric cancer (GC) remains a leading cause of cancer-related morbidity and mortality worldwide, particularly in East Asia. Nanomedicine has emerged as a promising strategy to enhance the precision and efficacy of GC diagnosis and therapy. This study aimed to systematically evaluate global research trends and knowledge structures in GC nanomedicine during the early 21st century using bibliometric and visualization analyses. Relevant publications during the early 21st century were retrieved from the Web of Science Core Collection (WoSCC) and analyzed using R, VOSviewer, CiteSpace, Scimago Graphica, and Pajek. The included records spanned 2003-2026, with the two items labeled "2026" reflecting early access articles already indexed in WoSCC at the time of data retrieval. A total of 1,717 publications authored by 9,857 researchers across 2,192 institutions in 83 countries were included. China, Iran, and the United States were identified as leading contributors, with major research hubs at the Chinese Academy of Sciences, Shanghai Jiao Tong University, and Islamic Azad University. Research output exhibited a sustained upward trend, with citation frequency and H-index steadily increasing. Five primary research hotspots were identified: nano-drug delivery and chemotherapy optimization, targeted therapy and signaling pathway modulation, photothermal/photodynamic synergistic therapies, functionalized nanomaterials for diagnostics, and immunotherapeutic/antibacterial nanomedicine. Over time, the field has evolved from early nanocarrier design (2003-2015) to targeted therapy and diagnostic applications (2015-2020), and more recently toward immunotherapy and green synthesis strategies (2020-2026). Importantly, most studies in this field remain at the preclinical or early translational stage, underscoring the need for further validation using standardized preclinical models and well-designed clinical trials. This bibliometric study provides a comprehensive overview of the developmental trajectory, key contributors, and emerging trends in GC nanomedicine, offering valuable insights to guide future interdisciplinary research, clinical translation, and strategic resource allocation.

This paper addresses the need for real-time monitoring and early warning of the psychological state of emergency personnel in the unique environment of the plateau. To address the challenges faced by existing methods in plateau hypobaric, hypoxic, and high-stress tasks, such as strong signal interference, large individual differences, and insufficient real-time performance, a real-time early warning model for psychological abnormalities based on multimodal physiological signals is proposed. First, a physiological signal database for plateau conditions is constructed, then, a spatiotemporal convolutional network incorporating an attention mechanism is designed, an adaptive threshold learning mechanism is introduced, finally, a lightweight edge computing deployment solution is constructed to achieve low-latency, real-time early warning. Experimental results demonstrate that the proposed model achieves an F1 score of 0.891 in the psychological abnormality recognition task and an average F1 score of 0.852 in the cross-individual generalization test. The edge inference latency fully meets the application requirements. Overall, the proposed model strikes a good balance between performance, real-time performance, and resource consumption, making it feasible for practical deployment.

BackgroundMonocyte chemoattractant protein-1 (MCP-1) has been utilized as a prognostic indicator for sepsis patients; however, the findings have been inconsistent. This meta-analysis seeks to elucidate the prognostic significance of MCP-1 in individuals diagnosed with sepsis.MethodsWe systematically queried four databases (Web of Science, PubMed, EMBASE, Cochrane Library) to identify studies published from the establishment of the databases until January 2025. Hazard ratios (HRs) with 95% CIs were employed to evaluate the prognostic significance of MCP-1 in this patient population. In our study, the Newcastle-Ottawa Quality Assessment Scale was evaluated for quality assessment, and Funnel plot was used for publication offset.ResultsA total of 592 patients from eight studies were included in the evaluation. The findings revealed that MCP-1 levels were significantly elevated in non-survivors compared to those who survived [SMD = 0.54, 95% CI: 0.25–0.82, P = 0.0002]. Our study demonstrated that elevated levels of MCP-1 were significantly associated with an unfavorable prognosis in patients with sepsis, with a hazard ratio (HR) of 1.36 (95% confidence interval: 1.25–1.48; P < 0.00001) using a random-effects model. The funnel plot indicated no publication bias.ConclusionThis meta-analysis suggests that elevated levels of MCP-1 could serve as a valuable prognostic indicator in sepsis patients, however, this conclusion still requires validation through higher-quality studies with longer-term follow-up.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12865-025-00783-8.

This study is to investigate the role of Endothelin-converting enzyme-like 1 (ECEL1) in neuropathic pain (NP). The expression of ECEL1 was modulated by injecting adeno-associated virus 5 (AAV5) carrying Ecel1 shRNA or full-length Ecel1 into the dorsal root ganglion (DRG) of mice with a chronic constriction injury (CCI) model. Then, various nociceptive responses were evaluated. Additionally, leukemia inhibitory factor (LIF) was intrathecally injected, or its function was blocked, to observe the changes in ECEL1 expression. Our findings demonstrate that downregulating ECEL1 expression alleviates CCI-induced pain and reduces the hyperexcitability of injured DRG neurons, which is achieved by inhibiting sympathetic sprouting in the DRG. Conversely, overexpressing ECEL1 in DRG neurons leads to pain hypersensitivity. Additionally, we observed that LIF upregulated ECEL1 expression, while blocking LIF reduced ECEL1 expression and mitigated CCI-induced nociception in mice. ECEL1 promotes hyperalgesia following CCI and is regulated by LIF, suggesting it could be a new target for NP treatment.

Acute kidney injury (AKI) refers to clinical syndromes culminating in rapidly reduced renal function associated with inflammation and the demise of renal tubular epithelial cells. Current research aims to develop strategies which prevent tubular cell death. Here, based on the involvement of histone deacetylases (HDACs) in renal physiology and their established role in renal fibrosis, we investigated the mechanistic contributions of HDACs using a mouse model together with in vitro studies employing human renal epithelial cells. We found HDAC3 expression was upregulated in mouse renal tubules after ischemia/reperfusion and cisplatin treatment. Instructively, treatment with the HDAC3 selective inhibitor RGFP966 exerted potent protective effects, attenuates acute kidney injury in both in vivo and in vitro models. Moreover, RGFP966 was found to reduce inflammation and injury caused by cisplatin and hypoxia-reoxygenation in HK2 cells with transcriptome sequencing revealing that RGFP966 significantly inhibited the upregulation of the necroptosis initiator, RIPK1. Cellular thermal displacement assay and molecular docking demonstrated the physical binding of RGFP966 to HDCA3. In addition, RIPK1 knockdown cell assay signified that RGFP966 targeted RIPK1 and inhibited RIPK1 kinase activity. In summary, these findings established the efficacy of the HDAC3 inhibitor RGFP966 in treating AKI.

Deepfake detection aims to authenticate the authenticity of facial images and videos, thereby providing crucial theoretical and technical support for portrait rights protection, false information identification, and online fraud prevention. Recent research has introduced a deep neural network Transformer, leveraging a self-attention mechanism to model long-range dependencies and global receptive fields. This advanced architecture enhances the ability to capture contextual relationships in images and temporal dependencies in videos, thereby improving the representational capacity of the detection system. In this paper, we propose a dual-branch feature extraction framework, incorporating a spatial domain feature extraction branch based on EfficientNet and a frequency domain feature extraction branch, to enrich the feature representation compared to single-branch methods. The extracted features are subsequently integrated with the Transformer's encoder structure and cross-attention mechanism, enabling effective modeling of feature correlations across global regions. Our proposed method achieves detection accuracies of 83.5%, 70.25%, and 78.5% for Deepfake, Face2Face, and NeuralTextures forged images, respectively.