Radiotherapy is a crucial treatment modality for head and neck tumors. However, while effectively killing tumor cells, it significantly disrupts the homeostasis of the oral microecology, which is closely associated with various complications such as radiation-induced oral mucositis. Literature review indicates that as radiotherapy doses accumulate and treatment durations extend, the richness and diversity of the oral microbiota show a declining trend, with the genus Streptococcus decreasing most markedly. In contrast, radiotherapy selectively promotes the proliferation of bacterial phyla such as Proteobacteria and Bacteroidetes, which are rich in opportunistic pathogens. Mechanistically, radiotherapy activates the nuclear factor-kappa B pathway, triggering chronic inflammation and oxidative stress, damaging the epithelial barrier, suppressing local immunity, and causing damage to organs such as the salivary glands. It can also induce systemic diseases via the oral-gut axis, forming a multi-level, interconnected pathogenic network. In terms of interventions, treatment strategies including probiotics and prebiotics have shown promising efficacy against side effects such as radiation-induced oral mucositis. Saliva-based oral microbiota transplantation is an emerging strategy that is expected to become widely utilized for restoring oral microecological balance. Existing interventions provide preliminary pathways for clinical practice, but this field still faces several key scientific questions. The association between oral microecology and systemic diseases remains largely correlative, lacking causal evidence. Furthermore, critical parameters for oral microbiota transplantation, such as donor screening criteria, transplantation protocols, and long-term safety, are not yet well-defined. Therefore, future research should focus on conducting large-scale clinical trials to establish standardized protocols and safety evaluation systems for oral microecological interventions, and explore combined treatment therapies such as probiotics, prebiotics, and microbiota transplantation to advance the development of personalized precision modulation. These will enable more effective management of radiotherapy-induced oral microecological dysbiosis and improve treatment outcomes and quality of life for patients with head and neck tumors.

Background Lung tissue fibrosis caused by exposure to free silica (SiO₂) dust is the core pathological feature of silicosis. Lipocalin-2 (LCN2), as a secreted glycoprotein, plays an important role in various inflammatory diseases, but its specific role in silicosis-induced inflammatory injury remains unclear. Objective To identify differentially expressed genes (DEGs) and enriched pathways in the lung tissues of rats with silicosis, and to explore the regulatory mechanism of LCN2 in silicosis inflammation. Methods Twenty specific pathogen-free male SD rats were randomly divided into a control group and a silicosis model group (n=10 per group). The silicosis model was established via a single intratracheal instillation of silicon dioxide (SiO₂) suspension (50 mg), while the control group received an equal volume of normal saline. In vitro, NR8383 cells were divided into a control group and a SiO₂ treatment group. Hematoxylin-eosin (HE) staining and Van Gieson (VG) staining were used to evaluate alveolar structure damage, inflammatory infiltration, and collagen deposition in the lung tissues. Transcriptomic sequencing was applied to screen for DEGs and enriched signaling pathways. Western blot was used to detect the protein expression of interleukin-6 (IL-6), E-cadherin (ECA), cluster of differentiation 36 (CD36), liver X receptor (LXR), ATP-binding cassette transporter A1 (ABCA1), and LCN2 in both rat lung tissues and NR8383 cells. Immunohistochemistry was used to further assess LCN2 expression and localization. Additionally, NR8383 cells were treated with the LCN2 inhibitor ZINC00640089 and assigned to four groups: control, ZINC00640089-only, SiO₂, and SiO₂+ZINC00640089. Western blot was used to evaluate the expression of LCN2, IL-6, and interleukin-1β (IL-1β). Results HE and VG staining revealed disorganized lung tissue structure, damaged alveolar walls, nodule formation, and increased collagen deposition in the silicosis model group compared to the controls. Transcriptomic analysis identified 2723 DEGs in the lung tissues of silicosis rats (312 down-regulated and 2411 up-regulated) with LCN2 expression being significantly up-regulated. These DEGs were primarily enriched in pathways related to the innate immune response, extracellular matrix (ECM)-receptor interaction, and inflammatory response. Immunohistochemical staining confirmed elevated LCN2 levels in the lung tissues of the model rats. Western blot demonstrated that the protein levels of IL-6, CD36, and LCN2 were significantly increased (P<0.05), whereas ABCA1 and ECA were significantly decreased in the silicosis group (P<0.05). Similarly, in SiO₂-treated NR8383 cells, IL-6, CD36, and LCN2 expressions significantly increased (P<0.05), while ABCA1 and LXR significantly decreased (P<0.05). Compared with the SiO₂ group, treatment with ZINC00640089 significantly reduced the expression levels of LCN2, IL-6, and IL-1β (P<0.05). Conclusion LCN2 is significantly upregulated in both the lung tissues of silicosis rats and SiO₂-treated NR8383 cells. Inhibiting LCN2 expression effectively reduces the SiO₂-induced inflammatory response, indicating that LCN2 serves as a potential therapeutic target for silicosis.

Stratified diagnosis and treatment is a crucial approach in precision medicine， aiming to optimize medical care by grouping patients based on clinical manifestations， biomarkers， and pathological characteristics. Based on clinical stages， symptoms， age， gene expression， and pathology， research on Sjögren's syndrome （SS） has proposed various stratification methods， incorporating both traditional Chinese medicine （TCM） and Western medicine perspectives. These methods provide essential support for early diagnosis， risk assessment， and personalized treatment. Key strategies include moving SS intervention time forward， to leverage TCM's preventive principles， integrating TCM and Western tools to enhance precision， innovating clinical trial designs， developing multifactorial risk prediction models and digital imaging technologies， and constructing combined prognostic models for personalized follow-up and big data-driven treatment. These insights offer a comprehensive framework for advancing SS precision medicine.

The 2025 European Lung Cancer Congress (ELCC) convened in Paris, France, centering on the optimization and innovation of immunotherapy for non-small cell lung cancer (NSCLC). Key topics at the congress included the application strategies for perioperative immunotherapy, breakthroughs in combination therapy models for advanced NSCLC, and the emerging roles of biomarkers in predicting diverse treatment outcomes. This paper integrates data from several key pivotal studies to systematically analyze the clinical value of neoadjuvant therapy within the perioperative setting, the potential of targeted combination regimens, and the challenges of managing drug resistance, thus offering new directions for clinical practice.

Pinellia ternata， belonging to the Pinellia genus within the Araceae family， is a medicinal plant due to its tubers. There are severe issues with unclear germplasm and mixed varieties in its cultivation， necessitating urgent new variety protection efforts. The distinctness， uniformity， and stability （DUS） testing of the plant variety is the basis for protecting new plant varieties， and the DUS test guidelines are the technical basis for DUS testing. To develop the DUS test guidelines for P. ternata， agronomic traits of 229 germplasm of P. ternata were observed and measured during its two growth stages over the years， and each character was graded and described. A total of 38 traits were selected as the test traits of the DUS test guideline for P. ternata. There were three plant traits， 19 leaf traits， six flower traits， two fruit traits， two tuber traits， five bulbil traits， and one ploidy trait. These traits could be divided into 22 quality characters， 12 quantitative characters， and four pseudo-quantitative characters， as well as seven groups， including plants， leaves， flowers， fruit， tubers， bulbils， and ploidy. By searching for standard traits， 10 standard varieties were ultimately determined. Preparing these guidelines will have great significance for reviewing and protecting P. ternata varieties， safeguarding breeders' rights， and promoting the development of the P. ternata industry.

Using Real-time PCR technology, a highly specific and sensitive method for detecting DNA residues of Escherichia coli host cells in levodopa was established, validated, and preliminarily applied. Escherichia coli strain MB6 16S ribosomal RNA gene was selected as the target gene to design multiple pairs of primers and the target fragment by specific amplification of PCR was obtained. The target fragment was cloned into the pLENTI-BSD-CON vector and the recombinant plasmid was constructed and named pLENTI-BSD-CON-E.coli-16S. A quantitative PCR detection method (SYBR Green method) with magnetic bead extraction and purification methods was established with the reference standard of the recombinant plasmid. Furthermore, the established method was validated, including linear and range, accuracy, precision, specificity, and quantification limit, and applied to the detection of levodopa raw materials. Meanwhile, the detection method was compared with the Taqman probe method by the commercial kit. The primer sequences of the quantitative PCR detection method (SYBR Green method) were TTCGATGCAACGCGAAGAAC (forward) and GTGTAGCCCTGGTCGTAAGG (reverse). The standard curve of DNA was in the range of 10 fg/μL to 3 ng/μL with good linearity (R2≥ 0.98). The quantitative limit was 10 fg/μL. In addition, the detection recovery rate was in the range of 59.7% to 80.7%, with RSD at less than 15%. Nine batches of levodopa were detected by this method, and the amount of E.coli DNA residue was below the limit. The developed qPCR method can be used for quantitative detection of residual DNA in biological products produced by E.coli as host cells, such as levodopa . The results indicate that the sensitivity of the detection method for recombinant plasmid construction standards is superior than the reagent kit detection method.

Among numerous medical imaging modalities, diffusion weighted imaging (DWI) is extremely sensitive to acute ischemic stroke lesions, especially small infarcts. However, magnetic resonance imaging is time-consuming and expensive, and it is also prone to interference from metal implants. Therefore, the aim of this study is to design a medical image synthesis method based on generative adversarial network, Stroke-p2pHD, for synthesizing DWI images from computed tomography (CT). Stroke-p2pHD consisted of a generator that effectively fused local image features and global context information (Global_to_Local) and a multi-scale discriminator (M ²Dis). Specifically, in the Global_to_Local generator, a fully convolutional Transformer (FCT) and a local attention module (LAM) were integrated to achieve the synthesis of detailed information such as textures and lesions in DWI images. In the M ²Dis discriminator, a multi-scale convolutional network was adopted to perform the discrimination function of the input images. Meanwhile, an optimization balance with the Global_to_Local generator was ensured and the consistency of features in each layer of the M ²Dis discriminator was constrained. In this study, the public Acute Ischemic Stroke Dataset (AISD) and the acute cerebral infarction dataset from Yantaishan Hospital were used to verify the performance of the Stroke-p2pHD model in synthesizing DWI based on CT. Compared with other methods, the Stroke-p2pHD model showed excellent quantitative results (mean-square error = 0.008, peak signal-to-noise ratio = 23.766, structural similarity = 0.743). At the same time, relevant experimental analyses such as computational efficiency verify that the Stroke-p2pHD model has great potential for clinical applications.
Humans ; Tomography, X-Ray Computed/methods* ; Diffusion Magnetic Resonance Imaging/methods* ; Cerebral Infarction/diagnostic imaging* ; Stroke/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Algorithms

To address the problem of data silos in dental specialties caused by equipment heterogeneity, this study developed an Intelligent Internet of Things (IoT)-enabled dental chair platform (hereinafter referred to as the intelligent platform) based on the concept of medical-engineering integration. The platform adopts a three-tier chair-domain interconnection architecture: the bottom tier integrates multi-source sensors and standardized interfaces for automated data acquisition and linkage with hospital information systems; the middle tier provides clinic-level management and remote teaching collaboration; and the top tier employs a blockchain-based secure cloud database for resource allocation and data management. Clinical validation at The Affiliated Stomatological Hospital of Nanjing Medical University demonstrated that, compared with a control group from the same period in 2023, the trial group achieved a 38.0% increase in average daily patient visits (80.6±6.8 vs. 58.4±5.2, t=15.16, P<0.001), a 24.6% reduction in average treatment time [(36.1±6.3) min vs. (47.9±8.5) min, t=7.72, P<0.001], a 39.2% reduction in waiting time [23.3 (16.5, 30.1) min vs. 38.3 (28.3, 48.3) min, U=32.00, P<0.001], a 30.4% reduction in equipment idle rate [8.7% (5.1%, 12.3%) vs. 12.5% (7.4%, 17.6%), U=251.00, P=0.003], and an increase in patient satisfaction from 88.2% (1 519/1 723) to 94.3% (2 186/2 318) ( t=7.26, P<0.001). User research confirmed that the functions most favored by clinicians and patients were "dental chair parameter updating and clinical data integration" [74.7% (80/107)] and "chairside display of diagnostic images" [76.8% (119/155)], respectively. Looking forward, the intelligent platform has the potential to integrate artificial intelligence-assisted diagnosis and 5G-enabled multicenter collaboration to further expand its clinical applications and accelerate the digital transformation of dental healthcare.

Stroke is a critical health issue in China, and carotid artery stenosis and plaque play key roles in its prevalence. Despite the acknowledged significance of this condition, detailed information regarding the prevalence of carotid artery stenosis and plaque across the Chinese population has been scarce. This study analyzed data from the China Stroke High-risk Population Screening and Intervention Program for 2020-2021, focusing on 194 878 Chinese adults aged 40 years and above. It assessed the prevalence of carotid artery stenosis and plaque and identified their associated risk factors. Results revealed a standardized prevalence of 0.40% for carotid artery stenosis and 36.27% for carotid plaque. Notably, the highest rates of stenosis were observed in north and south China at 0.61%, while southwestern China exhibited the highest plaque prevalence at 43.17%. Key risk factors included older age, male gender, hypertension, diabetes, stroke, smoking, and atrial fibrillation. This study highlights significant geographical and demographic disparities in the prevalence of these conditions, underlining the urgent need for targeted interventions and policy reforms. These measures are essential for reducing the incidence of stroke and improving patient outcomes, addressing this significant health challenge in China.
Humans ; China/epidemiology* ; Male ; Female ; Prevalence ; Middle Aged ; Carotid Stenosis/epidemiology* ; Risk Factors ; Aged ; Adult ; Plaque, Atherosclerotic/epidemiology* ; Stroke/epidemiology* ; Aged, 80 and over

Trophoblast cell surface antigen 2 (Trop2) is a transmembrane glycoprotein. It promotes development and coordinates intracellular calcium signal transduction in healthy tissues, but is overexpressed in a variety of solid malignant tumors (such as gastrointestinal tumors, ovarian cancer, prostate cancer, and breast cancer). Moreover, it is closely associated with poor tumor prognosis and metastasis risk. As a powerful tool, molecular imaging technology can directly characterize and quantify the molecular metabolic changes after the combination of probes and targets through imaging technology at the in vivo level. It has become a key technology for Trop2 research and plays an important role in specific diagnosis, tumor staging, and therapeutic effect monitoring. A variety of Trop2-targeted molecular probes based on monoclonal antibodies, antibody-drug conjugates, and antibody fragments have been used in preclinical and clinical studies of various solid tumors. This article reviews the latest progress of Trop2-related research in the field of tumor molecular imaging technology.