Youjiang Medical College for Nationalities

Respiratory diseases are increasingly influenced by meteorological variability, yet few studies have applied high-resolution environmental data and advanced forecasting models to quantify this relationship in climate-sensitive regions such as southern China.This study aims to characterize how climate variability drives respiratory disease dynamics by quantifying the influence of meteorological factors and constructing predictive models for early intervention. It further seeks to advance climate-resilient health surveillance through the application of machine learning and time-series forecasting techniques.A longitudinal dataset comprising daily meteorological indicators (temperature, humidity, precipitation, wind speed, atmospheric pressure, air quality index) and annual respiratory disease incidence was analyzed. Feature engineering, dimensionality reduction via Principal Component Analysis (PCA), and temporal lags were applied. Predictive models-Random Forest, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX)-were evaluated using coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Variable importance and Pearson correlation were used to interpret model behavior and epidemiological relevance.Respiratory incidence showed marked interannual fluctuations, with notable peaks in 2014, 2018, 2022, and 2024. Random Forest identified total precipitation (27%), average temperature (22%), and relative humidity (18%) as the most influential predictors. Air quality and wind speed also contributed to model accuracy. LSTM and SARIMAX models achieved high fidelity in forecasting both long-term and weekly trends, with low residual variance and strong temporal alignment with observed case data.This study provides strong empirical evidence that respiratory disease patterns in Baise City are closely linked to meteorological conditions. The integration of machine learning and time-series modeling establishes a scalable framework for real-time forecasting and early detection of climate-sensitive health risks. These findings advance the frontier of climate-health analytics and offer actionable pathways for embedding environmental intelligence into public health policy, resource planning, and resilience-building strategies in vulnerable regions.

Breast cancer remains a major global health burden, with persistent challenges including recurrence, metastasis, and drug resistance limiting the efficacy of current treatments. The identification of novel therapeutic targets is essential for advancing precision oncology. We employed a 2-sample Mendelian randomization (MR) framework utilizing large-scale eQTL and genome-wide association study (GWAS) datasets from European cohorts to identify genetic targets for breast cancer. Colocalization analysis, phenome-wide association studies (PheWAS), protein–protein interaction networks, and functional enrichment analyses (GO/KEGG) were conducted. Single-cell gene expression was also analyzed. Candidate drugs were predicted using pharmacogenomic databases and subsequently validated through molecular docking simulations. MR analysis identified 480 candidate targets, among which 51 were successfully validated. Colocalization analysis highlighted 7 genes – DNPH1, SYT11, RCCD1, LAMB2, SLC22A5, CBX6, and FAAH – with strong evidence of causal association. Functional annotation and protein–protein interaction (PPI) network analysis revealed their involvement in key cancer-related pathways. Their expression patterns were also analyzed at the single-cell level. Molecular docking confirmed stable binding affinities between the identified target proteins and their predicted drug candidates. This integrative genomics and bioinformatics approach identified 7 promising drug targets for breast cancer. These findings offer novel avenues for the development of targeted therapies and underscore the importance of genetic epidemiology in guiding drug discovery.

ABSTRACTLung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer‐related deaths worldwide. Unlike conventional treatments, the targeted therapies or emerging immunotherapies have shown significant advantages in the management of advanced lung cancer. Therefore, exploring novel predictive biomarkers or therapeutic targets is still of far‐reaching significance for the future treatment of lung cancer. This study revealed that low expression of IGSF10, an important member of the immunoglobulin superfamily, significantly correlates with poor overall survival of lung adenocarcinoma (LUAD) patients and strong tumorigenic capacity of LUAD cells. Mechanistically, high expression of IGSF10 can inhibit the epithelial‐mesenchymal transition of LUAD cells via p53‐triggering ferroptosis and impede G1/S cell cycle transition of LUAD cells via the p53‐p21 axis, leading to suppression of LUAD cell migration, growth and tumorigenic capacity. Our findings clarified the specific role of IGSF10 in LUAD, and theoretically suggested new avenues for the presumable IGSF10‐targeting therapy of lung cancer in the future.

The Hanging Coffin represents a distinctive mortuary tradition practiced across southern China, Southeast Asia (e.g., the Log Coffin), and the Pacific approximately 3,000 years. Historical records attribute this funerary practice to the Bo people, a group that largely disappeared from documented history by the end of the Ming Dynasty (1,368–1,644 AD). Here we report eleven ancient genomes from four Hanging Coffin sites in China, alongside thirty whole genomes from the extant Bo people in Southwest China. We also sequence four ancient genomes from Log Coffin sites in northwestern Thailand. Our findings indicate that present-day Bo people derive a substantial proportion of their ancestry from practitioners of the Hanging Coffin mortuary tradition. Both ancient and modern groups exhibit elevated genetic affinity with coastal Neolithic populations from southern East Asia, who are ancestral to Tai-Kadai and Austronesian speakers. Unexpectedly, we also find evidence of long-range interactions and cultural inclusivity between Northeast Asian and Yellow River farmers and Hanging Coffin communities over 1,200 years ago. Finally, shared genetic components between Hanging (Log) Coffin populations in China and Thailand point to a common origin and a broader genetic and cultural network underlying this distinctive mortuary tradition across southern China and Southeast Asia.

IntroductionThe integration of artificial intelligence (AI) into medical text generation is transforming public health by enhancing clinical documentation, patient education, and decision support. However, the widespread deployment of AI in this domain introduces significant ethical challenges, including fairness, privacy protection, and accountability. Traditional AI-driven medical text generation models often inherit biases from training data, resulting in disparities in healthcare communication across different demographic groups. Moreover, ensuring patient data confidentiality while maintaining transparency in AI-generated content remains a critical concern. Existing approaches either lack robust bias mitigation mechanisms or fail to provide interpretable and privacy-preserving outputs, compromising ethical compliance and regulatory adherence.MethodsTo address these challenges, this paper proposes an innovative framework that combines privacy-preserving AI techniques with interpretable model architectures to achieve ethical compliance in medical text generation. The method employs a hybrid approach that integrates knowledge-based reasoning with deep learning, ensuring both accuracy and transparency. Privacy-enhancing technologies, such as homomorphic encryption and secure multi-party computation, are incorporated to safeguard sensitive medical data throughout the text generation process. Fairness-aware training protocols are introduced to mitigate biases in generated content and enhance trustworthiness.Results and discussionThe proposed approach effectively addresses critical challenges of bias, privacy, and interpretability in medical text generation. By combining symbolic reasoning with data-driven learning and embedding ethical principles at the system design level, the framework ensures regulatory alignment and improves public trust. This methodology lays the groundwork for broader deployment of ethically sound AI systems in healthcare communication.

This paper proposes a model for constructing a mechatronic control knowledge graph based on multimodal semantic enhancement, aiming to improve the construction efficiency, accuracy and application value of the mechatronic control knowledge graph through the fusion and semantic analysis of multimodal data. The model uses multimodal information such as natural language processing (NLP), computer vision and sensor data to extract domain-specific entities, attributes and their complex relationships from unstructured and structured data such as technical literature, equipment manuals, operation videos, sensor data, etc. in the field of mechatronic control. First, semantic enhancement technology is used in combination with methods such as named entity recognition (NER) to accurately extract key entities and their semantic relationships in the field of mechatronic control. Then, through multimodal fusion technology, text, images, videos and sensor data are aligned across modalities to generate a unified domain knowledge representation. In order to ensure the reasoning ability and dynamic update of the knowledge graph, the model adopts a knowledge representation and reasoning framework based on graph neural network (GNN) to support real-time update and intelligent reasoning of the graph. In summary, the mechatronic control knowledge graph construction model based on multimodal semantic enhancement proposed in this paper not only solves the shortcomings of traditional knowledge management methods, but also provides an efficient and intelligent solution.

Periodontitis, a chronic infectious disease, presents significant treatment challenges due to antibiotic resistance and high recurrence rates, necessitating novel therapeutics. Network pharmacology identified ISL's potential targets in periodontitis, focusing on NF-κB signaling pathway. Toxicity was assessed via MTT assay and long-term toxicity studies in vivo. Anti-inflammatory effects were evaluated using enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), while antibacterial activity was tested in vivo. Micro-computed Tomography (Micro-CT) and hematoxylin and eosin (H&E) staining analyzed periodontal tissue recovery. Western blotting measured NF-κB-p65 and IκB phosphorylation. Molecular docking and dynamics simulations explored ISL's targets. ISL exhibited low toxicity and reduced IL-6, IL-1β, and TNF-α levels in vitro and in vivo. It demonstrated strong antibacterial effects and mitigated alveolar bone loss. Phosphorylation of NF-κB-p65 and IκB decreased in immune cells, with IKKB (-8.4 docking score) identified as a stable target. ISL effectively treats periodontitis by combining antibacterial and anti-inflammatory actions, targeting IKKB to suppress NF-κB signaling pathway. This study highlights ISL's therapeutic potential and provides a foundation for developing periodontitis treatments.