OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective The aim of this study was to investigate the correlation between the interaction of uric acid and inflammatory factors and hyperuricemia in overweight/obese patients. Methods The personnel with hyperuricemia who underwent physical examination in our hospital from September 2021 to September 2022 were selected as the study subjects, and they were divided into 100 cases of overweight group and 90 cases of obese group according to the BMI index; 120 cases of healthy and non-hyperuricemic personnel were randomly selected as the control group; venous blood of the three groups was collected in 5 mL after 8 h of fasting, and were tested respectively for serum uric acid, lipid indexes and inflammatory factors: IL-6, IL-2, IFN-γ, TNF-α, IL-4, IL-10. Results Glucose, triglycerides, total cholesterol, and LDL were significantly higher in the obese group versus the overweight group (P<0.001), while HDL was significantly lower than the control group (P<0.001), and these changes were more pronounced in the obese group (P<0.001).The Pearson correlation coefficient pointed out that the levels of serum uric acid in patients with hyperuricosuric acid were significantly associated with the pro-inflammatory factors IL- 6, IL-2, IFN-γ, and TNF-α were significantly positively correlated (P<0.001), whereas they were significantly negatively correlated with the anti-inflammatory factors IL-4, IL-10 (P<0.001). Conclusion High uric acid levels in overweight/obese patients can cause enhanced inflammatory responses and reduced expression levels of anti-inflammatory factors, and the interaction between uric acid and pro-inflammatory factors aggravates the condition of patients with hyperuricemia.

BackgroundThe middle school stage represents a crucial period for the development of borderline personality features. Negative parenting styles and emotional regulation strategies are associated with the formation of borderline personality features. However， the moderating role of emotional regulation strategies between negative parenting styles and borderline personality features among middle school students remains unclear. ObjectiveTo explore the moderating influence of emotional regulation strategies in the relationship between negative parenting styles and borderline personality features among middle school students， and to provide references for the intervention of borderline personality features. MethodsIn October 2023， a total of 5 965 middle school students from three middle schools in Nanning， Guangxi Zhuang Autonomous Region were selected by cluster sampling， and assessed by the Borderline Personality Features Scale for Children （BPFS-C）， the Egna Minnen Barndoms Uppfostran （EMBU）， and the Emotion Regulation Questionnaire-Chinese Revised Version （ERQ-CRV）. Pearson correlation analysis was used to test the correlation between the scores of each scale， and the model 1 of the Process macro program was used to conduct the moderating effect test. ResultsA total of 5 572 middle school students （93.41%） completed this study， and 1 388 of them （24.91%） were identified as having high borderline personality features. The BPFS-C score of middle school students was positively correlated with the score of the negative parenting style dimension of EMBU （r=0.367， P<0.01）， negatively correlated with the score of the cognitive reappraisal dimension of ERQ-CRV （r=-0.168， P<0.01）， and positively correlated with the score of the expression inhibition dimension of ERQ-CRV （r=0.344， P<0.01）. Cognitive reappraisal played a negative moderating effect between negative parenting styles and borderline personality features （β=-0.072， 95% CI： -0.104–-0.041， P<0.01）， while expressive suppression played a positive moderating effect （β=0.076， 95% CI： 0.055–0.097， P<0.01）. ConclusionCognitive reappraisal strategy may help mitigate the negative influence of negative parenting styles on middle school students' borderline personality features， while expressive suppression may exacerbate the harm of negative parenting styles to the borderline personality features of middle school students.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To explore the efficacy and safety of neoadjuvant immunotherapy combined with chemotherapy in patients with locally advanced resectable non-small cell lung cancer (NSCLC). Methods The clinical data of patients with stage ⅡA-ⅢB NSCLC who underwent surgical treatment after receiving neoadjuvant immunotherapy combined with chemotherapy or chemotherapy alone at the First People’s Hospital of Jining between April 2021 and January 2024 were retrospectively analyzed. According to the preoperative neoadjuvant regimens, the patients were divided into a combined group and a chemotherapy group, and the clinical data of the two groups were compared. Results A total of 66 patients were included, including 61 males and 5 females. There were 53 patients in the combined group with a mean age of (63.40±6.80) years, and 13 patients in the chemotherapy group with a mean age of (58.62±8.30) years. There was a statistical difference in age between the two groups (P＜0.001), while no statistical difference was observed in other baseline data (P>0.05). The major pathological response rates in the combined group and the chemotherapy group were 54.7% and 23.1%, respectively (P=0.041), and the pathological complete response rates were 39.6% and 0.0%, respectively (P=0.006). The operative time in the combined group was shorter (P=0.039). There were no statistical differences between the two groups in intraoperative blood loss, duration of postoperative tube carrying, incidence of postoperative complications, overall survival, or event-free survival (P>0.05). Conclusion Surgical treatment following neoadjuvant immunotherapy combined with chemotherapy is safe and feasible, and its long-term efficacy requires further follow-up for confirmation.

Objective To observe the efficacy and safety of neoadjuvant sintilimab combined with chemotherapy in the treatment of patients with locally advanced resectable esophageal squamous cell carcinoma (ESCC). Methods Clinical data were collected from patients with locally advanced resectable ESCC who received neoadjuvant immunotherapy combined with chemotherapy followed by surgical treatment at the Department of Thoracic Surgery of Jining No.1 People's Hospital from April 2020 to April 2022. The endpoints included major pathological response (MPR), pathological complete response (pCR), R0 resection rate, safety, and postoperative survival. Results A total of 43 patients with ESCC who received at least one cycle of neoadjuvant immunotherapy before surgery were included. Among them, there were 31 males and 12 females with a median age of 65 years (interquartile range: 58-69). All patients successfully completed the surgery without any surgical delays. The pCR rate was 14.0% (6/43), the MPR rate was 58.1% (25/43), and the R0 resection rate was 97.7% (42/43). Patients exhibited reliable safety during neoadjuvant therapy and postoperatively. The 2-year overall survival and disease-free survival rates were 90.7% and 81.4%, respectively. Kaplan-Meier survival analysis and log-rank test revealed lower recurrence rates and better survival in the MPR patients compared to the non-MPR patients. Conclusion The combination of neoadjuvant sintilimab and chemotherapy in the treatment of patients with locally advanced resectable ESCC has demonstrated significant clinical efficacy, while also being safe and reliable.

ObjectiveTo explore the clinical safety of Feilike Mixture （FLK） in the real world. MethodsThe safety of all children who received FLK from 29 institutions in 12 provinces between January 21，2021 and December 25，2021 was evaluated through prospective centralized surveillance and a nested case control study. ResultsA total of 3 035 juveniles were included. There were 29 research centers involved，which are distributed across 12 provinces，including one traditional Chinese medicine （TCM） hospital and 28 general hospitals. The average age among the juveniles was （4.77±3.56） years old，and the average weight was （21.81±12.97） kg. Among them，119 cases （3.92%） of juveniles had a history of allergies. Acute bronchitis was the main diagnosis for juveniles，with 1 656 cases （54.46%）. FLK was first used in 2 016 cases （66.43%），and 142 juvenile patients had special dosages，accounting for 4.68%. Among them，92 adverse drug reactions （ADRs） occurred，including 73 cases of gastrointestinal system disorders，10 cases of metabolic and nutritional disorders，eight cases of skin and subcutaneous tissue diseases，two cases of vascular and lymphatic disorders，and one case of systemic diseases and various reactions at the administration site. The manifestations of ADRs were mainly diarrhea，stool discoloration，and vomiting，and no serious ADRs occurred. The results of multi-factor analysis indicated that special dosages （the use of FLK）［odds ratio （OR） of 2.642， 95% confidence interval （CI） of 1.105-6.323］，combined administration： spleen aminopeptide （OR of 4.978， 95%CI of 1.200-20.655），and reason for combined administration： anti-infection （OR of 1.814， 95%CI of 1.071-3.075） were the risk factors for ADRs caused by FLK. Conclusion92 ADRs occurred among 3 035 juveniles using FLK. The incidence of ADRs caused by FLK was 3.03%，and the severity was mainly mild or moderate. Generally，the prognosis was favorable after symptomatic treatment such as drug withdrawal or dosage reduction，suggesting that FLK has good clinical safety.

INTRODUCTION: Lecanemab has shown promise in treating early Alzheimer's disease (AD), but its safety and efficacy in Chinese populations remain unexplored. This study aimed to evaluate the safety and 6-month clinical outcomes of lecanemab in Chinese patients with mild cognitive impairment (MCI) or mild AD. METHODS: In this single-arm, real-world study, participants with MCI due to AD or mild AD received biweekly intravenous lecanemab (10 mg/kg). The study was conducted at Hainan Branch, Ruijin Hospital Shanghai Jiao Tong University School of Medicine. Patient enrollment and baseline assessments commenced in November 2023. Safety assessments included monitoring for amyloid-related imaging abnormalities (ARIA) and other adverse events. Clinical and biomarker changes from baseline to 6 months were evaluated using cognitive scales (mini-mental state examination [MMSE], montreal cognitive assessment [MoCA], clinical dementia rating-sum of boxes [CDR-SB]), plasma biomarker analysis, and advanced neuroimaging. RESULTS: A total of 64 patients were enrolled in this ongoing real-world study. Safety analysis revealed predominantly mild adverse events, with infusion-related reactions (20.3%, 13/64) being the most common. Of these, 69.2% (9/13) occurred during the initial infusion and 84.6% (11/13) did not recur. ARIA-H (microhemorrhages/superficial siderosis) and ARIA-E (edema/effusion) were observed in 9.4% (6/64) and 3.1% (2/64) of participants, respectively, with only two symptomatic cases (one ARIA-E presenting with headache and one ARIA-H with visual disturbances). After 6 months of treatment, cognitive scores remained stable compared to baseline (MMSE: 22.33 ± 5.58 vs . 21.27 ± 4.30, P = 0.733; MoCA: 16.38 ± 6.67 vs . 15.90 ± 4.78, P = 0.785; CDR-SB: 2.30 ± 1.65 vs . 3.16 ± 1.72, P = 0.357), while significantly increasing plasma amyloid-β 42 (Aβ42) (+21.42%) and Aβ40 (+23.53%) levels compared to baseline. CONCLUSIONS: Lecanemab demonstrated a favorable safety profile in Chinese patients with early AD. Cognitive stability and biomarker changes over 6 months suggest potential efficacy, though high dropout rates and absence of a control group warrant cautious interpretation. These findings provide preliminary real-world evidence for lecanemab's use in China, supporting further investigation in larger controlled studies. REGISTRATION ClinicalTrials.gov , NCT07034222.
Humans ; Alzheimer Disease/drug therapy* ; Male ; Female ; Aged ; Middle Aged ; Cognitive Dysfunction/drug therapy* ; Aged, 80 and over ; Amyloid beta-Peptides/metabolism* ; Biomarkers ; East Asian People

With the advancement in big data and artificial intelligence technologies, the extensive application of omics technologies in traditional Chinese medicine(TCM) research has generated large experimental datasets, enabling the exploration of cross-scale correlations among massive data and thereby resulting in the shift toward a data-intensive research paradigm. The emerging approach of multi-omics data fusion analysis, emphasizing technical and computational tools, presents a potential breakthrough in this field. The holistic perspective of TCM aligns with the concept of multi-omics data fusion, yet the data types encountered exhibit high dimensionality with small sample sizes, necessitating data processing techniques such as dimensionality reduction. The current challenge lies in selecting suitable analytical methods for these data to enhance the systematic understanding of physiological functions and disease diagnosis/treatment processes. This paper explores the theories and frameworks of multi-omics data fusion, analyzes methods for fusing high-dimensional, small-sample multi-omics data in TCM, and aims to provide insights for advancing TCM research.
Medicine, Chinese Traditional/methods* ; Humans ; Computational Biology/methods* ; Genomics/methods* ; Sample Size ; Artificial Intelligence ; Multiomics