ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Objective:To investigate the level of beliefs about medication in young patients with newly treated pulmonary tuberculosis and analyze its influencing factors.Methods:This was a cross-sectional study. Using the convenient sampling method, a total of 320 young newly treated pulmonary tuberculosis patients who visited the designated tuberculosis hospitals in Kashgar Prefecture and Hotan Prefecture of Xinjiang Uygur Autonomous Region from January 2022 to April 2023 were selected as the research objects. The investigation was carried out with the General Information Questionnaire, Beliefs about Medicines Questionaire Specific (BMQ-S), 8-Item Morisky Medication Adherence Scale (MMAS-8), Brief Illness Perception Questionnaire (BIPQ) and Tuberculosis-related Stigma Scale (TSS). Multiple linear regression was used to analyze the influencing factors of medication beliefs in young patients with newly treated tuberculosis.Results:A total of 320 questionnaires were distributed in this study, and 302 valid questionnaires were collected, with an effective response rate of 94.38% (302/320). The total score of BMQ-S of 302 young patients with newly treated tuberculosis was -1.00 (-2.00, 1.00), MMAS-8 score was 5.38 (4.75, 5.75), BIPQ score was 37.00 (24.00, 44.00) and TSS score was 48.00 (44.00, 52.00). The results of multiple linear regression analysis showed that comorbidities, medication adherence, disease perception and stigma were the influencing factors of medication beliefs in young newly treated pulmonary tuberculosis patients ( P<0.01) . Conclusions:The medication belief level of young patients with newly treated pulmonary tuberculosis needs to be improved. Medical staff should correct the negative cognition of the patient's disease, emphasize the benefits of drug treatment and enhance the patients' beliefs in the necessity of medication.

The lip plays a crucial role in facial aesthetics,making them a focal point of interest for orthodontists and patients through-out orthodontic treatment due to their three-dimensional changes in morphology and position.The degree of improvement in lip morphol-ogy significantly impacts post-treatment satisfaction among orthodontic patients.Therefore,understanding the influencing factors of lip morphology and its correlation with orthodontic interventions enables orthodontists to predict post-treatment changes in lip morphology more accurately,facilitating the customization of orthodontic treatment plans.In this paper,we reviewed the progress of methods for evaluating lip morphology and its influencing factors in order to provide reference for clinical practice.

Eukaryotic organisms constantly face a wide range of internal and external factors that cause damage to their DNA. Failure to accurately and efficiently repair these DNA lesions can result in genomic instability and the development of tumors (Canela et al., 2017). Among the various forms of DNA damage, DNA double-strand breaks (DSBs) are particularly harmful. Two major pathways, non-homologous end joining (NHEJ) and homologous recombination (HR), are primarily responsible for repairing DSBs (Katsuki et al., 2020; Li and Yuan, 2021; Zhang and Gong, 2021; Xiang et al., 2023). NHEJ is an error-prone repair mechanism that simply joins the broken ends together (Blunt et al., 1995; Hartley et al., 1995). In contrast, HR is a precise repair process. It involves multiple proteins in eukaryotic cells, with the RAD51 recombinase being the key player, which is analogous to bacterial recombinase A (RecA) (Shinohara et al., 1992). The central event in HR is the formation of RAD51-single-stranded DNA (ssDNA) nucleoprotein filaments that facilitate homology search and DNA strand invasion, ultimately leading to the initiation of repair synthesis (Miné et al., 2007; Hilario et al., 2009; Ma et al., 2017).
Recombinational DNA Repair ; DNA-Binding Proteins/metabolism* ; DNA Repair ; DNA Damage ; DNA

Listeria monocytogenes is recognized as a significant foodborne pathogen, capable of causing listeriosis in humans, which is a global public health concern. This pathogen is particularly dangerous for pregnant women, as it can lead to invasive listeriosis in fetuses and neonates, posing a significant threat to both maternal and fetal health. Therefore, establishing suitable in vitro and in vivo models for L. monocytogenes placenta infection, as well as analyzing and exploring the infection process and its pathogenic mechanism, are important approaches to prevent and control L. monocytogenes infection in mothers and infants. In this study, we reviewed the in vitro and in vivo placental models used for studying the infection of L. monocytogenes in maternal and infant, summarized and discussed the advantages and limitations of each model, and explored the potential of in vitro cell models and organoids for the study of L. monocytogenes infection. This paper aims to support the study of the infection pathway and pathogenesis of listeriosis and provide scientific references for the prevention and control of L. monocytogenes infection.
Female ; Humans ; Pregnancy ; Listeria monocytogenes ; Listeriosis/prevention & control* ; Placenta/pathology* ; Public Health ; Infant, Newborn

ObjectiveTo compare the characteristics of adult and juvenile callers at high risk of suicide from psychological assistance hotline， to screen factors influencing the crisis intervention effect， so as to develop specific intervention programs for different age groups on psychological assistance hotline. MethodsFrom January 2021 to June 2021， a total of 2 229 callers at high risk for suicide were recruited from Beijing psychological assistance hotline. The selected individuals were divided into adult group （n=1 344） and juvenile group （n=885）. Callers were interviewed by the hotline operators. Their general information， suicidal ideation， socio-psychological characteristics and chief complaints were recorded and compared between two groups. Before and after hotline crisis interventions， psychological distress， suicidal ideation intensity and hope level of the callers were assessed， and the improvements in the three assessed dimensions were compared between two groups. Then Logistic regression was adopted to compare the crisis intervention effects on three dimensions and their associated factors. ResultsCompared with adult group， juvenile group reported higher rates of family relationship problems， learning problems， history of suicide attempts and fear of assault， with statistically significant differences （χ²=55.604， 24.706， 41.944， 106.527， P<0.01）. After hotline crisis intervention， the proportion of callers with increased level of hope was larger in juvenile group than that in adult group （42.74% vs. 30.97%， χ²=26.042， P<0.01）. Multivariate Logistic regression analysis showed that the chances of improvement in psychological distress for major depressive disorder （OR=0.650， 95% CI： 0.510~0.827）， the chances of improvement in the level of hope for those with substance dependents （OR=0.550， 95% CI： 0.327~0.926）， and the chances of improvement in the intensity of suicidal ideation for those with stressful life events （OR=0.565， 95% CI： 0.328~0.973） were all higher in juvenile group than those in adult group after crisis intervention. ConclusionAmong the callers at high risk for suicide from psychological assistance hotline， adult callers are more concerned about extra-familial relationships， work and economic problems， while juvenile callers are more concerned about family relationship and learning problems. Furthermore， the effect of crisis intervention in juvenile callers is less affected by major depressive disorder， substance use problems and stressful life events.

Cancer stem cell-like cells (CSCs) play an integral role in the heterogeneity, metastasis, and treatment resistance of head and neck squamous cell carcinoma (HNSCC) due to their high tumor initiation capacity and plasticity. Here, we identified a candidate gene named LIMP-2 as a novel therapeutic target regulating HNSCC progression and CSC properties. The high expression of LIMP-2 in HNSCC patients suggested a poor prognosis and potential immunotherapy resistance. Functionally, LIMP-2 can facilitate autolysosome formation to promote autophagic flux. LIMP-2 knockdown inhibits autophagic flux and reduces the tumorigenic ability of HNSCC. Further mechanistic studies suggest that enhanced autophagy helps HNSCC maintain stemness and promotes degradation of GSK3β, which in turn facilitates nuclear translocation of β-catenin and transcription of downstream target genes. In conclusion, this study reveals LIMP-2 as a novel prospective therapeutic target for HNSCC and provides evidence for a link between autophagy, CSC, and immunotherapy resistance.
Humans ; Autophagy ; Carcinoma, Squamous Cell/pathology* ; Cell Line, Tumor ; Glycogen Synthase Kinase 3 beta/metabolism* ; Head and Neck Neoplasms/pathology* ; Neoplastic Stem Cells/pathology* ; Squamous Cell Carcinoma of Head and Neck/pathology* ; Lysosome-Associated Membrane Glycoproteins

Objective To investigate the computed tomography (CT) features of solitary nodular invasive mucinous lung adenocarcinoma (IMA) in stage IA and establish its prediction model. Methods We included 53 lesions of 53 patients with stage-IA IMA and 141 control lesions of 141 patients with invasive non-mucinous lung adenocarcinoma (NIMA) that were confirmed by surgical pathology in our hospital from January 2017 to December 2019. Univariable analysis was used to compare the demographics and CT signs of the two groups. Multivariable logistic regression analysis was performed to determine the main factors influencing solitary nodular IMA. A risk score prediction model was constructed based on the regression coefficients of the main influencing factors. A receiver operating characteristic (ROC) curve was used to assess the performance of the model. Results The univariable analysis showed significant differences between the two groups in age, largest nodule diameter, tumor-lung interface, lobulation, spiculation, air-bronchogram or vacuole sign, vessel abnormalities (P < 0.05). The spiculation sign was different between the two groups, which was longer and softer in the IMA group while shorter and harder in the NIMA group. There was no significant difference in sex, nodule shape, or pleural retraction (P > 0.05), but irregular shapes were slightly more frequent in the IMA group. The multivariable logistic regression analysis showed that obscure tumor-lung interface (odds ratio (OR = 20.930, P < 0.05), air-bronchogram or vacuole sign (OR = 7.126, P < 0.05), spiculation sign (OR = 4.207, P < 0.05), and vessel abnormalities (OR = 0.147, P < 0.05) were the main influencing factors. The prediction model based on those factors’ regression coefficients had an area under the ROC curve of 0.829 (P < 0.05). Conclusion Compared with those with NIMA, patients with solitary nodular IMA in stage IA were older and more likely to have the CT features of obscure tumor-lung interface, air-bronchogram or vacuole sign, and longer and softer spiculation. Based on the regression coefficients of tumor-lung interface, air-bronchogram or vacuole sign, spiculation, and vessel abnormalities, the risk score prediction model showed good predictive performance for solitary nodular IMA.