Chronic post-surgical pain (CPSP) is a common long-term complication following lung surgery. Its high incidence significantly impacts patients’ quality of life and functional recovery, and imposes a substantial socioeconomic burden. This consensus aims to systematically establish a standardized integrated Chinese and Western medicine diagnostic and treatment framework for chronic post-lung surgery pain (CPLSP). Based on the latest domestic and international evidence-based medical research and multidisciplinary clinical experience, the working group comprehensively elaborates on core issues regarding CPLSP, including its definition, epidemiology, pathogenesis, clinical assessment, Western medical treatment, traditional Chinese medicine (TCM) treatment, and integrated strategies. The consensus emphasizes a patient-centered approach, adhering to the principles of multimodality, individualization, and stepwise management, highlighting the synergistic advantages of integrating Chinese and Western medicine throughout the entire perioperative management cycle encompassing "perioperative anti-inflammation, acute analgesia, and chronic rehabilitation." Through systematic literature retrieval and evidence integration, a total of 9 core recommendations were established to provide scientifically sound and clinically practical guidance.

Objective To explore the association between serum uric acid levels and the cumulative incidence risk of atrial fibrillation, and to evaluate the predictive value of different uric acid levels for the onset of atrial fibrillation. Methods A retrospective selection of 451 879 participants from the large-scale prospective epidemiological cohort UK Biobank, aged 40-69 years, all completed a median follow-up of 13.6 years. Participants were divided into groups based on the interquartile range of serum uric acid levels (Q1–Q4) related to gender and whether they were diagnosed with hyperuricemia. Cox proportional hazards model, sensitivity analysis, and other methods were used to compare baseline data and atrial fibrillation incidence during follow-up among each group of participants. Results Individuals with higher baseline uric acid levels tended to be older, more obese, and had lower education levels and a history of cancer, along with significantly higher levels of triglyceride, low-density lipoprotein cholesterol, and C-reactive protein, but lower high-density lipoprotein cholesterol levels (P<0.001); the highest uric acid group showed the highest diabetes prevalence (6.49%). Participants with higher serum uric acid levels (log-rank P<0.05) or diagnosed with hyperuricemia had significantly higher incidence of atrial fibrillation (P<0.001). After adjusting for potential confounders, compared to Q1 uric acid level group, the Q4 level was associated with a 20% increased risk of atrial fibrillation (HR=1.20, 95%CI 1.16–1.25). Each 74.7 μmol/L increase in uric acid level was associated with a 9% increased incidence risk of atrial fibrillation (HR=1.09, 95%CI 1.08–1.11). Individuals with hyperuricemia had a 20% increased incidence risk of atrial fibrillation (HR=1.20, 95%CI 1.17–1.24). A nonlinear association was observed between uric acid levels and the incidence risk of atrial fibrillation (P for nonlinearity <0.01). Subgroup analysis indicated significant heterogeneity of the risk effect across subgroups, with a higher risk associated with hyperuricemia in females. Conclusions Elevated blood uric acid levels may increase the cumulative risk of atrial fibrillation, and this pathogenic effect is significantly correlated with age, race, cancer history, body mass index, and sex.

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.

The lung collaterals form a network that branches from the lung meridian, traversing the lung system and extending across the body′s surface. Lung collateral disease refers to the structural alterations or dysfunction in these collaterals caused by external or internal pathogens. Research into the structural and physiological functions of children′s lung collaterals, as well as the pathogenesis and syndrome differentiation for treating lung collateral diseases in children, holds significant value in guiding the prevention and treatment of pediatric respiratory conditions. Drawing on the theory of collateral disease, the clinical insights of both historical and contemporary physicians, and modern research findings—while considering the unique physiological and pathological characteristics of children′s respiratory systems—this study provides a foundational summary of the morphology and spatial distribution of children′s lung collaterals. The characteristics of these collaterals are highlighted as thin, sparse, short, narrow, brittle, and tender. From this structural understanding, the unique physiological functions of children′s lung collaterals are analyzed. The study further explores the interactions between pathogenic factors and lung collaterals, elucidating the pathogenesis and progression of children′s lung collateral diseases. It proposes treatment principles centered on "seeking treatment in the collaterals and employing the method of unblocking collaterals, "which align with the unique features of pediatric lung collaterals. Common treatment approaches, and relevant prescriptions for managing these diseases are summarized. This paper lays the foundation for a theoretical system encompassing the structure, function, pathogenesis, and syndrome differentiation for treating children′s lung collateral diseases. It offers valuable insights for the clinical diagnosis and management of pediatric respiratory diseases linked to collateral dysfunction and serves as a reference for the systematic development of a broader theoretical framework for children′s collateral diseases.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Ribosome is an intracellular ribonucleoprotein particle that serves as the site of protein biosynthesis. Ribosomal dysfunction caused by mutations in genes encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RBFs) can lead to a spectrum of diseases, collectively known as ribosomopathy. Phase separation is a thermodynamic process that produces multiple phases from a homogeneous mixture. The formation of membraneless organelles and intracellular structures, including ribosomes and nucleoli, cannot occur without the involvement of phase separation. Here, ribosome structure, biogenesis, and their relationship with ribosomopathy are systematically reviewed. The tissue specificity of ribosomopathy and the role of phase separation in ribosomopathy are particularly discussed, which may offer some clues for understanding the mechanisms of ribosomopathy. Then, some new ideas for the prevention, diagnosis, and treatment of ribosomopathy are provided.
Humans ; Ribosomes/physiology* ; Ribosomal Proteins/metabolism* ; Mutation ; Animals ; Cell Nucleolus/metabolism* ; Protein Biosynthesis ; Phase Separation

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Objective To investigate application value of three-dimensional light sheet microscopy imaging for evaluating intraplaque neovascularization in arterial plaques and the efficacy of intervention,and to assess the effect of melatonin(MLT)on neovascularization by these means.Methods Thirty-six ApoE-/-model mice were randomly divided into three groups(n=12):vehicle group,MLT group,and MLT+GW9662 intervention group(MLT+GW).The mice were treated with vehicle,MLT alone,or MLT combined with peroxisome proliferator activated receptor-γ(PPARγ)inhibitor GW9662,respectively.The carotid arteries of the models were three-dimensionally imaged using a light sheet microscopy,and the length,volume and other indicators of neovascularization were quantitatively analyzed using Imaris software.Subsequently,CD31 immunohistochemical staining was performed for verification.Results The light sheet microscopy preliminarily achieved the three-dimensional visualization of intraplaque neovascularization,and its structure was observed to be three-dimensionally reticular and scattered.The results of Imaris quantitative analysis showed that,compared with vehicle group,the total intraplaque neovas cularization length in the MLT group was shortened[(15.79±12.90)mm vs.(33.42±11.16)mm,P<0.05],the total volume was reduced[(1.34±1.47)×10-3 mm3 vs.(13.44±7.35)×10-3 mm3,P<0.05],and the volume ratio was decreased(0.44%±0.47%vs.3.76%±1.74%,P<0.05).The above indicators in MLT+GW group were significantly increased compared with those in MLT group[total length:(35.31±4.69)mm,total volume:(8.87±3.46)×10-3 mm3,volume ratio:2.89%±0.38%;P<0.05].The CD31 immunohistochemical staining also supported the above findings(P<0.05).Conclusions Based on the light sheet microscopy imaging technology,the three-dimensional visualization and quantitative analysis of intraplaque neovascularization were preliminarily realized.It was found that MLT could reduce the overall burden of intraplaque neovascularization,and PPARγ might be involved in its regulatory process.

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.