Objective To analyze the risk factors of type 2 diabetes mellitus (T2DM) with metabolic-associated fatty liver disease (MAFLD) and explore their relationship with BMI management. Methods A retrospective analysis was conducted of 310 patients with type 2 diabetes who underwent physical examinations at the 363 hospital between March 2023 and March 2025. Among these patients, those with MAFLD were counted. The risk factors of T2DM with MAFLD were analyzed by logistic regression analysis. The relationship between T2DM with MAFLD and BMI management was explored by Spearman correlation coefficient analysis. Results Compared with the non-MAFLD group, the levels of alanine aminotransferase (ALT), fasting insulin (I₀), fasting blood glucose (G₀), BMI, triglyceride (TG), aspartate aminotransferase (AST), and serum uric acid (SUA) were higher while the level of high-density lipoprotein cholesterol (HDL-C) was lower in the MAFLD group (P<0.05). Logistic regression analysis showed that BMI, SUA, I₀, ALT, G₀, and BMI control scale score were risk factors of T2DM with MAFLD (P<0.05). The score of BMI control scale of patients in the MAFLD group was higher than that in the non-MAFLD group (P<0.05). Correlation analysis indicated that T2DM with MAFLD was negatively correlated with BMI management (P<0.05). Conclusion BMI, SUA, I₀, ALT, and G₀ are all risk factors of T2DM with MAFLD. BMI management is negatively correlated with T2DM with MAFLD. Patients with T2DM should control BMI and blood glucose to reduce the occurrence of MAFLD.

Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

AIM:To evaluate the optimal timing of preoperative intravitreal anti vascular endothelial growth factor(VEGF)therapy in proliferative diabetic retinopathy(PDR)using intraoperative fluorescein angiography(IOFA).METHODS:A retrospective case series study was conducted on patients who underwent vitrectomy for PDR with vitreous hemorrhage(VH)at Sichuan Provincial People's Hospital from January 2023 to February 2025. Patients were divided into three groups according to the interval between intravitreal conbercept injection and surgery: Group A(3 d before surgery), Group B(7 d before surgery), and Group C(14 d before surgery). IOFA was used to assess the number and size of retinal neovascularization(NV). Additional data were collected including preoperative best corrected visual acuity(BCVA), vitreous hemorrhage grading, operative time, frequency of intraoperative endodiathermy, duration of high perfusion pressure, vitreoretinal adhesion grade, postoperative BCVA, and central macular thickness(CMT). Multidimensional analyses were performed.RESULTS:This study enrolled a total of 91 patients(94 eyes)with PDR accompanied by vitreous hemorrhage. Among them, Group A consisted of 31 patients(31 eyes; 18 males, 13 females; mean age 53.26±12.38 y), Group B consisted of 34 patients(37 eyes; 21 males, 13 females; mean age 51.61±14.16 y), and Group C consisted of 26 patients(26 eyes; 18 males, 8 females; mean age 51.00±12.02 y), with baseline characteristics comparable among the three groups(all P>0.05). Comparative analysis of NV visualized via IOFA revealed that both the number and size of NVs were significantly lower in Groups B and C than in Group A(all P<0.0167), while no statistically significant differences were observed between Groups B and C(both P>0.05). No significant differences were found among the three groups regarding other intraoperative parameters, including operation time, frequency of electrocoagulation application, duration of high perfusion pressure, or grading of vitreoretinal adhesion(all P>0.05).CONCLUSION:IOFA confirms that preoperative anti-VEGF therapy administered 7 or 14 d before surgery is more effective than a 3 d interval in suppressing retinal NV activity in PDR patients.

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 To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Objective: To analyze the space time characteristics of poor vision among primary and secondary school students in Chengdu, in order to provide the reference for formulating myopia prevention and control policies for students. Methods: The data relating to poor vision among primary and secondary school students in Chengdu from 2021 to 2023 were sourced from the Sichuan Students Physical Health Big Data Center. The districts and counties of Chengdu were divided into three circles, including the main urban area, suburban districts and counties, and suburban districts and counties. The Chi square test was used for inter group comparison, and the Cochran-Armitage test was used to analyze the trend of changes. Global and local Moran s I were used to analyze spatial clustering. Results: The detection rates of poor vision among primary and secondary school students in Chengdu from 2021 to 2023 were 62.47%, 61.61% and 60.78%, respectively, showing a decreasing trend ( Z=-32.01, P <0.01). For each year, the higher detection rate of poor vision among students was detected in the higher level of education, and differences were statistically significant ( χ 2=161 549.47, 173 471.87, 233 459.09, P <0.01). The rate of poor vision among primary and secondary school students gradually decreased from the central districts and counties of Chengdu to the surrounding districts and counties for each year, and the differences were statistically significant ( χ 2=299.20, 776.22, 633.16, P <0.01). The spatial autocorrelation analysis showed that the first circle of Chengdu City was mainly characterized by high-high agglomeration ( P <0.01), with the rate of poor vision among primary school students in Wuhou District in 2023 exhibiting a low-high anomaly. The third circle was mainly characterized by low-low aggregation ( P <0.01), while the spatial clusterings of the second circle was not significant ( P >0.05). Conclusions The myopia prevention and control work in Chengdu has achieved preliminary results. It should continue to consolidate existing achievements and implement targeted myopia prevention and control measures based on regional characteristics.

Curcumin is a natural yellow pigment， a natural phenolic antioxidant extracted from the rhizomes of Curcuma longa and Curcumae Rhizoma of the ginger family， with anti-inflammatory， anti-tumor and antioxidant properties. In recent years， it has been found that curcumin also has good antidepressant properties， and it is considered a safe and effective antidepressant potential drug. The mechanism of curcumin’s antidepressant efficacy mainly includes regulating neurotransmitters， modulating the hypothalamic-pituitary-adrenal axis， regulating brain-derived neurotrophic factor， inhibiting neuroinflammation， inhibiting oxidative stress， and regulating gut microbiota， etc.， and there is an overlapping and synergistic therapeutic effect of the above mechanisms. At present， the antidepressant mechanism of curcumin is still not fully understood， and will be combined with multi-omics technology， new formulation technology， and clinical trials to obtain further breakthroughs in the future.

CRISPR/Cas9-based therapeutics face significant challenges in penetrating the dense microenvironment of solid tumors, resulting in insufficient gene editing and compromised treatment efficacy. Current nanostrategies, which mainly focus on the paracellular pathway attempted to improve gene editing performance, whereas their efficiency remains uneven in the heterogenous extracellular matrix. Here, the nanoCRISPR system is prepared with self-cascading mechanisms for gene editing-mediated robust apoptosis and transcellular penetration. NanoCRISPR unlocks its self-cascade capability within the matrix metallopeptidase 2-enriched tumor microenvironment, initiating the transcellular penetration. By facilitating cellular uptake, nanoCRISPR triggers robust apoptosis in edited malignancies, promoting further transcellular penetration and amplifying gene editing in neighboring tumor cells. Benefiting from self-cascade between robust apoptosis and transcellular penetration, nanoCRISPR demonstrates continuous gene transfection/tumor killing performance (transfection/apoptosis efficiency: 1st round: 85%/84.2%; 2nd round: 48%/27%) and homogeneous penetration. In xenograft tumor-bearing mice, nanoCRISPR treatment achieves remarkable anti-tumor efficacy (∼83%) and significant survival benefits with minimal toxicity. This strategy presents a promising paradigm emphasizing transcellular penetration to enhance the effectiveness of CRISPR-based antitumor therapeutics.