Based on LI Gao's Academic Thought， focusing on the process of qi transformation and taking the regulation and restoration of metabolism and immunity as the entry point， a "source-pivot-convergence" diagnostic and therapeutic model for malignant tumors is constructed. In this model， spleen and stomach internal injury is the source of malignant tumor occurrence， while the disorder of ascending and descending is the pivot of the disease development， and the generation of yin fire is the convergence of malignant tumor progression. Based on this， the three major therapeutic methods of clearing the source， harmonizing the pivot， and resolving the convergence are established. To fortify spleen and boost qi， consolidate the root and clear the source， modified Buzhong Yiqi Decoction（补中益气汤）can be used. To raise the clear and direct the turbid downward， regulate qi and harmonize the pivot， modified Shengyang Yiwei Decoction （升阳益胃汤） is suggested. To restore balance and promote circulation， disperse accumulation and resolve convergence， modified Shengyang Sanhuo Decoction （升阳散火汤） is selected. In clinical practice， these formulas can be used in combination according to the complexity of the pathogenesis， and further adapted with prescriptions for promoting dispersion and penetrating pathogenic factors， resolving phlegm and promoting circulation， activating blood and eliminating concretions， which can provide a reference for the prevention and treatment of tumor diseases.

Based on LI Gao's Academic Thought， focusing on the process of qi transformation and taking the regulation and restoration of metabolism and immunity as the entry point， a "source-pivot-convergence" diagnostic and therapeutic model for malignant tumors is constructed. In this model， spleen and stomach internal injury is the source of malignant tumor occurrence， while the disorder of ascending and descending is the pivot of the disease development， and the generation of yin fire is the convergence of malignant tumor progression. Based on this， the three major therapeutic methods of clearing the source， harmonizing the pivot， and resolving the convergence are established. To fortify spleen and boost qi， consolidate the root and clear the source， modified Buzhong Yiqi Decoction（补中益气汤）can be used. To raise the clear and direct the turbid downward， regulate qi and harmonize the pivot， modified Shengyang Yiwei Decoction （升阳益胃汤） is suggested. To restore balance and promote circulation， disperse accumulation and resolve convergence， modified Shengyang Sanhuo Decoction （升阳散火汤） is selected. In clinical practice， these formulas can be used in combination according to the complexity of the pathogenesis， and further adapted with prescriptions for promoting dispersion and penetrating pathogenic factors， resolving phlegm and promoting circulation， activating blood and eliminating concretions， which can provide a reference for the prevention and treatment of tumor diseases.

OBJECTIVE To investigate the protective effect of 2-dodecyl-6-methoxy-2，5-diene-1，4-cyclohexanedione （DMDD）， an active component from Averrhoa carambola root， on myocardial injury in diabetic mice based on the nuclear receptor coactivator 4/ferritin heavy chain 1/autophagy-related protein 8 （NCOA4/FTH1/ATG8） axis. METHODS The successfully modeled diabetic mice were randomly divided into model group and DMDD low-， medium-， and high-dose （12.5， 25， 50 mg/kg） groups， while an additional non-modeled control group was established， with 6 mice in each group. Each group received the corresponding drug solution or an equal volume of normal saline intragastically once daily for 21 consecutive days. After the administration， the levels of fasting blood glucose （FBG）， serum lactate dehydrogenase （LDH）， and creatine kinase isoenzyme MB （CK-MB） were measured. Myocardial pathological changes， degree of fibrosis， and myocardial cell ultrastructure were observed. Myocardial cell death index and NCOA4 protein positive index were detected. The protein expression levels of NCOA4， FTH1， ATG8， solute carrier family 7 member 11 （SLC7A11）， and glutathione peroxidase 4 （GPX4） in cardiac tissue were measured. RESULTS Compared with model group， each DMDD group showed significant alleviation of cardiac pathological injury and varying degrees of improvement in the myocardial cell ultrastructure. The FBG and serum LDH and CK-MB levels， the myocardial cell death index and NCOA4 protein positive index，the protein expression levels of NCOA4， FTH1， and ATG8 in cardiac tissue were significantly decreased （ P ＜0.001）， while the protein expression levels of SLC7A11 and GPX4 were significantly increased （ P ＜0.001）. CONCLUSIONS DMDD can reduce blood glucose levels， alleviate myocardial histopathological injury， and inhibit cell death in diabetic mice. The mechanism is associated with inhibiting excessive activation of the NCOA4/FTH1/ATG8 axis and reducing ferritinophagy.

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.

We report a case of a female farmer from the Yichang suburb, aged 68 years, who had been suffering from the infection for 3 years and had a history of seeking medical treatment at a community hospital and receiving blood transfusions. Her hemoglobin was as low as 28 g/L during this visit, and she had been ill for a long time with severe anemia. Routine stool examination revealed hookworm eggs. After active treatment, the patient recovered and was discharged from the hospital.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

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.

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.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Objective To investigate the diagnostic accuracy and clinical applicability of the Linknet-VGG16 deep learning algorithm for measuring the effective optical zone(EOZ)after corneal refractive surgery.Methods This single-center retrospective cohort study included 69 patients(69 eyes)who underwent femtosecond laser-assisted in situ kerato-mileusis(FS-LASIK)(34 eyes)or small incision lenticule extraction(SMILE)(35 eyes)at the Refractive Surgery Center of Affiliated Zhengzhou Aier Eye Hospital of Henan University from June 2023 to June 2024.Data from the right eyes of all patients were selected for statistical analysis.During the surgery,patients in the FS-LASIK group adopted the VisuMax fem-tosecond laser system combined with the Amaris 750S excimer laser system,while those in the SMILE group only used the VisuMax femtosecond laser system.A total of 276 Pentacam images were re-examined postoperatively.A Linknet segmenta-tion model based on the VGG16 encoder was constructed,and image normalization techniques were applied to accelerate model convergence.Model performance was assessed using accuracy,intersection over union(IoU),and the Dice coeffi-cient.The traditional EOZ measurement method based on corneal tangential curvature served as the reference standard.Bland-Altman analysis was conducted to evaluate consistency across all images and within each group,and the time effi-ciency of both methods was compared.Results Six representative medical image segmentation architectures(U-Net,U-Net++,DeepLabv3-ResNet50,DeepLabv3+-ResNet50,Unet-Densenet169,and Linknet-VGG16)were systematically evaluated.The Linknet-VGG16 model demonstrated superior performance over the other 5 models in pixel-level accuracy,IoU and Dice coefficient,which were 99.83％,99.48％and 99.74％,respectively.Although there was no significant differ-ence in accuracy and Dice coefficient between Linknet-VGG16 and U-Net models(whose accuracy was 99.82％and Dice coefficient was 99.72％),the inference speed of the U-Net model(62.46 ms)was 31.76％slower than that of the Linknet-VGG16 model(42.62 ms).The evaluation results of a clinically applicable comprehensive scoring model(weights:accura-cy 20％,IoU 20％,Dice coefficient 20％,speed 25％,model size 15％)showed that the Linknet-VGG16 model achieved a score of 88.01,surpassing other architectures(U-Net:86.29;DeepLabv3+-ResNet50:80.41;DeepLabv3-ResNet50:73.82;U-Net++:73.22;Unet-Densenet169:66.66).Bland-Altman analysis revealed that the mean difference of the 136 images in the FS-LASIK group was 0.01 mm[95％limits of agreement(LoA):-0.36 to 0.35 mm],with 96.3％of data points falling within the LoA.The mean difference of the 140 images in the SMILE group was-0.01 mm(95％LoA:-0.36 to 0.33 mum),with 95.7％of data points falling within the LoA.The mean difference of all 276 images was 0.00 mm(95％LoA:-0.36 to 0.34 mm),with 96.4％of data points falling within the LoA.These results indicated excellent consistency.The average measurement time per image using the traditional EOZ measurement method was 13.00 minutes,whereas the deep learning model required only 3.22 seconds.Conclusion The traditional EOZ measurement method based on corne-al tangential curvature exhibits good consistency with the fully automatic EOZ measurement method based on deep learning algorithms,achieving high image recognition accuracy.Additionally,the deep learning algorithm significantly reduces measurement time,compared with the traditional method based on corneal tangential curvature.