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:Disorders in iron metabolism increase the risk of type 2 diabetes mellitus.Hepcidin play an important role in maintaining iron homeostasis in the body,but its level increases with increased inflammation.Changes in hepcidin and iron homeostasis and the extent of their association with inflammation in people with and without type 2 diabetes mellitus are unknown.Meta-analysis was used to evaluate the effect of inflammation on serum hepcidin and iron metabolism related parameters in patients with type 2 diabetes mellitus.METHODS:CNKI,PubMed,Web of Science and EBSCOhost databases were searched by computer to collect observational studies related to inflammatory index and hepcidin in patients with type 2 diabetes mellitus.The search time was from September 1,2000 to September 30,2024.Three researchers independently screened the literature,extracted data and evaluated the quality of the included literature.Meta-analysis was performed by Review Manager 5.3,Stata 17.0 and GraphPad Prism 8.0.2 software.RESULTS:A total of 15 articles(17 studies)involving 3 159 participants,including 1 357 patients with type 2 diabetes mellitus,were included.Meta-analysis results showed that compared with the control group,patients with type 2 diabetes mellitus had higher levels of serum hepcidin[standardized mean difference(SMD)=0.35,95％confidence interval(CI)(0.05,0.65),P＜0.05],serum ferritin(SMD=0.49,95％CI(0.21,0.78),P＜0.01)and serum transferrin(SMD=0.19,95％CI(0.00,0.37),P＜0.05).Subgroup analysis results indicated that inflammation had a significant effect on serum hepcidin(SMD=0.76,95％CI(0.17,1.34),P＜0.05)and serum ferritin(SMD=0.77,95％CI(0.06,1.47),P＜0.05)in patients with type 2 diabetes mellitus.CONCLUSION:Hepcidin concentration is positively correlated with type 2 diabetes mellitus.Inflammation is one of the risk factors of type 2 diabetes mellitus.Early prevention of inflammation has certain significance in preventing iron metabolism disorder in patients with type 2 diabetes mellitus.

OBJECTIVE:Disorders in iron metabolism increase the risk of type 2 diabetes mellitus.Hepcidin play an important role in maintaining iron homeostasis in the body,but its level increases with increased inflammation.Changes in hepcidin and iron homeostasis and the extent of their association with inflammation in people with and without type 2 diabetes mellitus are unknown.Meta-analysis was used to evaluate the effect of inflammation on serum hepcidin and iron metabolism related parameters in patients with type 2 diabetes mellitus.METHODS:CNKI,PubMed,Web of Science and EBSCOhost databases were searched by computer to collect observational studies related to inflammatory index and hepcidin in patients with type 2 diabetes mellitus.The search time was from September 1,2000 to September 30,2024.Three researchers independently screened the literature,extracted data and evaluated the quality of the included literature.Meta-analysis was performed by Review Manager 5.3,Stata 17.0 and GraphPad Prism 8.0.2 software.RESULTS:A total of 15 articles(17 studies)involving 3 159 participants,including 1 357 patients with type 2 diabetes mellitus,were included.Meta-analysis results showed that compared with the control group,patients with type 2 diabetes mellitus had higher levels of serum hepcidin[standardized mean difference(SMD)=0.35,95％confidence interval(CI)(0.05,0.65),P＜0.05],serum ferritin(SMD=0.49,95％CI(0.21,0.78),P＜0.01)and serum transferrin(SMD=0.19,95％CI(0.00,0.37),P＜0.05).Subgroup analysis results indicated that inflammation had a significant effect on serum hepcidin(SMD=0.76,95％CI(0.17,1.34),P＜0.05)and serum ferritin(SMD=0.77,95％CI(0.06,1.47),P＜0.05)in patients with type 2 diabetes mellitus.CONCLUSION:Hepcidin concentration is positively correlated with type 2 diabetes mellitus.Inflammation is one of the risk factors of type 2 diabetes mellitus.Early prevention of inflammation has certain significance in preventing iron metabolism disorder in patients with type 2 diabetes mellitus.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Objective: To assess the nutritional supply status of school meals for primary and secondary school students in Henan Province, so as to provide a basis for scientific guidance of school meals. Methods: During 2021-2023, 115 lunch and dinner samples were collected from 25 primary and secondary schools in Zhoukou, Anyang and Luoyang of Henan Province by a direct selection method, and 13 nutrients were determined for each sample. The nutrient supply was evaluated based on Nutrition Guidelines of School Meals and Reference Intake of Dietary Nutrients for Chinese Residents (2023 Edition). Mann-Whitney U test was used to compare the differences of nutritional supply between urban and rural schools. Results: The median values for energy (709.77 kcal,1 kcal=4.18 kJ), fat energy supply ratio (0.27) and carbohydrate energy supply ratio (0.55) in the 66 lunches and dinners from primary school were within the recommended range. The supply of protein (28.39 g) and sodium (1 464.59 mg) was excessive. The median values of zinc (2.62 mg) and dietary fiber (5.19 g) were lower than the reference values. No statistically significant differences were observed in the supply of 13 nutrients between urban and rural primary schools( U = 427.00 -633.00, P > 0.05 ). Among 49 samples from secondary schools, the median value of energy supply (930.02 kcal), carbohydrate energy ratio ( 0.54 ) and fat energy supply ratio(0.25) were within the recommended range; and the median values of protein (38.82 g) and sodium (2 556.80 mg) were higher than the standard; and the median values of calcium (250.32 mg) and vitamin B1 (0.16 mg) were lower than the standard. Additionally, the differences in the level of vitamin B2 ( U =372.00) and zinc ( U =375.00) between the urban and rural secondary schools were statistically significant ( P <0.05). Conclusion Nutrient supply of primary and secondary school meals in three cities of Henan Province is inadequate and imbalanced, and the recipe need to be further optimized and improved.

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 construct a preoperative prediction model for proliferative hepatocellular carcinoma(HCC)based on enhanced CT image features,and to analyze the prognosis of the disease.Methods A retrospective case-control study was conducted on 603 patients with pathologically confirmed HCC.Among them,519 cases from the First Affiliated Hospital of Army Medical University were randomly divided into a training group(n=363)and an internal verification group(n=156)in a ratio of 7:3.Another 84 patients from the Second Affiliated Hospital of Chongqing Medical University served as an external validation group.All patients underwent abdominal CT scan with contrast before surgery.The clinical data and CT imaging characteristics of proliferative and non-proliferative HCC patients were observed.Binary logistic regression analysis was used to identify the independent risk factors of proliferative HCC,and a nomogram prediction model was constructed.Receiver operating characteristic(ROC)curve was plotted to evaluate its diagnostic performance,and calibration curve and decision curve analysis(DCA)were applied to evaluate its calibration performance and clinical application value.The model was validated in both the internal and external validation groups.Kaplan-Meier survival curves were employed to compare the prognosis between proliferative and non-proliferative HCC.Results Multivariate analysis showed that negative result of HBV-DNA quantification,incomplete tumor capsule,intratumoral necrosis or ischemia(≥20%),and annular hyperenhancement in arterial phase were independent predictors in predicting proliferative HCC(P<0.05).Our nomogram model for predicting proliferative HCC based on the above clinical imaging features had an AUC value of 0.805(95%CI:0.756～0.854),a sensitivity of 83.19%and a specificity of 64.80%in the training group.For the internal validation group,the AUC value was 0.793(95%CI:0.721～0.854),the sensitivity was 67.86%,and the specificity was 75.00%.In the external validation group,the AUC value was 0.842(95%CI:0.746～0.912),the sensitivity was 72.41%,and the specificity was 90.91%.Calibration curve and DCA showed that the model had good calibration performance and clinical applicability.The disease free survival(DFS)of the patients with proliferative HCC diagnosed by pathologically confirmed/predictive models was significantly shorter than that of non-proliferative HCC patients(training group:P=0.005,P<0.001;internal validation group:P=0.006,P=0.006;external validation group:P=0.002,P=0.015).Conclusion Our prediction model based on clinical and imaging features can accurately diagnose proliferative HCC before surgery,and the prognosis of proliferative HCC is generally poor.

Objective: : The objective of this study was to assess the relationship between spermine synthase (SMS) expression, tumor occurrence, and prognosis in lower-grade gliomas (LGGs). Methods: : A total of 523 LGG patients and 1152 normal brain tissues were included as controls. Mann-Whitney U test was performed to evaluate SMS expression in the LGG group. Functional annotation analysis was conducted to explore the biological processes associated with high SMS expression. Immune cell infiltration analysis was performed to examine the correlation between SMS expression and immune cell types. The association between SMS expression and clinical and pathological features was assessed using Spearman correlation analysis. In vitro experiments were conducted to investigate the effects of overexpressing or downregulating SMS on cell proliferation, apoptosis, migration, invasion, and key proteins in the protein kinase B (AKT)/epithelialmesenchymal transition signaling pathway. Results: : The study revealed a significant upregulation of SMS expression in LGGs compared to normal brain tissues. High SMS expression was associated with certain clinical and pathological features, including older age, astrocytoma, higher World Health Organization grade, poor disease-specific survival, disease progression, non-1p/19q codeletion, and wild-type isocitrate dehydrogenase. Cox regression analysis identified SMS as a risk factor for overall survival. Bioinformatics analysis showed enrichment of eosinophils, T cells, and macrophages in LGG samples, while proportions of dendritic (DC) cells, plasmacytoid DC (pDC) cells, and CD8+ T cells were decreased. Conclusion : High SMS expression in LGGs may promote tumor occurrence through cellular proliferation and modulation of immune cell infiltration. These findings suggest the prognostic value of SMS in predicting clinical outcomes for LGG patients.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

Objective:To examine the influence of physical function status on supportive care needs (SCNS) in patients with primary liver cancer (PLC) and to explore the mediating roles of perceived social support and anxiety-depression, with the aim of providing evidence for nursing interventions to improve patients' care experience.Methods:A convenience sampling method was used to select PLC inpatients admitted to The First Affiliated Hospital of Jinan University between December 2022 and February 2024. Data were collected using a general information questionnaire, the 34-item Supportive Care Needs Survey-Short Form, the Hospital Anxiety and Depression Scale, the Perceived Social Support Scale, and the Karnofsky Performance Status Scale to conduct a cross-sectional survey. Pearson correlation analysis was performed to examine associations among SCNS, perceived social support, anxiety-depression, and physical function status. Structural equation modeling was used to assess the mediating effects of perceived social support and anxiety-depression on the relationship between physical function status and SCNS.Results:A total of 304 patients were enrolled, including 260 males and 44 females, 31 cases aged 18-40 years old, 160 cases aged 41 to 60 years old, and 113 cases over 60 years old. The mean total SCNS score was (98.32 ± 21.75) points, the mean anxiety-depression score was (23.31 ± 9.64) points, the mean perceived social support score was (56.62 ± 13.05) points, and the mean physical function status score was (66.28 ± 12.31) points. Pearson correlation analysis showed that physical function status was negatively correlated with SCNS and anxiety-depression ( r =-0.509, -0.447; both P<0.05) and positively correlated with perceived social support ( r =0.439, P<0.05). Physical function status had a direct effect of 60.74% on SCNS, with specific mediating effects of perceived social support and anxiety-depression accounting for 23.15% and 11.85%, respectively, and a chain mediating effect of 4.26%. Conclusions:Patients with PLC exhibited a moderately high level of supportive care needs. Physical function status not only exerted a direct negative effect on SCNS but also indirectly influenced SCNS through the chain mediation of perceived social support and anxiety-depression. Clinical medical staff should focus on enhancing patients' perception of social support and effectively alleviating anxiety and depressive symptoms, thereby reducing supportive care needs to some extent.