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.

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.

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.

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.

Abstract Kidney stones are common and frequently occurring diseases in urology. In recent years, the incidence of urinary stones has increased year by year, affecting people′s quality of life and aggravating the social and economic burden. The occurrence and development of kidney stones are closely related to metabolic factors. This review mainly conducts a systematic overview of the research on the relationship between electrolyte metabolism abnormalities, dyslipidemias and other metabolic disorder diseases and kidney stones in recent years.By exploring the relationship between various metabolic disorder diseases and kidney stones, this review provides reference for exploring the occurrence and development of kidney stones from the perspective of metabolism.

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 To investigate the effect of gender on prognosis after transcatheter patent foramen ovale(PFO)closure in patients with cerebral infarction or transient ischemic attack.Methods A retrospective cohort study was conducted involving patients with cerebral infarction or transient ischemic attack(TIA)who underwent PFO closure at our hospital between January 2013 and December 2023.The patients were grouped by gender,and related data were collected,including age,comorbidities,Risk of Paradoxical Embolism(RoPE)score,laboratory results,findings of transthoracic/transesophageal echocardiography(TTE/TEE),and post-procedural complications,such as device-related thrombosis(DRT),recurrent stroke,bleeding,and atrial fibrillation(AF).Results A total of 112 patients were enrolled,including 59 males and 53 females,at a mean age of 42.47±12.35 years.The females had significantly higher preoperative RoPE score than the males(6.6±1.4 vs 6.0±1.5,P=0.046),and a statistical difference was observed in the distribution of infarction sites between them(Chi-square=10.25,P=0.006),indicating that the males were prone to posterior circulation infarction.Intraoperative transthoracic echocardiography revealed a greater distance from the PFO to the aortic root in the females(9.3±2.4 mm vs 7.6±2.0 mm,P<0.001).During a median follow-up of 4 years,the male group had 1 case of myocardial infarction,1 cerebral hemorrhage,1 paroxysmal AF,2 gingival bleeding episodes,and 1 skin ecchymosis.In the female group,1 case experienced pulmonary embolism,1 paroxysmal atrial fibrillation,3 gingival bleeding episodes,2 skin ecchymoses,2 recurrent cerebral infarctions,and 2 recurrent TIAs.There was no statistical difference in overall adverse events between gender(P=0.291).Although the females had higher rates of recurrent cerebral infarction and TIA,this difference lacked statistical significance(P=0.222).Multivariate Cox regression analysis indicated that after adjusting for various potential confounding factors,such as RoPE score,age,hypertension,coronary heart disease,and other factors,gender was not an independent predictor of composite endpoint events after surgery.Conclusion Gender does not significantly affect overall prognosis after PFO closure in patients with cerebral infarction or TIA.However,females showed a trend toward higher rates of recurrent cerebral infarction and TIA.

Objective To explore a nomogram model based on clinical characteristics and immune indicators for predicting the efficacy of tyrosine kinase inhibitor(TKI)against chronic myeloid leukemia(CML).Methods Clinical data was retrospectively collected from 100 patients with CML treated with TKI between January 2021 and January 2023 in Tangshan Gongren Hospital.Patients were divided into the best response and warning/treatment failure groups according to therapeutic efficacy.Factors affecting therapeutic efficacy were analyzed using logistic regression analysis,and a nomogram model was constructed.Results The best response and warning/treatment failure groups showed significant differences in red blood cell distribution width(RDW),platelet count(PLT),ELTS score,Th 1/CD4+,Treg/CD4+ratio,white blood cell count,and absolute value of natural killer cells(P＜0.05).Logistic regression confirmed that the above indicators were influencing factors(P＜0.05),indicating that the model was meaningful,and had a high goodness of fit as well as high predictive value.Conclusion The nomogram model constructed based on RDW,PLT,and other factors can effectively predict the thera-peutic efficacy of AKI in treating CML.

Gastric cancer has relatively high disease burden worldwide. With the advancement of endoscopic technology and increased public health awareness, the detection rate of early gastric cancer has gradually risen globally. Conventional total or distal gastrectomy frequently results in postoperative complications, including malnutrition and digestive disorders, which can significantly impair the patient's quality of life. Function-preserving gastrectomy (FPG), which aims to minimize the extent of resection and preserve the functional anatomy of the stomach while ensuring radical removal of the tumor, has shown significant advantages in improving postoperative quality of life. This review summarizes the indications for various FPG techniques and the challenges currently faced, emphasizing the necessity of cautious promotion of FPG to improve treatment outcomes and the quality of life for patients with gastric cancer.