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.

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: : 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.

Objective To explore the efficacy and safety of side-to-side anastomosis(SS)and end-to-side anastomosis(ES)of the ileocolon in laparoscopic right hemicolectomy of colon cancer,so as to provide evidence-based evidence for surgical selection.Methods PubMed,Embase,Web of Science,Cochrane Library,China National Knowledge Infrastructure,Wanfang Data,VIP database,Chinese BioMedical Literature database were searched from inception to November 2024 to collect relevant clinical studies of SS versus ES.The Newcastle-Ottawa Scale(NOS)was used to evaluate the literature quality of retrospective studies,and the Cochrane system was used to evaluate the literature quality of randomized controlled trials(RCT).Rev Man 5.3 software was used for Meta-analysis,sensitivity analysis,and publication bias analysis.Results 9 retrospective studies and 4 RCTs with a total of 2 632 patients were included.The Meta-analysis results of the retrospective study show that:Compared with SS,ES has a shorter tolerance time for liquid diet(MD=-0.20,95%CI:-0.40～0.00,P<0.05),fewer daily episodes of diarrhea(MD=-1.06,95%CI:-1.79～0.23,P<0.05),but a higher pain score at 12 hours post-surgery(MD=0.95,95%CI:0.50～1.40,P<0.05).Comparison of the overall complication rates of the two anastomosis methods showed no statistically significant difference((OR)=1.05,95%CI:0.22～5.14,P>0.05).Sensitivity analysis of the retrospective study shows:the incidence of ES bowel obstruction was higher than that of SS,with a statistically significant difference((OR)=2.18,95%CI:1.15～4.14,P<0.05);The sensitivity analysis of the RCT shows:the overall incidence of complications at the anastomotic site of SS was significantly higher than that of ES,with a statistically significant difference((OR)=5.26,95%CI:1.91～14.48,P<0.05),and the results of other outcome indicators did not show reversal.The analysis of publication bias risk showed no significant publication bias.Conclusion Ileocolonic ES has a slight advantage over SS in laparoscopic right hemicolectomy of colon cancer,both anastomoses are safe and effective,and the surgeon can choose the appropriate anastomosis technique according to the patient's specific situation,in order to improve the postoperative recovery.

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.

Objective:To construct and verify a risk prediction model for pulmonary infection in patients with aneurysmal subarachnoid hemorrhage (aSAH) after craniotomy and clipping, providing theoretical basis and practical guidance for improving the quality of postoperative care.Methods:Using the convenience sampling method, a retrospective selection was made of 397 patients with aSAH after craniotomy and clipping who were hospitalized in the Department of Neurosurgery of Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences) from January 2019 to December 2023 as the modeling group. They were randomly divided into the training set and the test set at a ratio of 7:3, with 278 cases in the training set and 119 cases in the test set. Patients were divided into the infection group and the non-infection group based on whether they developed pulmonary infection. Univariate analysis was used to model the risk factors of pulmonary infection after aSAH craniotomy and clamping in the group, and Lasso regression was used to construct a predictive model. A total of 119 patients with aSAH admitted to the neurosurgery department of the same hospital from January to April 2024 were selected for the external validation of the model. The predictive effect of the model was evaluated through the receiver operating characteristic (ROC) curve.Results:In the modeling group, there were 216 male patients and 181 female patients. The incidence of pulmonary infection was 38.54% (153/397). Finally, five influencing factors, namely stroke, Hunt-Hess classification, mechanical ventilation, indwelling nasogastric tube and the timing of initiating enteral nutrition, were included to construct a predictive model. The areas under the ROC curves of the nomogram prediction models of this model in the training set, test set, and external validation group were 0.859(95% CI 0.791-0.928), 0.843(95% CI 0.796-0.890), and 0.800(95% CI 0.711-0.889), respectively. The calibration curve shows that the model's prediction fits well with the actual situation and has a high degree of calibration. Decision curve analysis indicates that this model has high clinical application value under different risk thresholds. Conclusions:The risk prediction model for pulmonary infection in patients after craniotomy and clipping with aSAH has good discrimination and calibration, which can provide reference for medical staff to identify high-risk patients at an early stage and take preventive intervention measures.

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 explore the construction of a core competency evaluation indicator system for pediatric surgery operating room specialist nurses.Methods:The evaluation indicator system was constructed by reviewing literature, analyzing semi-structured interview results, and examining the characteristics of training specialized nurses in pediatric surgery operating rooms, as well as combining the characteristics of pediatric surgery and multi-source data. The research group discussed and proposed a preliminary core competency evaluation indicator system for specialized nurses in pediatric surgery operating rooms. This study used the Delphi expert inquiry method to conduct two rounds of expert consultation and ultimately established the core competency evaluation indicator system for pediatric surgery operating room specialist nurses. The expert positivity coefficient was represented by the questionnaire response rate, and the expert authority coefficient was calculated using the judgment coefficient and familiarity degree. The coefficient of variation and Kendall's W coefficient were used to measure the degree of coordination of expert opinions. Results:The effective response rates for the two rounds of expert consultation were 100.00% and 95.24%, respectively. The authority coefficients of the two rounds of expert consultation were 0.92 and 0.90, respectively. The coefficients of variation of indicators at various levels were less than 0.25. The Kendall's W coefficients for the first-, second-, and third-level indicators in the first and second rounds of expert consultation were 0.516-0.664 and 0.652-0.711, respectively ( P<0.001). The final core competency evaluation indicator system for pediatric surgery operating room specialist nurses included 3 first-level indicators, 15 second-level indicators, and 52 third-level indicators. Conclusions:The core competency evaluation indicator system for pediatric surgery operating room specialist nurses constructed in this study provides a scientific and practical guidance for training pediatric surgery operating room specialist nurses in China.