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 analyse the clinical efficiency of endolymphatic sac surgery (ESS) in the management of Meniere’s disease (MD). Methods A retrospective analysis was conducted on 274 patients with MD who were hospitalized for treatment in Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University from January 2009 to August 2023. All patients received lifestyle management and drug treatment such as diuretics. For those whose conditions were not well controlled 3 to 6 months after the initial treatment, intratympanic glucocorticoid (ITG) or ESS treatment was carried out. Six months after the treatment, the classes of vertigo relief and hearing changes in the patients were evaluated. After adjusting the confounding factors through propensity score matching (PSM), the impact of ESS on the prognosis of MD patients was evaluated. Results Among 274 patients, 194 and 80 patients underwent ITG and ESS, respectively. Eighty patients were enrolled into each group after PSM. Before and after PSM, the rate of patients reaching vertigo relief class A in ESS group was higher than that in the ITG group (P＝0.004); there was no significant difference in hearing preservation between the two groups. Kaplan-Meier curve analysis showed that vertigo relief in the ESS group was better than that in the ITG group (P=0.029); there was no statistically significant difference in hearing preservation between the two groups. Conclusion When the initial treatment for patients with MD is ineffective, choosing ESS is more beneficial than ITG for controlling vertigo.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Objective To explore the current status of medication literacy among urban elderly patients with chronic diseases in Anhui Province,aiming to reveal the factors influencing their medication literacy,and to propose targeted measures for improvement.Methods This research involved 381 participants aged 60 and above.It was conducted in Anhui province between December,2022 and January,2023,with data collected through face-to-face interviews by pharmacists.Single-factor analysis and ordinal multi-class logistic regression analysis were conducted to determine factors affecting medication literacy.Results Medication literacy cognition and medication literacy behavior were rated as good among urban older adults in Anhui province of the 294 valid questionnaires.Those who did not understood package insert exhibited significantly lower medication literacy behavior than those who fully understood[estimate=-1.224,95%CI=(-2.130,-0.317),P<0.01].Elderly patients with chronic diseases faced issues such as an inability to read or understand drug instructions in the investigation.90.48%of elderly patients with chronic diseases never heard or seldom heard of medication guidance services.Conclusion Medication literacy among urban elderly patients with chronic diseases is generally good in Anhui province.The ability to understood drug instructions significantly influenced the medication literacy of urban elderly patients with chronic diseases.Modifying the drug instructions to meet the reading needs of the elderly patients with chronic diseases and developing pharmaceutical care could effectively enhance rational drug use among this demographic.

Objective To investigate the relationship between systemic immune-inflammation index (SII)and lower extremity vascular disease in patients with type 2 diabetes mellitus (T2DM).Methods A cross-sectional study was conducted on 390 T2DM patients admitted in our department from January 2013 to January 2024.According to the diagnostic criteria for lower extremity vascular disease in T2DM patients,they were divided into a lower extremity vascular disease group (n=158)and a control group (n=232).General data and results of laboratory tests were compared between the 2 groups.Spearman correlation analysis was used to identify the related factors for lower extremity vascular diseases in T2DM patients.The correlation between SII and lower extremity vascular diseases in T2DM patients was analyzed using the Row Mean Scores and Cochran-Armitage Trend analysis.Multivariate logistic regression analysis was applied to identify the risk factors for lower limb vascular lesions in T2DM patients.Receiver operating characteristic (ROC)curve was plotted to evaluate the diagnostic efficacy of SII for lower extremity vascular disease in the patients.Results Compared with T2DMpatients without lower extremity vascular disease,those with lower extremity vascular disease were older,had higher levels of total cholesterol (TC),low-density lipoprotein cholesterol (LDL-C),SII,larger proportion of carotid vascular lesions,and increased proportion of no-taking statins.The lower extremity vascular disease in T2DM patients was positively correlated with SII/100 (r=0.429,P<0.001),age (r=0.517,P<0.001),TC (r=0.161,P=0.001),LDL-C (r=0.117,P=0.021),carotid artery lesions (r=0.101,P=0.047),no-taking statins (r=0.266,P<0.001).Logistic regression analysis showed that SII,age,LDL-C,and no-taking statins were the risk factors for lower extremity vascular lesions in T2DM patients (P<0.01).The area under the curve (AUC)value of SII combined with age,LDL-C,and no-taking statins in predicting lower extremity vascular disease in T2DM patients was 0.896.Conclusion SII is not only a risk factor,but also a simple marker for lower extremity vascular disease in T2DM patients,suggesting that inflammatory response plays an important role in the occurrence and development of lower extremity vascular disease in T2DM.

Acute basilar artery occlusion (ABAO) accounts for approximately 1% of all ischemic strokes, with high mortality and disability rates. Endovascular therapy is one of the effective treatment methods for ABAO, which can recanalize the occluded blood vessels, rescue ischemic penumbra, and improve the outcome of patients. This article reviews the current research status of endovascular treatment for patients with ABAO.

Objective:To investigate the risk factors of brain metastases in patients with limited-stage small cell lung cancer (SCLC) undergoing preventive brain radiotherapy after remission and to construct prediction model.Methods:A total of 231 patients with limited-stage SCLC who received chemoradiotherapy and achieved remission in 3201 Hospital from January 2015 to January 2023 were selected as the study objects. Logistic regression was used to analyze the influencing factors on the occurrence of brain metastases after remission in patients with limited-stage SCLC who received preventive brain radiotherapy. Binary logistic regression was used to construct a prediction model. Receiver operator characteristic (ROC) curve was used to evaluate the diagnostic efficacy of each indicator and the prediction model on the occurrence of brain metastases in patients.Results:The median follow-up time of the whole group was 73 months, and 42 cases of brain metastases occurred, with an incidence rate of 18.18%. There were statistically significant differences in the incidence of brain metastases among patients with different T stage ( Z=-4.97, P<0.001), clinical stage ( Z=-8.17, P<0.001), and time from initial treatment to thoracic radiotherapy ( χ2=21.38, P<0.001). Multivariate analysis showed that T stage (stage T 3: OR=6.29, 95% CI: 1.58-25.06, P=0.009; stage T 4: OR=12.91, 95% CI: 3.74-44.57, P<0.001), clinical stage (stageⅡ, OR=8.75, 95% CI: 2.89-26.51, P<0.001; stage Ⅲ, OR=18.43, 95% CI: 7.24-46.92, P<0.001), and time from initial treatment to thoracic radiotherapy ( OR=0.25, 95% CI: 0.11-0.56, P=0.001) were independent influencing factors on the occurrence of brain metastases after remission in patients with limited-stage SCLC who received preventive brain radiotherapy. The diagnostic prediction model based on the above indicators was logit ( P) =-19.91+1.84× stage T 3 +2.56× stage T 4+2.17× stage Ⅱ+2.91× stage Ⅲ-1.38× time from initial treatment to thoracic radiotherapy. ROC curve analysis showed that the area under the curve of T stage, clinical stage, time from initial treatment to thoracic radiotherapy, and the diagnostic prediction model for predicting the occurrence of brain metastasis after remission in patients with limited-stage SCLC who received preventive brain radiotherapy were 0.728, 0.660, 0.687, and 0.846, respectively, and the area under the curve of the diagnostic prediction model was significantly larger than those of the other indicators (all P<0.05) . Conclusion:T stage, clinical stage and the time from initial treatment to thoracic radiotherapy are all influential factors for the occurrence of brain metastases after remission in patients with limited-stage SCLC who received preventive brain radiotherapy. The diagnostic prediction model based on the above indicators can help to guide clinicians to accurately screen patients at high risk of brain metastases in the early stage.

Background::Growth retardation is a common complication of chronic kidney disease in children, which can be partially relieved after renal transplantation. This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease (ESRD) after kidney transplantation using machine learning algorithms based on genomic and clinical variables.Methods::A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing (WES), and another 39 children who underwent transplant from October 2021 to March 2022 were enrolled for external validation. Based on previous studies, we comprehensively collected 729 height-related single-nucleotide polymorphisms (SNPs) in exon regions. Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction.Results::The 110 children were divided into two groups according to change in height-for-age Z-score. After univariate analysis, age and 19 SNPs were incorporated into the model and validated. The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve (AUC) of 0.924, and also performed well in the external validation cohort (accuracy, 0.7949; AUC, 0.796). Conclusions::A model with good performance for predicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms. The model is expected to guide clinicians in the management of children after renal transplantation, including the use of growth hormone, glucocorticoid withdrawal, and nutritional supplementation, to alleviate growth retardation in children with ESRD.

Nasal drug delivery efficiency is highly dependent on the position in which the drug is deposited in the nasal cavity. However, no reliable method is currently available to assess its impact on delivery performance. In this study, a biomimetic nasal model based on three-dimensional (3D) reconstruction and three-dimensional printing (3DP) technology was developed for visualizing the deposition of drug powders in the nasal cavity. The results showed significant differences in cavity area and volume and powder distribution in the anterior part of the biomimetic nasal model of Chinese males and females. The nasal cavity model was modified with dimethicone and validated to be suitable for the deposition test. The experimental device produced the most satisfactory results with five spray times. Furthermore, particle sizes and spray angles were found to significantly affect the experimental device's performance and alter drug distribution, respectively. Additionally, mometasone furoate (MF) nasal spray (NS) distribution patterns were investigated in a goat nasal cavity model and three male goat noses, confirming the in vitro and in vivo correlation. In conclusion, the developed human nasal structure biomimetic device has the potential to be a valuable tool for assessing nasal drug delivery system deposition and distribution.