Objective To explore the construction of heart preservation model of empty beating donor based on extracorporeal membrane oxygenation (ECMO). Methods From January 2022 to August 2023, 20 Guangxi Bama miniature pigs weighing 25-30 kg were selected, half male and half female. Under general anesthesia and heparinization, a midline thoracotomy was performed. The pericardium was cut after freeing the anterior and posterior vena cavae, and a perfusion needle was inserted near the brachiocephalic artery in the ascending aorta, connected to a blood collection bag to collect 500-600 mL of blood. The anterior and posterior vena cavae were ligated, the aorta was blocked and perfused with HTK solution to stop the heart beating. The superior and inferior vena cavae were cut off, the right pulmonary vein was decompressed, the aorta and left and right pulmonary arteries and veins were cut off, and the whole heart was removed. An ECMO device was used to continuously perfuse a cardioprotective solution mainly composed of oxygenated warm blood, maintaining the isolated pig heart beating for 8 hours, monitoring (once/hour) ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, inflammatory factors, myocardial enzymes, myoglobin, and troponin levels. Myocardial tissue was taken for hematoxylin-eosin (HE) staining to observe myocardial cell damage and evaluate the quality of heart preservation. Results Among the 20 isolated beating pig hearts, 17 successfully resumed beating, 3 experienced ventricular fibrillation, resuscitated after intracardiac electrical defibrillation, and all 20 pig hearts successfully beat for 8 hours. There was no statistical difference in ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, and inflammatory factors at each time point (P>0.05). There were statistical increases in myocardial enzymes, myoglobin, and troponin levels (P<0.05). HE staining results suggested that there was no severe myocardial damage. Conclusion ECMO technology can be used for pig heart preservation with good results, and this study provides experimental evidence for improving heart preservation research in clinical heart transplantation.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Purpose: Our previous study showed that circulating tumor cell (CTC) count combined with gene mutation detection might help differentiate benign and malignant pulmonary nodules (PNs). Herein, we aimed to expand the study cohort and conduct further sequencing analysis. Materials and Methods: Patients with PNs were included, and CTCs were identified before operation. Low-coverage whole-genome sequencing (LC-WGS) and lung cancer-related targeted gene sequencing were performed on CTCs. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. The differences in CTC counts among subgroups classified by demographic–clinical characteristics were analyzed. LC-WGS–based copy number variation (CNV) analysis and targeted gene mutation analysis were conducted. Results: A total of 172 patients were included. CTC count of 2.5 was identified by the ROC curves as the optimal diagnostic cutoff. The sensitivity and specificity of CTC count for differentiating benign and malignant PNs were 54.2% and 78.6%, respectively. The diagnostic sensitivity and specificity of combined CTC count, radiological nodule type, and any malignant imaging features were 84.7% and 71.4%, respectively. The CTC counts were significantly greater in patients with aggressive tumors, later stage, and spread through air spaces. CTCs from malignant cases had more CNVs than those from benign cases. Conclusion CTC count can be used in identifying malignant PNs. The diagnostic efficacy can be improved if combined with computed tomography imaging characteristics. Further CNV analysis might help differential diagnosis. Greater CTC count might suggest more aggressive tumors. CTC detection can provide important information and guidance for subsequent management of PNs.

Echinococcosis is a neglected zoonotic parasitic disease across the world, which imposes a substantial burden on human health and socioeconomic development in endemic areas. From a One Health perspective, this review systematically summarizes the legal framework, scheme, integrated interventions, control achievements, and challenges in the national echinococcosis control programme of China. An integrated strategy centered on control of the source of Echinococcus infections has been implemented for echinococcosis control in China, and cross-sectoral and cross-regional multidisciplinary coordination mechanisms have been progressively established, resulting in remarkable achievements gained in human health, animal health, and integrated environmental management. The endemicity of echinococcosis remarkably reduced in China in 2023 as compared to results from the national echinococcosis epidemiological survey in China between 2012 and 2016 (baseline), with an 82.14% reduction in human prevalence, an 80.00% reduction in detection of echinococcosis among children, a reduction of prevalent echinococcosis patients to 25 400 cases, a 36.96% reduction in the number of dogs, an 88.24% reduction in the prevalence of Echinococcus infections in dogs, a 67.00% reduction in the number of stray dogs, an 80.77% reduction in the prevalence of echinococcosis in live-stock, and a 69.23% reduction in the prevalence of Echinococcus infections in rodents. Nevertheless, an estimated 14 600 canines were found to have Echinococcus infections in endemic areas across China in 2023, and the prevalence of Echinococcus infections increased by 241.03% in wild canines in 2023 relative to in 2021, which underscores the persistent threat to echinococcosis transmission. Future efforts are required to strengthen the One Health governance system, increase the financial investment, foster innovations in integrated control technologies, enhance cross-sectoral collaboration, and improve the capacity building at the grassroots level to advance the achievement of the national target of basically controlling echinococcosis in all endemic counties in China by 2030, thereby facilitating the process towards echinococcosis control and elimination.

Objective:To investigate, for multi-sensitized allergic rhinitis (AR) patients allergic to dust mites combined with other allergens (pollen, mold, animal dander, etc.), whether the single dust mite subcutaneous immunotherapy (SCIT) can improve the specific symptoms caused by other allergens in the patients, and to analyze the relationship between the effectiveness of symptom improvement in these patients and the type, quantity and severity of the allergens.Methods:A multicenter retrospective study was conducted to collect mul-sensitized AR patients from allergy or respiratory departments of 5 hospitals who received house dust mite allergen preparation SCIT for 12 to 36 months and met other inclusion and exclusion criteria from February to July 2024. General clinical data were collected and the perennial or seasonal symptoms before and after treatment were evaluated with visual analogue scale (VAS) to assess whether there was an perennial or allergen-specific symptom improvement (VAS score decrease ≥30%), by which the patients were divided into effective group and ineffective. R software was used to analyze the differences between groups by using Fisher′s exact test and Mann-Whitney U test. Results:A total of 62 patients were enrolled, and the treatment were effective in 39 of them, with an effective rate of 62.9%. For allergen-specific symptoms, the median age of the effective group was higher than that of the ineffective group (12 years old vs. 8 years old, P=0.039), and the effective rate in dust mite specific immunoglobin E (sIgE) grade ≤5 group was higher than that in sIgE grade >5 group (81.6% vs. 45.5%, P=0.008), and the effective rate of mold sIgE grade ≤2 group was higher than that of sIgE grade >2 group (83.3% vs. 28.6%, P=0.045), and there was no statistically significant correlation between the other allergen grades and the effective rate ( P>0.05). For perennial symptoms, the effective rate in the mold grade ≤2 group was higher than that in the sIgE grade >2 group (91.3% vs. 28.6%, P=0.010), and there was no statistically significant correlation between the other allergen grades and the effective rate ( P>0.05). There was no significant correlation between the treatment effectiveness of perennial or allergen-specific symptoms and the number of combined allergens, the grade of skin test, and the difference between the grade of combined allergens and that of dust mites ( P>0.05). Conclusion:Among the patients with multi-sensitized AR allergic to dust mites included in this study, single dust mite SCIT is effective in some of them, and for allergen-specific symptoms, the effective group was elder, and dust mite sIgE grade 6 and mold sIgE grade ≥2 was related to the low effective rate of SCIT. The present results are insufficient for selecting single or multiple AIT in any type of multi-sensitized patients.

Objective:To determine the safety and efficacy of the modified off-pump biatrial mini-maze procedure to treat isolated atrial fibrillation.Methods:This study retrospectively included isolated atrial fibrillation patients who underwent the modified off-pump biatrial mini-maze procedure at the Department of Cardiothoracic Surgery, Zhongshan Hospital, Fudan University from January 2021 to December 2023. These patients underwent a mini-maze procedure using the Dallas lesion set protocol. A purse-string suture was then performed on the right atrium, and 4 ablation lesions were made to the superior vena cava, the inferior vena cava, the appendix of the right atrium and the tricuspid valve annulus from the purse-string suture point using the bipolar radiofrequency clamp. After the operation, the patients were followed up at 3, 6 and 12months and every year thereafter.Results:A total of 301 patients were followed up. There were no deaths or strokes. A follow-up at intervals of 3, 6, 12, 24 and 36 months showed that the cumulative success rate free from isolated atrial fibrillation was 96.0%、91.0%、85.9%、79.5% and 72.7%.Conclusion:The modified off-pump biatrial mini-maze procedure is safe and effective and can be considered as an off-pump ablation option for patients and cardiac surgeons.

Lung cancer (LC) complicated with interstitial lung disease (ILD) is a relatively common comorbidity in clinical practice, and its management remains complex and challenging. As one of the primary treatment modalities for LC, radiotherapy carries a risk of inducing acute exacerbation of ILD and severe radiation pneumonitis; therefore, it should be used with caution in LC-ILD patients. Advances in radiotherapy technology now allow for more precise tumor targeting and better sparing of healthy lung tissues, potentially offering greater therapeutic benefits for these patients. In this article, current status and recent research progress in the application of radiotherapy in LC-ILD were reviewed, aiming to provide theoretical basis and reference for clinical practice.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.