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.

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.

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.

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.

Objective:To explore the impact of different blood transfusion thresholds on clinical outcomes in children with severe traumatic brain injury (TBI).Methods:A retrospective cohort study was conducted. Clinical data was collected from 64 children with severe TBI who received red blood cell transfusions and were admitted to the Pediatric Intensive Care Unit (PICU) of Beijing Children′s Hospital between January 2020 and December 2024. Data included basic clinical characteristics, mortality rate, neurological recovery (measured by Glasgow coma scale (GCS) at discharge, pediatric cerebral performance category (PCPC) score), length of stay in the PICU, duration of mechanical ventilation, and incidence of complications. Patients were divided into a liberal transfusion group (hemoglobin >70-<100 g/L at first transfusion) and a restrictive transfusion group (hemoglobin ≤70 g/L at first transfusion). Stratified analysis was performed based on age (children >5 and children ≤5 years old). Comparisons between groups were conducted using the independent samples t test, Mann-Whitney U test, χ2 test or Fisher′s exact test. Results:Among the 64 children with severe TBI (43 males and 21 females), the age was 4.9 (2.3, 10.0) years. There were 33 cases in the liberal transfusion group and 31 cases in the restrictive transfusion group. No statistically significant differences were observed in baseline data, including gender, age, trauma mechanism, GCS at admission, surgical intervention, presence of multiple injuries, or comorbidities (sepsis, shock, ventilator-associated pneumonia and acute kidney injury) between the 2 groups (all P>0.05). There were no statistically significant differences between the liberal and restrictive transfusion groups in mortality rate, GCS and PCPC score at discharge, length of PICU stay, duration of mechanical ventilation, or transfusion volume (all P>0.05). In the stratified analysis, 38 children aged over 5 years were included. The restrictive transfusion subgroup, which included 19 children, had a significantly longer PICU length of stay compared to the liberal transfusion subgroup, which also included 19 children (29.5 (18.0, 36.3) vs. 17.0 (6.3, 25.8) d, Z=2.11, P=0.035). Conclusions:There were no significant differences in PICU mortality or neurological functional recovery between the liberal and restrictive blood transfusion strategies in children with severe TBI. However, among children aged over than 5 years, a restrictive transfusion strategy might be associated with a longer length of PICU stay.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

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.