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.

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 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.

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.

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 application value of inventory management model of medical consumables based on the joint inventory management(JMI)theory in the informationization management for medical consumables.Methods:By analyzing the existed problems in the management for medical consumables,and establishing a JMI model information system,the strategy of inventory management of JMI model was formulated to conduct predictive regulation and control,and management for distribution on the requirement of medical consumables.A total of 1200 batches of medical consumables that were used in clinical practice at Heyuan People's Hospital from October 2022 to September 2024 were selected.Based on different inventory management modes,593 batches of medical consumables from October 2022 to September 2023 were managed by using the conventional independent inventory management mode,and 607 batches of medical consumables from October 2023 to September 2024 were managed by using the inventory management model(model management)based on JMI theory for informationization management for medical consumables.The efficiency and quality of management for inventory between two kinds of inventory management modes were compared.Results:The ratio of monthly average consumables to inventory of medical consumables of using the model management mode was 0.468±0.060,which was higher than that of the independent inventory management mode.The monthly average times of emergency ordering,the monthly average times of stockouts,and monthly average times of non-emergency ordering were respectively(23.84±6.02),(5.69±2.28)and(20.57±2.66)times,all of which were lower than those of the independent inventory management mode,and the differences were statistically significant(t=11.249,7.539,8.854,9.654,P<0.05).The consumed times of inbound,information verification,query and statistics,and outbound of medical consumables of adopting the model management mode were respectively(1.18±0.40),(0.25±0.06)h,(0.73±0.35)h,and(0.53±0.22)h,all of which were lower than those of the independent inventory management mode,and the differences were statistically significant(t=50.621,87.351,61.524,43.806,P<0.05).Conclusion:The application of inventory management model for medical consumables based on the JMI theory in informationization management for medical consumables can improve management efficiency for inventory,and optimize structure of inventory,and reduce costs of inventory,and decrease the frequency of temporary procurement for medical consumables.

BACKGROUND:Circadian rhythms are closely related to the life activities of most mammals and insects.Timless gene plays a crucial role in the generation of circadian rhythms as key components encoding the Timeless/Period gene complex.However,its specific mechanism in circadian rhythms is still unclear.OBJECTIVE:To study the relationship between Timless protein gene,Period gene,circadian rhythm,environment and cryptochrome gene,so as to have a more comprehensive understanding of the nucleation and accumulation mechanism of circadian cycle and the influence of environment on circadian rhythm.METHODS:Literature retrieval was conducted in the Web of science Core Collection database,PubMed and CNKI,and the relevant literature was searched,consulted and screened after the keyword was set as"Timless,Period,circadian rhythm,environment"in English and Chinese.Non-relevant literature was progressively excluded through full-text reading,and 126 papers were finally included for the review.RESULTS AND CONCLUSION:In the circadian clock,the circadian spontaneous output of the cyclins kaput and CYCLE activate the Timeless/Period gene,Timeless gene regulates the nucleation mechanism and stability of Period gene,and Period gene can also be nucleated individually by a number of mechanisms.Casein kinase 2,Shaggy protein kinase and double time genes can regulate circadian rhythms and participate in transcription by phosphorylating Timeless gene/Period gene.Cryptochrome gene-mediated degradation of Timeless gene has a very important role in transcriptional integrity.External factors such as environmental factors and dietary patterns can influence circadian rhythms through the Timeless gene/Period gene.Interestingly,time-restricted eating can be used as an effective way to improve circadian rhythm disturbances.

Objective To construct mice hepatocellular carcinoma models with different tumor immune microenvironments(TIME)and explore the differences.Methods H22 and hepa1-6 were used to construct subcutaneous transplantation tumor model of C57 mice as homologous hepatocellular carcinoma cell lines(denoted as H22 group and hepal-6 group,each n=8),and the differences of TIME were evaluated.Immunohistochemistry was used to detect and quantify the infiltration of T cells,CD4+T cells,CD8+T cells,regulatory T cells and B cells in TIME.Flow cytometry was performed to detect the differences of composition of immune cell subpopulations in peripheral blood and tumor parenchyma.Gene expression profile characteristics of tumor tissue were analyzed based on high-throughput transcriptome sequencing technology,and enrichment analyses of immune-related signaling pathways were evaluated combined with gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG).Results H22 group showed cold and hepa1-6 group showed hot TIME characteristics.The number of T cells,CD4+T cells and CD8+T cells in tumor tissue of H22 group were all lower,while the proportion of T cells,CD4+T cells and CD8+T cells in peripheral blood were all higher than those of hepa1-6 group(all P＜0.05).Compared with H22 group,up-regulated genes of tumor tissue in hepa1-6 group expressed significantly enriched in tumor immune activation-related signaling pathways.Conclusion H22 and hepa1-6 hepatocellular carcinoma models showed distinct TIME characteristics of cold and hot tumors,respectively,and the amount of immune cells in tumor tissue of the former were significantly lower than those in the latter.

Objective:To explore the application value of inventory management model of medical consumables based on the joint inventory management(JMI)theory in the informationization management for medical consumables.Methods:By analyzing the existed problems in the management for medical consumables,and establishing a JMI model information system,the strategy of inventory management of JMI model was formulated to conduct predictive regulation and control,and management for distribution on the requirement of medical consumables.A total of 1200 batches of medical consumables that were used in clinical practice at Heyuan People's Hospital from October 2022 to September 2024 were selected.Based on different inventory management modes,593 batches of medical consumables from October 2022 to September 2023 were managed by using the conventional independent inventory management mode,and 607 batches of medical consumables from October 2023 to September 2024 were managed by using the inventory management model(model management)based on JMI theory for informationization management for medical consumables.The efficiency and quality of management for inventory between two kinds of inventory management modes were compared.Results:The ratio of monthly average consumables to inventory of medical consumables of using the model management mode was 0.468±0.060,which was higher than that of the independent inventory management mode.The monthly average times of emergency ordering,the monthly average times of stockouts,and monthly average times of non-emergency ordering were respectively(23.84±6.02),(5.69±2.28)and(20.57±2.66)times,all of which were lower than those of the independent inventory management mode,and the differences were statistically significant(t=11.249,7.539,8.854,9.654,P<0.05).The consumed times of inbound,information verification,query and statistics,and outbound of medical consumables of adopting the model management mode were respectively(1.18±0.40),(0.25±0.06)h,(0.73±0.35)h,and(0.53±0.22)h,all of which were lower than those of the independent inventory management mode,and the differences were statistically significant(t=50.621,87.351,61.524,43.806,P<0.05).Conclusion:The application of inventory management model for medical consumables based on the JMI theory in informationization management for medical consumables can improve management efficiency for inventory,and optimize structure of inventory,and reduce costs of inventory,and decrease the frequency of temporary procurement for medical consumables.

BACKGROUND:Circadian rhythms are closely related to the life activities of most mammals and insects.Timless gene plays a crucial role in the generation of circadian rhythms as key components encoding the Timeless/Period gene complex.However,its specific mechanism in circadian rhythms is still unclear.OBJECTIVE:To study the relationship between Timless protein gene,Period gene,circadian rhythm,environment and cryptochrome gene,so as to have a more comprehensive understanding of the nucleation and accumulation mechanism of circadian cycle and the influence of environment on circadian rhythm.METHODS:Literature retrieval was conducted in the Web of science Core Collection database,PubMed and CNKI,and the relevant literature was searched,consulted and screened after the keyword was set as"Timless,Period,circadian rhythm,environment"in English and Chinese.Non-relevant literature was progressively excluded through full-text reading,and 126 papers were finally included for the review.RESULTS AND CONCLUSION:In the circadian clock,the circadian spontaneous output of the cyclins kaput and CYCLE activate the Timeless/Period gene,Timeless gene regulates the nucleation mechanism and stability of Period gene,and Period gene can also be nucleated individually by a number of mechanisms.Casein kinase 2,Shaggy protein kinase and double time genes can regulate circadian rhythms and participate in transcription by phosphorylating Timeless gene/Period gene.Cryptochrome gene-mediated degradation of Timeless gene has a very important role in transcriptional integrity.External factors such as environmental factors and dietary patterns can influence circadian rhythms through the Timeless gene/Period gene.Interestingly,time-restricted eating can be used as an effective way to improve circadian rhythm disturbances.