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.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Objective: To investigate the current status of iron metabolism among apheresis donors in Guangzhou and analyze the improvement effects of health education on iron deficiency in frequent apheresis donors. Methods: Using a generalized linear mixed model (GLMM), a 180-day follow-up was conducted on 261 eligible apheresis donors at the Guangzhou Blood Center from January to July 2024. Hemoglobin (Hb), serum ferritin (SF), unsaturated iron-binding capacity (UIBC), total iron-binding capacity (TIBC), and transferrin saturation (TS) were selected as outcome variables. The effects of gender, age group, and number of donations within 180 days on these outcomes were analyzed and modeled. A general linear model (GLM) with repeated measures was applied to 55 donors who received health education interventions, comparing changes in Hb and iron metabolism-related indicators before and after follow-up and health education. Results: No significant difference in Hb levels was observed between first-time and regular apheresis donors, but SF levels were significantly higher in first-time donors (F=6.195, P<0.05). The GLMM revealed that female donors exhibited more significant reductions in Hb (T=-12.546) and SF (T=-5.829)(P<0.05 for both). Age group showed no interactive effects on Hb or SF changes. While number of donations within 180 days had no interactive effect on Hb, SF levels significantly decreased with increased number of donations (using ≥9 donations as the reference group; P<0.05 for all groups). After health education, Hb levels remained unchanged, but SF increased compared to pre-intervention levels (mean difference: -18.571, P<0.05), though a declining trend persisted compared to baseline (mean difference from baseline to post-intervention: 23.068,P<0.05). Conclusion: Female and number of donations are primary factors contributing to SF reduction in apheresis donors. Health education interventions promote SF recovery. Extending donation intervals and reinforcing iron deficiency-related health education may improve iron status in donors.

This study analyzes the legal application of a dispute over employer identification in a case of occupational heat illness caused by part-time work to clarifying matters related to employer determination in occupational disease diagnosis using a case analysis method and factual reconstruction. The analysis is based on relevant civil laws and regulations, occupational disease diagnosis laws and regulations, and jurisprudential theories. The occupational disease diagnostic institution identified the part-time employer as responsible for the patient′s heat illness, which was both reasonable and lawful. This attribution safeguarded the rights of the worker, the primary employer, and the part-time entity. In occupational disease diagnosis, attention should be paid to de facto employment relationship, and the principle of "accountability lies with the actual employer at the time of the incident" should be followed to standardize employer identification. The health administrative department has supervisory responsibilities over occupational disease diagnoses. Workers′ compensation rights can be protected through the advance payment mechanism for work-related injury insurance. It is recommended to further improve internal procedures for occupational disease diagnosis, strengthen the dissemination of relevant laws and regulations and enhance the capabilities of occupational disease diagnosis physicians, to further protect workers' occupational health and their associated legal rights.

Although symptomatic treatment is widely applied in clinical practice， it is often regarded as a relatively low-level therapeutic method. Based on Traditional Chinese Medicine （TCM） state theory， the macroscopic， mesoscopic， and microscopic characterization parameters of TCM symptomatology are horizontally integrated， the full life cycle of states （pre-disease， incipient disease， manifest disease， post-disease） is vertically covered， and the cognitive system of "symptoms" is reconstructed from multiple dimensions. Accordingly， the application approach of symptomatic treatment at different state stages is proposed： implementing preventive intervention in the pre-disease state， strengthening the interception of disease progression in the incipient disease state， regulating dynamic development and treatment in the manifest disease state， and formulating a staged diagnosis and treatment strategy which focuses on functional rehabilitation in the post-disease state.

OBJECTIVE To investigate the independent risk factors for carbapenem-resistant Klebsiella pneumoniae （CRKP） infection， develop a nomogram prediction model and validate it. METHODS Clinical data of hospitalized patients infected with CRKP between April 2020 and May 2023 at Kunming First People’s Hospital were retrospectively collected and matched 1∶1 with patients infected with carbapenem-susceptible Klebsiella pneumoniae （CSKP） during the same period as the modeling group. Using the same criteria， data from patients hospitalized and infected with CRKP and matched CSKP between June 2023 and June 2024 were collected as the validation group. Univariate analysis， LASSO regression and multivariate Logistic regression were conducted to identify independent risk factors for CRKP infection and to develop a nomogram prediction model. Internal validation of the model was performed using Bootstrap resampling， and external validation was carried out using the data of validation group. The predictive performance of the model was evaluated using receiver operating characteristic （ROC） curves and calibration plots. RESULTS A total of 530 patients were enrolled， with 372 in the modeling group and 158 in the validation group. Cerebrovascular disease， indwelling gastric tube， mechanical ventilation， exposure to carbapenem antibiotics， and exposure to β-lactamase inhibitor compound agents were identified as independent risk factors for CRKP infection （P＜0.05）. The nomogram predicting CRKP infection risk achieved an area under ROC of 0.729 and 0.803 in internal and external validations， respectively. Calibration curves indicated a high degree of consistency between predicted and observed probabilities. CONCLUSIONS Cerebrovascular disease， indwelling gastric tube， mechanical ventilation， exposure to carbapenem antibiotics， and exposure to β-lactamase inhibitor compound agent are independent risk factors for CRKP infection. The developed nomogram model for predicting CRKP infection risk demonstrates good predictive performance and can aid in the early identification of patients at high risk for CRKP infection.

Objective To compare the midterm outcomes of the Bentall procedure versus isolated aortic valve replacement(AVR)in patients with bicuspid aortic valve(BAV)complicated with severe stenosis and ascending aortic dilation in order to assess the therapeutic value of these surgical approaches for this complex cardiac condition.Methods A retrospective cohort study was conducted on 96 eligible patients who underwent surgical treatment in our institute between January 2018 and December 2022.According to surgical approaches,they were divided into an AVR group(65 cases)and a Bentall group(31 cases).Demographic features,comorbidities,preoperative status,and echocardiographic parameters were collected in all patients.Propensity score matching(PSM)was applied in a 1:1 ratio to balance baseline characteristics.Perioperative indicators and follow-up data were compared and analyzed between matched cohorts after control of cofounding factors.Results After PSM,25 matched pairs were screened out and analyzed with comparable baseline data(all P>0.05).The Bentall group demonstrated significantly more superior intraoperative effective orifice area(EOA,2.69±0.47 vs 2.35±0.47 cm2,P=0.013)and EOA index(EOAI,1.69±0.30 vs 1.47±0.29 cm2/m2,P=0.010),and obviously longer cardiopulmonary bypass time[190.00(147.00,257.00)vs 101.00(88.50,124.50)min,P<0.01]and aortic cross-clamp time[141.00(120.00,166.00)vs 66.00(55.00,81.50)min,P<0.01]when compared with the AVR group.During a median follow-up of 28 months,the AVR group had notably larger aortic sinus diameter[32.00(30.00,34.00)vs 26.80(26.00,28.00)mm,P<0.01]and ascending aortic diameter[38.00(34.50,42.00)mm vs 26.00(26.00,28.00)mm,P<0.01],with ongoing dilation in the ascending aorta,while the Bentall group maintained stable dimensions.The Bentall group also showed statistically lower peak aortic valve pressure gradients[21.00(15.50,27.00)vs 25.00(19.50,31.00)mmHg,P=0.049].Conclusion Both Bentall procedure and AVR are effective in treatment of BAV complicated with severe stenosis and ascending aortic dilation.But,Bentall procedure offers advantages in hemodynamic optimization and aortic stability.

Objective:To verify and compare the correlation and diagnostic efficacy of international bowel ultrasound segmental activity score（IBUS-SAS），bowel ultrasound score（BUSS），simple ultrasound score for Crohn's disease（SUS-CD），and simple ultrasound score（Simple-US）with endoscopic disease activity in Crohn's disease（CD）patients. To provide external validation of the diagnostic efficacy of intestinal ultrasound（IUS）score and theoretical basis for clinical selection of optimal IUS score.Methods:A total of 160 patients with clinical diagnosis of CD combined with IUS and intestinal endoscopy were retrospectively analyzed in the Second Affiliated Hospital of Zhejiang University School of Medicine from January 2022 to August 2024. IUS parameters were measured and scored with IBUS-SAS，SUS-CD，BUSS and Simple-US scores. Endoscopic SES-CD was used to evaluate intestinal disease activity in patients without history of intestinal resection，and Rutgeerts score was used to evaluate intestinal disease activity in patients with history of intestinal resection. Endoscopic remission in patients with CD was defined as SES-CD < 3 or Rutgeerts score i0 and i1，mild endoscopic disease activity was defined as 7 > SES-CD≥3 or Rutgeerts score = 2，moderate endoscopic disease activity was defined as 15 > SES-CD≥7 or Rutgeerts score i3，severe endoscopic disease activity was defined as SES-CD≥15 or Rutgeerts i4. The correlation and diagnostic efficacy of IBUS-SAS，SUS-CD，BUSS and Simple-US scores with endoscopic disease activity in patients with CD were compared and analyzed.Results:IUS scores including IBUS-SAS，SUS-CD，BUSS and Simple-US were significantly correlated with endoscopic intestinal disease activity，SES-CD and Rutgeerts scores in CD patients（ r s = 0.706，0.492，0.502，0.526；0.825，0.581，0.584，0.603；0.541，0.434，0.437，0.467；all P＜0.05）. Among them，IBUS-SAS showed better correlation than the other three IUS scores. ROC curve showed that IBUS-SAS，SUS-CD，BUSS，and Simple-US had high predictive values for endoscopic disease activity and endoscopic disease moderate-severe activity in patients with CD（AUC = 0.886，0.748，0.730，0.756；all P＜0.05）. The diagnostic efficacy of IBUS-SAS on the presence of endoscopic disease activity in patients with CD was significantly higher than those of the other three IUS scores . Conclusions:IBUS-SAS，SUS-CD，BUSS and Simple-US are significantly correlated with the endoscopic results of on intestinal disease activity of CD，and have high predictive values for intestinal disease activity status，among which IBUS-SAS is superior to the other three IUS scores. It is recommended that IBUS-SAS be used first to evaluate intestinal disease activity in patients with CD.