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 symptom management experience and needs of tuberculosis patients undergoing home-based anti-tuberculosis chemotherapy.Methods:This was a descriptive qualitative study. Purposeful sampling was used to select tuberculosis patients receiving home-based chemotherapy and healthcare professionals with tuberculosis experience from Beijing Chest Hospital, Capital Medical University between March and July 2024. Semi-structured interviews focused on the management experience and needs related to chemotherapy symptoms. Data were analyzed using thematic analysis.Results:A total of 13 tuberculosis patients and six healthcare professionals were interviewed. A total of four core themes were identified: multiple concurrent symptoms exacerbate the difficulty of home disease management and cause multidimensional distress; tuberculosis patients have insufficient self-management skills for symptoms; there is a high and diverse demand for symptom management during home chemotherapy; and the out-of-hospital follow-up and monitoring system struggles to address symptom management effectively.Conclusions:Tuberculosis patients undergoing home chemotherapy face significant and challenging symptom management burdens. Future improvements should include enhancing continuity of care outside the hospital, developing specific symptom assessment tools, and establishing an efficient, multi-symptom integrated management strategy combining home and hospital care to improve symptom management experience and outcomes for tuberculosis patients.

Objective:To explore the symptom management experience and needs of tuberculosis patients undergoing home-based anti-tuberculosis chemotherapy.Methods:This was a descriptive qualitative study. Purposeful sampling was used to select tuberculosis patients receiving home-based chemotherapy and healthcare professionals with tuberculosis experience from Beijing Chest Hospital, Capital Medical University between March and July 2024. Semi-structured interviews focused on the management experience and needs related to chemotherapy symptoms. Data were analyzed using thematic analysis.Results:A total of 13 tuberculosis patients and six healthcare professionals were interviewed. A total of four core themes were identified: multiple concurrent symptoms exacerbate the difficulty of home disease management and cause multidimensional distress; tuberculosis patients have insufficient self-management skills for symptoms; there is a high and diverse demand for symptom management during home chemotherapy; and the out-of-hospital follow-up and monitoring system struggles to address symptom management effectively.Conclusions:Tuberculosis patients undergoing home chemotherapy face significant and challenging symptom management burdens. Future improvements should include enhancing continuity of care outside the hospital, developing specific symptom assessment tools, and establishing an efficient, multi-symptom integrated management strategy combining home and hospital care to improve symptom management experience and outcomes for tuberculosis patients.

Objective:To explore the interventional effect of β-sitosterol on ovalbumin(OVA)-induced allergic asthma rats and its potential mechanism.Methods:SD male rats were randomly divided into normal group(CON),model group(M),positive drug dexamethasone group(DEX,0.075 mg/kg)and β-sitosterol group(Sit,50 mg/kg).A rat model of allergic asthma was estab-lished by intraperitoneal injection of OVA with aluminum hydrogen solution,and nebulized inhalation of OVA to stimulate.Rats were given intragastric administration 30 min before aerosol challenge,and after continuous administration for 7 days,the indicators of cough and asthma and tracheal phenol red excretion were detected.HE staining was used to observe pathological changes of lung tis-sue.Flow cytometry was used to detect reactive oxygen species(ROS)generation,apoptosis level and ratios of Th17 and Treg cells in peripheral blood.Biochemical method was used to detect contents of MDA,and activities of T-SOD and GSH-Px in rat lung tissues.ELISA was used to detect levels of Th17 and Treg-related cytokines(TNF-α,IL-4,IL-6,IL-17A,and IL-35).Results:Compared with model group,β-sitosterol significantly prolonged the incubation period of cough and gasp in rats with allergic asthma,reduced the frequency of cough and gasping,and promoted the excretion of phenol red in trachea;significantly reduced inflammatory infiltration in lung tissue of asthmatic rats;observably reduced MDA content in lung tissue,ROS of primary lung cell and apoptosis levels of asthmatic rats,increased the activities of T-SOD and GSH-Px;markedly reduced proportion of Th17 cells and levels of pro-inflammatory cyto-kines TNF-α,IL-4,IL-6 and IL-17A,increased proportion of Treg cells and levels of anti-inflammatory cytokine IL-35.Conclusion:β-sitosterol can ameliorate airway inflammation and oxidative damage in OVA-induced allergic asthmatic rats,and its mecha-nism may be related to the regulation of β-sitosterol on Th17/Treg immune imbalance and oxidative stress response.

Objective:To investigate the preventive effect of Xiyanping injection on radioactive esophagitis in patients with radical radiotherapy and chemotherapy for esophageal cancer.Methods:A total of 70 patients with esophageal cancer undergoing radical radiotherapy and chemotherapy were selected from the Department of Radiation Oncology of Jiangsu Provincial People′s Hospital from January to September 2020. They were divided into experimental group ( n=35) and control group ( n=35) according to random number table method. The control group received standard concurrent radiotherapy and chemotherapy, and the experimental group received concurrent radiotherapy and chemotherapy combined with Xiyanping. The white blood cell count, neutrophil count, procalcitonin (PCT) and interleukin-6 (IL-6) levels before and after treatment were compared between the two groups, as well as the occurrences of radioactive esophagitis and radioactive pneumonia during radiotherapy. Results:Before treatment, there were no significant differences in white blood cell count [4.57 (2.52)×10 9/L vs. 5.59 (2.23)×10 9/L] and neutrophil count [2.95 (1.66)×10 9/L vs. 3.69 (1.56)×10 9/L] between the control group and experimental group ( Z=1.44, P=0.151; Z=1.52, P=0.130). After treatment, there were no statistically significant differences in white blood cell count [4.28 (2.50)×10 9/L vs. 4.25 (1.88)×10 9/L] and neutrophil count [2.99 (2.50)×10 9/L vs. 2.94 (1.61)×10 9/L] between the two groups ( Z=0.67, P=0.503; Z=0.69, P=0.489). There were no statistically significant differences in white blood cell count and neutrophil count between the patients after treatment and before treatment in the two groups ( Z=0.77, P=0.443; Z=1.08, P=0.279; Z =1.06, P=0.289; Z=0.68, P=0.495). Before treatment, there were no statistically significant differences in serum inflammation indexes PCT [0.02 (0.03) μg/L vs. 0.02 (0.05) μg/L] and IL-6 [0.04 (0.21) μg/L vs. 0.04 (0.12) μg/L] between the two groups ( Z=0.70, P=0.482; Z=0.77, P=0.439). After treatment, there were statistically significant differences in serum inflammation indexes PCT [0.06 (0.17) μg/L vs. 0.03 (0.08) μg/L] and IL-6 [0.10 (0.25) μg/L vs. 0.01 (0.08) μg/L] between the two groups ( Z=2.08, P=0.038; Z=2.92, P=0.003). There were no statistically significant differences in serum inflammation indexes PCT and IL-6 in the experimental groups after treatment compared with before treatment ( Z=1.20, P=0.230; Z=1.19, P=0.235). In the control group, the serum inflammation index PCT level increased after treatment compared with before treatment, with a statistically significant difference ( Z=2.82, P=0.005), and the serum inflammation index IL-6 level increased compared with before treatment, but with no statistically significant difference ( Z=1.41, P=0.158). During the treatment, the incidence of radioactive esophagitis in the two groups was mainly grade Ⅰ-Ⅱ, with 24 cases in the control group and 28 cases in the experimental group. There were 8 patients with grade Ⅲ-Ⅳ radioactive esophagitis in the control group and 1 in the experimental group. There was a statistically significant difference in the occurrence of radioactive esophagitis between the two groups ( χ2=10.34, P=0.035). During the treatment period, most patients with radioactive pneumonia were rated as grade 0. There were 10 cases of mild radioactive pneumonia (grade Ⅰ-Ⅱ) in the control group had and 6 cases in the experimental group. There were 2 cases of grade Ⅲ radioactive pneumonia in the control group and experimental group respectively. There was no grade Ⅳ radioactive pneumonia in either group. There was no significant difference in radioactive pneumonia between the two groups ( χ2=1.34, P=0.720). Conclusion:Xiyanping injection can prevent the rise of serum inflammatory index PCT and reduce the severity of radioactive esophagitis in patients with esophageal cancer after treatment.

Medical big data is a hot research topic in China,and it is also the main research direction in the field of emergency medicine.The current situation of the construction of the first-aid big data platform and the construction of the first-aid clinical decision support system were analyzed,the problems existing in the development of the first-aid big data research field were enumerated,to explore the theoretical methods for promoting the development of domestic first-aid big data,so as to provide references for the research in related fields.

Objective Analyzed surgical outcome following Flex-3D thoracoscopy among 429 cases with lobectomy and segmentectomy in this paper to define its safety and efficacy.Methods From the completion of the Olympus Flex-3D integrated operation room in Shanghai Chest Hospital in June 2015 up to December 2016,a single surgeons team carried out 429 cases of Flex-3D anatomic video-assisted thoracic surgery.Demography,preoperative condition,perioperative period complications and pathology for these patients were analyzed and discussed.Results There was a total of 429 patients including 258 males and 171 females.The age at diagnosis was ranged 21-81 yds.Lobectomy was performed in 313 cases,segmentectomy in 116 cases.Among those with 389 primary malignant tumors,39 benign tumors and 1 MALT were anatomically resected.The mean number of lymph nodes resected was 11.10 ±4.58(1-30) and mean sampled lymph node stations 6.10 ± 1.34(1-10).1patient was converted to thoracotomy because of vessel injury.The average operation time was 98.00 ±24.61 min(range,35-274 min) and the average blood loss was(97.9 ±24.6)ml(range,50-400 ml).The postoperative hospital stay was(5.6 ± 1.3) days on average.There was no operative death,and operative complications occurred in 18 patients(4.1％).The 1-year overall survival and 1-year disease-free survival for the lung cancer group were 100％ and 99.8％,respectively.Conclusion Flex-3D video-assisted thoracic surgery is a safe and effective surgical procedure featured by its added depth perception to facilitate operation and short learning curve.

Objective To perform the comparative analysis on the test results acquired from three different types of hematology analyzers ,to explore their results comparability or whether their deviations being within the allowable range .Methods The 30 d in‐ternal quality control results collected from the three types of analyzers ,Sysmex XE2100 ,Sysmex XN3000 and Sysmex XT4000i , were analyzed and analyze .The XE2100 hematology analyzer with excellent results in repeatedly participating in the national exter‐nal quality control assessment was taken as the reference instrument ,while the Sysmex XN3000 and Sysmex XT4000i analyzers served as the contrastive instruments .Four blood samples from different patients with high ,medium and low values were randomly selected and detected for 5 d .The results of white blood cell(WBC) ,red blood cell(RBC) ,hematocrit(HCT) ,hemoglobin(HGB) and platelet(PLT) were performed the comparative analysis .Then the accuracy and comparability of the detection results were judged by F test ,regression equation and correlation coefficient .Results The detection result of WBC ,RBC ,HCT ,HGB and PLT had no statistical differences among three instruments (P> 0 .05) .The correlation of various indexes had close correlation (r≥0 .975) .The deviations conformed to the related requirements with comparability .Conclusion When conducting detection by multi‐ple instruments ,the quality control of experimental instrument should be paid attention to ,the different detection instruments should be conducted the calibration and comparison at regular intervals for ensuring the accuracy and consistency of detection re‐sults ,thus better serve clinic .

BACKGROUND:Microleakage between restoration, tooth structure and bonding agent can cause the entry of bacteria and liquid in the mouth into the gap, thereby damaging the bonding interface between the restoration and tooth tissues, and leading to bond failure. Microleakage detection can directly show whether the closure of the root canal of post and core system is good or bad. The severity of microleakage directly affects the restorative effects of post and core. OBJECTIVE: To evaluate the effects of different root canal cleaning methods on the microleakage between the fiber post and root canal dentin. METHODS: Thirty fresh non-caries premolar posts with free root canalin vitro were randomly divided into five groups, and the root canal wal was respectively washed with saline, 5.25% sodium hypochlorite solution+17% ethylenediamine tetra-acetic acid (EDTA)+saline, 3% hydrogen peroxide solution+5.25% sodium hypochlorite+ saline, 3% hydrogen peroxide solution+2% chlorhexidine solution+saline, and 2% chlorhexidine solution+17% EDTA+saline in different groups. Super-bond C&B adhesive agent was used for bonding fiber post, and the microleakage of each sample was observed under stereomicroscope. RESULTS AND CONCLUSION: The severity of microleakage in the al groups was ranged as folows: saline group > 3% hydrogen peroxide solution+5.25% sodium hypochlorite+saline group > 5.25% sodium hypochlorite solution+17% EDTA+saline and 3% hydrogen peroxide solution+2% chlorhexidine solution+saline groups > 2% chlorhexidine solution+17% EDTA+saline group.