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

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.

Objective:To investigate the correlation between metabolic syndrome (MS), its different components and the risk of cholecystolithiasis and gallbladder polyp in Uygur population in rural areas of southern Xinjiang.Methods:This study was a prospective cohort study. A baseline survey was conducted in August 2016. A typical sampling method was used to select 10 476 Uygur people in rural areas of southern Xinjiang as the research objects. Baseline clinical data were collected, including demographic data such as age, gender, and education level, and laboratory examination indicators such as blood glucose and triglyceride levels. According to the MS diagnostic criteria of the relevant guidelines, 10 476 subjects were divided into the MS group (3 475 cases) and the non-MS group (7 001 cases). The incidence of cholecystolithiasis and gallbladder polyp was followed up in 2019, 2021 and 2023, respectively. Cox regression was used to analyze the correlation between MS, its different components and the risk of cholecystolithiasis and gallbladder polyp. Chi-square test and independent sample t test were used for statistical analysis. Results:The median follow-up time was 6.43 years in 10 476 subjects, and the overall cumulative incidence of cholecystolithiasis and gallbladder polyp was 5.43% (569/10 476). The cumulative incidence of cholecystolithiasis and gallbladder polyp in the MS group was 10.73% (373/ 3 475), which was significantly higher than that in the non-MS group (2.80% (196/7 001)); χ2= 284.62, P<0.001). The results of multivariate Cox regression analysis showed that, 41 to 59 years old ( HR=1.26, 95% confidence interval (95% CI): 1.03 to 1.54, P=0.025), ≥60 years old ( HR=1.88, 95% CI: 1.45 to 2.45, P<0.001), female ( HR=1.34, 95% CI: 1.13 to 1.60, P=0.001), MS ( HR=2.19, 95% CI: 1.59 to 3.01, P<0.001), hypertriglyceridemia ( HR=1.47, 95% CI: 1.18 to 1.83, P=0.001), hypertension ( HR=1.30, 95% CI: 1.04 to 1.62, P=0.023), and hyperglycemia ( HR=1.24, 95% CI: 1.01 to 1.52, P=0.041) were independent risk factors for cholecystolithiasis and gallbladder polyp. After the adjustment of age and gender, MS ( HR=3.39, 95% CI: 2.82 to 4.07, P<0.001), hypertriglyceridemia ( HR=2.37, 95% CI: 2.00 to 2.81, P<0.001), hypertension ( HR=2.00, 95% CI: 1.66 to 2.41, P<0.001), and hyperglycemia ( HR=1.86, 95% CI: 1.55 to 2.23, P<0.001) were still correlated with cholecystolithiasis and gallbladder polyp, and there was the srtongest correlation between MS and cholecystolithiasis and gallbladder polyp. The results of univariate Cox regression analysis showed that along with the increase of accumulated of MS components, the risk of cholecystolithiasis and gallbladder polyp significantly increased (1 to 5 components corresponding HR (95% CI) were 1.92 (1.13 to 3.24), 2.21 (1.32 to 3.69), 6.91 (4.22 to 11.30), 8.56 (5.15 to 14.22), and 10.73 (5.66 to 20.33); P=0.015, =0.002, <0.001, <0.001, and <0.001); after age and gender were adjusted, this trend still existed (1 to 5 components corresponding HR (95% CI) were 1.81(1.07 to 3.06), 1.95(1.16 to 3.27), 5.64(3.42 to 9.32), 6.69(3.97 to 11.25), and 7.76(4.04 to 14.91); P=0.028, =0.012, <0.001, <0.001, and <0.001). Conclusion:MS and its components can increase the risk of cholecystolithiasis and gallbladder polyp, and the risk of cholecystolithiasis and gallbladder polyp significantly increases along with the increase of accumulated of MS components.

Objective:To develop and design a during-treatment records auto-review program to comply the quality assurance(QA)requirement of radiotherapy chart auditing,and thereby improve the review efficiency and accuracy.Methods:Based on the items the guideline required,the Aria Oncology Information System database backup files was analyzed by Java,Vue,and etc.languages and the corresponding review logic was formulated.A total of 530 treatment records generated at Shanghai Concord Cancer Center from January to March 2024(10 weeks)were auto-reviewed and compared with the manual results for evaluating the accuracy and efficiency of the program.Results:The auto-review program was running smoothly.Overall with the above data,the sensitivity,specificity,accuracy and the error-miss rate were 73.4%,14.3%,87.7%and 12.3%respectively.For sub-set items,the source-skin distance(SSD)error detecting rate was 100%,the wrong session reporting was 100%correlated with the plans switching and the wrong fraction reporting was 100%related to plan revision.For the other items,auto and manual reviews gave out the same accuracy.Conclusion:The none-error results from the program are all true,so the manual rechecking could limit to those auto-review error records,which can reduce the workload by 73.4%,therefore improve the effectiveness and accuracy of the radiotherapy data review.

The escalating phenomenon of global population aging is posing multi-dimensional challenges to society, the economy and medical healthcare system.Among the significant health threats to the elderly population are cardiovascular diseases(CVD)and cognitive frailty(CF), both of which profoundly affect the quality of life and increase the risks of adverse health outcomes, including disability, hospitalization, and death.The concurrent presence of CVD and CF in elderly patients is prevalent, as these conditions share many common risk factors and underlying pathophysiological mechanisms, such as atherosclerosis, microcirculation dysfunction, and inflammation, which interact to perpetuate a vicious cycle.Notably, CF exhibits a certain degree of reversibility; thus, the implementation of a diagnosis and treatment paradigm that incorporates "comprehensive geriatric assessment and geriatric interdisciplinary teams" should be established as a conventional management strategy for elderly patients affected by both CVD and CF.Cognitive digital therapeutics, along with personalized exercise prescriptions based on cardiopulmonary exercise tests, may represent more appropriate precision interventions for these patients.Consequently, there is a necessity for further in-depth research in this area moving forward.

Objective:To investigate the anti-tumor effect of the Histone Deacetylase 6 (HDAC6) inhibitor ACY-738 and its underlying mechanisms in Diffuse Large B-cell Lymphoma (DLBCL) .Methods:The expression of HDAC6 in various tumors and DLBCL was analyzed using bioinformatics. DLBCL cells were treated with different concentrations of ACY-738. Cell viability, DNA synthesis, and clone formation were assessed by CCK-8 assay, EdU assay, and soft agar assay, respectively. Intracellular reactive oxygen species (ROS) levels were detected by fluorescence microscopy. Morphological changes in cells were observed using transmission electron microscopy (TEM). Mitochondrial ROS levels and apoptosis were measured by flow cytometry. The expression levels of apoptosis-related and autophagy-related proteins were detected by Western blotting.Results:HDAC6 was highly expressed in DLBCL ( P<0.05). ACY-738 inhibited the proliferation, DNA synthesis, and colony formation of DLBCL cells in a dose-dependent manner ( P<0.05). Treatment with ACY-738 increased intracellular and mitochondrial ROS levels in DLBCL cells in a dose-dependent manner ( P<0.05). TEM revealed that after ACY-738 treatment, mitochondria in cells were swollen and ruptured, mitochondrial cristae were reduced or absent, autolysosomes appeared, and features characteristic of apoptosis were observed. Western blotting showed that after ACY-738 treatment, the expression of the anti-apoptotic protein BCL-2 was downregulated, while the expression of Cleaved-PARP, Cleaved caspase-3, and BAX was upregulated ( P<0.05). The expression of autophagy-related proteins Atg7, Atg3, LC3B, and P62 was downregulated, and the expression of acetylated P53 protein was upregulated ( P<0.05) . Conclusion:The HDAC6 inhibitor ACY-738 induces mitochondria-dependent apoptosis and autophagy in DLBCL cells by acetylating P53, thereby inhibiting DLBCL cell proliferation.

Objective To examine the causes of incision infections following lower extremity open fractures and develop a predictive model for assessing the risk.Methods A total of 104 patients with open fractures of the lower extremity,who received internal fixation from January 2022 to August 2023.According to whether there was incision infection after the operation,the patients were divided into infection group and non-infection group.The aim of the study was to analyze the distribution of pathogenic bacteria causing postoperative incision infections.Single-factor and multifactor Logistic regression analyses were employed to examine the factors influencing postoperative incisional infections.Subsequently,a risk prediction model for these infections was developed.The predictive capacity of this model was assessed using ROC curves.Results In the cohort of 104 patients with open fractures of the lower limb,the occurrence rate of postoperative incision infections was 19.23％.A total of 45 non-repeated pathogenic bacteria were isolated,among which gram-positive bacteria accounted for 53.33％,gram-negative bacteria 42.22％,fungi 4.44％.Gram-positive bacteria showed 100％resistance to ampicillin/sulbactam and penicillin,while resistance rates for erythromycin and clindamycin exceeded 90％.Among gram-negative bacteria,resistance rates to cefazolin,sulfamethoxazole/trimethoprim,levofloxacin,ampicillin/sulbactam,ciprofloxacin,and gentamicin were all above 67％.Notably,resistance rates for cefazolin,sulfamethoxazole,and trimethoprim surpassed 90％.Univariate and multifactorial logistic stepwise regression analysis highlighted that time elapsed from injury to surgery,duration of surgery,length of hospital stay,perioperative prophylactic medication,and Gustilo classification were significant risk factors for postoperative incisional infections in patients with the condition(P＜0.05).The ROC curves illustrated that the risk prediction model accurately forecasted the incidence of postoperative incisional infections in patients with open fractures of the lower extremity,with an area under the curve of 0.861(95％ CI:0.811 to 0.911),boasting a sensitivity of 90.50％and a specificity of 72.92％.Conclusion The main pathogen of wound infection after open fracture of lower extremity is Gram-negative,the time from injury to operation,operation time,hospitalization time,prophylactic medication during perioperative period and GUSTILO classification were the influencing factors of postoperative wound infection.In addition,the establishment of risk prediction model has a good prediction effect on the incidence of postoperative wound infection in patients with this disease.

The worksite survey of occupational disease diagnosis is a series of occupational health investigations in the workplace initiated by the occupational disease diagnosis institution or the public health administrative department in order to understand whether there is a causal relationship between the workers' diseases and the workplace in the process of occupational disease diagnosis and verification. The main purpose of the worksite survey is to find out whether there are occupational hazards that cause health damage to workers in the workplace, and to analyze whether there is a causal relationship between the exposure to occupational hazards at the corresponding concentration (intensity) and the diseases suffered by workers. In actual work, it is necessary to determine whether it is necessary to organize worksite survey according to the legal situation and actual work of occupational disease diagnosis. The mainly works of worksite survey includes three aspects: preliminary preparation, survey implementation and survey report writing. It is necessary to pay attention to the key and difficult tasks such as preparation before survey, survey plan and questionnaire, complexity and uncertainty of worksite survey and sampling and detection of occupational hazard factors in workplace. After the worksite survey,it is necessary to write a written occupational disease on-site investigation report to provide objective, reliable and scientific evidence for occupational disease diagnosis.