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.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Objective: To understand the relationship between working hours and occupational stress among front-line employees in manufacturing enterprises, so as to provide a basis for the prevention and control of occupational stress. Methods: Front-line employees from 5 manufacturing enterprises in Hangzhou City were selected from June to November 2024 using random cluster sampling. Demographic information and occupational information such as daily average working hours and weekly working hours, were collected through questionnaires. The effort-reward imbalance questionnaire was used to investigate occupational stress. The association between working hours and occupational stress was analyzed using a multivariable logistic regression model. Results: A total of 926 people were surveyed, among whom 572 were male, accounting for 61.77%, and 354 were female, accounting for 38.23%. The average age was (32.98±8.28) years. There were 515 people (55.62%) who had a monthly personal income of more than 5 000 yuan but less than 9 000 yuan. There were 353 people (38.12%) who had a working seniority of less than 5 years. There were 784 people (84.67%) who had an average daily working hours of >8 hours and 645 people (69.65%) who had a weekly working day of more than 5 days. There were 338 people (36.50%) on the shift work system, and 331 people (35.75%) worked night shifts. A total of 707 people were detected with occupational stress, with a detection rate of 76.35%. Multivariable logistic regression analysis showed that after adjusting for gender, age, educational level, marital status, number of children, only-child status, monthly personal income, working seniority, weekly working hours, assembly-line work, shift work system and night shift, compared with employees with an average daily working hours of ≤8 hours, the risk of occupational stress increased by 118.7% for those with an average daily working hours of >8-<10 hours (OR=2.187, 95%CI: 1.434-3.336) and by 248.4% for those with an average daily working hours of ≥10 hours (OR=3.484, 95%CI: 2.034-5.966). Conclusions The detection rate of occupational stress among front-line employees in manufacturing enterprises in Hangzhou City is high. Long average daily working hours can increase the risk of occupational stress among employees in manufacturing enterprises. It is recommended to reasonably arrange work intensity and working hours.

Aim To investigate the possible mechanism of the myocardial protective effect of Danzhi Jiangtang capsules(DJC)on db/db mice based on NLRP3 in-flammasome-mediated pyroptosis.Methods The db/db mice were randomly divided into the model group,DJC low,medium,and high dose groups,and the met-formin group,and the db/m mice were taken as the blank group.The administration lasted for eightweeks.At the end of drug administration,blood glucose,blood lipids,cardiac enzymes and inflammatory factors were detected in each group of mice.HE and Masson stai-ning was performed to observe the morphology and fi-brosis of myocardial tissue.TUNEL staining was per-formed to detect apoptosis.RT-qPCR was performed to detect the mRNA expression of ANP,BNP and β-MHC,and Western blot was performed to detect the protein expression of NLRP3,ASC,caspase-1,cleaved-caspase-1,GSDMD and GSDMD-NT in myocardial tis-sue.Results DJC could alleviate myocardial patho-logical damage,reduce collagen deposition and apopto-sis,reduce the levels of blood glucose,blood lipid,myo-cardial enzyme and inflammatory factors in db/db mice.DJC could reduce the mRNA expressions of ANP,BNP and β-MHC,and the protein expressions of NLRP3,ASC,caspase-1,cleavedcaspase-1,GSDMD and GSDMD-NT in myocardial tissues.Conclusion DJC attenuates myocardial injury in db/db mice,prob-ably by inhibiting the activation of NLRP3 inflamma-somes,attenuating cardiomyocyte pyroptosis,and amel-iorating the inflammatory state.

Objective To explore the clinical characteristics of the patients with follicular cysts and menstrual disorders.Methods A total of 178 women with follicular cysts complicated by menstrual disorders were recruited,who were aged 23-53(38.62±6.35)years and came to the Department of Gynecological Endocrinology,Beijing Obstetrics and Gynecology Hospital between August 2022 and July 2024.The participants were divided into three groups according to the age,with 27 women in the age group of 21-30 years(Group 1),77 in the age group of 31-40 years(Group 2),and 74 in the age group of>40 years(Group 3).The age and menstruation of these patients were collected,the body height,weight,follicle-stimulating hormone(FSH),luteinizing hormone(LH),estradiol(E2),progesterone(Po),total testosterone(TT),prolactin(PRL),anti-müllerian hormone(AMH)and thyroid function[blood routine and blood clotting for patients with abnormal uterine bleeding(AUB)]were measured,and the diameter of ovarian cyst and the thickness of endometrium were examined.The clinical characteristics of the patients with follicular cysts complicated by menstrual disorders were described and compared with each other among the three age groups.Results ① Among 178 patients,61(34.3%)patients presented with complaints of cessation of regular menstruation or amenorrhea and 117(65.7%)with complaints of AUB.Out of 178 patients,13(7.3%)patients developed anemia due to AUB,and all the patients were treated with medication.② Follicular cysts disappeared in 1 month for 124(69.7%)patients,disappeared in 2 months for 176(98.9%)patients,and disappeared in 3 months for 178(100%)patients.A total of 177(99.4%)patients had follicular cysts with a diameter less than 5 cm,and only 1(0.6%)patient had follicular cysts with a diameter≥5 cm.③ The overall prevalence of diminished ovarian reserve(DOR)in patients with follicular cysts complicated by menstrual disorders was 82.0%,and the prevalence of DOR was 70.2%for the patients under 40 years old.The prevalence of DOR in three age groups was 37.0%(10/27),81.8%(63/77),and 98.6%(73/74),respectively.The prevalence of DOR gradually increased with age.④ The median AMH values for Group 1,Group 2,and Group 3 were 1.55,0.51,and 0.15 ng/mL,respectively.The FSH level in Group 3 was significantly higher than that in Groups 1 and 2(adjusted P<0.05),while the AMH level was significantly lower than that in Groups 1 and 2(adjusted P<0.05).The AMH level in Group 2 was lower than that in Group 1(adjusted P<0.05).Conclusion Patients with follicular cysts complicated by menstrual disorders are recommended to receive medication treatment,rather than just expecting treatment.The prevalence of DOR in patients with follicular cysts complicated by menstrual disorders is significantly higher than that in normal women,even in women younger than 40 years old.Therefore,it is essential to evaluate the ovarian reserve of these patients while treating menstrual disorders,particularly for young women with fertility needs.

Objective To explore the correlation between thyroid autoimmunity(TAI)and gestational diabetes mellitus(GDM)in Chinese euthyroid women.Methods A total of 508 euthyroid women were enrolled in the cross-sectional study,who performed their entire clinical/biological workup and oral glucose tolerance test(OGTT)from the department of Gynecology and Endocrinology of the Beijing Obstetrics and Gynecology Hospital,Capital Medical University from June 2023 to June 2024.At median 8(6-10)weeks of gestation,thyroid-stimulating hormone(TSH),free thyroxine(fT4),and thyroid peroxidase antibodies(TPOAb)were measured,baseline characteristics were recorded,and an OGTT was performed between 24 and 28 weeks of pregnancy.According to the OGTT results,they were divided into GDM group(n=169)and non GDM group(n=339).Thyroid parameters,the demographic and obstetric parameters,and the prevalence of TAI were compared with two groups.The factors associated with GDM were analyzed with multivariate Logistic regression analysis.Results The age,body mass index(BMI),and proportion of obese women before pregnancy in the GDM group were all significantly higher than those in the non-GDM group,with statistically significant differences(P<0.001).The proportion of pregnant women over 30 years old in the GDM group was significantly higher than that in the non-GDM group(59.17%vs 6.79%,χ2=168.667,P<0.001).The proportion of obese mothers(BMI≥28 kg/m2)before pregnancy in the GDM group was 24.26%,which was significantly higher than that in the non-GDM group(8.26%)(χ2=24.599,P<0.001).The incidence of TAI in the GDM group was 54.44%,while it was 15.93%in the non-GDM group.The difference between the two groups was statistically significant(χ2=81.659,P<0.001).The results of Logistic regression analysis showed that maternal age over 30 years and pre-pregnancy obesity increased the risk of GDM occurrence in TAI women by 6.08 times(OR=6.08,95%CI 3.61-10.25,P<.001).Conclusion Among early pregnancy women with normal thyroid function,as age increases during follow-up(especially over 30 years old),pre-pregnancy BMI increases(especially in obese individuals),and those with pre-pregnancy TAI,the risk of developing GDM during pregnancy significantly increases.It is necessary to explore preventive strategies for GDM in euthyroid TAI women,with a view to improving adverse pregnancy outcomes.

Objective To evaluate the impact of slow-freezing process on human ovarian tissue with the standard cryopreservation-thawing protocol of Fertility Protection Center of Beijing Obstetrics and Gynecology Hospital,Capital Medical University.Methods Ovarian tissues of 12 patients were divided into fresh ovarian tissue group(fresh group)and freezing-thawing ovarian tissue group(F-T group).The freezing-thawing protocol was the standard protocol in our center.The number and activity of follicle were examined with Hematoxylin-eosin(HE)staining and calcein-AM(calcein acetoxymethylester)staining,and the proliferation and apoptosis was evaluated with the immunohistochemical staining of Ki-67 and caspase-3.The expressions of apoptosis-related proteins such as caspase-3,bax and FasL between the two groups were compared with Western blotting.Results There were no statistically significant differences in follicle counting and follicle activity in ovarian tissues pre-and post-freezing-thawing(P>0.05),and the positive rate of Ki-67 in ovarian tissues after freezing-thawing was significantly lower than that in fresh ovarian tissues(P<0.05),and there was no statistically significant difference in the positive rate of caspase-3 between the two groups(P>0.05).The expression of caspase-3 protein in ovarian tissues after freezing-thawing was significantly higher than that in fresh ovarian tissues(P<0.05),while the expressions of other apoptosis-related proteins such as bax and FasL were not significantly different(P>0.05).Conclusion The standard cryopreservation-thawing regimen in our center can effectively maintain the follicle number,morphology,and activity in ovarian tissues.After freezing and thawing,the cell proliferation level is decreased.The expression of apoptosis-related proteins such as bax and FasL are not increased,and the expression of caspase-3 is relatively increased.These results suggest our freezing-thawing regimen is good for human ovarian tissue.

Female infertility is recognized as a global public health issue by the World Health Organization.Female fertility remodeling includes ovarian function reconstruction and uterus/endometrium reconstruction,etc.It is emerging as a hot technology since it is ready to prepare autologous platelet-rich concentrate and it is safer and more acceptable in autologous application.It plays an important role in regenerative medicine,and it is currently widely applied in maxillofacial and plastic surgery,dermatology and other clinical practices.This article mainly reviews the progresses of the application of autologous platelet-rich concentrate in female fertility remodeling.

Objective To compare ovulation recovery rate and metabolic indicators between ethinyl-estradiol/drospirenone(EE/DRSP)combined with orlistat and EE/DRSP alone in overweight/obese patients with polycystic ovary syndrome(PCOS).Methods This study was a randomized controlled clinical trial conducted based on the 2004 Rotterdam criteria.From October 2020 to December 2023,180 overweight/obese PCOS patients aged 20-40 were recruited from the Department of Department of Gynecological Endocrinology,Beijing Obstetrics and Gynecology Hospital,Capital Medical University.The patients were randomly divided into two groups in a 1∶2 ratio.Among them,60 patients received treatment with EE/DRSP(EE20 μg,DRSP 3 mg),while 120 patients received a combination treatment of EE/DRSP and orlistat(360 mg/d).The height,weight,waist circumference,hip circumference,and blood pressure of the patients were measured before treatment and after 12 weeks of treatment.Laboratory tests included measurements of follicle-stimulating hormone(FSH),luteinizing hormone(LH),fasting insulin(FINS),homeostasis model assessment of insulin resistance(HOMA-IR),free androgen index(FAI),alanine transaminase(ALT),aspartate transaminase(AST),gamma-glutamyl transferase(GGT),fasting plasma glucose(FPG),total cholesterol(TC),triglycerides(TG),high density lipoprotein-cholesterol(HDL-C),low density lipoprotein-cholesterol(LDL-C),lipoprotein(a)[Lp(a)],sex hormone-binding globulin(SHBG),total testosterone(T),and free testosterone(FT).After 12 weeks of treatment,the medication was discontinued,and natural ovulation was observed.Results After 12 weeks of treatment,the ovulation rate of the EE/DRSP combined with orlistat group reached 70.8%,while the natural ovulation rate of the EE/DRSP group alone was only 35%,indicating that the ovulation rate was significantly increased after EE/DRSP combined with orlistat treatment.After 12 weeks of treatment,both groups showed a significant decrease in total testosterone,free testosterone,and low-density lipoprotein levels(all P<0.05),and the decrease in BMI,waist circumference,fasting insulin,and HOMA-IR in the EE/DRSP combined with orlistat group was greater than that in the EE/DRSP group alone(P<0.05).After treatment,both groups showed a significant increase in high-density lipoprotein and triglyceride levels(all P<0.05),with no significant changes in total cholesterol and fasting blood glucose(all P>0.05).Conclusion After 12 weeks of treatment,EE/DRSP combined with orlistat can significantly improve the ovulation rate of PCOS patients.It is superior to EE/DRSP alone in reducing androgen levels,body weight,insulin resistance,and low-density lipoprotein levels.