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.

Diabetic kidney disease（DKD） is a chronic kidney structural and functional disorder caused by diabetes. With the global prevalence of diabetes continuing to rise， DKD has gradually become a major cause of chronic kidney disease and end-stage renal disease（ESRD）， posing a serious threat to patients' quality of life and long-term health outcomes. Studies have shown that apoptosis plays a pivotal role in the development and progression of DKD， with its mechanisms involving abnormal activation of multiple signaling pathways such as Toll-like receptor 4（TLR4）/nuclear transcription factor-κB（NF-κB）/B-cell lymphoma-2（Bcl-2）/cysteinyl aspartate-specific proteinase（Caspase）-3， protein kinase R-like endoplasmic reticulum kinase（PERK）/eukaryotic initiation factor 2α（eIF2α）/activating transcript factor 4（ATF4）/CCAAT enhancer-binding protein homologous protein（CHOP）， phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt）/glycogen synthase kinase-3β（GSK-3β）， Janus kinase 2（JAK2）/signal transducer and activator of transcription 3（STAT3）， adenosine monophosphate-activated protein kinase（AMPK）/mammalian target of rapamycin（mTOR） and silent information regulator 1（SIRT1）/tumor suppressor protein 53（p53）， thereby accelerating renal pathological damage in DKD. Extensive evidence-based medical studies have confirmed that traditional Chinese medicine（TCM）， leveraging its unique therapeutic advantages of multi-target， multi-component and multi-pathway approaches， has demonstrated remarkable efficacy and favorable safety profiles in treating DKD. Recent studies have demonstrated that active components of TCM can specifically target and modulate key effectors in apoptotic signaling pathways. Meanwhile， traditional compound formulations exert synergistic effects through multiple approaches such as replenishing deficiency and activating blood circulation， detoxifying and dredging collaterals， tonifying kidney essence， and removing stasis and purging turbidity， thereby comprehensively regulating critical pathological processes including endoplasmic reticulum stress and mitochondrial apoptosis pathways. This combined therapeutic approach of molecular targeting and holistic regulation provides novel strategies for delaying the progression of DKD. Based on this， this paper provides an in-depth analysis of key apoptotic signaling pathways and their regulatory mechanisms， while systematically summarizing recent research advances regarding the therapeutic effects of TCM active components， compound formulations， and proprietary Chinese medicines on DKD through modulation of these pathways， with particular emphasis on their underlying molecular mechanisms. These findings not only elucidate the modern scientific connotation and theoretical basis of TCM in treating DKD but also establish a solid theoretical and practical foundation for promoting the wider clinical application and further research of TCM in the field of DKD treatment.

OBJECTIVE: To explore the genotype-phenotype correlation in a Chinese family with structural brain abnormalities due to variant of the TUBB gene. METHODS: A family undergoing prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital in October 2024 was selected as the study subject. Clinical data were collected. Amniotic fluid sample was subjected to chromosomal copy number variation sequencing (CNV-seq). Trio whole-exome sequencing (Trio-WES) was carried out on the amniotic fluid and parental blood samples, and candidate variant was verified by Sanger sequencing. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2023-KY-076-01). RESULTS: Both prenatal ultrasound and fetal MRI showed deviation of brain midline, unilateral lateral ventriculomegaly, and bilateral gyral asymmetry. Trio-WES revealed that the fetus has harbored a maternally derived heterozygous missense variant of the TUBB gene [NM_178014.4: c.155A>G (p.N52S)]. Sanger sequencing confirmed that the woman and a previously terminated fetus both harbored the same variant. Both the proband and two fetuses exhibited similar neuroimaging abnormalities including midline deviation and asymmetrical gyri. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was classified as likely pathogenic (PM2_Supporting+PS2_Moderate+PS3). CONCLUSION The heterozygous c.155A>G (p.N52S) variant was the TUBB gene probably underlay the pathogenesis of the structural brain abnormalities in this family. Above findings have expanded the phenotypic spectrum associated with the variant and facilitated the prenatal diagnosis for this family.
Humans ; Female ; Pregnancy ; Prenatal Diagnosis ; Tubulin/genetics* ; Adult ; Brain/diagnostic imaging* ; Male ; Pedigree ; DNA Copy Number Variations/genetics* ; Exome Sequencing ; Genetic Association Studies ; Magnetic Resonance Imaging

Objective: To analyze the effects of maternal gestational weight gain and pre pregnancy body mass index (BMI) on the weight of preschool children,so as to provide scientific basis for prevention and treatment of overweight and obesity in children. Methods: Based on Jiangmen maternal and child health information platform, annual physical examination data of 3-6 years old preschool children from all nurseries and kindergartens in Jiangmen were collected from January to December 2024. A unique identification was made according to the mother s ID number and delivery date, and retrospective data collection was conducted on the platform to obtain pre pregnancy and pregnancy related information for 46 481 mothers. The Chi-square test,two way ordered variable analysis and Logistic regression analysis were used to compare the effects of maternal pre pregnancy BMI and gestational weight gain on overweight and obesity among preschool children. Results: A total of 5 168 (11.12%) children were overweight and obese, and the proportion of overweight and obesity in the 6 year old group was the highest (12.86%). There were significant differences in the detection rates of overweight and obesity between boys and girls ( χ 2=155.38), and there were also significant gender differences in the age groups of 4, 5 and 6 years ( χ 2=17.08, 96.97, 66.27)(all P <0.01). Through trend χ 2 test, the overall detection rates of overweight and obesity, as well as those for boys, increased with age ( χ 2 trend =49.36,60.54, both P <0.01). The BMI group of preschool children was correlated with the BMI group of their mothers before pregnancy and the weight gain group during pregnancy （χ 2= 1 250.64, 157.01, both P <0.01) and the proportion of children with higher BMI levels showed an upward trend with the improvement of their mothers pre-pregnancy BMI levels or gestational weight gain levels ( Gamma =0.13, 0.10, both P <0.01). Multiple Logistic regression analysis showed that pre pregnancy BMI groups as overweight ( OR =1.590, 1.922), obesity ( OR =2.100, 2.921 ), and male gender of the children ( OR =1.213, 1.763)，and newborns excessive birth weight( OR =1.001，1.001) increased the risks of overweight and obesity in preschool children; maternal gestational weight gain insufficiency ( OR =1.374) and advanced maternal age at the first prenatal visit ( OR =1.012) increased the risks of obesity in preschool children; maternal gestational weight gain deficiency or excess ( OR =1.324,1.118) increased the risk of overweight in preschool children (all P <0.01). Conclusions Maternal pre-pregnancy overweight and obesity and insufficient or excessive gestational weight gain increase the risk of overweight and obesity in preschool children. It is necessary to strengthen weight management before and during pregnancy to reduce the occurrence of childhood overweight and obesity.

Background Sunshine duration is closely associated with population mental health and emotional states, although its relationship with mental and behavioral disorders (MBD) remains insufficiently studied. Objective To analyze the effect of sunshine duration on hospital admissions for MBD in Zigong City, Sichuan Province. Methods Hospital admission records for MBD from 10 medical institutions, meteorological data, and ambient air pollutant concentrations were collected in Zigong City from January 1, 2019 to December 31, 2024. A distributed lag non-linear model (DLNM) was employed to calculate single-day and cumulative lag effects of different sunshine duration exposures—0 h (P₀, P₅, P₂₅), 6 h (P₇₅), and 10.4 h (P₉₅)—on hospitalization risks for MBD, stratified by diagnostic category, sex, and age groups. Results This study analyzed 36597 hospital admissions for MBD in Zigong City. The exposure-response curve for sunshine duration at lag 0 d exhibited an inverted N-shaped pattern, with minimum relative risk (RR) of hospitalizations observed at 14 h sunshine duration. The immediate effects of different levels of sunshine duration on the hospitalization risk for MBD, schizophrenia, schizotypal and delusional disorders (SSDD), and neurotic, stress-related and somatoform disorders (NSSD) were significant, with the cumulative effect peaking at lags 12–13 days. For extremely short sunshine duration 0 h, the maximum relative risk (RR) values (with 95% confidence intervals, 95%CI) of hospitalization risk were 1.209 (1.001, 1.459) at lag 1 d for SSDD patients and 1.398 (1.133, 1.724) at lag 0 d for NSSD patients, respectively. The cumulative effect on hospitalization risk induced by short sunshine duration 6 h was higher than that induced by extremely short sunshine duration 0 h and long sunshine duration 10.4 h. Among populations stratified by gender and age, male patients and those aged over 70 years had higher hospitalization risks. For short sunshine duration 6 h, the cumulative RR (CRR) value (95%CI) of hospitalization risk for male MBD patients was 3.373 (1.362, 8.358) at cumulative lag 0–12 d, which was higher than that for female patients [2.943 (1.471, 5.889) at cumulative lag 0–13 d]. The population aged over 70 years was more sensitive to single-day and cumulative lag effects on hospitalization risk than other age groups. For short sunshine duration 6 h, the RR and CRR values (95%CI) of single-day and cumulative lag effects on hospitalization risk were 1.336 (1.002, 1.783) at lag 0 day and 5.085 (1.680, 15.392) at cumulative lag 0–12 d, respectively. At lag 13 d, long sunshine duration 10.4 h exposure was associated with depression hospitalizations in patients <20 years with RR=1.127 (95%CI: 1.002, 1.270). Conclusion When the daily sunshine duration is less than 14 h, it will increase the hospitalization risk of MBD in the population to varying degrees, with both single-day and cumulative lag effects. It is recommended that the public, especially vulnerable groups such as people with MBD aged 70 years and above and those with depression under 20 years, strengthen health protection to reduce the health risks caused by changes in sunshine.

Xuanfuhua Decoction （旋覆花汤） is considered as the theoretical prototype of the method of unbloc-king collaterals with acrid and moistened medicinals. Guided by the theories of "chronic disease entering the collaterals" and "collaterals performing their functions when there is free flow"， YE Gui further developed this approach into a systematic method. The core of this approach lies in dispersing and opening constraint with acrid medicinals， nourishing and harmonizing collaterals with moistened medicinals， eliminating pathogens and unblocking collaterals with insect medicinals. The disease course of idiopathic pulmonary fibrosis （IPF） is prolonged， with a complex of deficiency and excess， and chronic disease entering the collaterals. The core pathogenesis involves lung collaterals obstruction， fluid depletion with blood stasis， and chronic disease entering the collaterals. Treatment can be guided by the method of unblocking collaterals with acrid and moistened medicinals based on Xuanfuhua Decoction， following a strategy of "dispersing and unblocking， moistening and nourishing， penetrating and venting". Specifically， for lung collaterals obstruction， acrid medicinals can be used to disperse lung qi and open bi （痹）. In case of fluid depletion and blood stasis， moistened medicinals for nourishing lung collaterals are suggested to restore vitality. For chronic disease ente-ring collaterals， it is advised to search and eliminate collateral pathogens in order to dissipate masses.

Objective To investigate the correlation of solute carrier protein 17 family member 1 (SLC17A1) gene single nucleotide polymorphisms (SNPs) and susceptibility to hyperuricemia (HUA) in automotive manufacturing workers. Methods A total of 192 Han male workers diagnosed with HUA were selected as the case group, 192 Han male workers without HUA from the same enterprises were selected as the control group. These workers were determined by the matching factor of age, total length of service, and body mass index by the 1∶1 case-control study method. Peripheral venous blood from the workers was collected for DNA extraction. Two SNPs of SLC17A1 were genotyped by MassArray system. Results The gene frequency distributions of SLC17A1 rs2096386 and rs1183201 of workers in the control group were in consistent with the Hardy-Weinberg equilibrium test (both P>0.05). The allele frequency distribution of rs2096386, and the genotype and allele frequency distribution of rs1183201 were significantly different between workers in the two groups (all P<0.05). There was no significant difference in genotype frequency distribution of rs2096386 between workers in the two groups (P>0.05). The results of conditional logistic regression analysis showed that workers with G allele at rs2096386 increased the risk of HUA [odds ratio (OR)=1.43, 95% confidence interval (CI)=1.01-2.04], workers with T allele at rs1183201 increased the risk of HUA (OR=2.03, 95%CI =1.29-3.19), after adjusting for confounding factors such as serum creatinine, blood urea nitrogen, alanin aminotransferase and aspartate aminotransferase. While workers with TA and TA+AA genotypes at rs1183201 had a lower risk of HUA than those with TT genotype (OR=0.51, 95%CI =0.30-0.85; OR=0.50, 95%CI =0.30-0.83), workers with TA genotype at rs1183201 had a lower risk of HUA than those with TT+AA genotype (OR=0.53, 95%CI =0.32-0.88). Conclusion The polymorphisms at rs2096386 and rs1183201 of SLC17A1 gene may be correlated with HUA susceptibility among automobile manufacturing workers in Guangzhou City.

Objective: To evaluate the clinical efficacy of digitally guided precision crown lengthening in secondary aesthetic rehabilitation cases, and to provide a clinical reference for digitally guided crown lengthening procedures and secondary aesthetic restorations. Methods: We present a case of a patient with tetracycline-stained teeth, partial detachment of anterior resin veneers, and gingival margin discrepancies. The patient underwent digitally guided precision crown lengthening followed by secondary aesthetic rehabilitation. Multimodal data, including intraoral, facial, and CBCT scans, were integrated to construct a four-dimensional virtual patient model (incorporating teeth, face, bone, and occlusion) for surgical planning and 3D-printed guide fabrication. Secondary aesthetic restoration was performed after achieving stable post-surgical outcomes. Based on this case, we conducted a detailed analysis and reviewed relevant literature on crown lengthening in secondary aesthetic rehabilitation. Results: The gingival contour of the anterior teeth exhibited significant improvement, with enhanced symmetry and stable gingival margin positioning that closely matched the preoperative design. The crown lengthening procedure demonstrated high precision, and the final outcome was aesthetic and functional. Literature review indicated that secondary restorations frequently present challenges such as gingival contour discrepancies and inflammation. Aesthetic crown lengthening in the anterior region should optimize both soft and hard tissue morphology to meet aesthetic standards, with digital technology improving procedural accuracy. Conclusion Precision crown lengthening effectively addresses gingival margin discrepancies in secondary aesthetic rehabilitation, ensuring stable gingival positioning and superior aesthetic outcomes. This approach is particularly suitable for cases with high aesthetic demands.

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.