ObjectiveTo investigate the trends in diabetes mortality and potential years of life lost (PYLL) among residents of Huangpu District, Shanghai from 1993 to 2021, to analyze the long-term trends of diabetic patients with different characteristics and to provide a reference for scientific prevention and control of diabetes in aging urban areas. MethodsDiabetes mortality data were obtained from the Huangpu District cause of death registration records in the Shanghai death cause registration system. Indicators such as crude mortality rate, standardized mortality rate, potential years of life lost (PYLL), average years of life lost (AYLL), annual percentage change (APC), and average annual percentage change (AAPC) were used to analyze diabetes-related mortality and life loss. Statistical analyses were performed using software SPSS 21.0 and Joinpoint 5.0.2. ResultsFrom 1993 to 2021, the average annual crude mortality rate of diabetes in Huangpu District was 46.56/100 000, and the average annual standardized mortality rate was 20.44/100 000. The crude mortality rate and standardized mortality rate of diabetes for female residents were higher than those for males. The crude mortality rate showed an overall increasing trend [AAPC=2.81% (95%CI: 0.20%‒5.49%), P<0.05], while the increase in standardized mortality rate significantly slowed [AAPC=0.15% (95%CI: -2.27%‒2.63%)], P<0.05]. The mortality rate rose rapidly in the 70‒74 years age group and peaked in the 85‒ years age group (607.69/100 000). Diabetes accounted for a cumulative PYLL of22 741 person-years, with an average annual AYLL of 1.88 years and an average annual potential years of life lost rate (PYLLR) of 0.82‰. Male residents had higher PYLL, AYLL, and PYLLR than females. ConclusionDiabetes mortality rates in Huangpu District have increased year by year, resulting in significant life loss. However, the age-standardized mortality rate increase has markedly slowed. Efforts should focus on elderly diabetic patients aged ≥70 years, by leveraging platforms such as community-based chronic disease health support centers, efforts should be made to enhance diabetes screening service for middle-aged and elderly residents. Consequently, elderly diabetic patients’ awareness of diabetes and responce to related complications is improved, which would be conducive to controling the progression of complications and reducing the mortolity risk of diabetes.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

OBJECTIVE: To analyze the factors related to spontaneous re-eruption after intruded injury in permanent anterior teeth in children and adolescents. METHODS: Clinical data from 5- to 17-year-old patients who sustained intrusive luxation of permanent anterior teeth and treated in the Department of Pedia-tric Dentistry of Peking University School and Hospital of Stomatology from June 2015 to August 2024 were reviewed. Information of age, gender, degree of intrusion, direction of intrusion, tooth development, concomitant injuries, luxation and post-osteoclastic eruption of the adjacent teeth were recorded. The patients were divided into two groups based on whether they showed spontaneous re-eruption during advised observation after intrusion. Univariate and multifactor analysis were performed using Logistic regression. RESULTS: Data from 170 teeth in 139 patients whose age ranging from 5.3-16.3 years [mean age (9.0± 2.1) years] were examined. A gender disparity was observed among the patients, with 84 being male and 55 being female. Among the 170 teeth, 112 were categorized as successfully spontaneous re-eruption during advised observation after intrusion, while 58 were not. In terms of the degree of intrusion, 45 teeth (26.47%) had intrusion less than 3 mm, 102 teeth (60.00%) experienced intrusion between 3-7 mm, and 23 teeth (13.53%) were faced with intrusion exceeding 7 mm. As for the direction of intrusion, 117 teeth (68.82%) were straight intrusion while mesial-distal and buccal-lingual intrusion respectively accounting for 17 (10.00%) and 23 (13.53%). Multivariate Logistic regression analysis showed that mesial-distal intrusion (OR=0.167, 95%CI: 0.031-0.9048, P=0.038), intrusion of >7 mm (OR=0.065, 95%CI: 0.014-0.299, P < 0.001) and luxation of adjacent teeth (OR=0.369, 95%CI: 0.144-0.944, P=0.037) were independent risk factors for spontaneous re-eruption of traumatically intruded permanent anterior teeth in children and adolescents during advised observation after intrusion, while intrusion of < 3 mm (OR=9.860, 95%CI: 2.430-40.009, P=0.001) and post-osteoclastic eruption of adjacent teeth (OR=4.712, 95%CI: 1.528-14.531, P=0.007) were independent protective factors. The possibility of spontaneous re-eruption in permanent anterior teeth during advised observation after intrusion was decreased by 61.1% with the increase of root development using Cvek' s classification (OR=0.611, 95%CI: 0.408-0.914, P=0.017). Age (OR=1.077, 95%CI: 0.763-1.521, P=0.673) and laceration of gingival (OR=0.865, 95%CI: 0.290-2.578, P=0.794) didn't significantly affect the spontaneous re-eruption during advised observation after intrusion. CONCLUSION In this study, mesial-distal intrusion, intrusion of >7 mm and luxation of adjacent teeth were independent risk factors for spontaneous re-eruption of traumatically intruded permanent anterior teeth in children and adolescents during advised observation, while intrusion of < 3 mm and post-osteoclastic eruption of adjacent teeth were served as independent protective factors.
Humans ; Adolescent ; Child ; Female ; Male ; Tooth Eruption/physiology* ; Child, Preschool ; Tooth Avulsion/therapy* ; Dentition, Permanent ; Incisor/injuries* ; Remission, Spontaneous

Cuproptosis, a recently discovered form of regulated cell death involving copper ion metabolism, has emerged as a promising approach for tumor therapy. This pathway not only directly eliminates tumor cells but also promotes immunogenic cell death (ICD), reshaping the tumor microenvironment (TME) and initiating robust anti-tumor immune responses. However, translating cuproptosis-based therapies into clinical applications is hindered by challenges, including complex metabolic regulation, TME heterogeneity, and the precision required for effective drug delivery. To address these limitations, nanoparticles offer transformative solutions by providing precise delivery of cuproptosis-inducing agents, controlled drug release, and enhanced therapeutic efficacy through simultaneous modulation of metabolic pathways and immune responses. This review systematically discusses recent advancements in nanoparticle-based cuproptosis delivery systems, highlighting nanoparticle design principles and their synergistic effects when integrated with other therapeutic modalities such as ICB, PTT, and CDT. Furthermore, we explore the potential of cuproptosis-based nanomedicine for personalized cancer treatment by emphasizing strategies for TME stratification and therapeutic optimization tailored to patient profiles. By integrating current insights from metabolic reprogramming, tumor immunotherapy, and nanotechnology, this review aims to facilitate the clinical translation of cuproptosis nanomedicine and significantly contribute to the advancement of precision oncology.

The research on the cardiovascular toxicity of air pollutants is in urgent need of collaborative innovation across multiple models. This paper systematically reviewed the advantages and limitations of four principal research models of cardiotoxicity, including epidemiological model, mammalian model, zebrafish model, and in vitro model. Epidemiological models have been used to demonstrate a significant correlation between exposure to PM_2.5 and both the incidence and mortality of cardiovascular diseases within populations; however, these models face challenges in establishing causal inferences and interpreting individual mechanisms. Mammalian models have been applied to elucidate the pathogenic mechanisms of PM_2.5 at both the systemic and organ-specific levels, yet they encounter difficulties related to interspecies differences and throughput constraints. Zebrafish models, with their transparent embryos and observable development, offer a distinctive opportunity for high-throughput screening and mechanistic investigation of PM_2.5-induced cardiac developmental toxicity. Nonetheless, their cardiac physiological structure diverges from that of mammals, limiting their capacity to accurately model chronic conditions such as coronary heart disease. In vitro models, particularly human heart organoids and chip technologies, have provided profound insights into the direct toxic mechanisms of PM_2.5, including disruptions in calcium homeostasis, cellular senescence, and electrophysiological irregularities at the cellular and molecular levels. Despite these advancements, the complexity and developmental maturity of these models present challenges to their broader application. This paper proposed that the key to overcoming the bottlenecks of single models lies in the construction of an integrated evaluation system that combines “epidemiological studies, mammalian models, zebrafish models, and in vitro models”. By focusing on three aspects, namely model integration, technological convergence, and policy support, it is intended to collaboratively address issues such as standardization of multi-model data, simulation of complex exposure scenarios and susceptible life stages, and transformation pathways. This will provide innovative methodological support for the analysis of the cardiotoxic mechanisms of air pollutants, the assessment of environmental health impacts, and the formulation of precise prevention and control strategies.

OBJECTIVE To analyze the metabolites of Zhideke granules and speculate its metabolic pathway in rats in vivo. METHODS Male SD rats were randomly divided into blank group and administration group （Zhideke granules， 9.45 g/kg）； they were given ultrapure water or relevant medicine， twice a day， every 6-8 h， for 3 consecutive days. Serum， urine and feces samples of rats were collected， and their metabolites were identified by UPLC-Q-Exactive-MS technique after intragastric administration of Zhideke granules； their metabolic pathways were speculated. RESULTS After intragastric administration of Zhideke granules， 16 prototype components （i.g. irisflorentin， baicalin， chlorogenic acid） and 11 metabolites （i.g. hydration products of kaempferol or luteolin， methylation products of chlorogenic acid， and hydroxylation products of baicalin） were identified in serum， urine and feces of rats. Among them， 8 prototype components and 4 metabolites were identified in serum samples； 10 prototype components and 7 metabolites were identified in urine samples； 8 prototype components and 5 metabolites were identified in the fecal samples. CONCLUSIONS The metabolites of Zhideke granules in rats mainly include baicalin， irisflorentin，chlorogenic acid， and the main metabolic pathways included methylation， hydroxylation， glucuronidation.

Objective:To explore the evaluation of 256 slice spiral computed tomography angiography(CTA)of coronary,serum lipoprotein associated phospholipase A2(Lp-PLA2)and angiopoietin like protein 3(ANGPTL3)on the severity of coronary artery stenosis of patients with coronary heart disease.Methods:A total of 102 patients with coronary heart disease who were diagnosed and treated at Hebei Chest Hospital from July 2022 to March 2023 were selected as the study subjects.According to the Gensini score about the severity of coronary artery stenosis,they were divided into mild stenosis group(0 score≤Gensini score≤20 scores),moderate stenosis group(20 scores60 scores),with 34 cases in each group.The minimum lumen diameter(MLD),percentage of area of stenosis(%AS),percentage of diameter of stenosis(%DS),minimum lumen area(MLA),Lp-PLA2 and ANGPTL3 among three groups were compared.The diagnostic efficiency of the severity of coronary artery stenosis was predicted according to receiver operating characteristic(ROC)curve.Results:The MLA and MLD values in severe stenosis group were significantly lower than those in moderate and mild stenosis groups,while%AS and%DS were significantly higher than those in moderate and mild stenosis groups(t=6.905,4.083,5.871,6.976,3.387,2.198,2.668,3.505,P<0.05),respectively.The Lp-PLA2 and ANGPTL3 values in severe stenosis group were significantly higher than those in moderate and mild stenosis groups(t=4.164,8.220,2.575,3.050,P<0.05),respectively.ROC curve analysis showed that the area under curve(AUC)values of MLA,MLD,%AS,%DS,CCTA comprehensive parameter,LpPLA2 and ANGPTL3 were respectively were 0.838,0.690,0.742,0.801,0.904,0.808 and 0.807 in predicting the severity of coronary artery stenosis.The sensitivities of them were respectively 91.20%,91.20%,64.70%,94.10%,97.10%,70.60%and 88.20%.The specificities of them were respectively 76.50%,57.40%,75.00%,50.00%,70.60%,97.10%and 70.60%.The AUC value of CCTA comprehensive parameter was respectively higher than that of LpPLA2 and ANGPTL3,but the difference was not statistically significant(P>0.05).Conclusion:256 slice spiral CCTA,serum Lp-PLA2 and ANGPTL3 have a certain efficiency in assessing the severity of coronary artery stenosis of coronary heart disease,and 256 slice spiral CCTA has higher predictive efficiency.