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.

To analyze the impact of salt reduction interventions on the knowledge, attitude and behavior regarding the salt reduction of students′ parents based on the home-school interaction model. In April 2021, parents of students in grades 3-5 from three primary schools in Yichang City were selected as the target population using a cluster sampling method, and the parent population was divided into an intervention group and a control group. In the intervention group, a comprehensive home-school interaction salt reduction intervention was implemented, and in the control group, no intervention measures were taken for students′ parents. Baseline and final surveys were conducted before and after the intervention period, which included general information, previous salt reduction interventions received, and salt reduction knowledge, attitude and behavior. Difference-in-difference (DID) method was used to compare the knowledge, attitude and behavior status of two groups before and after the intervention, and stratified analysis of parents with different literacy levels was conducted to assess the net effect of intervention implementation. The results showed that 740 parents completed the baseline and final surveys, with 231 in the intervention group and 509 in the control group. After propensity score matching, there were 231 (33.33%) in the intervention group and 462 (66.67%) in the control group. After the intervention, the proportion of the intervention group who obtained salt control spoons and pots, as well as salt reduction knowledge and advice through school, was 87.45%, 86.58% and 75.45%, respectively, which was significantly higher than that in the control group ( P<0.05). After the intervention, the proportion of parents with a high school and lower education who obtained salt control pots was higher in the intervention group (89.23%) than in the control group (74.49%), with significant differences ( P<0.05). The proportion of parents with a college degree or above who obtained salt control spoons and pots, as well as salt reduction knowledge and advice through school, was higher than that of the control group ( P<0.05). The results of DID method showed that after controlling for monthly income and other factors, the scores of parents′ salt reduction-related knowledge and low-salt behavior in the intervention group increased significantly higher than those in the control group, with DID values (95% CI) of 1.18 (0.15-2.21) and 0.62 (0.16-1.09), respectively, indicating a significant net effect of intervention implication. After stratification according to the education level of parents, this difference still existed in the college degree or above group, with DID values (95% CI) of 1.39 (0.13-2.66) and 0.76 (0.16-1.36), respectively. The home-school interaction model for salt reduction measures can improve the salt-related knowledge and low-salt behavioral choices of students′ parents.

Objective:This study aims to construct a quality evaluation index system for specialized disease databases. Through systematic assessment and optimization, it seeks to comprehensively enhance the quality and standardization of specialized disease cohort data. This initiative will provide more precise and reliable data support for disease research, the development of innovative drugs and medical devices, as well as policy formulation.Methods:By conducting a thorough analysis of domestic and international literature and policies related to clinical research data quality evaluation systems, preliminary quality evaluation indicators for specialized disease databases were established. Utilizing the Delphi method in two rounds, a quality evaluation system for specialized disease databases was constructed. The Analytic Hierarchy Process (AHP) and YAAHP 7.5 software were then employed to calculate the relative weights of indicators at various levels and their composite weights.Results:The two rounds of expert consultation achieved a 100.00% valid response rate, with an expert authority coefficient of 0.81 in both rounds. In the second round, the Kendall′s coordination coefficients for the first-level and second-level indicators reached 0.311 and 0.218, respectively ( P<0.05), indicating a good level of consensus among experts. The final specialized disease database quality evaluation system consists of 3 first-level indicators, 10 second-level indicators, and 32 third-level indicators. The first-level indicators include database construction, data quality, and cohort development, with weight coefficients of 31.82%, 41.49%, and 26.69%, respectively. The scientific validity of the indicator system was confirmed through reliability and validity analyses. When applied to assessing 58 specialized disease database projects from 36 medical institutions in a certain city, the results showed significant improvements in scores for database construction, data quality, and cohort development, with the most notable improvement observed in database construction. Conclusions:This study successfully developed a scientific, practical, and rationally weighted quality evaluation system for specialized disease databases, demonstrating high expert consensus and broad applicability.Validation studies have shown that this system effectively enhances the standardization and data quality of databases, providing robust technical support and assurance for specialized disease research and data resource sharing.

Objective:To construct a full-length infectious clone of enterovirus D68（EV-D68）expressing enhanced green fluorescent protein（EGFP）by reverse genetics in order to provide an efficient tool for studying the biological characteristics and screening antiviral drugs for EV-D68.Methods:Gene synthesis and overlap PCR techniques were used to construct the full-length clone plasmid pUC57-EV-D68 of EV-D68. The full-length viral sequence was then transferred into the pCAGGS plasmid to obtain the pCAGGS-EV-D68 plasmid. The EGFP gene was amplified and inserted into the pCAGGS-EV-D68 plasmid to construct the pCAGGS-EGFP-EV-D68 plasmid. Then，the two constructed plasmids were transfected into human rhabdomyosarcoma（RD）cells to rescue recombinant viruses RV-EV-D68 and RV-EGFP-EV-D68. The rescued viruses were identified using PCR，Western blot，and immunofluorescence techniques. The antiviral effect of doxycycline was evaluated using the rescued RV-EGFP-EV-D68. Statistical analysis was performed using the two independent samples t-test. Results:The recombinant virus RV-EGFP-EV-D68 capable of expressing EGFP was successfully rescued. Even after 15 serial passages，the virus retained EGFP expression with no significant difference in viral titers compared to the parental virus，indicating its stable passage in RD cells. Besides，the rescued strains exhibited similar replication characteristics to the parental virus. While at 24 and 36 h after infection，the titers of the rescued strains were significantly lower than that of the parental strain（both P<0.05）. This study demonstrated that doxycycline significantly reduced the fluorescence intensity of RV-EGFP-EV-D68-infected RD cells（ P<0.01）. Meanwhile，a negative correlation was observed between the doxycycline concentration and the fluorescence intensity，indicating that the rescued virus could be used for antiviral drug evaluation. Conclusions:This study successfully constructs an infectious clone of EV-D68 expressing EGFP. The rescued recombinant virus RV-EGFP-EV-D68 has been verified to be applicable for the evaluation of antiviral drugs.

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.

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.