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.

In recent years, China has been facing the dual challenges of declining fertility rates and births, with male reproductive health issues, especially the decline in semen quality, identified as a pivotal contributor to this phenomenon. Meanwhile, accumulating evidence indicates that air pollutants, an increasingly severe environmental problem, can damage semen quality not only directly through their biological toxicity but also indirectly by disrupting the composition of microbial communities in the gut and semen, thereby dysregulating immune function, endocrine homeostasis, and oxidative stress responses. The gut microbiota and semen microbiota, as important components of the human microecosystem, play crucial roles in maintaining reproductive health. This article comprehensively reviewed the research progress on the potential effects of air pollutants (particulate matter and gaseous pollutants), gut microbiota, and semen microbiota on semen quality. Specifically, it elucidated the mechanisms of interaction between these factors and explored how they affect male fertility.

BACKGROUND: The effect of the interval between previous Helicobacter pylori (H. pylori) eradication and rescue treatment on therapeutic outcomes remains unknown. The aim of this study was to investigate the association between eradication rates and treatment interval durations in H. pylori infections. METHODS: This prospective observational study was conducted from December 2021 to February 2023 at six tertiary hospitals in Shandong, China. We recruited patients who were positive for H. pylori infection and required rescue treatment. Demographic information, previous times of eradication therapy, last eradication therapy date, and history of antibiotic use data were collected. The patients were divided into four groups based on the rescue treatment interval length: Group A, ≥4 weeks and ≤3 months; Group B, >3 and ≤6 months; Group C, >6 and ≤12 months; and Group D, >12 months. The primary outcome was the eradication rate of H. pylori . Drug compliance and adverse events (AEs) were also assessed. Pearson's χ2 test or Fisher's exact test was used to compare eradication rates between groups. RESULTS: A total of 670 patients were enrolled in this study. The intention-to-treat (ITT) eradication rates were 88.3% (158/179) in Group A, 89.6% (120/134) in Group B, 89.1% (123/138) in Group C, and 87.7% (192/219) in Group D. The per-protocol (PP) eradication rates were 92.9% (156/168) in Group A, 94.5% (120/127) in Group B, 94.5% (121/128) in Group C, and 93.6% (190/203) in Group D. There was no statistically significant difference in the eradication rates between groups in either the ITT ( P = 0.949) or PP analysis ( P = 0.921). No significant differences were observed in the incidence of AEs ( P = 0.934) or drug compliance ( P = 0.849) between groups. CONCLUSION: The interval duration of rescue treatment had no significant effect on H. pylori eradication rates or the incidence of AEs. REGISTRATION ClinicalTrials.gov , NCT05173493.
Humans ; Helicobacter Infections/drug therapy* ; Helicobacter pylori/pathogenicity* ; Male ; Female ; Prospective Studies ; Middle Aged ; Anti-Bacterial Agents/adverse effects* ; Adult ; Aged ; Treatment Outcome ; Proton Pump Inhibitors/therapeutic use*

BACKGROUND: The profile and clinical significance of the oral microbiome in patients with non-obstructive hypertrophic cardiomyopathy (noHCM) and obstructive hypertrophic cardiomyopathy (oHCM) remain unexplored. The objective of this study was to evaluate the difference of oral microbiome between noHCM and oHCM patients. METHODS: This cross-sectional study enrolled 18 noHCM patients and 26 oHCM patients from Fuwai Hospital, Chinese Academy of Medical Sciences between 2020 and 2021. Clinical and periodontal evaluations were conducted, and subgingival plaque samples were collected. Metagenomic sequencing and subsequent microbial composition and functional analyses were performed. RESULTS: Compared to oHCM patients, those with noHCM had higher systolic blood pressure (138.1 ± 18.8 mmHg vs . 124.2 ± 13.8 mmHg, P = 0.007), a larger body circumference (neck circumference: 39.2 ± 4.0 cm vs . 35.1 ± 3.7 cm, P = 0.001; waist circumference: 99.7 ± 10.5 cm vs . 92.2 ± 10.8 cm, P = 0.027; hip circumference: 102.5 ± 5.6 cm vs . 97.5 ± 9.1 cm, P = 0.030), a greater left ventricular end-diastolic diameter (46.6 ± 4.9 mm vs . 43.1 ± 4.9 mm, P = 0.026), and a lower left ventricular ejection fraction (64.1 ± 5.7 % vs . 68.5 ± 7.8%, P = 0.048). While overall biodiversity and general microbial composition were similar between the noHCM and oHCM groups, ten taxa displayed significant differences at the genus and species levels, with Porphyromonas gingivalis showing the highest abundance and greater enrichment in noHCM (relative abundance: 7.79535 vs . 4.87697, P = 0.043). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis identified ten distinct pathways, with pathways related to energy and amino acid metabolism being enriched in oHCM patients, and those associated with genetic information processing less abundant in the oHCM group. Metabolic potential analysis revealed ten significantly altered metabolites primarily associated with amino sugar and nucleotide sugar metabolism, porphyrin metabolism, pentose and glucuronate interconversion, and lysine degradation. CONCLUSIONS The higher abundance of Porphyromonas gingivalis , which is known to impact cardiovascular health, in noHCM patients may partially account for clinical differences between the groups. Pathway enrichment and metabolic potential analyses suggest microbial functional shifts between noHCM and oHCM patients, potentially reflecting inherent metabolic changes in HCM.
Humans ; Cardiomyopathy, Hypertrophic/microbiology* ; Female ; Male ; Microbiota/genetics* ; Middle Aged ; Cross-Sectional Studies ; Adult ; Mouth/microbiology* ; Aged

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

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