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 quantitatively evaluate the accuracy of data obtained from liquid-interference surfaces using an intraoral 3D scanner (IOS) integrated with a compressed airflow system, so as to provide clinical proof of accuracy for the application of the compressed airflow system-based scanning head in improving data quality on liquid-interference surfaces. METHODS: The study selected a standard model as the scanning object, adhering to the "YY/T 1818-2022 Dental Science Intraoral Digital Impression Scanner" guidelines, a standard that defined parameters for intraoral scanning. To establish a baseline for accuracy, the ATOS Q 12M scanner, known for its high precision, was used to generate true reference values. These true values served as the benchmark for evaluating the IOS performance. Building on the design of an existing scanner, a new scanning head was developed to integrate with a compressed airflow system. This new design aimed to help the IOS capture high-precision data on surfaces where liquid-interference, such as saliva, might otherwise degrade scanning accuracy. The traditional scanning method, without airflow assistance, was employed as a control group for comparison. The study included five groups in total, one control group and four experimental groups, to investigate the effects of scanning lens obstruction, airflow presence, liquid media, and the use of the new scanning head on scanning process and accuracy. Each group underwent 15 scans, generating ample data for a robust statistical comparison. By evaluating trueness and precision in each group, the study assessed the impact of the compressed airflow system on the accuracy of IOS data collected from liquid-interference surfaces. Additionally, we selected Elite and Primescan scanners as references for numerical accuracy values. RESULTS: The scanning accuracy on liquid-interference surfaces was significantly reduced in terms of both trueness and precision [Trueness: 18.5 (6.5) vs. 38.0 (6.7), P < 0.05; Precision: 19.1 (8.5) vs. 31.7 (15.0), P < 0.05]. The use of the new scanning head assisted by the compressed airflow system significantly improved the scanning accuracy [Trueness: 22.3(7.6) vs. 38.0 (6.7), P < 0.05; Precision: 25.8 (9.6) vs. 31.7 (15.0), P < 0.05]. CONCLUSION The scanning head based on the compressed airflow system can assist in improving the accuracy of data obtained from liquid-interference surfaces by the IOS.
Imaging, Three-Dimensional/methods* ; Humans ; Dental Impression Technique/instrumentation*

Image 1.

In fixed prosthodontics, clear exposure of the preparation margin is the prerequisite for obtaining accurate digital impressions and improving the marginal fit of restorations. To resolve the issues associated with the cord retraction technique, such as pain, acute injury, and prolonged procedural time, this study proposes a new technology for intraoral digital impression taking with pneumatic gingival retraction. The new scanning head blows a high-speed airflow that instantaneously separates the free gingiva, locally exposing the subgingival preparation margin. Combined with the farthest point preservation stitching algorithm based on the distance from the normal vector and high-speed laser scanning photography, it achieves global preparation edge data and gingival reconstruction, realizing painless, non-invasive, and efficient precise acquisition of the preparation margin. Using this new technique, a patient with a full porcelain crown restoration on a posterior tooth was treated. The digital impression revealed a clear margin of the preparation, and the crown made from this data has a good marginal fit.

To study the clinical correlation between fasting plasma glucose, lipid metabolism, prostate-specific antigen and prostate volume in patients with benign prostatic hyperplasia, and to explore the combined effect as diagnostic indicators. A total of 108 patients with benign prostatic hyperplasia treated in Beijing University of Chinese Medicine Third Affiliated Hospital from June 2021 to March 2023 were retrospectively analyzed as the hyperplasia group, and 98 healthy physical examination personnel were selected as the control group during the same period. Compare the differences in levels of fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), small and dense low-density lipoprotein cholesterol (sdLDL-C), homocysteine, lipoprotein a (LPa), prostate specific antigen (PSA), and free prostate specific antigen (fPSA) between two groups of patients. Using Pearson analysis method to analyze the correlation between the above indicators and the size of prostate volume in patients with benign prostatic hyperplasia; using multiple linear regression to analyze the influencing factors of prostate volume enlargement; draw receiver operating characteristic (ROC) curves and analyze the application value of individual and combined detection of HDL, FPG, PSA, and fPSA. The results showed that there were significant differences in HDL, FPG, PSA, and fPSA levels between the control group and the proliferative group( P<0.05). The size of prostate volume is negatively correlated with HDL( r=-0.183, P<0.05) and positively correlated with FPG ( r=0.202, P<0.05), PSA( r=0.412, P<0.05), and fPSA( r=0.425, P<0.05). The results of multiple linear regression analysis showed that HDL( P=0.000), FPG( P=0.048), PSA( P=0.044), and fPSA ( P=0.012) were risk factors for increased volume of benign prostatic hyperplasia; ROC curve analysis shows that the AUC of HDL, FPG, PSA, and fPSA combined detection is 0.823, which is better than individual detection. In conclusion,HDL, FPG, PSA, fPSA has close correlation with hyperplasia of prostate, the joint detection may has better prediction for benign prostatic hyperplasia.

Objective This study aimed to explore the prognostic value of 18F-FDG PET/CT Metabolic and Heterogeneity Parameters Combined with Clinical Features Before Definitive Chemoradiotherapy(D-CRT)in predicting the prognosis of esophageal squamous cell carcinoma(ESCC)Patients.Methods A retrospective analysis was conducted on clinical data from 106 patients with ESCC who received D-CRT at the first affiliated Hospital of University of Science and Technology of China between January 2017 and December 2021.All patients underwent 18F-FDG PET/CT examination before the treatment.The primary tumor′s metabolic and heterogeneity parameters were obtained through data processing.All patients were followed up for overall survival.The Kaplan-Meier method and Cox proportional hazards models were used to analyze the association between clinical features,tumor metabo-lism and heterogeneity parameters and patient prognosis.Results The 1-and 1.5-year overall survival rates of all patients were 77.4%and 51.9%.The median survival time was 20 months.Univariate analysis showed that N stage,M stage,metabolic tumor volume,total lesion glycolysis,heterogeneity index-2(HI-2),and coefficient of variation with a threshold of 40%maximum standard uptake value(CV40%)were correlated with the prognosis of ESCC(all P<0.05).Multivariate analysis showed that N stage and CV40%were independent predictors of prognosis in patients with ESCC(P = 0.039 and P<0.001,respectively).Conclusion N stage and tumor metabolic heterogeneity parameter CV40%,which offering a degree of predictive value,are closely related to the prognosis of patients with ESCC treated with D-CRT.

To study the clinical correlation between fasting plasma glucose, lipid metabolism, prostate-specific antigen and prostate volume in patients with benign prostatic hyperplasia, and to explore the combined effect as diagnostic indicators. A total of 108 patients with benign prostatic hyperplasia treated in Beijing University of Chinese Medicine Third Affiliated Hospital from June 2021 to March 2023 were retrospectively analyzed as the hyperplasia group, and 98 healthy physical examination personnel were selected as the control group during the same period. Compare the differences in levels of fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), small and dense low-density lipoprotein cholesterol (sdLDL-C), homocysteine, lipoprotein a (LPa), prostate specific antigen (PSA), and free prostate specific antigen (fPSA) between two groups of patients. Using Pearson analysis method to analyze the correlation between the above indicators and the size of prostate volume in patients with benign prostatic hyperplasia; using multiple linear regression to analyze the influencing factors of prostate volume enlargement; draw receiver operating characteristic (ROC) curves and analyze the application value of individual and combined detection of HDL, FPG, PSA, and fPSA. The results showed that there were significant differences in HDL, FPG, PSA, and fPSA levels between the control group and the proliferative group( P<0.05). The size of prostate volume is negatively correlated with HDL( r=-0.183, P<0.05) and positively correlated with FPG ( r=0.202, P<0.05), PSA( r=0.412, P<0.05), and fPSA( r=0.425, P<0.05). The results of multiple linear regression analysis showed that HDL( P=0.000), FPG( P=0.048), PSA( P=0.044), and fPSA ( P=0.012) were risk factors for increased volume of benign prostatic hyperplasia; ROC curve analysis shows that the AUC of HDL, FPG, PSA, and fPSA combined detection is 0.823, which is better than individual detection. In conclusion,HDL, FPG, PSA, fPSA has close correlation with hyperplasia of prostate, the joint detection may has better prediction for benign prostatic hyperplasia.