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 investigate the risk factors of pleural effusion after spinal separation surgery for patients with spinal metastatic tumors.Methods:A total of 427 patients with spinal metastatic tumors from January 2014 to January 2022 in the Second Affiliated Hospital of Zhejiang University School of Medicine were retrospectively analyzed. There were 252 males and 175 females, with an average age of 59±12 years (range, 15-87 years). All patients underwent separation surgery. Based on the chest CT within 1 month after surgery, the volume of pleural effusion was measured individually by reconstruction software. Pleural effusion was defined as small volume (0-500 ml), moderate volume (500-1 000 ml), and large volume (above 1 000 ml). Baseline data and perioperative clinical outcomes were compared between the groups, and indicators with statistically significant differences were included in a binary logistic regression analysis to determine the independent risk factors for the development of pleural effusion after isolation of spinal metastatic cancer. Receiver operating characteristic (ROC) curves were conducted to calculate the area under the curve (AUC) for each independent risk factor.Results:All patients successfully completed the operation. Among the 427 patients, there were 35 cases of large pleural effusion, 42 cases of moderate pleural effusion, and 350 cases of small pleural effusion. There were significant differences in tumor size (χ 2=9.485, P=0.013), intraoperative blood loss ( Z=-2.503, P=0.011), blood transfusion ( Z=-2.983, P=0.003), preoperative total protein ( Z=2.681, P=0.007), preoperative albumin ( Z=1.720, P= 0.085), postoperative hemoglobin ( t=2.950, P=0.008), postoperative total protein ( Z=4.192, P<0.001), and postoperative albumin ( t=2.268, P=0.032) in the large pleural effusion group versus the small and moderate pleural effusion group. Logistic regression analysis showed that decreased preoperative albumin ( OR=0.89, P=0.045) and metastases located in the thoracic spine ( OR=4.01, P=0.039) were independent risk factors for the occurrence of large pleural effusion after separation surgery. The ROC curve showed that the AUC and 95% CI for preoperative albumin, lesion location, and the combined model were 0.637 (0.54, 0.74), 0.421 (0.36, 0.48), and 0.883 (0.81, 0.92). The combined predictive model showed good predictive value. Conclusion:The volume of pleural effusion can be measured individually and quantitatively based on chest CT. Decreased preoperative albumin and metastases located in the thoracic spine are independent risk factors for the occurrence of large pleural effusion after separation surgery. The combined prediction of the two factors has better predictive efficacy.

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.

Objective To utilize a 3D technology in the design of a female bed urinal and to evaluate its clinical efficacy.Methods A total of 102 adult female fracture patients with normal urination function admitted to a tertiary hospital in Hangzhou City from October 2022 to June 2023 were included in the study.They were divided into a control group(n=51)and an experimental group(n=51)according to random number method.Patients in control group used a regular urinal,while patients in the experimental group used the 3D technology-based female bed urinal.The level of physical pain caused by urination,the rate of urine immersion in the sacrococcygeal or gluteal cleft and the rate of bed unit or clothing of contamination were compared between the 2 groups.Results There was no significant difference in the rate of bed unit or clothing contamination between the 2 groups(P＞0.05).However,the experimental group experienced significantly lower pain caused by urination,a lower rate of urine impregnation in the sacrococcygeal or gluteal fissure(P＜0.001),compared to the control group.Conclusion The 3D technology-based female bed urinal has reasonable structure and simple operation,which can significantly reduce the physical pain caused by the change of body position,reduce the incidence of urine immersion events.

Heme/metabolism* ; Cryoelectron Microscopy ; Membrane Transport Proteins ; Escherichia coli Proteins/metabolism*

Objective:To investigate the necessity and feasibility of the virtual simulation teaching experiment software of the bronchoscopy intelligent navigation-based fiducial marker implantation technology in the clinical application of radiotherapy.Methods:This study developed a 3D virtual operation and interactive system using the Unity3D engine, tools including 3Dmax and Maya, and the SQL database. The scenes in the system were produced using the currently popular next-generation production process. Targeting the priorities and difficulties in the implantation of fiducial markers, the system developed in this study allowed for simulated demonstration and training based on 12 steps and 10 knowledge points. Internal tests and remote evaluation tests were adopted in this system to obtain the test result of each subject. Then, the application value of the system was analyzed based on the test result.Results:As of May 1, 2022, the system had received 2 409 views and 425 test participants, with an test completion rate of 100% and an experiment pass rate of 96.5%. Moreover, this system won unanimous praise from 167 users, primarily including the students majoring in multilevel medical imaging technology and medical imaging science from the Fujian Medical University, as well as the radiotherapy-related staff of this university.Conclusions:The virtual simulation teaching experiment software of the bronchoscopy intelligent navigation-based fiducial marker implantation technology can be applied to the teaching of students and the training of related professionals.

Objective:To improve the quality assurance (QA) skills of radiotherapy personnel and medical students and reduce the radiation risk of training by developing a remote training system for QA of medical electronic linear accelerators.Methods:This training system was built based on radiotherapy technology and quality control contents of medical electronic linear accelerators, and a virtual reality interactive software was developed using extended reality (XR) technology Unity 3D. A remote control module of multi-terminal platform was also developed. A multi-perspective evaluation system was adopted and a questionnaire was designed to analyze the application value of this system.Results:The training system reproduced the live environment and physical objects of medical electronic linear accelerator treatment room. It built a multi-terminal virtual simulation training system with radiotherapy technology as well as QA knowledge system. This system could provide 5G remote control of medical electronic linear accelerator for off-site quality control demonstration and guidance. By March 1, 2022, a total number of 133 people had been trained using this system, 76 valid questionnaires had been taken, of which 90.79% (69/76) of the respondents trusted the experimental results shown by the system and 88.16% (67/76) of the respondents considered the training system necessary.Conclusions:The training effect of this system is widely recognized. It fundamentally reduces the training radiation hazard and provides reference for the reform and progress of QA training mode of medical electronic linear accelerators.