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.

Lymphatic metastasis is the main metastatic route for colorectal cancer, which increases the risk of cancer recurrence and distant metastasis. The properties of the lymph node metastatic colorectal cancer (LNM-CRC) cells are poorly understood, and effective therapies are still lacking. Here, we found that hypoxia-induced fibroblast activation protein alpha (FAPα) expression in LNM-CRC cells. Gain- or loss-function experiments demonstrated that FAPα enhanced tumor cell migration, invasion, epithelial-mesenchymal transition, stemness, and lymphangiogenesis via activation of the STAT3 pathway. In addition, FAPα in tumor cells induced extracellular matrix remodeling and established an immunosuppressive environment via recruiting regulatory T cells, to promote colorectal cancer lymph node metastasis (CRCLNM). Z-GP-DAVLBH, a FAPα-activated prodrug, inhibited CRCLNM by targeting FAPα-positive LNM-CRC cells. Our study highlights the role of FAPα in tumor cells in CRCLNM and provides a potential therapeutic target and promising strategy for CRCLNM.

Objective To observe the value of augmented reality(AR)navigation system for assisting CT-guided puncture of pulmonary nodules in dog models.Methods Five healthy dogs were selected,and 4 target lung rings were implanted in each dog to build pulmonary nodule models.Deferring to crossover design,CT-guided punctures were performed with or without AR navigation 2 and 4 weeks after successful modeling,respectively,while punctures with AR navigation were regarded as AR group and the others as conventional group,respectively.The time duration of puncturing,the times of CT scanning,of needle adjustment,and the deviation distance between needle pinpoint to the center of pulmonary nodule shown on three-dimensional reconstruction were compared between groups.Results The duration time of puncture in AR group and conventional group was(13.62±5.11)min and(20.16±4.76)min,respectively.In AR group,the times of CT scanning,of needle adjustment,and the deviation distance was 2.40±0.50,2.75±0.44 and(2.94±1.92)mm,respectively,while in conventional group was 3.10±0.64,3.70±0.57 and(4.90±3.38)mm,respectively.The introduction of AR navigation was helpful to shortening the duration of puncture,reducing times of CT scanning and needle adjustment,also decreasing positioning error of needle pinpoint(all P＜0.05).In contrast,the variance of puncture sequences and dogs had no obvious effect on the results(both P＞0.05).Conclusion AR navigation system could improve accuracy and efficiency in CT-guided puncture of pulmonary nodules in dog models.

Objective To explore the value of computer-aided measurement of the mass-based consolidation-to-tumor ratio(CTRmass)in predicting preoperative clinical stage IA lung adenocarcinoma invasion.Methods A total of 56 patients with stage IA lung adenocarcinoma were retrospectively selected from Linfen Central Hospital and the National Lung Screening Trial(NLST)database(project number NLST-1053),and all of them were confirmed by pathology.Computer-aided software was used to automatically cal-culate parameters such as the volume-based consolidation-to-tumor ratio(CTRvol),CTRmass,average CT value,and maximum axial diameter,and its correlation with pathological results was analyzed.Results When the CTRmass≥30.95％,it indicated that lung nodules were more likely to be invasive adenocarcinoma(IAC).Conclusion CTRmass,based on computer-aided software measure-ment,can be used as one of the important indicators to evaluate the preoperative invasion of clinical stage IA lung adenocarcinoma,guiding the selection of surgical methods,and obtaining better curative effects.

Objective To investigate a novel stroke recurrence risk prediction model,which utilized radiomics machine learning methods and specifically combined carotid computed tomography angiography(CT A)with the Essen stroke risk score(ESRS).Methods A total of 136 patients who underwent carotid CT A were analyzed retrospectively.The features of carotid plaque were extrac-ted by machine learning to construct a radiomics feature model,as well as combined with ESRS.Based on clinical outcomes at one-year follow-up,the stroke recurrence risk prediction model was constructed using the logistic regression(LR)machine learning model.To construct an effective and robust model,the dataset was divided into a training set and a validation set in a ratio of 7∶3.The performance of this model was evaluated using area under the curve(AUC)of receiver operating characteristic(ROC)curve,sensi-tivity and specificity.Results The model had strong predictive value.In the training set,AUC,sensitivity and specificity of this model were 0.903,0.796 and 0.761,respectively.In the validation set,AUC,sensitivity and specificity of this model were 0.869,0.667 and 0.850,respectively.Conclusion The stroke recurrence risk prediction model constructed based on radiomics analysis of carotid plaque characteristics in carotid CTA,in combination with the ESRS,can provide reliable predictions for stroke prognosis.

Objective To investigate the efficacy of femoral artery infusion therapy in treatment of diabetic peripheral neuropathy(DPN).Methods A total of 86 DPN patients hospitalized in Department of Endocrinology of the Fourth Affiliated Hospital of Nanchang University from October 2021 to June 2022 were selected and divided into observation group and control group according to random number table method,with 43 cases in each group.The patients of control group was given conventional treatment,and the patients of observation group was given microarterial infusion of alprostadil and mecobalamin.Fasting blood glucose,scores of self-made questionnaire and vibrating perception threshold(VPT)of two groups were compared before and after treatment.Results There was no significant difference in fasting blood glucose between two groups before and after treatment(P>0.05).Before treatment,there were no significant differences in scores of self-made questionnaire and VPT between two groups(P>0.05).After treatment,scores of self-made questionnaire and VPT of patients in observation group were significantly lower than those in control group(P<0.05).Conclusion Femoral artery infusion therapy can obviously improve the symptoms and signs of DPN patients,and the recent effect is good,and it is a safe and effective method,which can be popularized in clinical practice.

Objective:To explore the effect of mind mapping-based health education on the discharge readiness of patients undergoing laryngectomy.Methods:From August 2022 to July 2023, convenience sampling was used to select patients who underwent first-time laryngectomy in the Otolaryngology and Head and Neck Surgery at Beijing Tongren Hospital as participants. The patients were divided into an observation group and a control group according to admission time, with 60 cases in each group. The control group received routine health education, while the observation group was treated with mind mapping-based health education. After the intervention, the discharge readiness and discharge guidance quality of two groups of patients were compared.Results:A total of 120 questionnaires were distributed and 120 valid questionnaires were collected, with a valid response rate of 100.00%. After the intervention, the discharge readiness and discharge guidance quality scores of the observation group were higher than those of the control group, and the difference was statistically significant ( P<0.01) . Conclusions:Mind mapping-based health education can improve the discharge guidance quality and discharge readiness for patients undergoing laryngectomy and provide a reference for medical and nursing staff.