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.

BACKGROUND: The proportion of patients with non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations is relatively high in China. However, these patients currently lack significant benefits from available neoadjuvant treatment options. This study aims to explore the potential application value of neoadjuvant targeted therapy by evaluating its efficacy and safety in patients with EGFR-mutant resectable lung adenocarcinoma. METHODS: A multicenter retrospective study was used to analyze the treatment effect of patients with stage IIA-IIIB EGFR-mutant lung adenocarcinoma who underwent surgical resection after receiving neoadjuvant targeted therapy from July 2019 to October 2024. RESULTS: A total of 24 patients with EGFR-mutant lung adenocarcinoma from three centers were included in this study. All patients successfully underwent surgery and achieved R0 resection of 100.0%. The objective response rate (ORR) was 83.3% (20/24) . The major pathologic response (MPR) rate was 37.5% (9/24), with 2 patients (8.3%) achieving pathological complete response (pCR). During neoadjuvant therapy, 13 out of 24 patients (54.2%) experienced adverse events of grade 1-2, with no occurrences of ≥ grade 3. The most common treatment-related adverse events were rash (n=4, 16.7%), mouth sores (n=2, 8.3%), and diarrhea (n=2, 8.3%). The median follow-up time was 33.0 months, no deaths occurred in all patients, and the overall survival (OS) rate was 100.0%. The 1-year disease-free survival (DFS) rate was 91.1%, and the 2-year DFS rate remained at 86.2%. CONCLUSIONS The application of neoadjuvant targeted therapy in patients with EGFR-mutant resectable lung adenocarcinoma is safe and feasible, and is expected to become a highly promising neoadjuvant treatment option for the patients with EGFR-mutant lung adenocarcinoma.
Humans ; ErbB Receptors/metabolism* ; Male ; Female ; Middle Aged ; Adenocarcinoma of Lung/surgery* ; Neoadjuvant Therapy ; Lung Neoplasms/surgery* ; Aged ; Retrospective Studies ; Mutation ; Adult

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. METHODS: The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. RESULTS: The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. CONCLUSIONS The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.
Humans ; Female ; Ultrasonography, Prenatal/methods* ; Pregnancy ; Fetus/diagnostic imaging* ; Neural Networks, Computer ; Video Recording

Neoadjuvant chemoradiotherapy has emerged as the standard treatment for locally advanced rectal cancer, esophageal cancer and gastroesophageal junction cancer which can not only improve the rate of local control but also induce pathological complete response in some patients. For patients who have achieved clinical complete response after neoadjuvant therapy, the watch & wait strategy and organ preservation could reduce unnecessary surgery and minimize the risk of postoperative complications, meanwhile greatly improve patients' quality of life without affecting the oncologic outcome. At present, a variety of methods, including white light endoscopy, endoscopic forceps biopsy, image enhanced endoscopy, endoscopic ultrasound, endoscopic ultrasound guided fine needle aspiration, endoscopic submucosal dissection, artificial intelligence assisted technology, etc., have become important assistance for the evaluation of tumor response after neoadjuvant chemoradiotherapy and have been widely used in clinical practice. This review will briefly introduce the application of the endoscopic approaches mentioned above and some novel endoscopic techniques and developing trends in response evaluation for patients with locally advanced rectal cancer, esophageal cancer and gastroesophageal junction cancer patients receiving neoadjuvant chemoradiotherapy.

Neoadjuvant chemoradiotherapy has emerged as the standard treatment for locally advanced rectal cancer, esophageal cancer and gastroesophageal junction cancer which can not only improve the rate of local control but also induce pathological complete response in some patients. For patients who have achieved clinical complete response after neoadjuvant therapy, the watch & wait strategy and organ preservation could reduce unnecessary surgery and minimize the risk of postoperative complications, meanwhile greatly improve patients' quality of life without affecting the oncologic outcome. At present, a variety of methods, including white light endoscopy, endoscopic forceps biopsy, image enhanced endoscopy, endoscopic ultrasound, endoscopic ultrasound guided fine needle aspiration, endoscopic submucosal dissection, artificial intelligence assisted technology, etc., have become important assistance for the evaluation of tumor response after neoadjuvant chemoradiotherapy and have been widely used in clinical practice. This review will briefly introduce the application of the endoscopic approaches mentioned above and some novel endoscopic techniques and developing trends in response evaluation for patients with locally advanced rectal cancer, esophageal cancer and gastroesophageal junction cancer patients receiving neoadjuvant chemoradiotherapy.

Objective To investigate the value of thrombomodulin(TM),thrombin-antithrombin complex(TAT),α2 plasmin inhibitor-plasmin complex(PIC)and tissue plasminogen activator-inhibitor complex(t-PAIC)in the diagnosis and prognosis of neonatal disseminated intravascular coagulation(DIC).Methods Eighty-seven DIC neonates(the observation group)were included and divided into the survival group(66 cases)and the death group(21 cases)based on their outcomes at discharge.And 50 healthy newborns born in the same period were selected as the control group.The clinical data of neonates were collected,and risk factors of neonatal DIC were analyzed by Logistic regression.The differences of TM,TAT,PIC and t-PAIC levels in different groups were analyzed.The receiver operating characteristic(ROC)curve was used to analyze values of TM,TAT,PIC and t-PAIC in the diagnosis and prognosis of neonatal DIC.Results The incidence of low Apgar score,birth asphyxia,IVH,sepsis and maternal pregnancy induced hypertension syndrome(PIH)were higher in the observation group than those in the control group(P＜0.05).Multivariate Logistic regression analysis showed that low Apgar score,birth asphyxia,sepsis and PIH were independent risk factors for neonatal DIC.TM,TAT,PIC and t-PAIC levels were higher in the observation group than those in the control group(P＜0.05).ROC curve showed that the combined diagnosis value of TM,TAT,PIC and t-PAIC was better than that of single diagnosis of neonatal DIC.TM and TAT levels were higher in the death group than those in the survival group(P＜0.05),and there were no significant differences in PIC and t-PAIC levels between the two groups.Multivariate Logistic regression analysis showed that elevated TAT level was an independent risk factor for neonatal DIC prognosis.ROC curve showed that when TAT was 21.72 μg/L,the area under the curve for predicting neonatal DIC prognosis was 0.772(95%CI:0.666-0.878),and the sensitivity and specificity were 76.2%and 71.2%,respectively.Conclusion The combined application of TM,TAT,PIC and t-PAIC has important clinical value in diagnosis and prognosis evaluation of neonatal DIC.