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 safety and efficacy of flow diverter (FD) in the treatment of middle cerebral artery (MCA) dissection aneurysms.Methods:Patients with MCA dissection aneurysm received FD treatment at the Department of Neurosurgery, Jinjiang Hospital and the Cerebrovascular Disease Center of the First Affiliated Hospital of Naval Medical University from January 2021 to December 2023 were included retrospectively. The success rate of procedure, incidence of complications, occlusion rate of aneurysms, and clinical outcome were evaluated.Results:A total of 23 patients were included, with a success rate of 100% for FD implantation and a periprocedural complication rate of 8.7%. Nineteen patients (82.6%) completed angiography follow-up within an average of 7.2 months, of which the aneurysms of 16 patients (84.2%) were completely occluded, 3 (15.8%) were partial occluded, and 2 (10.5%) experienced in-stent restenosis; 14 (73.7%) showed stenosis of covered branch openings, of which 2 (10.5%) had branch occlusions, with no relevant clinical symptoms. The median clinical follow-up time was 23.2 months, with 95.7% of patients achieving good outcome (modified Rankin scale score ≤2).Conclusion:FD is safe and effective in the treatment of MCA dissection aneurysms, and precise device selection and release is the key to improving procedural safety.

Objective:To investigate the relationship between the quick sequential organ failure assessment (qSOFA) score, procalcitonin (PCT), blood lactate, hypersensitive C-reactive protein (hs-CRP) levels, and the prognosis of patients with severe acute pancreatitis (SAP).Methods:This retrospective analysis included 225 pancreatitis cases admitted to the Department of Gastroenterology at Ganzi Tibetan Autonomous Prefecture People's Hospital between February 2020 and February 2025. Patients were categorized into three groups based on severity: SAP group ( n=125), moderately severe acute pancreatitis (MSAP) group ( n=50), and mild acute pancreatitis (MAP) group ( n=50). General patient data, qSOFA scores, PCT, lactate, and hs-CRP levels were compared among the groups. Within the SAP group, patients were further divided into a favorable prognosis group ( n=101) and a poor prognosis group ( n=24) based on 28-day survival. The relationship between qSOFA score, PCT, lactate, hs-CRP, and SAP prognosis was analyzed. Normally distributed continuous data were expressed as Mean±SD. Independent samples t-test was used on comparison between two groups, while one-way ANOVA was used on comparison among multiple groups. LSD test was used on post-hoc pairwise comparison. Categorical data were expressed as n(%) and compared by χ2 test. Spearman correlation analysis assessed the correlation between qSOFA score, PCT, lactate, hs-CRP levels, and acute pancreatitis (AP) severity. Multivariable logistic regression modeling was used for joint prediction, incorporating qSOFA score, serum PCT, lactate, and hs-CRP levels to calculate combined predictive efficacy. The predictive performance of each indicator for prognosis was evaluated using receiver operating characteristic (ROC) curves. The areas under the ROC curves (AUC) were compared using DeLong's test. Results:Significant differences were observed among the MAP, MSAP, and SAP groups for qSOFA score [(0.63±0.29), (1.32±0.37), (1.70±0.33) points], serum PCT [(4.06±0.74), (5.35±0.89), (6.12±0.98) μg/L], lactate [(1.74±0.32), (2.77±0.70), (4.29±1.06) mmol/L], and hs-CRP levels [(15.38±3.75), (27.64±5.09), (35.26±6.42) mg/L] ( F values=187.51, 91.83, 169.18, 223.92, respectively, all P<0.001). The SAP group had significantly higher levels of qSOFA score, serum PCT, lactate, and hs-CRP compared to both the MAP and MSAP groups (all P<0.05), and the MSAP group levels were significantly higher than the MAP group (all P<0.05). The poor prognosis group had significantly higher levels of qSOFA score [(1.92±0.19) points], PCT [(6.65±0.47) μg/L], lactate [(5.14±0.62) mmol/L], and hs-CRP [(40.65±4.73) mg/L] compared to the favorable prognosis group [(1.65±0.25) points, (5.99±0.84) μg/L, (4.09±0.94) mmol/L, (34.16±6.09) mg/L] ( t values=4.96, 3.71, 5.20, 4.88, respectively, all P<0.001). Multivariate logistic regression analysis identified qSOFA score ( OR=14.658, 95% CI: 2.612-82.253), PCT ( OR=2.809, 95% CI: 1.288-6.129), lactate ( OR=3.658, 95% CI: 1.635-8.187), and hs-CRP ( OR=1.168, 95% CI: 1.055-1.293) as independent influencing factors for SAP patient prognosis ( P=0.002, 0.010, 0.002, 0.003, respectively). ROC curve analysis showed that the AUCs for qSOFA score, PCT, lactate, and hs-CRP in predicting poor prognosis in SAP patients were 0.754, 0.856, 0.766, and 0.809, with sensitivities of 87.50%, 83.33%, 83.33%, 85.50%, and specificities of 60.40%, 77.20%, 64.40%, 75.20%, respectively. The combined prediction model yielded an AUC of 0.934, with a sensitivity of 95.80% and specificity of 72.30%. Conclusion:The qSOFA score, PCT, blood lactate, and hs-CRP levels are associated with the severity of AP and possess reference value for predicting the prognosis of SAP patients.

Objective:To investigate, for multi-sensitized allergic rhinitis (AR) patients allergic to dust mites combined with other allergens (pollen, mold, animal dander, etc.), whether the single dust mite subcutaneous immunotherapy (SCIT) can improve the specific symptoms caused by other allergens in the patients, and to analyze the relationship between the effectiveness of symptom improvement in these patients and the type, quantity and severity of the allergens.Methods:A multicenter retrospective study was conducted to collect mul-sensitized AR patients from allergy or respiratory departments of 5 hospitals who received house dust mite allergen preparation SCIT for 12 to 36 months and met other inclusion and exclusion criteria from February to July 2024. General clinical data were collected and the perennial or seasonal symptoms before and after treatment were evaluated with visual analogue scale (VAS) to assess whether there was an perennial or allergen-specific symptom improvement (VAS score decrease ≥30%), by which the patients were divided into effective group and ineffective. R software was used to analyze the differences between groups by using Fisher′s exact test and Mann-Whitney U test. Results:A total of 62 patients were enrolled, and the treatment were effective in 39 of them, with an effective rate of 62.9%. For allergen-specific symptoms, the median age of the effective group was higher than that of the ineffective group (12 years old vs. 8 years old, P=0.039), and the effective rate in dust mite specific immunoglobin E (sIgE) grade ≤5 group was higher than that in sIgE grade >5 group (81.6% vs. 45.5%, P=0.008), and the effective rate of mold sIgE grade ≤2 group was higher than that of sIgE grade >2 group (83.3% vs. 28.6%, P=0.045), and there was no statistically significant correlation between the other allergen grades and the effective rate ( P>0.05). For perennial symptoms, the effective rate in the mold grade ≤2 group was higher than that in the sIgE grade >2 group (91.3% vs. 28.6%, P=0.010), and there was no statistically significant correlation between the other allergen grades and the effective rate ( P>0.05). There was no significant correlation between the treatment effectiveness of perennial or allergen-specific symptoms and the number of combined allergens, the grade of skin test, and the difference between the grade of combined allergens and that of dust mites ( P>0.05). Conclusion:Among the patients with multi-sensitized AR allergic to dust mites included in this study, single dust mite SCIT is effective in some of them, and for allergen-specific symptoms, the effective group was elder, and dust mite sIgE grade 6 and mold sIgE grade ≥2 was related to the low effective rate of SCIT. The present results are insufficient for selecting single or multiple AIT in any type of multi-sensitized patients.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To investigate, for multi-sensitized allergic rhinitis (AR) patients allergic to dust mites combined with other allergens (pollen, mold, animal dander, etc.), whether the single dust mite subcutaneous immunotherapy (SCIT) can improve the specific symptoms caused by other allergens in the patients, and to analyze the relationship between the effectiveness of symptom improvement in these patients and the type, quantity and severity of the allergens.Methods:A multicenter retrospective study was conducted to collect mul-sensitized AR patients from allergy or respiratory departments of 5 hospitals who received house dust mite allergen preparation SCIT for 12 to 36 months and met other inclusion and exclusion criteria from February to July 2024. General clinical data were collected and the perennial or seasonal symptoms before and after treatment were evaluated with visual analogue scale (VAS) to assess whether there was an perennial or allergen-specific symptom improvement (VAS score decrease ≥30%), by which the patients were divided into effective group and ineffective. R software was used to analyze the differences between groups by using Fisher′s exact test and Mann-Whitney U test. Results:A total of 62 patients were enrolled, and the treatment were effective in 39 of them, with an effective rate of 62.9%. For allergen-specific symptoms, the median age of the effective group was higher than that of the ineffective group (12 years old vs. 8 years old, P=0.039), and the effective rate in dust mite specific immunoglobin E (sIgE) grade ≤5 group was higher than that in sIgE grade >5 group (81.6% vs. 45.5%, P=0.008), and the effective rate of mold sIgE grade ≤2 group was higher than that of sIgE grade >2 group (83.3% vs. 28.6%, P=0.045), and there was no statistically significant correlation between the other allergen grades and the effective rate ( P>0.05). For perennial symptoms, the effective rate in the mold grade ≤2 group was higher than that in the sIgE grade >2 group (91.3% vs. 28.6%, P=0.010), and there was no statistically significant correlation between the other allergen grades and the effective rate ( P>0.05). There was no significant correlation between the treatment effectiveness of perennial or allergen-specific symptoms and the number of combined allergens, the grade of skin test, and the difference between the grade of combined allergens and that of dust mites ( P>0.05). Conclusion:Among the patients with multi-sensitized AR allergic to dust mites included in this study, single dust mite SCIT is effective in some of them, and for allergen-specific symptoms, the effective group was elder, and dust mite sIgE grade 6 and mold sIgE grade ≥2 was related to the low effective rate of SCIT. The present results are insufficient for selecting single or multiple AIT in any type of multi-sensitized patients.