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.

INTRODUCTION: Lecanemab has shown promise in treating early Alzheimer's disease (AD), but its safety and efficacy in Chinese populations remain unexplored. This study aimed to evaluate the safety and 6-month clinical outcomes of lecanemab in Chinese patients with mild cognitive impairment (MCI) or mild AD. METHODS: In this single-arm, real-world study, participants with MCI due to AD or mild AD received biweekly intravenous lecanemab (10 mg/kg). The study was conducted at Hainan Branch, Ruijin Hospital Shanghai Jiao Tong University School of Medicine. Patient enrollment and baseline assessments commenced in November 2023. Safety assessments included monitoring for amyloid-related imaging abnormalities (ARIA) and other adverse events. Clinical and biomarker changes from baseline to 6 months were evaluated using cognitive scales (mini-mental state examination [MMSE], montreal cognitive assessment [MoCA], clinical dementia rating-sum of boxes [CDR-SB]), plasma biomarker analysis, and advanced neuroimaging. RESULTS: A total of 64 patients were enrolled in this ongoing real-world study. Safety analysis revealed predominantly mild adverse events, with infusion-related reactions (20.3%, 13/64) being the most common. Of these, 69.2% (9/13) occurred during the initial infusion and 84.6% (11/13) did not recur. ARIA-H (microhemorrhages/superficial siderosis) and ARIA-E (edema/effusion) were observed in 9.4% (6/64) and 3.1% (2/64) of participants, respectively, with only two symptomatic cases (one ARIA-E presenting with headache and one ARIA-H with visual disturbances). After 6 months of treatment, cognitive scores remained stable compared to baseline (MMSE: 22.33 ± 5.58 vs . 21.27 ± 4.30, P = 0.733; MoCA: 16.38 ± 6.67 vs . 15.90 ± 4.78, P = 0.785; CDR-SB: 2.30 ± 1.65 vs . 3.16 ± 1.72, P = 0.357), while significantly increasing plasma amyloid-β 42 (Aβ42) (+21.42%) and Aβ40 (+23.53%) levels compared to baseline. CONCLUSIONS: Lecanemab demonstrated a favorable safety profile in Chinese patients with early AD. Cognitive stability and biomarker changes over 6 months suggest potential efficacy, though high dropout rates and absence of a control group warrant cautious interpretation. These findings provide preliminary real-world evidence for lecanemab's use in China, supporting further investigation in larger controlled studies. REGISTRATION ClinicalTrials.gov , NCT07034222.
Humans ; Alzheimer Disease/drug therapy* ; Male ; Female ; Aged ; Middle Aged ; Cognitive Dysfunction/drug therapy* ; Aged, 80 and over ; Amyloid beta-Peptides/metabolism* ; Biomarkers ; East Asian People

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.

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Objective To investigate the risk factors of subdelirium syndrome in adult ICU patients,then establish a risk prediction model and have it verified.Method A total of 380 adult ICU patients in our hospital between June 2022 and January 2024 were selected in this study.Among the patients,224(70％)were assigned to the model group and 114(30％)to the validation group.Independent risk factors were screened by comparison of the general data,disease and treatment,laboratory indicators and other relevant data between the patients with and without subdelirium.A risk prediction model was established with Logistic regression.Results A risk prediction model was finalised and established.It composed six risk factors of age(OR=1.023),APACHE Ⅱ score(OR=1.093),critical care pain observation tool(OR=2.216),duration of mechanical ventilation(OR=1.003),constraint(OR=2.615)and sepsis(OR=2.081).In internal validation,it was found that the calibration curve closely overlapped with the ideal curve and the area under the ROC curve was 0.816,with a predictive cut off value of 85 points.In external validation,the calibration curve was found closely overlapped with the ideal curve and the area under ROC curve was 0.808.Conclusion The risk prediction model for subdelirium syndrome in adult ICU patients established in the study has good consistency and prediction efficiency,thereby it provides a basis for early clinical screening and intervention of subdelirium syndrome.

Objective To investigate the risk factors of subdelirium syndrome in adult ICU patients,then establish a risk prediction model and have it verified.Method A total of 380 adult ICU patients in our hospital between June 2022 and January 2024 were selected in this study.Among the patients,224(70％)were assigned to the model group and 114(30％)to the validation group.Independent risk factors were screened by comparison of the general data,disease and treatment,laboratory indicators and other relevant data between the patients with and without subdelirium.A risk prediction model was established with Logistic regression.Results A risk prediction model was finalised and established.It composed six risk factors of age(OR=1.023),APACHE Ⅱ score(OR=1.093),critical care pain observation tool(OR=2.216),duration of mechanical ventilation(OR=1.003),constraint(OR=2.615)and sepsis(OR=2.081).In internal validation,it was found that the calibration curve closely overlapped with the ideal curve and the area under the ROC curve was 0.816,with a predictive cut off value of 85 points.In external validation,the calibration curve was found closely overlapped with the ideal curve and the area under ROC curve was 0.808.Conclusion The risk prediction model for subdelirium syndrome in adult ICU patients established in the study has good consistency and prediction efficiency,thereby it provides a basis for early clinical screening and intervention of subdelirium syndrome.

Objective:To report a large family of epileptic seizures caused by DEPDC5 gene variation, and to conduct a clinical genetic analysis on a proband in this family with both DEPDC5 gene mutation and a de novo GABRA1 gene mutation. Methods:The medical records of a family suffering from epilepsy due to a newly identified DEPDC5 gene variant were compiled from cases admitted to Hebei General Hospital in January 2024. The relevant genetic detection was carried out by sampling the peripheral blood of the family members. The whole exome sequencing techniques were employed for the identification of pathogenic mutation sites in the proband. The next generation sequencing technology was utilized for other family members to identify disease-causing mutation sites associated with the clinical phenotype of patients, and these findings were confirmed using first-generation Sanger sequencing technology. Results:The proband, who experienced seizures in early childhood and harbored two gene mutation sites, exhibited an early onset age along with significant delays in both intellectual and motor development. The primary clinical manifestations included focal seizures, myoclonus, and tonic-clonic symptoms. When compared with other family members who had the onset of epilepsy during adolescence, carried only one mutation site, and had generalized epileptic seizures and mostly accompanied by attention deficit hyperactivity disorder, the proband showed obvious clinical heterogeneity. The results of whole exome sequencing indicated that the proband had both GABRA1 c.640C>T(p.Arg214Cys) and DEPDC5 c.4348A>T(p.Lys1450 *) mutations inherited from the father. The mutation inherited from the father was reported here for the first time and had not been reported before, with the paternal mutation traceable to the proband's grandfather, while the proband's mother and grandmother were found to be devoid of the mutation. In this family, 5 patients with similar seizure phenotype all had pathogenic mutations at the same locus of the DEPDC5 gene(c.4348A>T, p.Lys1450 *), and the remaining 3 patients with seizure symptoms were not tested. Conclusions:The DEPDC5 gene mutation is the cause of the disease in this family, and the c.4348A>T is the newly discovered mutation site. The proband carries the mutation sites of both GABRA1 gene and DEPDC5 gene. The clinical manifestations of proband are significantly heterogeneous compared with those of his family members.

Objective:To compare the imaging quality and metabolic quantitative parameters of pulmonary nodules between Q. Flex whole information five-dimensional (5D) and conventional three-dimensional (3D) PET/CT imaging for clinical evaluation.Methods:Fifty-four patients (30 males, 24 females, age: 60(42, 75) years; 78 solid pulmonary nodules (maximum diameter≤3 cm) with abnormal uptake of 18F-FDG) from Tianjin Cancer Hospital Airport Hospital between June 2022 and August 2022 were enrolled in this retrospective study. All patients underwent 5D scanning and 3D, 5D reconstruction. Image quality scores, signal-to-noise ratio (SNR), SUV max, SUV mean and metabolic tumor volume (MTV) of pulmonary nodules of 5D group and 3D group were evaluated and compared with χ2 test and Wilcoxon signed rank test. Correlation of quantitative parameters between 2 groups were analyzed by using Spearman rank correlation analysis. Results:Thirty-five of 78(45%) pulmonary nodules with image quality score≥4 were found in 5D group, which were more than those in 3D group (22/78(28%); χ2=4.67, P=0.031). Meanwhile, SNR, SUV max, SUV mean, and MTV were significantly positively correlated between the 2 groups ( rs values: 0.86, 0.86, 0.85, and 0.95, all P<0.001). SNR, SUV max and SUV mean of pulmonary nodules in 5D group were significantly higher than those in 3D group, which were 37.46(18.42, 62.00) vs 32.72(16.97, 54.76) ( z=-4.07, P<0.001), 9.71(5.48, 13.82) vs 8.96(4.82, 12.63) ( z=-3.05, P<0.001) and 6.30(3.39, 8.94) vs 5.61(2.99, 7.63)( z=-4.07, P<0.001) respectively. MTV of pulmonary nodules in 5D group was significantly lower than that in 3D group, which was 1.72(0.66, 2.74) cm 3vs 1.98(1.06, 4.63) cm 3 ( z=-7.13, P<0.001). Quantitative parameters of lower lung field and nodules with maximum diameters of >10 mm and ≤20 mm based on 5D scanning changed most significantly compared with those based on 3D scanning ( z values: from -5.23 to -2.48, all P<0.05). Conclusion:Q. Flex 5D PET significantly improves the quantitative accuracy of SUV and MTV of pulmonary nodules, and the improvement of image quality is substantial without increasing the radiation dose, which has clinical practical value.