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 association between intraoperative hypotension and post-operative acute kidney injury(AKI)in patients undergoing brain tumor resections.Methods A total of 428 patients undergoing elective craniotomy for tumor resection were selected,276 males and 152 females,aged≥18 years,BMI 15-36 kg/m2,ASA physical statusⅡ orⅢ.Based on postoperative occurrence of AKI,the patients were divided into two groups:the AKI group and the control group.This study defined three thresholds for hypotension,including MAP during surgery below 65 mmHg,60 mmHg,and 55 mmHg.Multivariate logistic regression was used to analyze the correlation between intraoperative hypotension and postoperative AKI under three thresholds.Results A total of 107 patients had postoperative AKI.The re-sults of multivariable regression analysis indicated that intraoperative MAP＜65 mmHg(OR = 1.11,95%CI 1.03-1.20,P = 0.010)and intraoperative MAP＜60 mmHg(OR = 1.12,95%CI 1.02-1.23,P = 0.017)were associated with postoperative AKI.Conclusion Intraoperative MAP＜65 mmHg or 60 mmHg is associated with postoperative AKI in patients undergoing brain tumor resection.

OBJECTIVE To analyze the metabolites of Zhideke granules and speculate its metabolic pathway in rats in vivo. METHODS Male SD rats were randomly divided into blank group and administration group （Zhideke granules， 9.45 g/kg）； they were given ultrapure water or relevant medicine， twice a day， every 6-8 h， for 3 consecutive days. Serum， urine and feces samples of rats were collected， and their metabolites were identified by UPLC-Q-Exactive-MS technique after intragastric administration of Zhideke granules； their metabolic pathways were speculated. RESULTS After intragastric administration of Zhideke granules， 16 prototype components （i.g. irisflorentin， baicalin， chlorogenic acid） and 11 metabolites （i.g. hydration products of kaempferol or luteolin， methylation products of chlorogenic acid， and hydroxylation products of baicalin） were identified in serum， urine and feces of rats. Among them， 8 prototype components and 4 metabolites were identified in serum samples； 10 prototype components and 7 metabolites were identified in urine samples； 8 prototype components and 5 metabolites were identified in the fecal samples. CONCLUSIONS The metabolites of Zhideke granules in rats mainly include baicalin， irisflorentin，chlorogenic acid， and the main metabolic pathways included methylation， hydroxylation， glucuronidation.

OBJECTIVE To provide reference for basic analysis of the pharmacodynamic substance in Stahlianthus involucratus. METHODS Overall 24 SD male rats were randomly divided into blank group （purified water）， and administration group （ethanol extract of S. involucratus， 15.75 g/kg， calculated by crude drug）， with 12 rats in each group. They were given drug liquid/purified water intragastrically， twice a day， every 6-8 h， for consecutive 3 days. After medication， the blood， urine and fecal samples were collected from two groups of rats. UPLC-Q-Exactive-MS technology was used to identify the chemical constituents in the ethanol extract of S. involucratus， and metabolites in the blood， urine and fecal of rats after intragastrical administration of the ethanol extract of S. involucratus. Multivariate statistical analysis was employed to screen various serum metabolites. Metabolic pathways were analyzed by MetaboAnalyst 5.0 platform. RESULTS A total of 38 chemical constituents were identified from the ethanol extract of S. involucratus， including fourteen prototype components and three metabolites identified from 5 urine samples， nine prototype components identified from fecal samples， and ten prototype components and one metabolite identified from serum samples. A total of 71 differential metabolites were screened from two groups of rat serum samples， of which 44 differential metabolites， such as ferulic acid， glycyrrhizin， were up-regulated and 27 differential metabolites， such as arachidonic acid， phenylacetylglutamine， were down-regulated. The 71 differential metabolites were mainly enriched in 11 metabolic pathways， including phenylalanine metabolism， linoleic acid metabolism， arachidonic acid metabolism， and tryptophan metabolism. CONCLUSIONS Ferulic acid， liquiritigenin， isofraxidin and formononetin may be the material basis that directly exert pharmacological effects of S. involucratus. S. involucratus may exert anti-inflammatory effects by affecting metabolic pathways， including arachidonic acid metabolism and tryptophan metabolism.

Objective:To compare image quality and diagnostic parameters of whole-brain CT perfusion scans under different scanning conditions and assess the utility of deep learning image reconstruction algorithm (DLIR) in reducing tube current during low-dose scans.Methods:Method A total of 105 patients with suspected acute ischemic stroke (AIS) were prospectively enrolled in the First Affiliated Hospital of Zhengzhou University from March, 2022 to March, 203 and their baseline information was recorded. All patients underwent head non-contrast CT and CT perfusion (CTP) examinations. CTP scanning was performed at 80 kV in two groups with the tube current of 150 mA (regular dose) and 100 mA (low dose), respectively. The CTP images of 150 mA group were reconstructed using filtered back-projection algorithm as well as adaptive statistical iterative reconstruction-V (ASIR-V) at 40% and 80% strength levels, which were denoted as groups A-C. The CTP images of 100 mA group were reconstructed using ASIR-V80%, DLIR-M, and DLIR-H, which were denoted as groups D-F. Clinical baseline characteristics and radiation doses were compared between the two groups under different scanning conditions. Furthermore, we assessed the subjective and objective image quality, conventional perfusion parameters, and abnormal perfusion parameters of AIS patients across the six groups of reconstructed CTP images.Results:Under the scanning conditions of 150 mA and 100 mA, 47 and 48 patients were diagnosed with AIS, respectively. There were no significant differences in the baseline characteristics between the two groups. However, there was a significant difference in the mean effective radiation dose (5.71 mSv vs. 3.80 mSv, t = 2 768.30, P < 0.001). The standard deviation (SD) of noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of gray matter (GM) and white matter (WM) were significantly different among the six groups of reconstructed images ( F = 40.58-212.13, P < 0.001). In GM, the SD values in groups C, D, and F were lower than those in other groups ( P < 0.05), and the SNR values in groups C and F were higher than those in other groups ( P < 0.05). In WM, the SD and SNR values in groups C and F were significantly different from those in other groups ( P < 0.05). Additionally, CNR values in groups C and F were higher than those in other groups ( P < 0.05). There was no significant difference in subjective scores among groups B, C, and F ( P > 0.05). Regarding perfusion parameters in the brain GM, groups D and E had lower cerebral blood volume (CBV) values compared to groups A to C ( P < 0.05), and group F had lower CBV values than group B ( P < 0.05). In the brain WM, group D had consistently lower mean transit time (MTT) values compared to the other groups ( P < 0.05). Notably, there were no significant differences in AIS lesion detection rates and relevant diagnostic parameters across the six image groups. Conclusions:Low-tube current CTP scan combined with the DLIR-H algorithm can enhance image quality without affecting perfusion parameters such as CBV and MTT, while reducing radiation dose by 30%. This algorithm can be routinely applied in brain CTP examinations.

Background::Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants.Methods::A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79 years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson’s correlation, multiple linear regression, and analyses of covariance.Results::All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5 μm/year in women and 5.8 μm/year in men) and diameter (0.03 mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. Conclusions::The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.

Objective To investigate the impact of a peer support model on the mental health of caregivers and the perceived social support and psychiatric symptoms of the mental disorder patients under their care. Methods Patients with mental disorders undergoing long-term community-based rehabilitation and their primary caregivers were recruited for this study. A total of 44 pairs of eligible patients and caregivers were selected based on a 1∶1 matching ratio. Systematic peer support activities were conducted exclusively for the caregivers. The General Health Questionnaire (GHQ) and the Symptom Checklist-90 (SCL-90) were administered before and after the intervention to assess the mental health status of caregivers. The Perceived Social Support Scale (PSSS) and the Brief Psychiatric Rating Scale (BPRS) were employed to evaluate the patients' perceived social support and psychiatric conditions before and after the intervention. Results A total of 44 valid questionnaires from caregivers and 42 from patients were collected. The GHQ score and the total scores, the number of positive item, positive total scores, and positive mean scores of and SCL-90 of caregivers were significantly lower after the intervention compared to pre-intervention (P < 0.05 or P < 0.001). For patients, the family support score, extracurricular support score, and total score of the PSSS were significantly higher post-intervention compared to pre-intervention (P < 0.05 or P < 0.001). Compared to pre-intervention, patients'scores on anxiety and depression, lack of energy, thought disturbance, activation, hostility and suspicion, and the total score on the BPRS were significantly lower after the caregiver peer support intervention (P < 0.001). Conclusion The peer support model contributes to improving the mental health of caregivers for patients with mental disorders and enhancing the patients'psychiatric conditions.