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.

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Objective To investigate the clinical application value of contrast-enhanced chest CT in the detection of responsible vessels for hemoptysis before selective arterial embolization(SAE).Methods The clinical data of 74 patients with hemoptysis trea-ted with interventional therapy and preoperative contrast-enhanced chest CT and digital subtraction angiography(DSA)were ana-lyzed retrospectively.The responsible vessels were identified and then embolized via angiography.The detection of the responsible vessels via preoperative contrast-enhanced chest CT was analyzed.The patients were followed up to observe the efficacy and compli-cations,and the influencing factors of interventional efficacy and recurrence were analyzed.Results A total of 245 responsible ves-sels were detected by preoperative contrast-enhanced chest CT,including bronchial arteries(n=178),ectopic bronchial arteries(n=10)and non-bronchial systemic artery(NBSA)(n=57),which could accurately show the anatomical information of responsible vessels.A total of 4 posterior intercostal arteries were missed.The diagnostic accuracy was 98.4％(245/249).All patients were followed up for 12 to 25.6 months.The immediate hemostasis rate was 93.2％(69/74)and the effective rate was 79.7％(59/74),respectively.The factors affecting the efficacy were bronchial artery to pulmonary circulation fistula,pleural thickening at the bleeding site,and underly-ing lung disease.Among the 59 patients with effective treatment,underlying lung disease was the influencing factor for postoperative recurrence.Conclusion Contrast-enhanced chest CT can provide anatomical information about the responsible vessels for interven-tional therapy of hemoptysis,improving surgical efficiency and reducing the recurrence rate of hemoptysis.

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 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.

Objective To explore the association between lipid profiles and left ventricular hypertrophy in a Chinese general population. Methods We conducted a retrospective observational study to investigate the relationship between lipid markers [including triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein (HDL) cholesterol, non-HDL-cholesterol, apolipoprotein A-I, apolipoprotein B, lipoprotein[a], and composite lipid profiles] and left ventricular hypertrophy. A total of 309,400 participants of two populations (one from Beijing and another from nationwide) who underwent physical examinations at different health management centers between 2009 and 2018 in China were included in the cross-sectional study. 7,475 participants who had multiple physical examinations and initially did not have left ventricular hypertrophy constituted a longitudinal cohort to analyze the association between lipid markers and the new-onset of left ventricular hypertrophy. Left ventricular hypertrophy was measured by echocardiography and defined as an end-diastolic thickness of the interventricular septum or left ventricle posterior wall > 11 mm. The Logistic regression model was used in the cross-sectional study. Cox model and Cox model with restricted cubic splines were used in the longitudinal cohort. Results In the cross-sectional study, for participants in the highest tertile of each lipid marker compared to the respective lowest, triglycerides [odds ratio (OR): 1.250, 95％CI: 1.060 to 1.474], HDL-cholesterol (OR: 0.780, 95％CI: 0.662 to 0.918), and lipoprotein(a) (OR: 1.311, 95％CI: 1.115 to 1.541) had an association with left ventricular hypertrophy. In the longitudinal cohort, for participants in the highest tertile of each lipid marker at the baseline compared to the respective lowest, triglycerides [hazard ratio (HR): 3.277, 95％CI: 1.720 to 6.244], HDL-cholesterol (HR: 0.516, 95％CI: 0.283 to 0.940), non-HDL-cholesterol (HR: 2.309, 95％CI: 1.296 to 4.112), apolipoprotein B (HR: 2.244, 95％CI: 1.251 to 4.032) showed an association with new-onset left ventricular hypertrophy. In the Cox model with forward stepwise selection, triglycerides were the only lipid markers entered into the final model. Conclusion Lipids levels, especially triglycerides, are associated with left ventricular hypertrophy. Controlling triglycerides level potentiate to be a strategy in harnessing cardiac remodeling but deserve to be further investigated.