OBJECTIVE To analyze the prototype components absorbed into blood and brain of Lithocarpus litseifolius leaves， so as to provide a reference for clarifying the pharmacological material basis of its prevention and treatment of central nervous system dis eases. METHODS The ethanol extract of L. litseifolius leaves， as well as the gastric lavage fluid and perfusion solution were prepared. Using rats as subjects， plasma samples of intestinal wall metabolism， intestinal flora metabolism and hepatic metabolism were prepared via in situ intestinal perfusion and closed intestinal loop method； while comprehensive metabolic plasma samples， brain tissue samples， and cerebrospinal fluid samples were collected after intragastric administration. UPLC-HRMS technology was utilized to analyze and identify chemical components and prototype components absorbed into blood and brain of L. litseifolius leaves. RESULTS A total of 66 chemical constituents were identified in L. litseifolius leaves， primarily consisting of flavonoids， organic acids， and others. A total of 16， 13， 11， and 5 prototype components were identified in intestinal wall metabolism， intestinal flora metabolism， hepatic metabolism， and comprehensive metabolic plasma samples， respectively. Additionally， 4 prototype components were detected in brain tissue and 9 in cerebrospinal fluid. Phloridzin， trilobatin， phloretin-2- O -malonyl hexoside， and phloretin were identified as common components across all sample types. CONCLUSIONS Prototype components absorbed into blood and brain of L. litseifolius leaves， such as phloridzin， trilobatin， phloretin， and other components may serve as the pharmacological material basis for their therapeutic effects on central nervous system diseases.

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.

Pulmonary hypertension (PH) is a group of progressive diseases characterized by pulmonary vascular remodeling,and some patients already have right heart insufficiency at the time of diagnosis. Therefore,early diagnosis of PH is essential to improve patients' quality of life and prolong survival. Biomarkers are an important indicator for early diagnosis of the disease,and there are many traditional biomarkers for PH diagnosis,but the sensitivity and specificity are low. With the progress of research,some new biomarkers have been shown to predict disease progression in early PH and play an important role in the early diagnosis of PH. This study reviews the research progress of biomarkers of PH from the aspects of right heart insufficiency,endothelial dysfunction,pulmonary artery smooth muscle dysfunction,inflammation,and in situ thrombosis to provide exploration direction and reference value for early diagnosis of PH.

Pulmonary hypertension(PH)is a fatal disease characterized by pulmonary vascular remodeling,ultimately leading to right heart failure and death.Current treatments for PH are suboptimal,with no substantial improvement in overall survival among patients with advanced PH.Despite some progress in understanding the pathogenesis of PH,further studies at the molecular level are needed to develop more effective treatments for PH.Recent research has shown that long non-coding RNAs(lncRNAs)have an important regulatory function in the pathophysiological process of PH,and may thus be potential disease biomarkers and therapeutic targets.In this paper,we review recent progress in our understanding of the molecular mechanisms of lncRNAs in PH.

Pulmonary hypertension(PH)is a fatal disease characterized by pulmonary vascular remodeling,ultimately leading to right heart failure and death.Current treatments for PH are suboptimal,with no substantial improvement in overall survival among patients with advanced PH.Despite some progress in understanding the pathogenesis of PH,further studies at the molecular level are needed to develop more effective treatments for PH.Recent research has shown that long non-coding RNAs(lncRNAs)have an important regulatory function in the pathophysiological process of PH,and may thus be potential disease biomarkers and therapeutic targets.In this paper,we review recent progress in our understanding of the molecular mechanisms of lncRNAs in PH.

Pulmonary hypertension (PH) is a group of progressive diseases characterized by pulmonary vascular remodeling,and some patients already have right heart insufficiency at the time of diagnosis. Therefore,early diagnosis of PH is essential to improve patients' quality of life and prolong survival. Biomarkers are an important indicator for early diagnosis of the disease,and there are many traditional biomarkers for PH diagnosis,but the sensitivity and specificity are low. With the progress of research,some new biomarkers have been shown to predict disease progression in early PH and play an important role in the early diagnosis of PH. This study reviews the research progress of biomarkers of PH from the aspects of right heart insufficiency,endothelial dysfunction,pulmonary artery smooth muscle dysfunction,inflammation,and in situ thrombosis to provide exploration direction and reference value for early diagnosis of PH.

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.