Background: Malignant vasovagal syncope (VVS) is characterized by cardiac arrest lasting more than 3 seconds during a syncope episode or head-up tilt test (HUTT). We aim to conduct a risk assessment for potential malignant VVS before the HUTT by using economic, simple and convenient demographic data, in order to prevent adverse outcomes for pediatric VVS.Purpose: To explore the correlation between demographic factors and pediatric malignant VVS, and verify the value of these factors in early risk assessment for malignant VVS before HUTT, so as to optimize test safety and reduce adverse events. Methods: We conducted a retrospective analysis of the clinical data of 3,734 children who were initially diagnosed with VVS due to unexplained syncope and presyncope. Finally, 122 children who met the diagnostic criteria for malignant VVS were included in the malignant VVS group, and 661 children who did not meet the criteria during the same period were matched as the control group. By analyzing demographic data and other factors, we attempted to clarify the association between these factors and malignant VVS. Results: Linear relationship: age and body mass index (BMI) have independent protective effects on malignant VVS. For every 1-year increase in age and every 1 kg/m2 increase in BMI, the risk of malignant VVS decreases by 12% and 9%, respectively. Nonlinear relationship: When the age is <12.9 years old, for every additional year of age, the risk of malignant VVS decreases by 20%. For ages 12.9 years and above, the efficacy is not significant. There is no significant nonlinear relationship between BMI and malignant VVS. Conclusion Age and BMI are independent protective factors for pediatric malignant VVS. Before the age of 12.9 years, the incidence of malignant VVS gradually decreases with the increase in age, and thereafter there is no significant impact.

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.

Signal detection is a critical task in drug safety regulation. However, it inevitably generates irrelevant or false signals, posing challenges for resource allocation by marketing authorization holders. To reasonably assess these signals, different countries have established various principles for prioritizing the evaluation of risk signals. This study systematically compares these principles and finds that the U.S. Food and Drug Administration(FDA) focuses on practical issues, such as identifying drug confusion or drug interactions. However, China's Good Pharmacovigilance Practices and the European Medicines Agency(EMA) emphasize a comprehensive evaluation framework. The Council for International Organizations of Medical Sciences(CIOMS) emphasizes the consistency of multiple data sources, highlighting the reliability of signal evaluation. China practices a multidisciplinary approach combining traditional Chinese and western medicine, and the risk signals related to traditional Chinese medicine(TCM) have unique characteristics, including complex components, cumulative toxicity, specific theoretical foundations, and drug interactions. The different priorities in risk signal evaluation principles across countries suggest that China should strengthen clinical trial research, emphasize corroboration with evidence of multiple sources, and pay particular attention to the risks of drug interactions in the TCM regulatory science. Establishing the risk signal prioritization principles that align with the characteristics of TCM enables more precise and efficient scientific regulation of TCM.
Humans ; Medicine, Chinese Traditional/standards* ; China ; Drugs, Chinese Herbal/adverse effects* ; United States ; United States Food and Drug Administration

Vasculature injury occurs rarely in traumatic brain injury but increases lifetime risk of ischemic or hemorrhage stroke. The diverse and nonspecific clinical manifestations make the diagnosis and treatment of these injuries highly challenging. With advancements in device design, endovascular treatments have become widely adopted, playing an increasingly vital role in the management of vascular diseases. The purpose of this review is to introduce and summarize endovascular treatments of traumatic cerebrovascular injury and other related pathological states after traumatic brain injury. Given the innovations of neuroendovascular devices and improvements in the techniques over the past decade, this review will outline several recent advancements in endovascular treatment strategies for cerebrovascular pathologies. Popularizing more treatment options to clinicians will benefit in dealing with a variety of clinical scenarios and reduce the overall morbidity of traumatic cerebrovascular injury.
Humans ; Endovascular Procedures/methods* ; Brain Injuries, Traumatic/complications* ; Cerebrovascular Trauma/therapy*

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.

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent

OBJECTIVE: Ulcerative colitis is closely associated with intestinal stem cell (ISC) loss and impaired intestinal mucus barrier. Sinisan (SNS), a compound Chinese herbal medicine, has a long history in the treatment of intestinal dysfunction, yet whether SNS can relieve acute experimental colitis by modulating ISC proliferation and secretory cell differentiation has not been studied. Our study tested the effect of SNS against acute colitis and focused on the mechanisms involving intestinal barrier recovery. METHODS: Network pharmacology analysis and blood entry component analysis of SNS were used to explore the underlying mechanism by which SNS affects the acute dextran sulfate sodium (DSS)-induced murine colitis model. RNA-sequencing was used to demonstrate the mechanism. Further, reverse transcription-quantitative polymerase chain reaction, immunofluorescence staining, and alcian blue and periodic acid-Schiff staining were performed in vivo and in the colonic organoids to investigate the cell lineage differentiation-related mechanism of SNS. Furthermore, potential active ingredients from SNS were predicted by network pharmacology analysis. RESULTS: SNS dramatically suppressed DSS-induced acute colonic inflammation in mice. RNA-sequencing analysis revealed downregulation of inflammation and apoptosis-related genes, and upregulation of lipid metabolism and proliferation-related genes, such as Irf7, Pparα, Clspn and Hspa5. Additionally, ISC renewal and intestinal secretory cell lineage commitment were significantly promoted by SNS both in vivo and in vitro in colonic organoids, leading to enhanced mucin expression. Furthermore, potential active ingredients from SNS that mediated inflammation, lipid metabolism, proliferation, apoptosis, stem cells and secretory cells were predicted using a network pharmacology approach. CONCLUSION Our study shed light on the underlying mechanism of SNS in attenuating acute colitis from the perspective of ISC renewal and secretory lineage cell differentiation, suggesting a of novel therapeutic strategy against colitis. Please cite this article as: Cai YJ, Lan JH, Li S, Feng YN, Li FH, Guo MY, et al. Sinisan, a compound Chinese herbal medicine, alleviates acute colitis by facilitating colonic secretory cell lineage commitment and mucin production. J Integr Med. 2025; 23(4): 429-444.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Mice ; Colon/pathology* ; Mucins/metabolism* ; Mice, Inbred C57BL ; Cell Differentiation/drug effects* ; Male ; Colitis/metabolism* ; Cell Lineage/drug effects* ; Dextran Sulfate ; Stem Cells/drug effects* ; Disease Models, Animal