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.

Cholestatic liver disease(CLD)is a group of hepatobiliary disorders caused by impaired bile production,secretion,or excretion.With the application of liquid biopsy,multi-parameter radiological examination,and genomics technology,significant progress has been made in the diagnosis and prognostic evaluation of CLD.At present,the therapeutic principles for CLD mainly focus on addressing the underlying causes and managing cholestasis,and commonly used drugs include ursodeoxycholic acid,S-adenosylmethionine,cholestyramine,fibrates,and obeticholic acid.Based on the latest clinical consensus statements and research advances in CLD,this article systematically elaborates on the clinical management strategies for CLD from the aspects of diagnosis,treatment,and prognostic evaluation.

BACKGROUND:Osteoporosis affects the quality of life of patients and is prone to pathological fractures,and plant-derived exosome-like nanovesicles play an important role in regulating bone balance in patients with osteoporosis.OBJECTIVE:To review the research status and prospect of different plant-derived exosome-like nanovesicles in the treatment of osteoporosis.METHODS:Relevant literature in PubMed,Web of Science,and CNKI databases were searched by computer.Search time was from January 1995 to March 2024.The Chinese and English search terms were"osteoporosis,plant-derived exosome-like nanovesicles,exosomes,osteoblasts,osteoclasts."Finally,53 articles were included and analyzed.RESULTS AND CONCLUSION:Plant-derived exosom-like nanovesicles have the advantages of low biocompatibility,low toxicity,and natural targeting.These properties not only ensure its stable presence and effective effect in the body,but also avoid unnecessary immune responses,laying the foundation for the design of long-term treatment strategies.Plant-derived exosome-like nanovesicles can be anti-osteoporosis by stimulating osteoblast proliferation,inhibiting osteoclast differentiation,and promoting osteo-inhibition osteoclasts.In addition,some plant-derived exosome-like nanovesicles may be widely used in the diagnosis and treatment of osteoporosis in the future as novel tracer targets and treatment options due to their bone-targeting.

Portal hypertensive biliopathy is a secondary condition of intrahepatic and extrahepatic bile duct abnormalities caused by portal hypertension, especially in extrahepatic portal venous obstruction. Most patients may remain asymptomatic for a long time, while a few may present with symptomatic portal hypertensive biliopathy, such as obstructive jaundice, cholelithiasis with or without cholangitis, gastrointestinal bleeding, and others. Such disease is rare in clinical practice and is prone to misdiagnosis and missed diagnosis. Improper treatment can lead to serious adverse consequences. We report a case of unexpected discovery of bile duct dilation due to abdominal pain, which was ultimately diagnosed as portal hypertensive biliopathy based on the medical history, manifestations of portal hypertension, and imaging examinations, especially intraductal ultrasonography.

Metabolic associated fatty liver disease (MAFLD) has replaced non-alcoholic fatty liver disease (NAFLD), emphasizing the core role of metabolic dysfunctions and necessitating a multidisciplinary (MDT) approach for diagnosis and treatment to concurrently manage liver disease and metabolic disorders.Downplaying the non-alcohol label while focusing more on metabolic driving factors allows it to coexist with other liver diseases. The gastroenterology and hepatology department is the initial assessment department, and MDT, which includes endocrinology, nutrition, sports medicine, surgery, and psychology, develops individualized treatment plans. With the aim of halting the progression of the disease (steatosis → fibrosis → cirrhosis) and eventually improving patient prognosis, MAFLD management must be based on metabolic intervention and accomplish "liver disease metabolic co-treatment" through multidisciplinary integration.

Cholestatic liver disease(CLD)is a group of hepatobiliary disorders caused by impaired bile production,secretion,or excretion.With the application of liquid biopsy,multi-parameter radiological examination,and genomics technology,significant progress has been made in the diagnosis and prognostic evaluation of CLD.At present,the therapeutic principles for CLD mainly focus on addressing the underlying causes and managing cholestasis,and commonly used drugs include ursodeoxycholic acid,S-adenosylmethionine,cholestyramine,fibrates,and obeticholic acid.Based on the latest clinical consensus statements and research advances in CLD,this article systematically elaborates on the clinical management strategies for CLD from the aspects of diagnosis,treatment,and prognostic evaluation.