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.

The correspondence between prescription and syndrome is the advantage and characteristic of traditional Chinese medicine(TCM)treatment.However,the pathogenesis of clinical diseases is complex and the condition is changeable,and the clinical application is difficult to achieve the maximum effect under the existing cognition of the correspondence between prescription and syndrome.In this paper,the five categories of physiological characteristics,pathological characteristics,constitution,syndrome,and disease of the longitudinal classification of"TCM state"are introduced into the correspondence of prescription and syndrome.Under the vertical perspective of"TCM state",the theoretical connotation of the correspondence between prescription and syndrome is interpreted as"correspondence between prescription and state",namely correspondence of"prescription-physiological characteristics",correspondence of"prescription-pathological characteristics",correspondence of"prescription-constitution",correspondence of"prescription-syndrome",and correspondence of"prescription-disease".It is hoped to accurately grasp the corresponding connotation of the correspondence between prescription and syndrome,in order to deepen the clinical thinking mode of TCM.

Objective To investigate the mechanism by which Erastin affects ferroptosis in lung fibroblasts.Meth-ods Mouse lung fibroblasts (C57/B6-L) were treated with varying concentrations of the iron death inducer Eras-tin.Cell viability was assessed using the cell counting Kit-8 (CCK-8) assay.Oxidative stress levels were visualized using a fluorescence microscope, and the expression of proteins related to the mitogen-activated protein kinase (MAPK) signaling pathway was analyzed using Western blot.Additionally, the p38 and extracellular regulated protein kinase (ERK) inhibitors SB203580 and U0126 were employed to further elucidate the mechanism by which Erastin induces iron death in lung fibroblasts.Results At a concentration of 100 μmol/L, Erastin effectively in-duced ferroptosis in lung fibroblasts, leading to an upregulation of oxidative stress.Furthermore, the phosphoryla-tion levels of p38 and ERK proteins in the MAPK pathway were elevated (P<0.05) .The addition of SB203580 and U0126 inhibitors resulted in a significant reduction in oxidative stress levels and a notable increased in cell ac-tivity in lung fibroblasts (P<0.05).Conclusion It can be concluded that Erastin induces ferroptosis in lung fi-broblasts, potentially through the mediation of oxidative stress via the MAPK signaling pathway.

Based on the"You Gu Wu Yun"theory in traditional Chinese medicine(TCM),this paper believes that"Gu"in"You Gu Wu Yun"is extended to"state"from the perspective of"TCM state".In order to avoid the adverse reactions of TCM,the macro,meso,and micro three views should be used together,and macro,meso,and micro parameters should be integrated.We should also carefully identify the physiological characteristics,pathological characteristics,constitution,syndrome,and disease of human body by combining qualitative and quantitative method,highlighting the relationship between the prescription and the"state".The correspondence between prescription and the"state"will reduce the risk of adverse reactions of TCM.In this paper,we hope to focus on the guiding role of the"You Gu Wu Yun"theory in TCM research,to give full play to the characteristics and advantages of TCM,and to dialectically treat the role of TCM.

In the past 20 years,near infrared spectrum technology has been widely used in human body monitoring due to its non-invasive and real-time characteristics.Oxygen,as the main metabolic substance of the human body,is consumed the most in brain tissue.In order to prevent complications caused by a decrease in brain tissue oxygen during treatment,the patient's brain tissue blood oxygen saturation needs to be monitored in real time.Currently,most of the clinically used non-invasive cerebral blood oxygen detection equipments use dual wavelengths.Other substances on the detection path will cause errors in the measurement results.Therefore,this article proposes a three-wavelength method based on the basic principle of non-invasive monitoring of cerebral blood oxygen using near-infrared spectrum.The brain tissue oxygen saturation monitoring method of detecting light sources was initially verified through the built system,laying the foundation for subsequent system engineering.

A cerebral oximeter based on 3-wavelength spatially resolved spectroscopy and the modified Lambert-Beer law is proposed.A platform for monitoring the regional cerebral oxygen saturation(rSO2)is established,and the system reliability is verified through spectral stability and background noise test.Eighteen volunteers are recruited to participate in the controlled hypoxia test for exploring the trend of rSO2 with the sequence of stepped hypoxia platform and discussing the relationship between rSO2 and arterial blood oxygen saturation.The results show that the established system can effectively monitor rSO2 and meet the measurement requirements.During the controlled hypoxia sequence,as the fraction of inspired oxygen decreases,rSO2 shows a downward trend,and the individual rSO2 has a high correlation with arterial blood oxygen saturation.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.