Psychological factors have become significant contributors to the onset and progression of insomnia. This article explored the treatment of insomnia from the perspective of “liver governs wei qi （defensive qi）”. The concept of “liver governs wei qi （defensive qi）” is summarized in three aspects， firstly， the liver assists the spleen and stomach in transformation and transportation， governing the generation of wei qi； secondly， the liver aids lung qi diffusion and dispersion， governing the distribution of wei qi； thirdly， the liver regulates circadian rhythms， governing the circulation of wei qi. It is proposed that the clinical treatment of insomnia should focus on the following methods： for regulating the liver to harmonize the five viscera， and facilitate the circulation of wei qi， medicinals entering the liver channel include Chaihu （Bupleuri radix）， Baishao （Paeoniae Radix Alba）， Zhizi （Gardeniae Fructus）， and Suanzaoren （Ziziphi Spinosae Semen） could be commonly used； for nourishing the liver， the treatment should align with the day-night rhythm， and herbs such as Baihe （Lilium）， Hehuan （Albizia julibrissin）， and Yejiaoteng （Polygoni multiflori caulis） are commonly used； for soothing the liver and address both mental and physical health to calm wei qi， treatment should advocate verbal counseling， psychological regulation， and health education. Ultimately， this treatment approach can free liver qi to flow， soothe qi movement， restore the motion of wei qi， regulate during day and night， balance yin and yang， and resolve insomnia effectively.

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.

Humans ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis ; Biomarkers, Tumor/genetics* ; Mutation/genetics* ; Colorectal Neoplasms/genetics* ; Proto-Oncogene Proteins B-raf/metabolism*

Diabetes mellitus is a complex metabolic disease involving multiple organ systems in the body.In recent years,its global incidence rate has increased year by year.In China,the blood glucose control of patients with diabetes mellitus who receive oral hypogly-cemic agents or insulin treatment remains poor.In the early disease stages,exercise is important to control blood glucose levels.Recently,many studies have found that the occurrence of type 2 diabetes mellitus was related to declining levels of irisin,an exercise-related muscle factor.Furthermore,studies have found that irisin improved insulin resistance,promoted the production of pancreatic isletβcells,and affected the body's glucose and lipid metabolism.In addition,its levels were also implicated in the occurrence of various complications,such as diabetic nephropathy and diabetes-related cardiovascular diseases.This article summarizes and analyzes the role of irisin in the occurrence and development of diabetes mellitus and further describes its impact and mechanism on various diabetic complications.

ObjectiveTo understand the characteristics of PM_2.5 pollution in the air of Pearl River Delta city in Guangdong Province under the COVID-19 epidemic and the health risks of inhaling elements in PM_2.5. MethodsIn 2022， 10 PM_2.5 monitoring points were set up in 10 districts in Guangzhou， Shenzhen， Foshan and Zhuhai， and air samples were collected for 7 consecutive days every month to analyze the concentration of PM_2.5 and the 12 elements in PM_2.5. The classic "four-step" method was used to evaluate the carcinogenic risk and chronic non-carcinogenic risk of the elements in air PM_2.5 on health. The age-sensitive characteristics of metal elements were combined in the carcinogenic risk assessment， and age-sensitive factors were introduced to analyze the impact of air pollution on population health. ResultsA total of818 samples were collected. and the average annual PM_2.5 concentration in the four cities of the Pearl River Delta was 30.17 （1.00-166.00， s=21.06） μg·m^-3， which was lower than the concentration limit of the secondary standard of the Ambient Air Quality Standard （GB 3095-2012）. The difference of PM_2.5 concentration in the four cities was statistically significant. The PM_2.5 concentrations in Zhuhai and Shenzhen， which were located near the sea， were lower than those in Guangzhou and Foshan. The monthly mean concentration of PM_2.5 in the four cities was the lowest at 13.70 （4.00-34.00， s=5.93） μg·m^-3 in July and the highest at 57.73 （14.00-146.00， s=27.96） μg·m^-3 in January， showing a low concentration from May to October and a high concentration from November to April of the following year. The average daily PM_2.5 concentration exceeded the secondary standard for 29 days， mainly distributed in January and November. The average annual mass concentration of elements in PM_2.5 in the four cities was Al>Mn>Pb>As>Ni>Cr>Se>Sb>Cd>Tl>Be>Hg. AS and Mn have chronic non-carcinogenic risk in population， while Cr， AS， Cd， Be and Ni have carcinogenic risk in population. ConclusionThe PM_2.5 pollution levels of the four cities in the Pearl River Delta are low and variable. Coastal cities are lower than non-coastal cities， which shows the characteristics of first decreasing and then increasing throughout the year. The order of mass concentration of metal elements of PM_2.5 in four cities is basically the same except Be and Ni. As and Mn in PM_2.5 show a certain degree of chronic non-carcinogenic risk， and As， Cr， Cd， Ni and Be have a certain degree of carcinogenic risk. The four cities need to take effective intervention measures to continue to strengthen the pollution control and health protection of Cr， As， Cd and Mn in the air， and control the health burden caused by air pollution.

Purpose To investigate the clinical pathologic features of five SMARCA4-deficient non-small lung cancers(SMARCA4-dNSCLCs).Methods Five cases of SMARCA4-dNSCLC was underwent by HE,immunohistochemical staining,and molecular detection,analyzed their clinicopathological char-acteristics and reviewed relevant literatures.Results All 5 ca-ses were male,and mean age was 66 years.Five patients had a history of smoking,three patients were treated with cough and blood in sputum as the first symptom,one was treated with a history of pulmonary tuberculosis combined with limb mobility disorder,and one was diagnosed with pulmonary nodules by physical examination.Under microscopic observation,tumor cells were poorly differentiated,with solid nest sheet distribu-tion,some with glandular structure,tumor cells had abundant e-osinophilic or transparent cytoplasm,vacuolar nuclear chroma-tin,nucleoli was visible,and nuclear mitosis was common.In-flammatory cell infiltration and sheet of necrosis were seen in the stroma.Immunohistochemical staining showed 5/5 diffuse ex-pression of CK(AE1/AE3)and CK7,5/5 loss expression of BRG1,1/5 diffuse expression of p40 and CK5/6,and Ki67 proliferating index ranged from 20％to 90％.FISH tests showed that 4/4 SMARCA4 genes missed.Five patients were followed up for 2-15 months,3 patients died and 2 patients survived.Conclusions SMARCA4-dNSCLC can have extensive morphologi-cal features,high degree of malignancy,and complicated treat-ment.BRG1 deficiency is helpful for diagnosis.Deepening the understanding of SMARCA4-dNSCLC can help the clinical cor-rect choice of treatment strategies and accurately evaluate patient prognosis.

Objective:To construct a reliability management model of medical equipment in the department of respiratory and critical care medicine,and to explore its application effect in the management of medical equipment in the department of respiratory and critical care medicine.Methods:Taking the reliability of equipment management content and management methods as evaluation indexes,standardized procedures of equipment use,cleaning and emergency management were formed,and a reliability management model for medical equipment in the department of respiratory and critical care medicine was constructed.A total of 63 medical devices in clinical use in the Department of Respiratory and Critical Care Medicine of Beijing Anzhen Hospital,Capital Medical University from January 2022 to January 2023 were selected.According to different management modes,conventional management mode(32 devices)and reliability management mode(31 devices)were adopted respectively.The equipment management index score,equipment goal achievement degree and equipment management defect rate,and the equipment management recognition scores of the engineers,equipment operation technicians and doctors of equipment use management were compared between the two management modes.Results:The average recognition scores of the engineers,operating technicians and doctors for the use of equipment of the reliability management model were(90.66±5.25)points,(91.54±4.14)points and(92.17±5.17)points,respectively,which were higher than those of the conventional management model,the difference was statistically significant(t=14.249,13.773,12.267,P<0.05).The average scores of equipment resource allocation,information technology,technical support and management performance indicators of the reliability management mode were(90.25±4.12)points,(92.45±3.26)points,(91.47±2.78)points and(90.25±3.11)points,respectively,which were higher than those of conventional management mode,the difference was statistically significant(t=12.122,18.379,15.581,14.141,P<0.05).The average scores of equipment use standardization,cleaning completion and emergency management timeliness of reliability management mode were(92.36±3.25)points,(90.69±3.69)points and(91.87±3.01)points,respectively,which were higher than those of the conventional management mode,the difference was statistically significant(t=14.953,15.030,14.401,P<0.05).The number of equipment damaged,repaired and factory repair of the reliability management mode was 1,1 and 2,respectively,and the defect rates were 3.22%,3.22%and 6.45%,respectively,which were lower than those of the conventional management mode,the difference was statistically significant(x2=8.581,9.908,8.782,P<0.05).Conclusion:The application of reliability-based medical equipment management model to the medical equipment management of respiratory and critical care medicine can improve the quality of equipment management and operation,reduce the failure rate of equipment,and improve the service level of equipment.