Objective: To explore the correlation and lag effects of environmental factors on pediatric respiratory disease visits at hospital, so as to provide scientific basis for disease prediction and optimizing clinical diagnosis and treatment. Methods: Data from 503 889 pediatric respiratory disease outpatient and emergency visits a central hospital in Minhang District of Shanghai between 2017 and 2019, along with concurrent meteorological data were collected. A distributed lag non-linear models (DLNM) was constructed to explore the specific relationship between pediatric respiratory disease consultations and various environmental factors and to quantify the cumulative lag effects of environmental factors on respiratory disease consultations. Results: Among the environmental factors, temperature, fine particulate matter (PM 2.5 ), inhalable particulate matter (PM 10 ), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) were associated with pediatric respiratory disease visits. After adjusting for temperature, PM 2.5 and PM 10 concentrations did not show significant immediate or lag effects. The relative risk (RR) of pediatric respiratory disease visits increased with rising NO 2 concentrations. When NO 2 concentration ≥55 μg/m 3, significant immediate and lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.05, 1.13, 1.17, and 1.21( P <0.05). The RR values showed an inverted “U” shaped relationship with SO 2 concentrations. When SO 2 concentration ≥5 μg/m 3, significant lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.03 , 1.03, and 1.04 ( P <0.05). Conclusion High concentrations of NO 2 and SO 2 increase the risk of pediatric respiratory disease visits, with observable lag effects.

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.

Diabetic nephropathy is one of the important causes of end-stage renal disease, and kidney transplantation is the best treatment option for patients with end-stage renal disease due to diabetes (DN-ESRD). However, patients with DN-ESRD have multiple complex factors that affect glucose homeostasis. Long-term hyperglycemia leads to disordered internal environment and extensive involvement of systemic organs, increasing the risks during the perioperative period of kidney transplantation. This article reviews the perioperative management strategies for kidney transplantation in patients with DN-ESRD, discusses the perioperative risk factors, preoperative evaluation and management, intraoperative volume and internal environment management and early postoperative management, and elaborates on the latest progress in this field.

Objective: To evaluate the efficacy and safety of plasma exchange (PE) in thymoma-associated myasthenia gravis (MG), thereby to provide theoretical support for its application in the treatment of thymoma-associated MG. Methods: A total of 133 patients with thymoma-associated MG admitted from January 2018 to September 2024 were retrospectively analyzed. Patients were matched using propensity score to reduce selection bias, yielding 22 matched pairs for both PE group (n=22) and non-PE group (n=22). Patient characteristics including gender, age of disease onset, course of disease, history of thymoma resection, clinical absolute scores [clinical absolute scores (CAS) and clinical relative scores (CRS)], and synchronized immunotherapy regimen of the two groups were analyzed. The CAS scores before and after treatment were compared between the two groups, and the CRS was used to assess the treatment efficiency. Safety of the two treatment regimens were also compared. Continuous variables were compared using the t-test or ANOVA, while categorical data were compared by the chi-square test. Results: A total of 133 patients were included and divided into two groups according to whether they underwent plasma exchange treatment: the PE group (n=22) and the non-PE group (n=111). To exclude bias caused by large difference in the number of cases between the two groups, we performed propensity score matching. After matching, the number of cases in both groups was 22. There was no significant difference in baseline clinical characteristics between the two groups (P>0.05), including gender, age of onset, duration of disease course, history of thymectomy and baseline CAS score before treatment. Compared to the non-PE group, patients in the PE group showed more significant improvement in CAS score (5.09±1.95 vs 3.59±1.50, P<0.05) and a higher CRS score (75.00% vs 50.00%, P<0.001). Compared to the non-PE group, PE group had significantly longer ICU stay, longer hospital stay and higher hospitalization cost (P<0.05). There was no statistically significant difference in adverse events between the two groups during treatment (P>0.05). During long-term follow-up, both the PE and non-PE groups showed relatively low 1-, 3-, and 5-year recurrence rate, with no significant difference between the two groups (P>0.05). Conclusion: This study indicates that plasma exchange has clear value in the treatment of patients with thymoma-associated myasthenia gravis. It can not only significantly improve patients' muscle strength to alleviate motor dysfunction and enhance quality of life, but also does not significantly increase the incidence of adverse reactions. Therefore, it can be regarded as one of the preferred treatment options that achieve a "balance between efficacy and safety" for such patients, and provides an important basis for optimizing treatment strategies, improving prognosis, and promoting the application of subsequent treatment regimens.

Objective:Aiming at the problem of target user electroencephalogram (EEG) recognition, an EEG recognition method was presented based on common spatial patterns (CSP) and transfer learning.Methods:Firstly, preprocess was adopted on the original EEG data, and time windows 0.5~2.5 s and broad frequency band 8~30 Hz EEG signals, which contained α and β wave, were selected. Here event-related desynchronization (ERD) phenomenon existed significant differences. Afterwards, by CSP preprocessed EEG signals of multi-user were conducted to extract feature and feature vectors were obtained, respectively. Finally, by transfer learning target user EEG recognition was completed.Results:In channel Cz, ERD of right hand motor imagery was higher than ERD of foot motor imagery. The classification accuracy of users aa, al, av, aw, and ay were 93.8%, 100.0%, 84.2%, 94.6%, and 94.4%, respectively. The average classification accuracy was 92.4%, which was better than the commonly used classifiers SVM and EM. The method was only lower than the method of the first winner in the competition adopted by Tsinghua University 1.8%.Conclusions:EEG recognition method based on CSP and transfer learning increased target user EEG recognition performance by using non-target users and had important implications for the study of motor imagery brain-computer interface.

Objective To make an epidemiological investigation on traditional Chinese medicine(TCM)dampness syndrome manifestations in the population at risk of cerebrovascular diseases in Guangdong area.Methods A cross-sectional study was conducted to analyze the clinical data related to the risk of cerebrovascular diseases in 330 Guangdong permanent residents.The diagnosis of dampness syndrome,quantitative scoring of dampness syndrome and rating of the risk of stroke were performed for the investigation of the distribution pattern of dampness syndrome and its influencing factors.Results(1)A total of 306(92.73%)study subjects were diagnosed as dampness syndrome.The percentage of dampness syndrome in the risk group was 93.82%(258/275),which was slightly higher than that of the healthy group(48/55,87.27%),but the difference was not statistically significant(χ2 = 2.91,P = 0.112).The quantitative score of dampness syndrome in the risk group was higher than that of the healthy group,and the difference was statistically significance(Z =-2.24,P = 0.025).(2)Among the study subjects at risk of cerebrovascular disease,evaluation time(χ2 = 26.11,P = 0.001),stroke risk grading(χ2= 8.85,P = 0.031),and history of stroke or transient ischemic attack(TIA)(χ2 = 9.28,P = 0.015)were the factors influencing the grading of dampness syndrome in the population at risk of cerebrovascular disease.Conclusion Dampness syndrome is the common TCM syndrome in the population of Guangdong area.The manifestations of dampness syndrome are more obvious in the population with risk factors of cerebrovascular disease,especially in the population at high risk of stroke,and in the population with a history of stroke or TIA.The assessment and intervention of dampness syndrome should be taken into account for future project of stroke prevention in Guangdong.

Objective To quantitatively assess the influence of various factors on the pharmacokinetic parameters of propofol and to develop a propofol population pharmacokinetic model tailored for Chinese patients.Methods Thirty patients scheduled for selective abdominal surgeries received an anesthesia dose of propofol at 2.0 mg·kg-1.The concentration of propofol in collected venous blood samples was measured using liquid chromatography-tandem mass spectrometry.Polymorphisms in metabolizing enzyme genes were detected through Sanger sequencing technology.Pharmacokinetic parameters were computed,and models were constructed and evaluated using Phoenix Winnonlin software.Results Through software analysis,the drug's in vivo process was best described by a three-compartment model.The population mean values for the central compartment clearance rate(CL),shallow peripheral compartment clearance rate(Q2),deep peripheral compartment clearance rate(Q3),central compartment volume of distribution(V),shallow peripheral compartment volume of distribution(V2),and deep peripheral compartment volume of distribution(V3)were 1.71 L·min-1,1.31 L·min-1,1.51 L·min-1,5.92 L,19.86 L and 99.06 L,respectively.Body weight was identified as a significant covariate affecting CL and V,and was incorporated into the model.Conclusion The evaluation of the final model demonstrates its substantial predictive capability,offering directional guidance for the clinical administration of propofol.