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.

Aromatherapy refers to the method of using the aromatic components of plants in appropriate forms to act on the entire body or a specific area to prevent and treat diseases. Essential oils used in aromatherapy are hydrophobic liquids containing volatile aromatic molecules， such as limonene， linalool， linalool acetate， geraniol， and citronellol. These chemicals have been extensively studied and shown to have a variety of functions， including reducing anxiety， relieving depression， promoting sleep， and providing pain relief. Terpenoids are a class of organic molecules with relatively low lipid solubility. After being inhaled， they can pass through the nasal mucosa for transfer or penetrate the skin and enter the bloodstream upon local application. Some of these substances also have the ability to cross the blood-brain barrier， thereby exerting effects on the central nervous system. Currently， the academic community generally agrees that products such as essential oils and aromatherapy from aromatic plants have certain health benefits. However， the process of extracting a single component from it and successfully developing it into a drug still faces many challenges. Its safety and efficacy still need to be further verified through more rigorous and systematic experiments. This article systematically elaborated on the efficacy of aromatic substances， including plant extracts and natural small molecule compounds， in antibacterial and antiviral fields and the regulation of nervous system activity. As a result， a deeper understanding of aromatherapy was achieved. At the same time， the potential of these aromatic substances for drug development was thoroughly explored， providing important references and insights for possible future drug research and application.

Objective To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database,to explore research hotspots and developmen-tal trends.Methods A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the litera-ture measuring tool CiteSpace.The authors,institution,country(region),title,journal,keywords,cited references and other information of relevant literatures were analyzed.Results A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries(regions)were identi-fied,with the number of articles published showing an increasing trend year by year.Among them,the United States had the highest number of publications and China ranked the second.Academy of Forensic Science had the highest number of publications among the institutions.Forensic Science Inter-national,Journal of Forensic Sciences,International Journal of Legal Medicine ranked high in publica-tion and citation frequency.Through the analysis of keywords,it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technol-ogy for sex and age estimation,cause of death analysis,postmortem interval estimation,individual identification and so on.Conclusion It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research.Exploring the combination of advanced ar-tificial intelligence technologies with forensic research will be a hotspot and direction for future re-search.

Objective:To investigate the effects of ubiquitin-specific protease (USP) 7/47 inhibitor (Cat. No. 1247825-37-1) on the proliferation and apoptosis of acute myeloid leukemia (AML) cells with or without internal tandem duplications of the Flt3 gene (Flt3-ITD). Methods:ATP assay was used to detect the effects of 1247825-37-1 on the cell viability of two AML cell lines (MOLM13 and MV4-11) harboring Flt3-ITD mutation and one AML cell line (THP-1) without Flt3-ITD mutation as well as the primary Flt3-ITD-mutant and non-mutant AML cells from patient samples. Flow cytometry was used to detect the apoptosis of AML cell lines treated by different concentrations of 1247825-37-1.Results:Compared with the control group, 1247825-37-1 was able to significantly inhibit the proliferation of MOLM13, MV4-11 and THP-1 cells ( P<0.000 1). Besides, the cell viability of primary AML cells was also inhibited by 1247825-37-1, and a stronger inhibitory effect on non-mutant AML cells was observed. The USP7/USP47 inhibitor 1247825-37-1 could inhibit the proliferation of AML cells in a dose-dependent manner and a low dose (2 or 4 μmol/L) of 1247825-37-1 would be effective. Moreover, 1247825-37-1 was also able to efficiently induce the apoptosis of above AML cell lines in a dose-dependent manner. Conclusions:The USP7/USP47 inhibitor 1247825-37-1 significantly inhibits the proliferation of AML cells with or without Flt3-ITD mutation.

Objective To analyze the risk factors influencing the occurrence of atherosclerosis in patients with type 2 diabetes, and to construct and evaluate a nomogram prediction model. Methods Multivariate logistic regression was used to analyze the risk factors of atherosclerosis in type 2 diabetes mellitus, and R software was used to build a nomogram prediction model. The accuracy and clinical validity of the model were verified by using H-L fit curve, area under ROC curve and calibration curve. Results The prevalence rate of atherosclerosis was 56.37%. Independent risk factors for atherosclerosis in type 2 diabetes mellitus (P<0.05) were body weight (OR=1.42，P<0.05), glycated serum protein (OR=1.35, P<0.05), lactate dehydrogenase (OR=1.17, P<0.05), alkaline phosphatase (OR=0.79, P<0.05), hyperlipidemia (OR=2.30, P<0.05), stroke (OR=4.20, P<0.05), coronary heart disease (OR=64.54, P<0.05), lower extremity artery disease (OR=24.52, P<0.05), and other endocrine diseases (OR=1.65 , P<0.05). The area under ROC curve was 0.91, the slope of the calibration curve was close to 1, and the H-L fit curve χ²=3.11. The internal verification result of the constructed nomogram prediction model was P=0.93. External verification of patients in the test set showed that the area under ROC curve was 0.91, indicating good differentiation and accuracy of the model. Conclusion The prediction model established by using the risk factors screened in this study has a high accuracy and differentiation, and medical staff can take effective prevention measures according to the individual factors of patients.

Objective:To establish the standard of nursing quality in bone oncology department, and provide the basis for scientific evaluation of nursing quality in bone oncology department.Methods:On the theoretical basis of Donabedian′s three-dimensional quality model of "structure-process-outcome", and through literature review and semi-structured interview method, the "evaluation index of nursing quality in bone oncology department" was preliminatively formulated from November 2022 to June 2023. The Delphi method was used to select 31 experts from 31 third-level A hospitals and nursing colleges in 27 provinces or municipalities across the country for two rounds of correspondence consultation. The criteria were screened and modified to determine the evaluation criteria of nursing quality in bone tumor specialty.Results:The questionnaire recovery rate of 2 rounds of expert consultation was 100.00%, the authority coefficient of 2 rounds of expert consultation was 0.93, and the coefficient of variation of 1, 2 and 3-grade standards were all less than or equal to 0.25. The Kendall′s coefficient of concordance of the primary standards of the two rounds of expert consultation were both 0.088, in the secondary standards were 0.103 and 0.140, in the tertiary standards were 0.119 and 0.110. Through 2 rounds of expert letter consultation, the evaluation criteria for the quality of care in bone tumor specialties were divided into three levels, including 3 primary standards (structural quality criteria, process quality criteria and outcome quality criteria), 21 secondary standards and 80 tertiary standards.Conclusions:The construction process of nursing quality standard in bone oncology department is scientific and reliable, reflecting specialty characteristics, and can provide scientific basis for the evaluation of nursing quality in bone oncology department and standardize nursing behavior.

Traditional Chinese Medicine has a long history and plays a decisive role in the fields of modern medicine and pharmacy.It is an important part of our country's traditional medicine.With the progress of the times,people are paying more and more attention to the innovation and development of traditional Chinese medicine.However,the current traditional Chinese medicine talents trained by major universities cannot meet the needs and requirements of society.This is closely related to the current talent training model of universities.Local high-level universities have unique advantages and potential in cultivating inno-vative talents in traditional Chinese medicine.They can incorporate traditional Chinese medicine culture with local characteristics into the teaching content and practical links of training traditional Chinese medicine talents,and build innovative traditional Chi-nese medicine talents integrating science and education.The training model is more conducive to cultivating top innovative talents that meet the needs of society and and the development of traditional Chinese medicine.This article analyzes and discusses how local high-level universities can develop innovative talent training models that suit the needs of traditional Chinese medicine by combining local characteristics and disciplinary advantages,so as to provide useful reference and inspiration for local high-level u-niversities in cultivating talents in traditional Chinese medicine.