Etiology and prognosis of non-Kawasaki disease induced coronary aneurysms in children: a retrospective case series study.

While Kawasaki disease (KD) induced coronary artery aneurysms (KD CAAs) in children are well studied, the features and prognosis of non-KD induced CAAs (non-KD CAAs) in the pediatric population are poorly documented. This case series study is to analyze the etiology and prognosis of non-KD CAAs in children and compare the characteristics of non-KD CAAs and KD CAAs. Non-KD CAA and KD CAA cases at our department from January 2022 to December 2023 were retrospectively collected. Etiologies and prognosis of non-KD CAAs were analyzed. Furthermore, demographic data, biochemical parameters and outcomes between children with Non-KD CAAs and children with KD CAAs were comparatively studied. Fifteen children with non-KD CAAs with a median age of 6 years and 117 children with KD CAAs with a median age of 2.0 years (p = 0.022) were included in this study. The causes of non-KD CAAs include: unknown etiologies (2 cases), coronary artery structural abnormalities (4), Takayasu arteritis (2), virus infection (2), cardiomyopathy (2), aplastic anemia with agranulocytosis (1), ANCA-associated vasculitis (1), and mucopolysaccharidosis (1). In the non-KD CAA group, there were a total of 19 CAAs with 3 being giant, 5 medium, and 11 small; 4 patients had complete CAA regression; an infant with a fistula between the right coronary artery and the coronary sinus complicated with cardiac enlargement died of heart failure. The KD group had significantly higher levels of CRP, white cells counts and ESR with zero mortality. Non-KD CAA cases had a significantly lower regression rate than KD-CAA cases (26.7% vs 66.7%, p = 0.004), and the probability of CAA regression in non-KD patients was 0.341 of that in KD patients (p = 0.006, OR = 0.341, 95% CI: 0.179-0.647). CONCLUSIONS: Various etiologies for Non-KD CAAs are identified. Patients with Non-KD CAAs were observed to have lower inflammatory indexes but poorer recovery than patients with KD CAAs. Therapeutic strategies different than those for KD may be needed for non-KD CAAs. WHAT IS KNOWN: • Coronary artery aneurysm (CAA) in children is most commonly induced by Kawasaki disease (KD CAA), with a 50 ~ 70% regression rate in 1 to 2 years. • CAA induced by diseases other than KD (non-KD CAA) in children is rare and its prognosis remains largely unknown. WHAT IS NEW: • Most non-KD CAA cases are caused by coronary artery structural malformations. • Non-KD CAA in children has poorer prognosis and lower regression rate compared with KD CAA. • In addition to guideline directed anti-platelet and anti-coagulant therapies, treatments targeting the causal factor are necessary for non-KD CAA.

Clinical biomarker profiles reveals gender differences and mortality factors in sepsis.

Background: Sepsis is a major contributor to global morbidity and mortality, affecting millions each year. Notwithstanding the decline in sepsis incidence and mortality over decades, gender disparities in sepsis outcomes persist, with research suggesting higher mortality rates in males. Methods: This retrospective study aims to delineate gender-specific clinical biomarker profiles impacting sepsis progression and mortality by examining sepsis cases and related clinical data from the past three years. Propensity score matching was used to select age-matched healthy controls for comparison. Results: Among 265 sepsis patients, a significantly higher proportion were male (60.8%, P<0.001). While mortality did not significantly differ by gender, deceased patients were significantly older (mean 69 vs 43 years, P=0.003), more likely to have hypertension (54% vs 25%, P=0.019), and had higher SOFA scores (mean ~10 vs 4, P<0.01) compared to survivors. Principal Component Analysis (PCA) showed clear separation between sepsis patients and healthy controls. 48 serum biomarkers were significantly altered in sepsis, with Triiodothyronine, Apolipoprotein A, and Serum cystatin C having the highest diagnostic value by ROC analysis. Gender-stratified comparisons identified male-specific (e.g. AFP, HDLC) and female-specific (e.g. Rheumatoid factor, Interleukin-6) diagnostic biomarkers. Deceased patients significantly differed from survivors, with 22 differentially expressed markers; Antithrombin, Prealbumin, HDL cholesterol, Urea nitrogen and Hydroxybutyrate had the highest diagnostic efficiency for mortality. Conclusion: These findings enhance our understanding of gender disparities in sepsis and may guide future therapeutic strategies. Further research is warranted to validate these biomarker profiles and investigate the molecular mechanisms underlying these gender differences in sepsis outcomes.

Role of Inflammation in the Development of COVID-19 to Parkinson's Disease.

Background: The coronavirus disease 2019 (COVID-19) can lead to neurological symptoms such as headaches, confusion, seizures, hearing loss, and loss of smell. The link between COVID-19 and Parkinson's disease (PD) is being investigated, but more research is needed for a definitive connection. Methods: Datasets GSE22491 and GSE164805 were selected to screen differentially expressed gene (DEG), and immune infiltration and gene set enrichment analysis (GSEA) of the DEG were performed. WGCNA analyzed the DEG and selected the intersection genes. Potential biological functions and signaling pathways were determined, and diagnostic genes were further screened using gene expression and receiver operating characteristic (ROC) curves. Screening and molecular docking of ibuprofen as a therapeutic target. The effectiveness of ibuprofen was verified by constructing a PD model in vitro, and constructing "COVID19-PD" signaling pathway, and exploring the role of angiotensin-converting enzyme 2 (ACE2) in PD. Results: A total of 13 DEG were screened from the GSE36980 and GSE5281 datasets. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the DEG were mainly associated with the hypoxia-inducible factor (HIF-1), epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, etc. After analysis, it is found that ibuprofen alleviates PD symptoms by inhibiting the expression of nuclear factor kappa-B (NF-κB), interleukin-1ß (IL-1ß), IL-6, and tumor necrosis factor-α (TNF-α). Based on signal pathway construction, the importance of ACE2 in COVID-19-induced PD has been identified. ACE2 is found to have widespread distribution in the brain. In the 1-methyl-4-phenyl-1,2,3,6-te-trahydropyridine (MPTP)-induced ACE2-null PD mice model, more severe motor and non-motor symptoms, increased NF-κB p65 and α-synuclein (α-syn) expression with significant aggregation, decreased tyrosine hydroxylase (TH), severe neuronal loss, and neurodegenerative disorders. Conclusion: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection increases the risk of PD through an inflammatory environment and downregulation of ACE2, providing evidence for the molecular mechanism and targeted therapy associated with COVID-19 and PD.

Plasma metabolomic profile in orthostatic intolerance children with high levels of plasma homocysteine.

BACKGROUND: Orthostatic intolerance, which includes vasovagal syncope and postural orthostatic tachycardia syndrome, is common in children and adolescents. Elevated plasma homocysteine levels might participate in the pathogenesis of orthostatic intolerance. This study was designed to analyze the plasma metabolomic profile in orthostatic intolerance children with high levels of plasma homocysteine. METHODS: Plasma samples from 34 orthostatic intolerance children with a plasma homocysteine concentration > 9 µmol/L and 10 healthy children were subjected to ultra-high-pressure liquid chromatography and quadrupole-time-of-flight mass spectrometry analysis. RESULTS: A total of 875 metabolites were identified, 105 of which were significantly differential metabolites. Choline, 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine, 1-(1Z-octadecenyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphocholine, histidine, isocitric acid, and DL-glutamic acid and its downstream metabolites were upregulated, whereas 1-palmitoyl-sn-glycero-3-phosphocholine, 1-stearoyl-sn-glycerol 3-phosphocholine, sphingomyelin (d18:1/18:0), betaine aldehyde, hydroxyproline, and gamma-aminobutyric acid were downregulated in the orthostatic intolerance group compared with the control group. All these metabolites were related to choline and glutamate. Heatmap analysis demonstrated a common metabolic pattern of higher choline, 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine, and DL-glutamic acid, and lower sphingomyelin (d18:1/18:0), 1-stearoyl-sn-glycerol 3-phosphocholine, and 1-palmitoyl-sn-glycero-3-phosphocholine in patients with certain notable metabolic changes (the special group) than in the other patients (the common group). The maximum upright heart rate, the change in heart rate from the supine to the upright position, and the rate of change in heart rate from the supine to the upright position of vasovagal syncope patients were significantly higher in the special group than in the common group (P < 0.05). Choline, 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine, and DL-glutamic acid were positively correlated with the rate of change in heart rate from the supine to the upright position in vasovagal syncope patients (P < 0.05). CONCLUSIONS: The levels of choline-related metabolites and glutamate-related metabolites changed significantly in orthostatic intolerance children with high levels of plasma homocysteine, and these changes were associated with the severity of illness. These results provided new light on the pathogenesis of orthostatic intolerance.

The genetic etiology of body fluids on chronic obstructive airways disease.

BACKGROUND: Numerous studies have documented significant alterations in the bodily fluids of Chronic Obstructive Pulmonary Disease (COPD) patients. However, existing literature lacks causal inference due to residual confounding and reverse causality. METHODS: Summary-level data for COPD were obtained from two national biobanks: the UK Biobank, comprising 1,605 cases and 461,328 controls, and FinnGen, with 6,915 cases and 186,723 controls. We also validated our findings using clinical data from 2,690 COPD patients and 3,357 healthy controls from the First Affiliated Hospital of Guangzhou Medical University. A total of 44 bodily fluid biomarkers were selected as candidate risk factors. Mendelian randomization (MR) and meta-analyses were used to evaluate the causal effects of these bodily fluids on COPD and lung function (FEV1/FVC). RESULTS: Mendelian randomization (MR) and meta-analyses, by integrating data from the UK Biobank and FinnGen cohort, found that 3 bodily fluids indicators (HDLC, EOS, and TP) were causally associated with the risk of COPD, two (EOS and TP) of which is consistent with our observational findings. Moreover, we noticed EOS and TP were causally associated with the risk of lung function (FEV1/FVC). CONCLUSIONS: The MR findings and clinical data highlight the independent and significant roles of EOS and TP in the development of COPD and lung function (FEV1/FVC), which might provide a deeper insight into COPD risk factors and supply potential preventative strategies.

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China.

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

The Construction and Validation of a Novel Ferroptosis-Related Gene Signature in Parkinson's Disease.

As a newly discovered regulated cell death mode, ferroptosis is associated with the development of Parkinson's disease (PD) and has attracted much attention. Nonetheless, the relationship between ferroptosis and PD pathogenesis remains unclear. The GSE8397 dataset includes GPL96 and GPL97 platforms. The differential genes were analyzed by immune infiltration and Gene Set Enrichment Analysis (GSEA) (p < 0.05), and differential multiple |logFC| > 1 and weighted gene coexpression network analysis (WGCNA) were used to screen differential expression genes (DEGs). The intersection with 368 ferroptosis-related genes (FRGs) was conducted for gene ontology/Kyoto encyclopedia of gene and genome (GO/KEGG) enrichment analysis, gene expression analysis, correlation analysis, single-cell sequencing analysis, and prognosis analysis (area under the curve, AUC) and to predict relevant miRNAs and construct network diagrams using Cytoscape. The intersection genes of differentially expressed ferroptosis-related genes (DEFRGs) and mitochondrial dysfunction genes were validated in the substantia nigra of MPTP-induced PD mice models by Western blotting and immunohistochemistry, and the protein-binding pocket was predicted using the DoGSiteScorer database. According to the results, the estimated scores were positively correlated with the stromal scores or immune scores in the GPL96 and GPL97 platforms. In the GPL96 platform, the GSEA showed that differential genes were mainly involved in the GnRH signaling pathway, B cell receptor signaling pathway, inositol phosphate metabolism, etc. In the GPL97 platform, the GSEA showed that differential genes were mainly involved in the ubiquitin-mediated proteolysis, axon guidance, Wnt signaling pathway, MAPK signaling pathway, etc. We obtained 26 DEFRGs, including 12 up-regulated genes and 14 down-regulated genes, with good correlation. The area under the prognostic analysis curve (AUC > 0.700) showed a good prognostic ability. We found that they were enriched in different neuronal cells, oligodendrocytes, astrocytes, oligodendrocyte precursor cells, and microglial cells, and their expression scores were positively correlated, and selected genes with an AUC curve ≥0.9 were used to predict miRNA, including miR-214/761/3619-5p, miR-203, miR-204/204b/211, miR-128/128ab, miR-199ab-5p, etc. For the differentially expressed ferroptosis-mitochondrial dysfunction-related genes (DEF-MDRGs) (AR, ISCU, SNCA, and PDK4), in the substantia nigra of mice, compared with the Saline group, the expression of AR and ISCU was decreased (p < 0.05), and the expression of α-Syn and PDK4 was increased (p < 0.05) in the MPTP group. Therapeutic drugs that target SNCA include ABBV-0805, Prasinezumab, Cinpanemab, and Gardenin A. The results of this study suggest that cellular DEF-MDRGs might play an important role in PD. AR, ISCU, SNCA, and PDK4 have the potential to be specific biomarkers for the early diagnosis of PD.

Exploring the association of PM2.5 with lung cancer incidence under different climate zones and socioeconomic conditions from 2006 to 2016 in China.

Air pollution generated by urbanization and industrialization poses a significant negative impact on public health. Particularly, fine particulate matter (PM2.5) has become one of the leading causes of lung cancer mortality worldwide. The relationship between air pollutants and lung cancer has aroused global widespread concerns. Currently, the spatial agglomeration dynamic of lung cancer incidence (LCI) has been seldom discussed, and the spatial heterogeneity of lung cancer's influential factors has been ignored. Moreover, it is still unclear whether different socioeconomic levels and climate zones exhibit modification effects on the relationship between PM2.5 and LCI. In the present work, spatial autocorrelation was adopted to reveal the spatial aggregation dynamic of LCI, the emerging hot spot analysis was introduced to indicate the hot spot changes of LCI, and the geographically and temporally weighted regression (GTWR) model was used to determine the affecting factors of LCI and their spatial heterogeneity. Then, the modification effects of PM2.5 on the LCI under different socioeconomic levels and climatic zones were explored. Some findings were obtained. The LCI demonstrated a significant spatial autocorrelation, and the hot spots of LCI were mainly concentrated in eastern China. The affecting factors of LCI revealed an obvious spatial heterogeneity. PM2.5 concentration, nighttime light data, 2 m temperature, and 10 m u-component of wind represented significant positive effects on LCI, while education-related POI exhibited significant negative effects on LCI. The LCI in areas with low urbanization rates, low education levels, and extreme climate conditions was more easily affected by PM2.5 than in other areas. The results can provide a scientific basis for the prevention and control of lung cancer and related epidemics.

Rasterizing CO2 emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013-2019.

Climate change caused by CO2 emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve CE estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEADS) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R2 = 0.95) than the NTL (R2 = 0.92) in predicting CE, particularly in rural regions where the R2 value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.

Rubiadin-1-methyl ether inhibits BECN1 transcription and Beclin1-dependent autophagy during osteoclastogenesis by inhibiting NF-κB p65 activation.

As an active substance isolated from the root of Morinda officinalis How., rubiadin-1-methyl ether (RBM), can improve osteoporosis due to its inhibition on osteoclastogenesis. Autophagy plays a key role in osteoclastogenesis. Our research aims to explore the relationship between RBM, autophagy, and osteoclastogenesis. Our results showed that RBM not only inhibited the differentiation level of osteoclasts and the proliferation ability of osteoclast precursors (OCPs), but also repressed the autophagic activity in OCPs (LC3 transformation and the number of autophagosomes observed by transmission electron microscopy). However, RBM-inhibited osteoclast differentiation and OCP autophagy (LC3 transformation and LC3-puncta formation) could be reversed by the application of TAT-Beclin1. Moreover, RBM administration reduced RANKL-induced p65 phosphorylation and p65 nuclear translocation in OCPs. In addition, the addition of RBM inhibited Beclin1 protein level and BECN1 (the gene form of Beclin1) mRNA level in OCPs increased by RANKL. Importantly, the reduction in the expression of BECN1 and Beclin1, LC3 transformation, and osteoclastic differentiation in OCPs caused by RBM were reversed by p65 overexpression. In conclusion, RBM may reduce the transcription of BECN1 by inhibiting the activation of nuclear factor kappa B (NF-κB) p65, thereby inhibiting Beclin1-dependent autophagy and RANKL-induced osteoclastogenesis.

An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs.

The smartphone has become an indispensable tool in our daily lives, and the Android operating system is widely installed on our smartphones. This makes Android smartphones a prime target for malware. In order to address threats posed by malware, many researchers have proposed different malware detection approaches, including using a function call graph (FCG). Although an FCG can capture the complete call-callee semantic relationship of a function, it will be represented as a huge graph structure. The presence of many nonsensical nodes affects the detection efficiency. At the same time, the characteristics of the graph neural networks (GNNs) make the important node features in the FCG tend toward similar nonsensical node features during the propagation process. In our work, we propose an Android malware detection approach to enhance node feature differences in an FCG. Firstly, we propose an API-based node feature by which we can visually analyze the behavioral properties of different functions in the app and determine whether their behavior is benign or malicious. Then, we extract the FCG and the features of each function from the decompiled APK file. Next, we calculate the API coefficient inspired by the idea of the TF-IDF algorithm and extract the sensitive function called subgraph (S-FCSG) based on API coefficient ranking. Finally, before feeding the S-FCSG and node features into the GCN model, we add the self-loop for each node of the S-FCSG. A 1-D convolutional neural network and fully connected layers are used for further feature extraction and classification, respectively. The experimental result shows that our approach enhances the node feature differences in an FCG, and the detection accuracy is greater than that of models using other features, suggesting that malware detection based on a graph structure and GNNs has a lot of space for future study.

CLDTLog: System Log Anomaly Detection Method Based on Contrastive Learning and Dual Objective Tasks.

System logs are a crucial component of system maintainability, as they record the status of the system and essential events for troubleshooting and maintenance when necessary. Therefore, anomaly detection of system logs is crucial. Recent research has focused on extracting semantic information from unstructured log messages for log anomaly detection tasks. Since BERT models work well in natural language processing, this paper proposes an approach called CLDTLog, which introduces contrastive learning and dual-objective tasks in a BERT pre-trained model and performs anomaly detection on system logs through a fully connected layer. This approach does not require log parsing and thus can avoid the uncertainty caused by log parsing. We trained the CLDTLog model on two log datasets (HDFS and BGL) and achieved F1 scores of 0.9971 and 0.9999 on the HDFS and BGL datasets, respectively, which performed better than all known methods. In addition, when using only 1% of the BGL dataset as training data, CLDTLog still achieves an F1 score of 0.9993, showing excellent generalization performance with a significant reduction of the training cost.

IL-17A inhibits the degradation of RANKL in osteoblasts by inhibiting BCL2-Beclin1-autophagy signaling.

The inflammatory cytokine IL-17A is known to have the capacity to promote osteoclastogenesis, thereby enhancing bone loss. Moreover, IL-17A can promote the expression of RANKL in osteoblasts, contributing to its pro-osteoclastogenic effect. IL-17A is an autophagy regulator, which is also responsible for its regulation on RANKL expression. However, the specific role of autophagy in IL-17A-regulated RANKL expression and the underlying mechanism of IL-17A-regulated osteoblast autophagy remain unclear. IL-17A is known to inhibit autophagy by preventing BCL2 degradation. This study aimed to explore the significance of BCL2-dependent autophagy in IL-17A-regulated RANKL expression. Our results showed that IL-17A at 50 ng/mL could inhibit autophagic activity and promote RANKL protein expression in MC3T3-E1 osteoblast line. Moreover, the corresponding concentration of IL-17A could enhance BCL2 protein expression and the protein interaction between BCL2 and Beclin1 in MC3T3-E1 cells. However, the protein expression of RANKL and BCL2 promoted by 50 ng/mL of IL-17A was blocked by autophagy activation with Beclin1 pharmacological upregulation. Furthermore, RANKL protein expression promoted by 50 ng/mL of IL-17A was also reversed by autophagy activation with BCL2 knockdown. Importantly, the supernatant from osteoblasts treated with 50 ng/mL of IL-17A made osteoclast precursors (OCPs) form larger osteoclasts, which was reversed by BCL2 knockdown in osteoblasts. In conclusion, high levels of IL-17A prevent the degradation of RANKL by inhibiting BCL2-Beclin1-autophagy activation signal transduction in osteoblasts, thereby indirectly promoting osteoclastogenesis.

Platelet, a key regulator of innate and adaptive immunity.

Platelets, anucleate blood components, represent the major cell type involved in the regulation of hemostasis and thrombosis. In addition to performing haemostatic roles, platelets can influence both innate and adaptive immune responses. In this review, we summarize the development of platelets and their functions in hemostasis. We also discuss the interactions between platelet products and innate or adaptive immune cells, including neutrophils, monocytes, macrophages, T cells, B cells and dendritic cells. Activated platelets and released molecules regulate the differentiation and function of these cells via platelet-derived receptors or secreting molecules. Platelets have dual effects on nearly all immune cells. Understanding the exact mechanisms underlying these effects will enable further application of platelet transfusion.

Global competence of medical students: An assessment scale and preliminary investigation in China.

INTRODUCTION: The importance of global competence has been acknowledged in medical care as well as medical education. This study aims to develop a scale assessing the global competence of medical students, determine the factor structure and internal consistency of the scale and explore the underlying factors influencing the global competence of Chinese medical students in 8-year programs. METHODS: A questionnaire (Global Competence Assessment Scale for Medical Students, MS-GCAS) was developed, and a cross-sectional multicenter survey was conducted in 1062 medical students from 10 medical schools in China. Questionnaire data were analyzed using exploratory factor analysis and multiple linear regression. RESULTS: The exploratory factor analysis revealed a three-factor scale. The MS-GCAS has good internal consistency (Cronbach's alpha = 0.79 to 0.87). In the multivariate regression analyses, medical education stage (p<0.05), the frequency of communicating with foreigners (p<0.001), multilingual ability (p<0.05) and grade level (p<0.05) are associated with the MS-GCAS scores. DISCUSSION: The MS-GCAS has the potential to serve as a tool to measure the global competence of medical students. This three-factor scale can be used by medical education researches to improve future versions. Medical schools should conduct further educational reforms to promote students' global competence.

[Serum free fatty acid level in children with primary hypertension]. / å„¿ç«¥åŽŸå‘æ€§é«˜è¡€åŽ‹è¡€æ¸ æ¸¸ç¦»è„‚è‚ªé ¸çš„æ°´å¹³åŠæ„ä¹‰.

OBJECTIVES: To examine the serum level of free fatty acid (FFA) in children with primary hypertension and its value in the pathogenesis, prevention, and treatment of primary hypertension in children. METHODS: In this retrospective study, 34 children with primary hypertension who were treated for the first time in Children's Hospital Affiliated to Capital Institute of Pediatrics from January to June, 2021, were enrolled as the hypertension group, and 32 children with normal blood pressure who underwent physical examination during the same period were enrolled as the control group. The two groups were compared in terms of the levels of fasting serum FFA, fasting serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). The multivariate logistic regression model was used to analyze the influence of FFA on the development of primary hypertension. RESULTS: Compared with the control group, the hypertension group had significantly higher body mass index (BMI), systolic blood pressure, and diastolic blood pressure (P<0.05), as well as significantly higher serum levels of FFA, TG, LDL-C, and non-HDL-C and a significantly lower serum level of HDL-C (P<0.05). Compared with the control group, the hypertension group had significantly higher rates of elevated serum FFA (>0.45 mmol/L for girls and >0.60 mmol/L for boys) (P<0.05) and abnormal blood lipid levels (abnormality in at least one index among serum TG, TC, LDL-C, HDL-C, and non-HDL-C) (P<0.05). A multivariate logistic regression equation was established based on age, sex, BMI, elevated serum FFA, and abnormal blood lipid levels, and the results showed that elevated serum FFA was an independent risk factor for primary hypertension in children (OR=17.560, 95%CI: 1.964-157.003, P<0.05). CONCLUSIONS: There is a significant increase in serum FFA level in children with primary hypertension, and the increase in serum FFA can increase the risk of primary hypertension in children.

Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign.

Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 µg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 µg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 µg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 µg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.

Frequency-Modulated Signal Measurement Using Closed-Loop Methodology.

Frequency-modulated (FM) signals are widely used in sensing, measurement, and signal detection due to their strong anti-interference and easy transmission characteristics. Although the high-precision measurement methods for static signals are quite complete, the high-precision measurement methods for dynamic FM signals still need to be studied, and the measurement accuracy in the high-sampling system still has room for improvement. Traditionally, the equal-precision measurement method is widely applied in most scenarios. However, its accuracy is limited by the quantization error of ±1 word and the sampling gate time, making it difficult to improve the frequency measurement accuracy while ensuring a high sampling rate at the same time. In this paper, a high-precision feedback frequency measurement system with the capability to eliminate the quantization error of ±1 word is proposed. The proposed system consists of two stages, the rough measurement stage based on the equal-precision measurement method and the precise measurement stage based on the negative feedback tracking architecture using the phase-frequency detector (PFD) and direct digital synthesizer (DDS). The effectiveness and feasibility of the system are verified by both simulation and experiment. At the sampling rate of 2 kHz, the frequency measurement accuracy is improved by more than 30 dB.

Increasing angiotensin-converting enzyme 1 regulated by histone 3 lysine 27 hyperacetylation in high-fat diet-induced hypertensive rat kidney.

BACKGROUND: Obesity is a key risk factor of hypertension. Angiotensin-converting enzyme 1 (ACE1) is a key enzyme involved in the renin-angiotensin-aldosterone system (RAAS), which contributes to obesity-related hypertension (OrHTN). Emerging evidence has shown that histone acetylation is also involved in OrHTN. As kidney is an effector organ that activates the RAAS by secreting renin after hypertension occurs, this study aimed to explore the regulatory role of histone acetylation on renal RAAS expression. METHODS: Nineteen male Wistar rats were randomly divided into a control group ( n  = 9, fed normal chow) and a high-fat diet (HFD) group ( n  = 10, fed HFD for 16 weeks). The renal transcriptome and histone acetylation spectrum was analyzed by RNA sequencing and tandem mass spectrometry and was further confirmed by RT-qPCR, western blot, and immunohistochemistry. Then, chromatin immunoprecipitation (ChIP)-qPCR analysis was performed for the detection of DNA-protein interaction. RESULTS: After 16-week HFD, the rats became obese with increased plasma triglyceride and high blood pressure. Increased ACE1 and histone 3 lysine 27 acetylation (H3K27ac) expression levels were found in OrHTN rat kidneys. The following ChIP-qPCR analysis illustrated that the upregulation of ACE1 transcription was mediated by increased H3K27ac. CONCLUSION: H3K27ac could be an important histone acetylation site that activates renal ACE1 in HFD-induced hypertensive rats.

Efficient Interfacial Charge Transfer Based on 2D/2D Heterojunctions of Fe-C3N4/Ti3C2 for Improving the Photocatalytic Degradation of Antibiotics.

Graphitic carbon nitride (g-C3N4) has shown to be a promising photocatalyst that, however, suffers from strong charge recombination and poor conductivity, while MXenes have shown to be perfect cocatalysts for the photocatalytic process but show poor stability. In this study, we successfully constructed 2D/2D heterojunctions of Fe-C3N4/Ti3C2 for the photocatalytic degradation of antibiotics. In this study, multilayer Ti3C2 was obtained by etching Ti3AlC2, and then Fe-C3N4/Ti3C2 photocatalyst was prepared by the one-pot microwave method and high-temperature calcination method. The synthesized samples were characterized by XRD, SEM, TEM, XPS, TGA, BET, DRS, PL, and other means. The photocatalytic degradation of tetracycline hydrochloride by Fe-C3N4/Ti3C2 was in accordance with the first-order reaction kinetics model, and the apparent rate constant k was 2.83, 2.06, and 1.77 times that of g-C3N4, Fe-C3N4, and g-C3N4/Ti3C2, respectively. Through the mechanism study, it was shown that the most active species in the reaction system was • O2 -, while h+ and •OH had a relatively lower effect on the degradation system.