Correlation between serum microRNA-122 and VEGF expression and pregnancy outcome in gestational diabetes mellitus patients.

Objective: Gestational diabetes mellitus (GDM) seriously influences the health of mothers and babies, and there are still no effective early diagnostic markers. Therefore, our study planned to probe the correlation between serum microRNA-122 and VEGF expression and pregnancy outcome in GDM patients. Methods: This was a retrospective study of the correlation between serum microRNA-122 and vascular endothelial growth factor (VEGF) expression and pregnancy outcome in GDM patients. Sixty GDM patients admitted to the Fourth Hospital of Shijiazhuang from January 2021 to October 2022 were included in the research group (RG), and another 60 healthy pregnant women were included in the control group (CG). Serum miR-122 and VEGF levels were quantified using quantitative real-time polymerase chain reaction. The value of miR-122 and VEGF in predicting adverse pregnancy outcomes was analyzed by receiver operating characteristic curve. Results: Serum miR-122 and VEGF levels in the RG were higher relative to the CG. The total occurrence of adverse pregnancy outcomes in the RG was higher relative to the CG (P<0.05). Serum miR-122 together with VEGF levels in the poor outcome group was higher relative to the good outcome group (P<0.05). ROC analysis revealed that miR-122 and VEGF could be used to predict adverse pregnancy outcome (P<0.0001). The area under the curve of miR-122 was 0.860, 95% confidence interval (CI) =0.793-0.926, and the area under the curve of VEGF was 0.780, 95% CI =0.694-0.866. Serum levels of miR-122, VEGF were positively related with abortion, preterm delivery, low birth weight infants, macrogenesis infants, and fetal development abnormalities (P<0.001). Conclusion: The higher serum miR-122 and VEGF levels in GDM patients with satisfactory blood glucose control, the greater the probability of adverse pregnancy outcome, which should be paid attention to by clinicians.

Associations between activities of daily living, leisure activities and subjective well-being in Chinese older adults.

OBJECTIVES: The study aimed to explore the internal pathway of the association between activities of daily living and subjective well-being among Chinese older adults using a large and representative sample. BACKGROUND: There is still a lack of systematic investigation into the association between activities of daily living and subjective well-being among Chinese older adults. MATERIALS AND METHODS: The study included 10 578 participants from the Chinese Longitudinal Healthy Longevity Survey. We applied multiple linear regression analysis and Spearman correlation analysis to examine the association between activities of daily living and subjective well-being and PROCESS macro to determine the mediating effect of leisure activities on the relationship between them. RESULTS: The mean total score of subjective well-being was 30.5 ± 4.4. The prevalence of basic activities of daily living and instrumental activities of daily living limitations was 18.0% and 60.8%, respectively. Correlation analysis showed that activities of daily living score were negatively correlated with subjective well-being. Leisure activities were positively correlated with subjective well-being. The effect of instrumental activities of daily living on subjective well-being was partially mediated by leisure activities and leisure activities completely mediated the association between basic activities of daily living and subjective well-being. CONCLUSIONS: The effect of instrumental activities of daily living on subjective well-being was partially mediated by leisure activities and leisure activities completely mediated the association between basic activities of daily living and subjective well-being.

Analysis for trends in disease burden of congenital birth defects among children under 5 years old in China. / 中国5å²ä»¥ä¸‹å„¿ç«¥å ˆå¤©æ€§å‡ºç”Ÿç¼ºé™·çš„ç–¾ç— è´Ÿæ‹ è¶‹åŠ¿åˆ†æž.

OBJECTIVES: Congenital birth defects are the main source of disease burden among children under 5 years old in China. This study aims to compare the trends in disease burden of different congenital birth defects among Chinese children under 5 years old from 1990 to 2019, and to provide a scientific basis for strengthening the comprehensive prevention and control of birth defects. METHODS: Based on data from the Global Burden Disease (GBD) in 2019, the incidence mortality rate, and disability-adjusted life years (DALYs) rate of congenital birth defects among Chinese children under 5 years old from 1990 to 2019 were selected as evaluation indicators. The Joinpoint regression model was used to analyze the trends in disease burden of different types with congenital birth defects over three decades. The study also compared the differences in disease burden of congenital birth defects among children under 5 years old by gender. RESULTS: Compared to 1990, the DALYs rates of congenital heart anomalies (1 931.91/100 000), digestive congenital anomalies (364.63/100 000), neural tube defects (277.20/100 000), congenital musculoskeletal and limb anomalies (133.33/100 000), and Down syndrome (128.22/100 000) in children under 5 years old in China in 2019 were decreased 70.78%, 71.61%, 86.21%, 36.84% and 73.65%, respectively. From 1990 to 2019, the mortality rates and DALYs rates of different congenital birth defects showed an overall downward trend, but the incidence of digestive congenital anomalies and Down syndrome showed an upward trend after 2005 and 2001, respectively. Except for congenital musculoskeletal and limb anomalies, incidence of the remaining categories of birth defects were higher in boys than that in girls. CONCLUSIONS: The disease burden of congenital birth defects in children under 5 years old in China is decreased substantially from 1990 to 2019, but the burden of congenital heart anomalies is still serious and the incidence of some birth defect diseases is on the rise, and it is still crucial to strengthen the prevention and treatment for birth defects in children and propose targeted measures according to their gender characteristics.

Distribution and spectral characteristics of chromophoric dissolved organic matter in a coastal bay in northern China.

The absorption spectra of chromophoric dissolved organic matter (CDOM), along with general physical, chemical and biological variables, were determined in the Bohai Bay, China, in the springs of 2011 and 2012. The absorption coefficient of CDOM at 350 nm (a350) in surface water ranged from 1.00 to 1.83 m⁻¹ (mean: 1.35 m⁻¹) in May 2011 and from 0.78 to 1.92 m⁻¹ (mean: 1.19 m⁻¹) in April 2012. Little surface-bottom difference was observed due to strong vertical mixing. The a350 was weakly anti-correlated to salinity but positively correlated to chlorophyll a (Chl-a) concentration. A shoulder over 260-290 nm, suggestive of biogenic molecules, superimposed the overall pattern of exponentially decreasing CDOM absorption with wavelength. The wavelength distribution of the absorption spectral slope manifested a pronounced peak at ca. 300 nm characteristic of algal-derived CDOM. All a250/a365 ratios exceeded 6, corresponding to CDOM molecular weights (Mw) of less than 1 kDa. Spectroscopically, CDOM in the Bohai Bay differed substantively from that in the Haihe River, the bay's dominant source of land runoff; photobleaching of the riverine CDOM enlarged the difference. Results point to marine biological production being the principal source of CDOM in the Bohai Bay during the sampling seasons. Relatively low runoff, fast dilution, and selective photodegradation are postulated to be among the overarching elements responsible for the lack of terrigenous CDOM signature in the bay water.

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification.

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.

Health related quality of life in pediatric hematological malignancies patients and survivors: A meta-analysis of comparative studies.

BACKGROUND: Pediatric patients with hematologic malignancies (HM) and survivors are at high risk for numerous negative effects including decreased health-related quality of life (HRQOL). In order to understand the association between HM and QOL, we conducted this meta-analysis to systematically compare QOL between pediatric HM patients and survivors and controls. METHOD: The PubMed, EMBASE, Web of Science and the Cochrane Library databases were systematically searched. Data were analyzed using the random-effects model. RESULTS: Of 6586 unique articles identified, 30 were included in this meta-analysis. Studies described 12 different HRQOL tools. Different QOL measures varied in their association with quality of life. When compared with Non-HM group, the Pediatric Quality of Life Inventory (PedsQL) has a moderate effect size (standard mean difference, SMD = 0.50, 95% CI: 0.32, 0.68; P < 0.001). When compared with health controls, it has a large effect size (SMD = -1.00, 95% CI: -1.47, -0.53; P < 0.001). In addition, Health utilities index mark (HUI), and the Pediatric Oncology Quality of Life Scale (POQOLS) have a large (SMD = -0.81, 95% CI: -1.29, -0.33; P = 0.001) and a small (SMD = -0.10, 95% CI: -0.42, 0.22; P = 0.534) effect sizes when comparing overall controls. CONCLUSION: Pediatric HM patients and survivors had lower QOL compared with healthy controls and higher QOL compared with Non-HM controls in most domains. Considering the negative impact of poor QOL on daily life and functional outcomes, future research should focus on proposing effective measures to improve QOL of this population.

Deep Fusion of Multi-Template Using Spatio-Temporal Weighted Multi-Hypergraph Convolutional Networks for Brain Disease Analysis.

Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI) can only reflect the relationship between pairwise brain regions. Thus, the hyper-connectivity network (HCN) has been widely used to reveal high-order interactions among multiple brain regions. However, existing HCN models are essentially spatial HCN, which reflect the spatial relevance of multiple brain regions, but ignore the temporal correlation among multiple time points. Furthermore, the majority of HCN construction and learning frameworks are limited to using a single template, while the multi-template carries richer information. To address these issues, we first employ multiple templates to parcellate the rs-fMRI into different brain regions. Then, based on the multi-template data, we propose a spatio-temporal weighted HCN (STW-HCN) to capture more comprehensive high-order temporal and spatial properties of brain activity. Next, a novel deep fusion model of multi-template called spatio-temporal weighted multi-hypergraph convolutional network (STW-MHGCN) is proposed to fuse the STW-HCN of multiple templates, which extracts the deep interrelation information between different templates. Finally, we evaluate our method on the ADNI-2 and ABIDE-I datasets for mild cognitive impairment (MCI) and autism spectrum disorder (ASD) analysis. Experimental results demonstrate that the proposed method is superior to the state-of-the-art approaches in MCI and ASD classification, and the abnormal spatio-temporal hyper-edges discovered by our method have significant significance for the brain abnormalities analysis of MCI and ASD.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Tectorigenin protects against cardiac fibrosis in diabetic mice heart via activating the adiponectin receptor 1-mediated AMPK pathway.

Diabetic cardiomyopathy (DCM) is a common severe complication of diabetes that occurs independently of hypertension, coronary artery disease, and valvular cardiomyopathy, eventually leading to heart failure. Previous studies have reported that Tectorigenin (TEC) possesses extensive anti-inflammatory and anti-oxidative stress properties. In this present study, the impact of TEC on diabetic cardiomyopathy was examined. The model of DCM in mice was established with the combination of a high-fat diet and STZ treatment. Remarkably, TEC treatment significantly attenuated cardiac fibrosis and improved cardiac dysfunction. Concurrently, TEC was also found to mitigate hyperglycemia and hyperlipidemia in the DCM mouse. At the molecular level, TEC is involved in the activation of AMPK, both in vitro and in vivo, by enhancing its phosphorylation. This is achieved through the regulation of endothelial-mesenchymal transition via the AMPK/TGFß/Smad3 pathway. Furthermore, it was demonstrated that the level of ubiquitination of the adiponectin receptor 1 (AdipoR1) protein is associated with TEC-mediated improvement of cardiac dysfunction in DCM mice. Notably the substantial reduction of myocardial fibrosis. In conclusion, TEC improves cardiac fibrosis in DCM mice by modulating the AdipoR1/AMPK signaling pathway. These findings suggest that TEC could be an effective therapeutic agent for the treatment of diabetic cardiomyopathy.

Relationship between trimethylamine N-oxide and the risk of hypertension in patients with cardiovascular disease: A meta-analysis and dose-response relationship analysis.

BACKGROUND: The gut microbiota-dependent metabolite trimethylamine N-oxide (TMAO) has recently been recognized to be one of the risk factors for cardiovascular disease (CVD). However, there is a scarcity of data on the relationship between circulating TMAO levels and hypertension in patients with CVD. Meta analysis and a dose-response relationship were used in this study to assess the relationship between circulating trimethylamine N-oxide levels and the risk of hypertension in patients with CVD. METHODS: CNKI, Wanfang Database, Pubmed, Embase, Cochrane Library, and Web of Science were searched up to June 01, 2023. Meta-analysis and dose-response analysis of relative risk data from prospective cohort studies reporting on the relationship between circulating TMAO levels and hypertension risk in patients with CVD were conducted. RESULTS: Fifteen studies with a total of 15,498 patients were included in the present meta-analysis. Compared with a lower circulating TMAO level, a higher TMAO level was associated with a higher risk of hypertension in patients with CVD (RR = 1.14,95%CI (1.08, 1.20)). And the higher the TMAO level, the greater the risk of hypertension. The dose-response analysis revealed a linear dose-response relationship between circulating TMAO levels and the risk of hypertension in patients with CVD. The risk of hypertension increased by 1.014% when the circulating TMAO level increased by 1 µ mol/L. CONCLUSION: In patients with CVD, the level of circulating TMAO is significantly related to the risk of hypertension. The risk of hypertension increased by 1.014% for every 1 µ mol/L increase in circulating TMAO levels.

Semaglutide ameliorates cardiac remodeling in male mice by optimizing energy substrate utilization through the Creb5/NR4a1 axis.

Semaglutide, a glucagon-like peptide-1 receptor agonist, is clinically used as a glucose-lowering and weight loss medication due to its effects on energy metabolism. In heart failure, energy production is impaired due to altered mitochondrial function and increased glycolysis. However, the impact of semaglutide on cardiomyocyte metabolism under pressure overload remains unclear. Here we demonstrate that semaglutide improves cardiac function and reduces hypertrophy and fibrosis in a mouse model of pressure overload-induced heart failure. Semaglutide preserves mitochondrial structure and function under chronic stress. Metabolomics reveals that semaglutide reduces mitochondrial damage, lipid accumulation, and ATP deficiency by promoting pyruvate entry into the tricarboxylic acid cycle and increasing fatty acid oxidation. Transcriptional analysis shows that semaglutide regulates myocardial energy metabolism through the Creb5/NR4a1 axis in the PI3K/AKT pathway, reducing NR4a1 expression and its translocation to mitochondria. NR4a1 knockdown ameliorates mitochondrial dysfunction and abnormal glucose and lipid metabolism in the heart. These findings suggest that semaglutide may be a therapeutic agent for improving cardiac remodeling by modulating energy metabolism.

Central blockage of sympathetic nerves inhibits the abnormal vital signs and disturbance of the gut microbiota caused by continuous light exposure.

Background: Continuous light exposure increases sympathetic excitation in rats, leading to hypertension, left ventricular hypertrophy, and fibrosis. This study was aimed to investigate whether continuous light exposure causes destabilization of vital signs and gut microbiota (GM) in Sprague Dawley (SD) rats and whether clonidine hydrochloride (CH), a central sympathetic depressant drug, could prevent these changes. Methods: Eight-week-old male SD rats were divided into three groups with different interventions for 14 weeks: control group (CG), 2-mL pure water gavaged daily while on a normal 12-h light/dark cycle; continuous illumination group (CI), 2-mL pure water gavaged daily while receiving continuous exposure to light (300 lx); and drug administration group (DA), CH (10 µg/kg) gavaged daily while receiving continuous exposure to light (300 lx). Results: The results showed that blood pressure, heart rate, and body weight were significantly higher in the CI group than in the CG and DA groups (P < 0.05). Moreover, the Shannon index was higher in the DA group than in the CI group (P = 0.012). The beta diversity index in the CG group was significantly higher in the CI group (P = 0.039). The pairwise comparison results of the linear discriminant analysis effect size showed that Oscillospirales were enriched in the DA group, whereas the Prevotellaceae lineage (family level) > Prevotella (genus level) > Prevotellaceae_bacterium (species level) were enriched in the CI group. The Muribaculaceae family was more abundant in the CG group than in the CI group. Conclusion: Sympathetic nerve inhibition restored the abnormal vital signs and GM changes under continuous light exposure.

High, low, and non-optimum temperatures exposure on road injuries in a changing climate: a secondary analysis based on the Global Burden of Disease Study 2019.

Global warming in the twenty-first century has gradually made high temperatures a major threat to the global population. Health problems associated with extreme temperatures have become a growing public health concern worldwide. This study aimed to estimate road injuries stratified by sex, age, geographic location, and sociodemographic status attribute to high, low, and non-optimal temperatures in 21 regional and global. We used the Global Burden of Disease (GBD) Study Results Tool to examine the age-standardized death rates (ASDR) and disability-adjusted life years (DALYs) due to road injuries in 2019 by Joinpoint regression. In addition, we reported high, low, and non-optimal temperature exposures for road injuries across different groups by gender, age, region, and disease. Moreover, we examined temporal trends in the burden of road disease caused by high, low, and non-optimum temperatures from 1990 to 2019. Trend analyzes were conducted for five sociodemographic index (SDI) regions. Globally, both ASDR and DALY declined from 1990 to 2019, with average annual percent change (AAPC) values of - 1.3% and - 1.2%, respectively. In 2019, the indicators (death and DALYs) steadily declined, while SDI quintile increased in most regions. Road injuries related to death and DALYs rate attributed to high temperatures were 0.17 and 8.50, respectively, in 2019. From 1990 to 2019, DALYs for road injuries caused by low temperatures showed the most significant upward trend in most regions, especially in low-latitude countries. This study provides a comprehensive understanding of the road injury burden caused by high, low, and non-optimum temperatures, which remains high in regions with low SDI. Therefore, special attention should be paid to road injuries in poor countries or in areas with extreme temperatures.

Association of cooking fuel use with risk of cancer and all-cause mortality among Chinese elderly people: a prospective cohort study.

When cooking with biomass and fossil fuels, their incomplete burning can lead to air pollution, which can trigger pernicious effects on people's health, especially among the elderly, who are more vulnerable to toxic and harmful environmental damage. This study explored the association between different cooking fuel types and the risk of cancer and all-cause mortality among seniors constructing Cox regression models. Data were obtained by linking waves of 6, 7, and 8 of the Chinese Longitudinal Healthy Longevity Survey, which included a total of 7269 participants who were 65 years old and over. Cooking fuels were categorized as either biomass, fossil, or clean fuels. And the effects of switching cooking fuels on death risk were also investigated using Cox regression models. The results indicate that, compared with the users of clean fuels, individuals using biomass or fossil fuels were at a greater death risk for cancer [HR (95% CI): biomass, 1.13 (1.05-1.20); fossil, 1.16 (1.06-1.25)] and all causes [HR (95% CI): biomass, 1.29 (1.16-1.42); fossil, 1.32 (1.22-1.50)]. Furthermore, compared with sustained users of biomass fuels, individuals converting from biomass to clean fuels significantly reduced death risk for cancer [HR (95% CI): 0.81 (0.72-0.95)] and all causes [HR (95% CI): 0.76 (0.64-0.93)]. Similarly, all-cause death risk [HR (95% CI): 0.77 (0.62-0.93)] was noticeably reduced among these participants converting from fossil to clean fuels than persistent users of fossil fuels. Subgroup analyses revealed that males had a greater cancer and all-cause death risk when exposed to unclean fuels. These findings can inform the development of policies and the implementation of measures related to cooking fuel use to promote the health of older people and reduce the burden of disease on society.

Personalized Functional Connectivity Based Spatio-Temporal Aggregated Attention Network for MCI Identification.

Functional connectivity (FC) networks deri- ved from resting-state magnetic resonance image (rs-fMRI) are effective biomarkers for identifying mild cognitive impairment (MCI) patients. However, most FC identification methods simply extract features from group-averaged brain templates, and neglect inter-subject functional variations. Furthermore, the existing methods generally concentrate on spatial correlation among brain regions, resulting in the inefficient capture of the fMRI temporal features. To address these limitations, we propose a novel personalized functional connectivity based dual-branch graph neural network with spatio-temporal aggregated attention (PFC-DBGNN-STAA) for MCI identification. Specifically, a personalized functional connectivity (PFC) template is firstly constructed to align 213 functional regions across samples and generate discriminative individualized FC features. Secondly, a dual-branch graph neural network (DBGNN) is conducted by aggregating features from the individual- and group-level templates with the cross-template FC, which is beneficial to improve the feature discrimination by considering dependency between templates. Finally, a spatio-temporal aggregated attention (STAA) module is investigated to capture the spatial and dynamic relationships between functional regions, which solves the limitation of insufficient temporal information utilization. We evaluate our proposed method on 442 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieve the accuracies of 90.1%, 90.3%, 83.3% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification tasks, respectively, indicating that our method boosts MCI identification performance and outperforms state-of-the-art methods.

WITHDRAWN: Associations between family support, loneliness, and underlying depression among children and adolescents in the China family panel studies: A mediation analysis.

OBJECTIVE: This study aimed to assess the association between depression and family support among Chinese children and adolescents and to investigate whether loneliness could mediate this relationship. METHODS: There were 2755 children and adolescents aged 18 and below from the China Family Panel Studies (CFPS) that participated in our study. Depression in children and adolescents was assessed by the Center for Epidemiologic Studies Depression Scale (CESD). Binary logistic regression combined with mediation analysis was used to examine the association between family support and depression and the mediating effect of loneliness on this relationship. RESULTS: Of the 2755 children and adolescents, 848 (30.8 %) reported feeling lonely and 310 (11.3 %) reported feeling depressed. Multivariate logistic regression analysis showed that compared with children and adolescents with family support, children and adolescents without family support had a higher risk of loneliness (odds ratio (OR) = 1.668, 95 % confidence interval (CI): 1.318-2.111) and depression (odds ratio (OR) = 2.252, 95 % confidence interval (CI) 1.669-3.039). Mediation analysis revealed that loneliness played a partial mediating role in the association between family support and depression (ß = -0.109, P < 0.001), and the mediation proportion was 38.11 %. CONCLUSION: Family support affects depression directly and indirectly through loneliness. The results suggest that parents should provide more support to children and adolescents to reduce the risk of loneliness and underlying depression during the COVID-19 pandemic.

Triad3A-Mediated K48-Linked ubiquitination and degradation of TLR9 impairs mitochondrial bioenergetics and exacerbates diabetic cardiomyopathy.

INTRODUCTION: Targeted protein degradation represents a promising therapeutic approach, while diabetic cardiomyopathy (DCM) arises as a consequence of aberrant insulin secretion and impaired glucose and lipid metabolism in the heart.. OBJECTIVES: Considering that the Toll-like receptor 9 (TLR9) signaling pathway plays a pivotal role in regulating energy metabolism, safeguarding cardiomyocytes, and influencing glucose uptake, the primary objective of this study was to investigate the impact of TLR9 on diabetic cardiomyopathy (DCM) and elucidate its underlying mechanism. METHODS: Mouse model of DCM was established using intraperitoneal injection of STZ, and mice were transfected with adeno-associated virus serotype 9-TLR9 (AAV9-TLR9) to assess the role of TLR9 in DCM. To explore the mechanism of TLR9 in regulating DCM disease progression, we conducted interactome analysis and employed multiple molecular approaches. RESULTS: Our study revealed a significant correlation between TLR9 expression and mouse DCM. TLR9 overexpression markedly mitigated cardiac dysfunction, myocardial fibrosis, oxidative stress, and apoptosis in DCM, while inflammation levels remained relatively unaffected. Mechanistically, TLR9 overexpression positively modulated mitochondrial bioenergetics and activated the AMPK-PGC1a signaling pathway. Furthermore, we identified Triad3A as an interacting protein that facilitated TLR9's proteasomal degradation through K48-linked ubiquitination. Inhibiting Triad3A expression improved cardiac function and pathological changes in DCM by enhancing TLR9 activity. CONCLUSIONS: The findings of this study highlight the critical role of TLR9 in maintaining cardiac function and mitigating pathological alterations in diabetic cardiomyopathy. Triad3A-mediated regulation of TLR9 expression and function has significant implications for understanding the pathogenesis of DCM. Targeting TLR9 and its interactions with Triad3A may hold promise for the development of novel therapeutic strategies for diabetic cardiomyopathy. Further research is warranted to fully explore the therapeutic potential of TLR9 modulation in the context of cardiovascular diseases.

Multi-scale dense selective network based on border modeling for lung nodule segmentation.

PURPOSE: Accurate quantification of pulmonary nodules helps physicians to accurately diagnose and treat lung cancer. We try to improve the segmentation efficiency of irregular nodules while maintaining the segmentation accuracy of simple types of nodules. METHODS: In this paper, we obtain the unique edge part of pulmonary nodules and process it as a single branch stream, i.e., border stream, to explicitly model the nodule edge information. We propose a multi-scale dense selective network based on border modeling (BorDenNet). Its overall framework consists of a dual-branch encoder-decoder, which achieves parallel processing of classical image stream and border stream. We design a dense attention module to facilitate a strongly coupled status of feature images to focus on key regions of pulmonary nodules. Then, during the process of model decoding, the multi-scale selective attention module is proposed to establish long-range correlation relationships between different scale features, which further achieves finer feature discrimination and spatial recovery. We introduce border context enhancement module to mutually fuse and enhance the edge-related voxel features contained in the image stream and border stream and finally achieve the accurate segmentation of pulmonary nodules. RESULTS: We evaluate the BorDenNet rigorously on the lung public dataset LIDC-IDRI. For the segmentation of the target nodules, the average Dice score is 92.78[Formula: see text], the average sensitivity is 91.37[Formula: see text], and the average Hausdorff distance is 3.06 mm. We further test on a private dataset from Shanxi Provincial People's Hospital, which verifies the excellent generalization of BorDenNet. Our BorDenNet relatively improves the segmentation efficiency for multi-type nodules such as adherent pulmonary nodules and ground-glass pulmonary nodules. CONCLUSION: Accurate segmentation of irregular pulmonary nodules can obtain important clinical parameters, which can be used as a guide for clinicians and improve clinical efficiency.

Identification of gene mutations in six Chinese patients with maple syrup urine disease.

Background: Maple syrup urine disease (MSUD) is a rare autosomal recessive amino acid metabolic disease. This study is to identify the pathogenic genetic factors of six cases of MUSD and evaluates the application value of high-throughput sequencing technology in the early diagnosis of MUSD. Methods: Clinical examination was carried out for patients and used blood tandem mass spectrometry (MS/MS), urine gas chromatography-mass spectrometry (GC/MS), and the application of high-throughput sequencing technology for detection. Validate candidate mutations by polymerase chain reaction (PCR)-Sanger sequencing technology. Bioinformatics software analyzed the variants' pathogenicity. Using Swiss PDB Viewer software to predict the effect of mutation on the structure of BCKDHA and BCKDHB proteins. Result: A total of six MSUD patients were diagnosed, including four males and two females. Nine variants were found in three genes of six MSUD families by high-throughput sequencing, including four missense mutations: c.659C>T(p.A220V), c.818C>T(p.T273I), c.1134C>G(p.D378E), and c.1006G>A(p.G336S); two non-sense mutations: c.1291C>T(p.R431*) and c.331C>T(p.R111*); three deletion mutations: c.550delT (p.S184Pfs*46), c.718delC (p.P240Lfs*14), and c.795delG (p.N266Tfs*64). Sanger sequencing's results were consistent with the high-throughput sequencing. The bioinformatics software revealed that the mutations were harmful, and the prediction results of Swiss PDB Viewer suggest that variation affects protein conformation. Conclusion: This study identified nine pathogenic variants in the BCKDHA, BCKDHB, and DBT genes in six MSUD families, including two novel pathogenic variants in the BCKDHB gene, which enriched the genetic mutational spectrum of the disease. High-throughput sequencing is essential for the MSUD's differential diagnosis, early treatment, and prenatal diagnosis.