Explore the vast range of titles available.

Inter-site interaction in densely populated single-atom catalysts has been demonstrated to have a crucial role in regulating the electronic structure of metal atoms, and consequently their catalytic performances. We herein report a general and facile strategy for the synthesis of several densely populated single-atom catalysts. Taking cobalt as an example, we further produce a series of Co single-atom catalysts with varying loadings to investigate the influence of density on regulating the electronic structure and catalytic performance in alkene epoxidation with O 2 . Interestingly, the turnover frequency and mass-specific activity are significantly enhanced by 10 times and 30 times with increasing Co loading from 5.4 wt% to 21.2 wt% in trans-stilbene epoxidation, respectively. Further theoretical studies reveal that the electronic structure of densely populated Co atoms is altered through charge redistribution, resulting in less Bader charger and higher d-band center, which are demonstrated to be more beneficial for the activation of O 2 and trans-stilbene. The present study demonstrates a new finding about the site interaction in densely populated single-atom catalysts, shedding insight on how density affects the electronic structure and catalytic performance for alkene epoxidation. The interaction among single sites in densely populated single-atom catalysts (SACs) has received much attention. Here the authors report a general and facile strategy for the synthesis of several densely populated SACs and decipher the influence of density on regulating their electronic structure and catalytic performance in alkene epoxidation.

Camouflaged object detection (COD) encounters substantial difficulties owing to the visual resemblance between targets and their environments, together with discrepancies in multiscale representation of features. Current methodologies confront obstacles with feature distraction, modeling far-reaching dependencies, fusing multiple-scale details, and extracting boundary specifics. Consequently, we propose ATDMNet, an amalgamated architecture combining CNN and transformer within a numerous phases feature extraction framework. ATDMNet employs Res2Net as the foundational encoder and incorporates two essential components: multi-head agent attention (MHA) and top-k dynamic mask (TDM). MHA improves local feature sensitivity and long-range dependency modeling by incorporating agent nodes and positional biases, whereas TDM boosts attention with top-k operations and multiscale dynamic methods. The decoding phase utilizes bilinear upsampling and sophisticated semantic guidance to enhance low-level characteristics, hence ensuring precise segmentation. Enhanced performance is achieved by deep supervision and a hybrid loss function. Experiments applying COD datasets (NC4K, COD10K, CAMO) demonstrate that ATDMNet establishes a new benchmark in both precision and efficiency.

Background Group B streptococcus is a leading cause of neonatal invasive diseases. ST12/serotype Ib GBS isolate, the second most prevalent lineage in East Asia, has aroused increasing attention, as a growing body of reports suggests they may be more virulent and fatal than the ST17/serotype III strain. Results Sag37, an ST12/serotype Ib isolate, was recovered from a fatal neonatal case and exhibited features suggestive of hypervirulence. To define this lineage’s prevalence and virulence, we analyzed 432 non-duplicate GBS isolates collected in Shanghai (2016 ~ 2022), and subtyped them by multilocus sequence typing (MLST) and capsular serotyping via a multiplex polymerase chain reaction. MLST revealed 50 sequence types, with ST10 and ST19 being most common. Nine clonal complexes were identified using the eBURST program, dominated by CC12 and CC19. Among seven serotypes identified, Ib was most prevalent, followed by III, V and Ia. Guided by the epidemiological data, additional clinical ST12/serotype Ib isolates, along with representative strains from other STs/serotypes, were selected for virulence assessment. For survival, 8-week-old CD1 mice were challenged subcutaneously with 1 × 10 8 CFU and monitored for 7 days. ST12/serotype Ib isolates caused 100% mortality within 48 h, whereas other GBS subtypes resulted in delayed or no mortality. For the median lethal dose (LD 50 ), mice received intraperitoneal doses from 10 3 to 10 8 CFU; logistic regression estimated LD 50 values ranging from ≤ 10 3 to ≥ 10 8 CFU within ST12/serotype Ib, indicating marked intra-lineage heterogeneity. To further investigate the molecular mechanisms underlying the hypervirulence, whole-genome sequencing was conducted on 33 ST12/serotype Ib GBS strains identified herein. A pyseer-based genome-wide association study was performed using LD 50 as a quantitative trait. Multiple genetic variations potentially related to virulence were identified, providing insights into the molecular basis of hypervirulence of ST12/serotype Ib GBS. Conclusions This study underscored the correlation between ST12/serotype Ib GBS isolates and hypervirulence, and further and revealed multiple candidate loci potentially associated with this phenotype. These findings highlighted the need for ongoing surveillance and functional investigation of emerging GBS lineages. Clinical trial number Not applicable.

This article presents a high-precision airborne video synthetic aperture radar (SAR) raw echo simulation method aimed at addressing the issue of simulation accuracy in video SAR image generation. The proposed method employs separate techniques for simulating targets and ground clutter, utilizing pre-existing SAR images for clutter simulation and employing the shooting and bouncing rays (SBR) approach to generate target echoes. Additionally, the method accounts for target-generated shadows to enhance the realism of the simulation results. The fast simulation algorithm is implemented using the C++ programming language and the Accelerated Massive Parallelism (AMP) framework, providing a fusion technique for integrating clutter and target simulations. By combining the two types of simulated data to form the final SAR image, the method achieves efficient and accurate simulation technology. Experimental results demonstrate that this method not only improves computational speed but also ensures the accuracy and stability of the simulation outcomes. This research holds significant implications for the development of algorithms pertaining to video SAR target detection and tracking, providing robust support for practical applications.

Current practice uses the latest measure of glycated hemoglobin (HbAlc) to facilitate clinical decision-making. Studies have demonstrated that HbAlc variability links the risk of death and complications of diabetes. However, the role of HbAlc variability is unclear in clinical practice. This systematic review summarized the evidence of visit-to-visit HbAlc variability regarding different metrics in micro- and macro-vascular complications and death in people with type 2 diabetes. We searched PubMed, EMBASE (via OVID), and Cochrane Central Register (CENTRAL, via OVID) for studies investigating the association between HbAlc variability and adverse outcomes in patients with type 2 diabetes and performed random-effects meta-analysis stratified by HbAlc variability metrics in terms of standard deviation (SD), coefficient of variation (CV), and HbAlc variability score (HVS). In people with type 2 diabetes, the highest quantile of all three HbAlc variability metrics (HbAlc-standard deviation [HbAlc-SD], HbAlc-coefficient of variance [HbAlc-CV], and HVS) is associated with increased risks of all-cause mortality, cardiovascular events, progression to chronic kidney disease, amputation, and peripheral neuropathy. For example, the hazard ratio of HbAlc-SD on all-cause mortality was l.89 with 95% confidence interval (95% CI) l.46-2.45 (HbAlc-CV l.47, 95% CI l.26-l.72; HVS l.67, 95% CI l.34-2.09). High HbAlc variability leads to micro- and macro-vascular complications of type 2 diabetes and related death. People with type 2 diabetes and high HbAlc variability need additional attention and care for the potential adverse outcomes.

Background: Dendritic cells (DCs) play an important role as a bridge between innate and adaptive immunity, and changes in gene expression of DCs during the immune response to Mycobacterium tuberculosis (M.tb) may affect the development of tuberculosis. Methods: Using systems biology methods, mRNA and miRNA expression profile data of DCs infected with M.tb were obtained. A total of 1398 differentially expressed mRNAs and 79 differentially expressed miRNAs were identified, and a corresponding miRNA–mRNA regulatory network was constructed using Cytoscape 3.9.1 software. The functional annotations and pathway classifications of the miRNA–mRNA network were identified using the DAVID tool. Then, the key pathway modules in the miRNA–mRNA network were screened and subjected to PPI network analysis to identify hub nodes. Subsequently the miRNA/mRNA axis was determined, validated by qRT-PCR, and evaluated through ROC curve analysis. Results: The TNF signaling pathway and the Tuberculosis pathway were key pathway modules, with miR-34a-3p/TNF and miR-190a-3p/IL1B being the greatest correlations with the two pathway modules. qRT-PCR results showed that IL1B and miR-190a-3p exhibited significant differences in both the H37Ra and BCG infection groups. The AUC of two factors (IL1B and miR-190a-3p) was 0.9561 and 0.9625, respectively, showing high sensitivity and specificity. Conclusions: Consequently, miR-190a-3p/IL1B might be a good candidate marker to characterize the immune response of DCs to M.tb and a transition signal from innate to adaptive immunity.

Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85–0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1–13.4 ml min −1 per 1.73 m 2 and 0.65–1.1 mmol l −1 , and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort. Deep-learning models trained on retinal fundus images can be used to identify chronic kidney disease and type 2 diabetes and to predict the risk of the progression of these diseases.

Maturity-onset diabetes of the young (MODY) is the most common monogenic diabetes. The aim of this study was to assess the prevalence of MODY in phenotypic type 2 diabetes (T2DM) among Chinese young adults. From April 2015 to October 2017, this cross-sectional study involved 2429 consecutive patients from 46 hospitals in China, newly diagnosed between 15 years and 45 years, with T2DM phenotype and negative for standardized glutamic acid decarboxylase antibody at the core laboratory. Sequencing using a custom monogenic diabetes gene panel was performed, and variants of 14 MODY genes were interpreted as per current guidelines. The survey determined 18 patients having genetic variants causing MODY (6 HNF1A , 5 GCK , 3 HNF4A , 2 INS , 1 PDX1 , and 1 PAX4 ). The prevalence of MODY was 0.74% (95% confidence interval [CI]: 0.40-1.08%). The clinical characteristics of MODY patients were not specific, 72.2% (13/18) of them were diagnosed after 35 years, 47.1% (8/17) had metabolic syndrome, and only 38.9% (7/18) had a family history of diabetes. No significant difference in manifestations except for hemoglobin A1c levels was found between MODY and non-MODY patients. The prevalence of MODY in young adults with phenotypic T2DM was 0.74%, among which HNF1A -, GCK -, and HNF4A -MODY were the most common subtypes. Clinical features played a limited role in the recognition of MODY.

Type 2 diabetes is an established risk factor for dementia. However, how its genetic heterogeneity affects different dementia subtypes remains unclear. In this study, we investigate the associations between genetic risk of type 2 diabetes and dementia subtypes among 33,136 older Chinese adults from the KARE cohort. We find that a higher overall polygenic risk score for type 2 diabetes is significantly associated with an increased risk of vascular dementia, but not Alzheimer’s disease. Further analyses using cluster-specific partitioned polygenic score show that elevated genetic risk specific to the hyperinsulinemia pathway is strongly associated with increased incidence of vascular dementia. These findings highlight the potential role of insulin-related metabolic abnormalities in the pathogenesis of vascular dementia and provide genetic evidence to support the use of the hyperinsulinaemia pathway as a clinically relevant marker for early risk stratification and precision prevention strategies. Type 2 diabetes is an established risk factor for dementia. Here, the authors use data from 33,136 older Chinese adults to show that polygenic risk for type 2 diabetes was associated with vascular dementia but not Alzheimer’s disease.

Background. Triglyceride-glucose (TyG) index is a convenient indicator of insulin resistance. It has been shown to be associated with macrovascular and microvascular complications in nonhospitalized diabetic patients. However, whether TyG index is a risk factor of diabetes vascular complications in hospitalized type 2 diabetic patients is unclear. We sought to explore the association between TyG index and the risk of macrovascular and microvascular complications in a large Chinese cohort of hospitalized patients. Method. A total of 4,721 patients with type 2 diabetes (T2D) who were hospitalized in the Department of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University were enrolled between January 2015 and November 2020. TyG index was calculated as lnfasting triglycerides mg/dL×fasting glucose mg/dL/2. Measures of macrovascular complications included brachial-ankle pulse wave velocity (ba-PWV) and ankle-brachial index (ABI), whilst urine microalbumin (MAU), chronic kidney disease (CKD), and diabetic retinopathy (DR) were evaluated for microvascular complications. Logistic regressions were used to examine the association between TyG index and diabetes complications. Results. In univariate logistic regressions, higher TyG index was significantly (p<0.002) associated with increased odds of MAU (OR=1.39, 95% CI: [1.22~1.59]) and ABI (OR=1.31, 95% CI: [1.10-1.57]) but not CKD, DR, or ba-PWV. After controlling for confounders such as age, sex, and body mass index (BMI), TyG index remained strongly (p<0.002) associated with MAU and ABI. These associations were more pronounced (p<0.001) in patients with poor glycemic control or in the elderly. Conclusion. Hospitalized patients with an elevated TyG index were at a higher risk of lower limb vascular stenosis and nephric microvascular damage. Close monitoring of TyG index in patients with younger age or poor glycemic control could potentially reduce the burden of diabetes complications and prevent readmission.