ObjectiveTo elucidate the mechanism whereby advanced glycation end products (AGEs) accelerate atherosclerosis (AS) and to explore novel therapeutic strategies for atherosclerotic cardiovascular disease.Methods and resultsThe effect of AGEs on low-density lipoprotein (LDL) transcytosis across endothelial cells (ECs) was assessed using an in vitro model of LDL transcytosis. We observed that AGEs activated the receptor for advanced glycation end products (RAGE) on the surface of ECs and consequently upregulated Caveolin-1, which in turn increased caveolae-mediated LDL transcytosis and accelerated AS progression. Our molecular assessment revealed that AGEs activate the RAGE-NF-κB signaling, which then recruits the NF-κB subunit p65 to the RAGE promoter and consequently enhances RAGE transcription, thereby forming a positive feedback loop between the NF-κB signaling and RAGE expression. Increased NF-κB signaling ultimately upregulated Caveolin-1, promoting LDL transcytosis, and inhibition of RAGE suppressed AGE-induced LDL transcytosis. In ApoE−/− mice on a high-fat diet, atherosclerotic plaque formation was accelerated by AGEs but suppressed by EC-specific knockdown of RAGE.ConclusionAGEs accelerate the development of diabetes-related AS by increasing the LDL transcytosis in ECs through the activation of the RAGE/NF-κB/Caveolin-1 axis, which may be targeted to prevent or treat diabetic macrovascular complications.

PurposeThe purpose of this study was to introduce the surgical method of K-wire combined with screw in the treatment of Arbeitsgemeinschaftfür Osteosynthesefragen (AO) type B3.1 phalangeal fractures and to compare its clinical, radiological and functional outcomes with K-wire fixation.MethodsThis was a retrospective comparative study. From January 2015 to February 2022, we treated 86 patients with AO type B3.1 phalangeal fractures. A total of 71 patients were finally included in the statistical analysis. Thirty-nine patients received K-wires combined with screw, and 32 patients received simple K-wires. The follow-up time was at least 6 months. Outcome measures included general information, operative time, total active motion (TAM), pinch strength, radiological union time, pain assessed by visual analog scale (VAS), Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) score, cost, and complications.ResultsThe follow-up time was 6–12 months, with an average of 7.9 months. All patients achieved clinical and radiological union. Compared with the K-wire fixation group, the TAM, radiological union time and VAS score of the K-wire combined with screw group had obvious advantages. Compared with the opposite healthy hand, the grip strength of the two groups was similar, and there was no significant difference in the QuickDASH score. The incidence rate of complications in the K-wire combined with screw group (2/39) was lower than that in the K-wire fixation group (7/32).ConclusionsCompared with simple K-wire fixation, K-wire combined with screw in the treatment of AO type B3.1 phalangeal fractures is a safer and reliable surgical method. K-wire controls the rotation and plays a role similar to a “lock”. The screw can exert pressure and fix it more firmly. It shortens the time of fracture healing and has a higher TAM and fewer postoperative complications.

The kidney reabsorbs large amounts of glucose through Na+-glucose cotransporter 2 (SGLT2). P4-ATPase acts together with the β-subunit TMEM30A to mediate the asymmetric distribution of phosphatidylserine (PS), phosphatidylethanolamine (PE), and other amino phospholipids, promoting plasma membrane and internal vesicle fusion, and facilitating vesicle protein transport. We observed reduced TMEM30A expression in renal tubules of DKD and IgA patients, suggesting a potential role of TMEM30A in renal tubular cells. To investigate the role of TMEM30A in renal tubules, we constructed a TMEM30A knockdown cell model by transfecting mouse kidney tubular epithelium cells (TCMK-1) with TMEM30A shRNA. Knockdown of TMEM30A in TCMK-1 cells attenuated vesicle transporter protein synthesis, resulting in reduced transport and expression of SGLT2, which in turn reduced glucose absorption. These data suggested that TMEM30A plays a crucial role in renal tubules.

BackgroundPreterm birth (PTB), defined as delivery before 37 gestational weeks, imposes significant public health burdens. A recent maternal genome-wide association study of spontaneous PTB identified a noncoding locus near the angiotensin II receptor type 2 (AGTR2) gene. Genotype-Tissue Expression data revealed that alleles associated with decreased AGTR2 expression in the uterus were linked to an increased risk of PTB and shortened gestational duration. We hypothesized that a causative variant in this locus modifies AGTR2 expression by altering transcription factor (TF) binding.MethodsTo investigate this hypothesis, we performed bioinformatics analyses and functional characterizations at the implicated locus. Potential causal single nucleotide polymorphisms (SNPs) were prioritized, and allele-dependent binding of TFs was predicted. Reporter assays were employed to assess the enhancer activity of the top PTB-associated non-coding variant, rs7889204, and its impact on TF binding.ResultsOur analyses revealed that rs7889204, a top PTB-associated non-coding genetic variant is one of the strongest eQTLs for the AGTR2 gene in uterine tissue samples. We observed differential binding of CEBPB (CCAAT enhancer binding protein beta) and HOXA10 (homeobox A10) to the alleles of rs7889204. Reporter assays demonstrated decreased enhancer activity for the rs7889204 risk “C” allele.ConclusionCollectively, these results demonstrate that decreased AGTR2 expression caused by reduced transcription factor binding increases the risk for PTB and suggest that enhancing AGTR2 activity may be a preventative measure in reducing PTB risk.

Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we propose a novel approach that combines surrogate models with optimization algorithms. Firstly, we introduce the Sparse Gaussian Process regression (SGP) into the surrogate model, proposing the SGP surrogate-assisted optimization method. This approach effectively overcomes the computational expense caused by the large amount of data required in Gaussian Process model. Secondly, we use grid partitioning to divide the optimization problem into multiple regions, and utilize the multi-objective particle swarm optimization algorithm to optimize particles in each region. By combining the advantages of grid partitioning and particle swarm optimization, which overcome the limitations of traditional optimization algorithms in handling multi-objective problems. Lastly, the effectiveness and robustness of the proposed method are verified through three types of 12 test functions and a wind farm layout optimization case study. The results show that the combination of meshing and SGP surrogate enables more accurate identification of optimal solutions, thereby improving the accuracy and speed of the optimization results. Additionally, the method demonstrates its applicability to a variety of complex multi-objective optimization problems.

The rapid development of renewable energy generation aggravates the imbalance between supply and demand in power grid, and exploring the potential of demand side resource can effectively improve such problems. Industrial users (IU) is an important demand response resource of power grid, and mining the load patterns of IU is the basis of studying the demand response ability of IU, which plays an important role in the safe operation and lean management of power grid. Lately, the popularity of advanced metering infrastructures provides data support for studying the load patterns of IU. However, the high dimensionality and the complex non-linear relationship of IU’s load data bring difficulties to the task of clustering. To solve the above problems, this paper proposes a load pattern extraction method based on multidimensional electrical consumption feature construction. Firstly, industrial load characteristic set of IU is created with five load characteristic indexes weighted by improved entropy weight method. In addition, convolutional autoencoder is established to extract the temporal feature of industrial load data which is combined with industrial load characteristic set to build a multidimensional feature set (MFS) for IU and finish multidimensional electrical consumption feature construction (MECFC). Then, MFS is used as the input of Self-Organization Map network to select the initial clustering centers of K-means algorithm, overcoming the problem of local optimal solution, and complete the IU daily load clustering. The experiment shows that the algorithm based on MECFC solves the local optimal problem and have better performance in stability and clustering effect than traditional methods.