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Abstract Periodontitis is a critical risk factor for the occurrence and development of diabetes. Porphyromonas gingivalis may participate in insulin resistance (IR) caused by periodontal inflammation, but the functional role and specific mechanisms of P. gingivalis in IR remain unclear. In the present study, clinical samples were analysed to determine the statistical correlation between P. gingivalis and IR occurrence. Through culturing of hepatocytes, myocytes, and adipocytes, and feeding mice P. gingivalis orally, the functional correlation between P. gingivalis and IR occurrence was further studied both in vitro and in vivo. Clinical data suggested that the amount of P. gingivalis isolated was correlated with the Homeostatic Model Assessment for IR score. In vitro studies suggested that coculture with P. gingivalis decreased glucose uptake and insulin receptor (INSR) protein expression in hepatocytes, myocytes, and adipocytes. Mice fed P. gingivalis tended to undergo IR. P. gingivalis was detectable in the liver, skeletal muscle, and adipose tissue of experimental mice. The distribution sites of gingipain coincided with the downregulation of INSR. Gingipain proteolysed the functional insulin-binding region of INSR. Coculture with P. gingivalis significantly decreased the INSR–insulin binding ability. Knocking out gingipain from P. gingivalis alleviated the negative effects of P. gingivalis on IR in vivo. Taken together, these findings indicate that distantly migrated P. gingivalis may directly proteolytically degrade INSR through gingipain, thereby leading to IR. The results provide a new strategy for preventing diabetes by targeting periodontal pathogens and provide new ideas for exploring novel mechanisms by which periodontal inflammation affects the systemic metabolic state.

Abstract Background Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that has been found to be associated with dysregulation of gastrointestinal functions and gut microbial homeostasis (the so-called “gut-brain axis”). ASD is often accompanied by poor performances in social interaction and repetitive behaviors. Studies on the gut-brain axis provide novel insights and candidate targets for ASD therapeutics and diagnosis. Based on the ASD mice model, this work aims to reveal the mechanisms behind the interaction of intestinal barrier function and probiotics in ASD mouse models. Results We found an altered intestinal barrier in both BTBR T+ Itpr3tf/J (BTBR) and valproic acid (VPA) mice, including increased intestinal permeability, decreased expression of intestinal tight junction proteins (claudin1, claudin3, and occludin), and increased levels of IL-6, TNF-α, and IFN-γ. Based on intestinal microbial alternation, C. butyricum can drive reduced expression of histone deacetylases 1 (HDAC1) and enhanced intestinal barrier function, significantly promoting behavioral abnormalities of ASD in BTBR mice. In parallel, we confirmed that C. butyricum was involved in the regulation of intestinal function by the Trek1 channel, indicating that it is a target of C. butyricum/butyric acid to improve intestinal barrier function in ASD mice. Conclusions Our finding provides solid evidence for the gut microbiota involved in ASD through the brain-gut axis. In addition, the probiotics C. butyricum hold promise to improve gut health and ameliorate behavioral abnormalities associated with ASD.

In order to investigate the microstructure uniformity and plasticity of hot-rolled 0Cr21A16 alloy sheet and im prove its high temperature and cold working characteristics， heat treatment tests and microstructure properties analysis were carried out at different temperatures. The microstructure and mechanical properties of 0Cr21Al6 alloy sheet before and after heat treatment were studied by optical microscopy， X-ray diffraction （XRD）， Gleeble high temperature thermal tensile test， scanning electron microscopy （SEM） and nanoindentation. The results show that the microstructure unifor mity of 0Cr21Al6 alloy sheet can be improved by heat treatment， and the average grain size is about 42 μm after holding at 960 ℃ for 6 min 40 s， and the overall grain size is relatively uniform and fine compared with at 940、980、1 000、1 020 ℃ heat treatment， the hardness and plasticity are the best combination， and the fracture toughness KIC is the best after 960 ℃ heat treatment. With the best comprehensive performance， it can effectively avoid the brittle fracture of materials in the production process. In addition， Gleeble test shows that the heat treatment temperature over 1 000 ℃ is no longer suitable for hot processing.

Abstract The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs.

Abstract Background Gastric intestinal metaplasia (GIM) is an essential precancerous lesion. Although the reversal of GIM is challenging, it potentially brings a state-to-art strategy for gastric cancer therapeutics (GC). The lack of the appropriate in vitro model limits studies of GIM pathogenesis, which is the issue this work aims to address for further studies. Method The air-liquid interface (ALI) model was adopted for the long-term culture of GIM cells in the present work. This study conducted Immunofluorescence (IF), quantitative real-time polymerase chain reaction (qRT-PCR), transcriptomic sequencing, and mucoproteomic sequencing (MS) techniques to identify the pathways for differential expressed genes (DEGs) enrichment among different groups, furthermore, to verify novel biomarkers of GIM cells. Result Our study suggests that GIM-ALI model is analog to the innate GIM cells, which thus can be used for mucus collection and drug screening. We found genes MUC17, CDA, TRIM15, TBX3, FLVCR2, ONECUT2, ACY3, NMUR2, and MAL2 were highly expressed in GIM cells, while GLDN, SLC5A5, MAL, and MALAT1 showed down-regulated, which can be used as potential biomarkers for GIM cells. In parallel, these genes that highly expressed in GIM samples were mainly involved in cancer-related pathways, such as the MAPK signal pathway and oxidative phosphorylation signal pathway. Conclusion The ALI model is validated for the first time for the in vitro study of GIM. GIM-ALI model is a novel in vitro model that can mimic the tissue micro-environment in GIM patients and further provide an avenue for studying the characteristics of GIM mucus. Our study identified new markers of GIM as well as pathways associated with GIM, which provides outstanding insight for exploring GIM pathogenesis and potentially other related conditions.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases globally. Hepatic stellate cells (HSCs) are the major effector cells of liver fibrosis. HSCs contain abundant lipid droplets (LDs) in their cytoplasm during quiescence. Perilipin 5 (PLIN 5) is a LD surface‐associated protein that plays a crucial role in lipid homeostasis. However, little is known about the role of PLIN 5 in HSC activation. Methods PLIN 5 was overexpressed in HSCs of Sprague–Dawley rats by lentivirus transfection. At the same time, PLIN 5 gene knockout mice were constructed and fed with a high‐fat diet (HFD) for 20 weeks to study the role of PLIN 5 in NAFLD. The corresponding reagent kits were used to measure TG, GSH, Caspase 3 activity, ATP level, and mitochondrial DNA copy number. Metabolomic analysis of mice liver tissue metabolism was performed based on UPLC‐MS/MS. AMPK, mitochondrial function, cell proliferation, and apoptosis‐related genes and proteins were detected by western blotting and qPCR. Results Overexpression of PLIN 5 in activated HSCs led to a decrease in ATP levels in mitochondria, inhibition of cell proliferation, and a significant increase in cell apoptosis through AMPK activation. In addition, compared with the HFD‐fed C57BL/6J mice, PLIN 5 knockout mice fed with HFD showed reduced liver fat deposition, decreased LD abundance and size, and reduced liver fibrosis. Conclusion These findings highlight the unique regulatory role of PLIN 5 in HSCs and the role of PLIN 5 in the fibrosis process of NAFLD.

Abstract Background Ureteropelvic junction obstruction (UPJO) is a common obstructive disease of the urinary tract. UPJO patients commonly exhibit coexistent renal calculi. The main aim of therapy is to relieve the obstruction and remove the stones at the same time. Methods This retrospective study included 110 patients diagnosed with UPJO coexisting with multiple renal calculi at Shanxi Bethune Hospital and the First Hospital of Shanxi Medical University between March 2016 and January 2022. Patients were divided according to the methods used for dealing with UPJO and renal calculi. In Group A, patients underwent traditional open pyeloplasty and pyelolithotomy. In Group B, patients underwent percutaneous nephrolithotomy first and then laparoscopic pyeloplasty. In Group C, patients underwent flexible cystoscopy to remove stones and then laparoscopic pyeloplasty. In Group D, patients underwent flexible vacuum-assisted ureteral access sheath (FV-UAS)assisted flexible ureteroscopy (f-URS) and underwent laparoscopic pyeloplasty. The stones were broken up using a holmium laser. The pyeloplasty success rate, stone clearance rate, operation time, bleeding amount, complication occurrence rate, postsurgical pain, length of stay, and hospitalization cost were compared between the groups. The follow-up period was at least 2 years. Results The use of f-URS and the FV-UAS, significantly increased the renal stone clearance rate and significantly reduced the complication incidence and operation time in UPJO patients with multiple coexisting renal calculi. Conclusions Laparoscopic pyeloplasty combined with f-URS and FV-UAS is safe and effective for treating UPJO in patients complicated by renal caliceal stones. Trial registration Retrospectively registered.

A risk-aware scheduling method of multienergy virtual power plant (MEVPP) is proposed to measure the uncertainty in MEVPP. First, a novel day-ahead uncertainty scenario generation method based on denoising diffusion probabilistic model is proposed, and historical data are employed to learn the error relationship between real power curves and predict power curves. The probability distribution of the prediction error which describes the day-ahead output power curve of renewable energy source is learned by parameter training. Subsequently, the effect of risk aversion on decision-making is investigated by implementing conditional value-at-risk in the optimization model, MEVPP operation mode under the carbon trading and green certificate trading mechanism is analyzed. Finally, the proposed scheme is implemented on a test MEVPP with carbon trading, and green certificate trading is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the MEVPP are investigated.

ObjectiveGastric intestinal metaplasia (IM) is a precancerous stage associated with gastric cancer. Despite the observed beneficial effects of metformin on IM, its molecular mechanism remains not fully elucidated. This study aims to reveal the effects and potential mechanisms of metformin in treating IM based on both bioinformatics and in vivo investigations.MethodsThe seven public databases (GeneCards, DisGeNET, OMIM, SuperPred, Pharm Mapper, Swiss Target Prediction, TargetNet) were used in this work to identify targeted genes related to intestinal metaplasia (IM) and metformin. The shared targeted genes between metformin and IM were further analyzed by network pharmacology, while the interactions in-between were investigated by molecular docking. In parallel, the therapeutic effect of metformin was evaluated in IM mice model, while the core targets and pathways effected by metformin were verified in vivo.ResultsWe screened out 1,751 IM-related genes and 318 metformin-targeted genes, 99 common genes identified in between were visualized by constructing the protein-protein interaction (PPI) network. The top ten core targeted genes were EGFR, MMP9, HIF1A, HSP90AA1, SIRT1, IL2, MAPK8, STAT1, PIK3CA, and ICAM1. The functional enrichment analysis confirmed that carcinogenesis and HIF-1 signaling pathways were primarily involved in the metformin treatment of IM. Based on molecular docking and dynamics, we found metformin affected the function of its targets by inhibiting receptor binding. Furthermore, metformin administration reduced the progression of IM lesions in Atp4a−/− mice model significantly. Notably, metformin enhanced the expression level of MUC5AC, while inhibited the expression level of CDX2. Our results also showed that metformin modulated the expression of core targets in vivo by reducing the activity of NF-κB and the PI3K/AKT/mTOR/HIF-1α signaling pathway.ConclusionThis study confirms that metformin improves the efficacy of IM treatment by regulating a complex molecular network. Metformin plays a functional role in inhibiting inflammation/apoptosis-related pathways of further IM progression. Our work provides a molecular foundation for understanding metformin and other guanidine medicines in IM treatment.

The randomness and fluctuation of large-scale distributed photovoltaic (PV) power will affect the stable operation of the distribution network. The energy storage system (ESS) can effectively suppress the power output fluctuation of the PV system and reduce the PV curtailment rate through charging/discharging states. In order to improve the operation capability of the distribution network and PV consumption rate, an optimal multi-objective strategy is proposed based on PV power prediction. First, the back propagation (BP) neural network with an improved genetic algorithm (GA) is used to predict PV power output. Furthermore, an adaptive variability function is added to the GA to improve the prediction accuracy. Then, the distribution network model containing distributed PV and the ESS is constructed. The optimal object contains network power loss, voltage deviation, and PV consumption. The model is solved based on the improved multi-objective particle swarm optimization (MOPSO) algorithm of Pareto optimality. The probabilistic amplitude is adopted to encode the particles for avoiding local optimal. Finally, the proposed optimal strategy is verified by the IEEE 33-bus distribution network. The results show that the proposed strategy has an obvious effect on reducing the network power loss and voltage deviation, as well as improving the PV consumption rate.

Background: Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC. Methods: CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model. Results: We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p

BackgroundBilateral ductus arteriosus (BDA) is a relatively rare vascular malformation. According to the double arch theory, BDA is formed when the distal ends of the sixth pairs of primitive arches on the left and right sides have not regressed. We describe a fetus with prenatal echocardiographic findings of BDA and right aortic arch mirror-image branching (RAA-MIB) combined with congenital heart disease. Furthermore, to gain a deeper understanding of the embryological mechanism of BDA, we review the literature on all combinations of BDA present in 40 fetuses/infants.Case summaryA 22-year-old female patient underwent fetal echocardiography at 23 weeks of gestation. Both the two-dimensional (2D) grayscale image and color Doppler flow imaging (CDFI) revealed dextro-transposition of the great arteries combined with a ventricular septal defect and RAA-MIB. The following scan revealed a rare vascular ring, which was identified as BDA extending from the confluent of the left pulmonary artery and right pulmonary artery, completely encircling the trachea to form an “O”-shaped vascular ring before finally converging into the descending aorta. A persistent left superior vena cava was also observed. We subsequently used four-dimensional (4D) color Doppler imaging with the spatiotemporal image correlation (STIC) HD live flow and STIC HD live flow silhouette mode to clearly display ventricular arterial connectivity and the direction of vessel travel. Adjusting the image quality and display angle is very important when applying STIC. The 4D images confirmed our diagnosis. After multidisciplinary counseling and discussion with her family, this female patient decided to terminate the pregnancy.ConclusionOur review of the literature summarized nine combinations classified into three types of BDA and aortic arch pathology. However, our case differs because it is a novel combination of intracardiac structural abnormalities and vascular rings in a fetus. Prenatal ultrasound diagnosis of BDA is important and requires a combination of 2D grayscale, CDFI, and STIC images to assist in scanning.

BackgroundThere is a lack of research evidence on the association between sugar-sweetened beverage (SSB) consumption and gestational diabetes mellitus (GDM) in China. ObjectiveTo explore the association between frequency of SSB consumption before pregnancy and risk of GDM in pregnant women in Shaanxi Province, and to provide a scientific basis for targeted interventions to control maternal blood glucose. MethodsThe recruitment to the China Birth Cohort study started in October 2020. Pregnant women at 6-16 weeks who had their first prenatal examination at five hospitals in Shaanxi Province were recruited. A maternal health questionnaire was used to collect basic information about pregnant women. A semi-quantitative food frequency questionnaire was used to collect the consumption of carbonated beverages, fruit and vegetable juice beverages, coffee beverages, and milk tea beverages in one year before pregnancy, which were summed to obtain the SSB consumption. Pregnant women were divided into three groups according to SSB consumption, namely 0.05) between SSB consumption and maternal age, pre-pregnancy BMI, or first labor or not. For each additional SSB consumption per day, the risk of GDM increased by 94% (OR=1.94, 95%CI: 1.37, 2.75); and the maternal OGTT 1-hour glucose and 2-hour glucose increased by 0.33 mmol·L−1 and 0.18 mmol·L−1, respectively (P0.05). ConclusionHigher SSB consumption before pregnancy increases the risk of GDM in pregnant women.

Abstract A method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one‐sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.

In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.

A two-stage robust planning method for energy storage in distribution networks based on load prediction is proposed to address the uncertainty of active load in energy storage planning. First, considering the uncertainty of active load, a short-term load forecasting model combining the mutual information method and BiLSTM is established based on k-means++ clustering. Second, based on the results of load forecasting, a comprehensive norm-constrained uncertainty set is constructed, and a two-stage robust model for distribution network energy storage planning is established. The first stage aims to minimize the annual investment cost of the energy storage system, while the second stage aims to minimize the daily operating cost of the distribution network. At the same time, a second-order cone relaxation transformation model with non-convex constraints is introduced to ultimately achieve the optimal economy of the distribution network in energy storage planning. Finally, the effectiveness of the proposed method and model is validated on the IEEE 33-node distribution network model using the MATLAB platform.