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Accumulating evidence has indicated that intestinal microbiota is involved in the development of various human diseases, including cardiovascular diseases (CVDs). In the recent years, both human and animal experiments have revealed that alterations in the composition and function of intestinal flora, recognized as gut microflora dysbiosis, can accelerate the progression of CVDs. Moreover, intestinal flora metabolizes the diet ingested by the host into a series of metabolites, including trimethylamine N‐oxide, short chain fatty acids, secondary bile acid and indoxyl sulfate, which affects the host physiological processes by activation of numerous signalling pathways. The aim of this review was to summarize the role of gut microbiota in the pathogenesis of CVDs, including coronary artery disease, hypertension and heart failure, which may provide valuable insights into potential therapeutic strategies for CVD that involve interfering with the composition, function and metabolites of the intestinal flora.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Two-dimensional (2D) Ruddlesden-Popper perovskites are currently drawing significant attention as highly-stable photoactive materials for optoelectronic applications. However, the insulating nature of organic ammonium layers in 2D perovskites results in poor charge transport and limited performance. Here, we demonstrate that Al 2 O 3 /2D perovskite heterostructure can be utilized as photoactive dielectric for high-performance MoS 2 phototransistors. The type-II band alignment in 2D perovskites facilitates effective spatial separation of photo-generated carriers, thus achieving ultrahigh photoresponsivity of >10 8  A/W at 457 nm and >10 6 A/W at 1064 nm. Meanwhile, the hysteresis loops induced by ionic migration in perovskite and charge trapping in Al 2 O 3 can neutralize with each other, leading to low-voltage phototransistors with negligible hysteresis and improved bias stress stability. More importantly, the recombination of photo-generated carriers in 2D perovskites depends on the external biasing field. With an appropriate gate bias, the devices exhibit wavelength-dependent constant photoresponsivity of 10 3 –10 8  A/W regardless of incident light intensity. Designing high-performance photodetectors based on hybrid perovskites remains a challenge. Here, the authors demonstrate that Al2O3/2D perovskite heterostructure can be utilized as photoactive dielectric for high-performance MoS2 phototransistors with broadband photoresponse, high photogain and reliability operation.

Two-dimensional (2D) dielectrics, integrated with high-mobility semiconductors, show great promise to overcome the scaling limits in miniaturized integrated circuits. However, the 2D dielectrics explored to date still face the challenges of low crystallinity, diminished dielectric constant, and the lack of effective synthesis methods. Here, we report the controllable synthesis of ultra-thin gadolinium oxychloride (GdOCl) nanosheets via a chloride hydrate-assisted chemical vapor deposition (CVD) method. The resultant GdOCl nanosheets display good dielectric properties, including a high dielectric constant (high-κ) of 15.3, robust breakdown field strengths ( E bd ) exceeding 9.9 MV/cm, and minimal gate leakage currents of approximately 10 −6 A/cm 2 . The top-gated GdOCl/MoS 2 field-effect transistors (FETs) exhibit commendable switch characteristics, a negligible hysteresis of ~5 mV and a subthreshold swing down to 67.9 mV dec −1 . The GdOCl/MoS 2 FETs can also be employed to construct functional logic gates. Our study underscores the significant potential of the 2D GdOCl dielectric for innovative high-speed operated nanoelectronic devices. van der Waals dielectric materials are required to promote the industrialization of miniaturized 2D electronics. Here, the authors report the growth of GdOCl single crystals with a dielectric constant of 15.3 and equivalent oxide thickness down to 1.3 nm, showing their application for the realization of high-performance 2D MoS 2 transistors.

We previously established a rat model of diabetic cardiomyopathy (DCM) and found that the expression of long non-coding RNA myocardial infarction–associated transcript (MIAT) was significantly upregulated. The present study was aimed to determine the pathologic role of MIAT in the development of DCM. MIAT knockdown was found to reduce cardiomyocyte apoptosis and improve left ventricular function in diabetic rats. High glucose could increase MIAT expression and induce apoptosis in cultured neonatal cardiomyocytes. The results of luciferase reporter assay and RNA immunoprecipitation assay revealed that MIAT was targeted by miR-22-3p in an AGO2-dependent manner. In addition, the 3′-untranslated region of DAPK2 was fused to the luciferase coding region and transfected into HEK293 cells with miR-22-3p mimic, and the results showed that DAPK2 was a direct target of miR-22-3p. Our findings also indicated that MIAT overexpression could counteract the inhibitory effect of miR-22-3p on DAPK2. Moreover, MIAT knockdown was found to reduce DAPK2 expression and inhibit apoptosis in cardiomyocytes exposed to high glucose. In conclusion, our study demonstrates that MIAT may function as a competing endogenous RNA to upregulate DAPK2 expression by sponging miR-22-3p, which consequently leads to cardiomyocyte apoptosis involved in the pathogenesis of DCM.

Cardiac hypertrophy is characterized by an increase in myocyte size in the absence of cell division. This condition is thought to be an adaptive response to cardiac wall stress resulting from the enhanced cardiac afterload. The pathogenesis of heart dysfunction, which is one of the primary causes of morbidity and mortality in elderly people, is often associated with myocardial remodelling caused by cardiac hypertrophy. In order to well understand the potential mechanisms, we described the molecules involved in the development and progression of myocardial hypertrophy. Increasing evidence has indicated that micro‐RNAs are involved in the pathogenesis of cardiac hypertrophy. In addition, molecular biomarkers including vascular endothelial growth factor B, NAD‐dependent deacetylase sirtuin‐3, growth/differentiation factor 15 and glycoprotein 130, also play important roles in the development of myocardial hypertrophy. Knowing the regulatory mechanisms of these biomarkers in the heart may help identify new molecular targets for the treatment of cardiac hypertrophy.

Palladium diselenide (PdSe 2 ), a stable layered material with pentagonal structure, has attracted extensive interest due to its excellent electrical and optoelectronic performance. Here, we report a reliable process to synthesize PdSe 2 via chemical vapor deposition (CVD) method. Through systematic regulation of temperature in the growth process, we can tune the thickness, size, nucleation density and morphology of PdSe 2 nanosheets. Field-effect transistors based on PdSe 2 nanosheets exhibit n-type behavior and present a high electron mobility of 105 cm 2 ·V −1 ·s −1 . The electrical property of the devices after 6 months keeping in the air show little change, implying outstanding air-stability of PdSe 2 . In addition, PdSe 2 near-infrared photodetector shows a photoresponsivity of 660 A·W −1 under 914 nm laser. These performances are better than those of most CVD-grown 2D materials, making ultrathin PdSe 2 a highly qualified candidate material for next-generation optoelectronic applications.

Modern distribution networks are increasingly affected by the widespread adoption of photovoltaic (PV) generation and electric vehicles (EVs). The variability of PV output and the fluctuating demand of EVs may cause voltage violations that threaten the safe operation of active distribution networks (ADNs). This paper proposes a voltage control strategy for ADNs to address the voltage violation problem by utilizing the control flexibility of EV charging stations (EVCSs). In the proposed strategy, a state-driven margin algorithm is first employed to generate training data comprising response capability (RC) of EVs and state parameters, which are used to train a multi-layer perceptron (MLP) model for real-time estimation of EVCS response capability. To account for uncertainties in EV departure times, a relevance vector machine (RVM) model is applied to refine the estimated RC of EVCSs. Then, based on the estimated RC of EVCSs, a second-order cone programming (SOCP)-based voltage regulation problem is formulated to obtain the optimal dispatch signal of EVCSs. Finally, a broadcast control scheme is adopted to distribute the dispatch signal across individual charging piles and the energy storage system (ESS) within each EVCS to realize the voltage regulation. Simulation results on the IEEE 34-bus feeder validate the effectiveness and advantages of the proposed approach.

With respect to the current campus energy systems, the research on energy storage deployment has focused mostly on single users or a single metric, making it difficult to accommodate diverse multiuser needs while efficiently utilizing the available resources. This results in narrow evaluation dimensions and underutilized storage assets. To address this issue, an integrated method for multiuser energy storage, optimal sizing and leasing is proposed in this paper; the method is aimed at improving the economics and utilization of storage. First, we construct a campus energy system architecture that includes an energy storage service provider and develop a storage sizing model that minimizes the average daily total cost, yielding the optimal power ratings and capacities for different users. Second, we construct a comprehensive evaluation framework from both economic and technical perspectives and apply quantitative methods to select the best configuration scheme. On this basis, we propose a multicriteria optimization-based storage leasing mechanism that enables resource sharing among users and maximizes the revenue received by the service provider. Simulation results reveal that across five typical user scenarios, the proposed method outperforms the traditional single-configuration models: the overall storage utilization rate increases by 3.84%, the cost-reduction rates for some users exceed 16%, and the investment payback period decreases by approximately one year. Compared with configuration-only approaches, the proposed integrated configuration–leasing framework simultaneously enhances user-side economics and the profitability of the service provider. The integrated sizing and leasing method not only demonstrates solid economic and technical feasibility but is also applicable to multiuser campuses, shared storage cases, and cloud storage scenarios, providing a reference path for future multidimensional value extraction processes and commercial operations.

Traditional symmetric voltage multiplier-based structures offer low current stress and high scalability. However, the equalization current flowing into each energy storage cell must overcome four diode voltage drops per switching cycle, significantly degrading energy transfer efficiency. A compact integrated equalizer based on multi-stacked buck-boost converters for large-scale energy storage systems is proposed. By replacing diodes with inductors, the design achieves high-efficiency cell balancing even at low cell voltages. The integrated design leverages the boost circuit’s inherent current ripple for driving the balancing system, eliminating extra switches and minimizing size and cost. Additionally, it provides independent balancing channels for each cell, eliminating equalization current superposition. This reduces cell current stress while enabling large-scale system balancing. Experimental validation on an eight-cell setup demonstrated successful balancing with 87.5% system efficiency.