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The purpose of this study was to permit bone marrow mesenchymal stem cells (BMSCs) to reach their full potential in the treatment of chronic wounds. A biocompatible multifunctional crosslinker based temperature sensitive hydrogel was developed to deliver BMSCs, which improve the chronic inflammation microenvironments of wounds. A detailed in vitro investigation found that the hydrogel is suitable for BMSC encapsulation and can promote BMSC secretion of TGF-β1 and bFGF. In vivo , full-thickness skin defects were made on the backs of db/db mice to mimic diabetic ulcers. It was revealed that the hydrogel can inhibit pro-inflammatory M1 macrophage expression. After hydrogel association with BMSCs treated the wound, significantly greater wound contraction was observed in the hydrogel + BMSCs group. Histology and immunohistochemistry results confirmed that this treatment contributed to the rapid healing of diabetic skin wounds by promoting granulation tissue formation, angiogenesis, extracellular matrix secretion, wound contraction and re-epithelialization. These results show that a hydrogel laden with BMSCs may be a promising therapeutic strategy for the management of diabetic ulcers.

This paper studies the framework of fault prognostics and health management for complex systems. Aiming at the problems of lack of effective organization and management of fault diagnosis resources and low resource sharing rate, this paper analyzes the characteristics of complex system and the shortcomings of traditional diagnosis mode, puts forward the service-oriented PHM system architecture, studies the system service mode and characteristics, analyzes the key technologies and main function modules of establishing the system, and carries out public PHM system service level for complex equipment field Taiwan research provides a new way of thinking.

This paper reviews major research and development issues relating to hydrogels as scaffolds for tissue engineering, the article starts with a brief introduction of tissue engineering and hydrogels as extracellular matrix mimics, followed by a description of the various types of hydrogels and preparation methods, before a discussion of the physical and chemical properties that are important to their application. There follows a short comment on the trends of future research and development. Throughout the discussion there is an emphasis on the genetic understanding of bone tissue engineering application.

As one of the most induced genes in activated macrophages, immune-responsive gene 1 ( IRG1 ) encodes a mitochondrial metabolic enzyme catalysing the production of itaconic acid (ITA). Although ITA has an anti-inflammatory property, the underlying mechanisms are not fully understood. Here we show that ITA is a potent inhibitor of the TET-family DNA dioxygenases. ITA binds to the same site on TET2 as the co-substrate α-ketoglutarate, inhibiting TET2 catalytic activity. Lipopolysaccharide treatment, which induces Irg1 expression and ITA accumulation, inhibits Tet activity in macrophages. Transcriptome analysis reveals that TET2 is a major target of ITA in suppressing lipopolysaccharide-induced genes, including those regulated by the NF-κB and STAT signalling pathways. In vivo, ITA decreases the levels of 5-hydroxymethylcytosine, reduces lipopolysaccharide-induced acute pulmonary oedema as well as lung and liver injury, and protects mice against lethal endotoxaemia, depending on the catalytic activity of Tet2. Our study thus identifies ITA as an immune modulatory metabolite that selectively inhibits TET enzymes to dampen the inflammatory responses. Chen et al. report that the immune modulatory metabolite itaconic acid selectively inhibits the activity of TET DNA dioxygenases to repress the inflammatory responses.

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.

Objectives Pretreatment evaluation of tumor biology and microenvironment is important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadoxetic acid-enhanced MRI to predict microvascular invasion (MVI), tumor differentiation, and immunoscore. Methods This retrospective study included 273 patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI. Patients were assigned to two groups: training ( N = 191) and validation ( N = 82). Univariable and multivariable logistic regression analyses were performed to investigate clinical variables and MRI features’ associations with MVI, tumor differentiation, and immunoscore. Nomograms were developed based on features associated with these three histopathological features in the training cohort, then validated, and evaluated. Results Predictors of MVI included tumor size, rim enhancement, capsule, percent decrease in T1 images (T1 D %), standard deviation of apparent diffusion coefficient, and alanine aminotransferase levels, while capsule, peritumoral enhancement, mean relaxation time on the hepatobiliary phase (T1 E ), and alpha-fetoprotein levels predicted tumor differentiation. Predictors of immunoscore included the radiologic score constructed by tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1 D %, relaxation time on plain scan (T1 P ), and alpha-fetoprotein and alanine aminotransferase levels. Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699), and immunoscore (0.737, 0.726) in the training and validation cohorts, respectively. Conclusion MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction and pretreatment decisions. Key Points • This study developed and validated three nomograms based on gadoxetic acid-enhanced MRI to predict MVI, tumor differentiation, and immunoscore in patients with HCC. • The pretreatment prediction of tumor microenvironment may be useful to guide accurate prognosis and planning of surgical and immunological therapies for individual patients with HCC.

Tetramethylpyrazine (TMP) is an active component of the Chuanxiong, effectively crosses blood-brain barrier (BBB). It exhibits neuroprotective potential in cerebral ischemia-reperfusion injury (CIRI). This study performed middle cerebral artery occlusion/reperfusion (MCAO/R) surgery in rats to evaluate TMP’s efficacy and mechanisms in mitigating CIRI. Rats received intraperitoneal TMP (40 mg/kg) for 3 days prior to MCAO/R and continued for 14 days post-surgery. Behavioral tests were conducted using mNSS and Morris water maze tests. Histopathological analyses, including HE, Nissl, and TUNEL staining. mRNA sequencing revealed that RhoA and ROCK2 were upregulated in the CIRI model and downregulated by TMP treatment. GO enrichment and KEGG enrichment showed RhoA and ROCK were related to neuroplasticity. Western blot and immunofluorescence staining confirmed that TMP inhibited RhoA, ROCK2, phosphorylated LIMK, and phosphorylated cofilin expression. Additionally, TMP increased the levels of neuroplasticity-related proteins PSD95 and MAP2, promoting synaptic and dendritic regeneration. Administration of lysophosphatidic acid (LPA), a RhoA/ROCK pathway agonist, attenuated TMP’s neuroprotective effects, validating the pathway’s role in TMP-mediated protection. These findings indicate that TMP confers neuroprotection in CIRI by inhibiting the RhoA/ROCK pathway and enhancing neuroplasticity, underscoring its therapeutic potential in CIRI.

The determination of rock mechanics parameters and in-situ stress during the development process of “horizontal well + volume fracturing” for shale oil reservoirs in Block Y of the Ordos Basin can provide a basis for fracturing schemes and production pressure difference design. Rock mechanics experiments are the most direct method for determining rock mechanics parameters. This article tested the in-situ stress of the Chang 7 shale oil reservoir in Block Y of the Ordos Basin through Kaiser acoustic emission experiments, calculated the static rock mechanics parameters of the block, and found that the vertical principal stress distribution of the Chang 7 section of the block is between 49.72 ~ 61.13 MPa, the maximum horizontal principal stress distribution is between 59.04 ~ 75.4 MPa, the minimum horizontal principal stress distribution is between 46.75 ~ 56.38 MPa, the horizontal stress difference is between 10.16 ~ 21.67 MPa, and the horizontal stress difference coefficient is between 0.21 ~ 0.42. The average maximum horizontal stress gradient is 2.534 MPa/100m, the average minimum horizontal stress gradient is 1.891 MPa/100m, and the average vertical stress gradient is 2.051 MPa/100m. In addition, dynamic rock mechanics parameters can be calculated using well logging curves, and a relationship model between dynamic and static rock mechanics can be established. Through calculation, the error can be obtained within 16%, which meets practical engineering requirements and can be applied in mining practice. The core experimental data is limited, discrete, and unable to reflect the trend of rock strength changes throughout the entire well section. By using logging curve data to predict rock strength parameters, continuous formation strength profiles can be obtained, providing important basis for later layer selection, section selection, and prediction of fracture direction.

Optimal transport (OT) is a powerful tool for measuring the distance between two probability distributions. In this paper, we introduce a new manifold named as the coupling matrix manifold (CMM), where each point on this novel manifold can be regarded as a transportation plan of the optimal transport problem. We firstly explore the Riemannian geometry of CMM with the metric expressed by the Fisher information. These geometrical features can be exploited in many essential optimization methods as a framework solving all types of OT problems via incorporating numerical Riemannian optimization algorithms such as gradient descent and trust region algorithms in CMM manifold. The proposed approach is validated using several OT problems in comparison with recent state-of-the-art related works. For the classic OT problem and its entropy regularized variant, it is shown that our method is comparable with the classic algorithms such as linear programming and Sinkhorn algorithms. For other types of non-entropy regularized OT problems, our proposed method has shown superior performance to other works, whereby the geometric information of the OT feasible space was not incorporated within.