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Traumatic brain injury (TBI) is an important cause of death in young adults and children. Till now, the treatment of TBI in the short- and long-term complications is still a challenge. Our previous evidence implied aquaporin 4 (AQP4) and hypoxia inducible factor-1α (HIF-1α) might be potential targets for TBI. In this study, we explored the roles of AQP4 and HIF-1α on brain edema formation, neuronal damage and neurological functional deficits after TBI using the controlled cortical injury (CCI) model. The adult male Sprague Dawley rats were randomly divided into sham and TBI group, the latter group was further divided into neutralized-AQP4 antibody group, 2-methoxyestradiol (2-ME2) group, and their corresponding control, IgG and isotonic saline groups, respectively. Brain edema was examined by water content. Hippocampal neuronal injury was assessed by neuron loss and neuronal skeleton related protein expressions. Spatial learning and memory deficits were evaluated by Morris water maze test and memory-related proteins were detected by western blot. Our data showed that increased AQP4 protein level was closely correlated with severity of brain edema after TBI. Compared with that in the control group, both blockage of AQP4 with neutralized-AQP4 antibody and inhibition of HIF-1α with 2-ME2 for one-time treatment within 30–60 min post TBI significantly ameliorated brain edema on the 1st day post-TBI, and markedly alleviated hippocampal neuron loss and spatial learning and memory deficits on the 21st day post-TBI. In summary, our preliminary study revealed the short-term and long-term benefits of targeting HIF-1α-AQP4 axis after TBI, which may provide new clues for the selection of potential therapeutic targets for TBI in clinical practice. [ABSTRACT FROM AUTHOR]

The remote and non-invasive optical thermometer exploiting fluorescence intensity ratio technology is of great importance in various intelligent optoelectronic sensors. Dual-emitting YNbO 4 :Bi3+, Eu3+ phosphor is synthesized and investigated for the development of temperature sensing materials. The phosphor excited at 318 nm shows a blue broad band emission and several red sharp peaks. The value of Bi3+→Eu3+ energy transfer can reach 98.2% when Eu3+ doping content is 0.14. Benefiting from diverse thermal response behaviors due to Bi3+ and Eu3+ ions completely different electronic configurations, a dual-emitting optical thermometer is designed. The maximum values of absolute and relative sensitivities are as high as 2.51% K−1 at 473 K and 1.43% K−1 at 423 K, respectively. The thermochromic phosphor possesses a great chromaticity shift (ΔE = 317×10−3) with temperature. Based on superior color discriminability, visual colorimetry temperature measurement relied on thermal characteristic is proposed. All the results demonstrate that YNbO 4 :0.01Bi3+, 0.005Eu3+ phosphor as a sensitive and reliable optical thermometer has great potentials in temperature measurement. [ABSTRACT FROM AUTHOR]

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm. [ABSTRACT FROM AUTHOR]

Background: Total hip arthroplasty (THA) is a successful treatment for many hip diseases. Length of stay (LOS) and hospital cost are crucial parameters to quantify the medical efficacy and quality of unilateral primary THA patients. Clinical variables associated with LOS and hospital costs haven't been investigated thoroughly. Methods: The present study retrospectively explored the contributors of LOS and hospital costs among a total of 452 unilateral primary THA patients from January 2019 to January 2020. All patients received conventional in-house rehabilitation services within our institute prior to discharge. Outcome parameters included LOS and hospital cost while clinical variables included patient characteristics and procedural variables. Multivariable linear regression analysis was performed to assess the association between outcome parameters and clinical variables by controlling confounding factors. Moreover, we analyzed patients in two groups according to their diagnosis with femur neck fracture (FNF) (confine THA) or non-FNF (elective THA) separately. Results: Among all 452 eligible participants (266 females and 186 males; age 57.05 ± 15.99 year-old), 145 (32.08%) patients diagnosed with FNF and 307 (67.92%) diagnosed with non-FNF were analyzed separately. Multivariable linear regression analysis revealed that clinical variables including surgery duration, transfusion, and comorbidity (stroke) among the elective THA patients while the approach and comorbidities (stoke, diabetes mellitus, coronary heart disease) among the confine THA patients were associated with a prolonged LOS (P < 0.05). Variables including the American Society of Anesthesiologists classification (ASA), duration, blood loss, and transfusion among the elective THA while the approach, duration, blood loss, transfusion, catheter, and comorbidities (stoke and coronary heart disease) among the confine THA were associated with higher hospital cost (P < 0.05). The results revealed that variables were associated with LOS and hospital cost at different degrees among both elective and confine THA. Conclusions: Specific clinical variables of the patient characteristics and procedural variables are associated the LOS and hospital cost, which may be different between the elective and confine THA patients. The findings may indicate that evaluation and identification of detailed perioperative factors are beneficial in managing perioperative preparation, adjusting patients' anticipation, decreasing LOS, and reducing hospital cost. [ABSTRACT FROM AUTHOR]

• A macrophage membrane coated curcumin-loaded biomimetic nanosponge system was designed for osteoarthritis alleviation. • This system can alleviate the progression of osteoarthritis by synergistically suppressing inflammation and ferroptosis. • We broadened the biomedical application of macrophage membrane as an anti-ferroptosis agent in OA treatment. Osteoarthritis (OA) is a chronic degenerative joint disease characterized by the degradation of articular cartilage while sustained inflammation and ferroptosis have been demonstrated to play crucial roles in the progression of OA. It is an effective strategy to fabricate nanomedicines for OA treatment. However, current research primarily concentrates on anti-inflammation, whereas nanomedicine systems for anti-ferroptosis in OA treatment are rarely reported. Additionally, there are few studies integrating inflammation inhibition with anti-ferroptosis within a single system to ameliorate OA progression. In this study, we introduce a curcumin-loaded biomimetic nanosponge (CM@Cur-NPs) to alleviate OA by synergistically suppressing inflammation and ferroptosis. CM@Cur-NPs were obtained by encapsulating curcumin within polymeric nanoparticles and coating them with macrophage cell membranes. The in vitro and in vivo synergistic anti-inflammatory and anti-ferroptotic effects of CM@Cur-NPs were investigated. It was discovered that the CM@Cur-NPs significantly reduced pro-inflammatory cytokines (TNF-α, IL-6), iNOS, ROS, and apoptosis levels of chondrocytes while concurrently increasing the anti-inflammatory cytokine (IL-4) and extracellular matrix (ECM) content in vitro. We also found that CM@Cur-NPs decreased the expression of the ferroptosis marker Fe2+, ACSL4, and MDA, coupled with an increase in the levels of SLC7A11 and GPX4 of chondrocytes. Lipid peroxidation of chondrocytes was attenuated by CM@Cur-NPs as well. In vivo results indicated that CM@Cur-NPs effectively alleviated OA progression and achieved cartilage protection by upregulating Aggrecan and collagen II expression, along with downregulating ADAMTS5 and MMP13 expression. This study demonstrated that CM@Cur-NPs exhibited enhanced chondroprotective effects through synergistic anti-inflammatory and anti-ferroptotic actions. It is a promising approach to integrate inflammation and ferroptosis inhibition by bioactive nanomaterials for OA treatment. [ABSTRACT FROM AUTHOR]

The key issue in the field of smart contract security is efficient and rapid vulnerability detection in smart contracts. Most of the existing detection methods can only detect the presence of vulnerabilities in the contract and can hardly identify their type. Furthermore, they have poor scalability. To resolve these issues, in this study, we developed a smart contract vulnerability detection model based on multi-task learning. By setting auxiliary tasks to learn more directional vulnerability features, the detection capability of the model was improved to realize the detection and recognition of vulnerabilities. The model is based on a hard-sharing design, which consists of two parts. First, the bottom sharing layer is mainly used to learn the semantic information of the input contract. The text representation is first transformed into a new vector by word and positional embedding, and then the neural network, based on an attention mechanism, is used to learn and extract the feature vector of the contract. Second, the task-specific layer is mainly employed to realize the functions of each task. A classical convolutional neural network was used to construct a classification model for each task that learns and extracts features from the shared layer for training to achieve their respective task objectives. The experimental results show that the model can better identify the types of vulnerabilities after adding the auxiliary vulnerability detection task. This model realizes the detection of vulnerabilities and recognizes three types of vulnerabilities. The multi-task model was observed to perform better and is less expensive than a single-task model in terms of time, computation, and storage. [ABSTRACT FROM AUTHOR]

To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]