AMPK activation eliminates senescent cells in diabetic wound by inducing NCOA4 mediated ferritinophagy.

BACKGROUND: Diabetic wounds are one of the long-term complications of diabetes, with a disordered microenvironment, diabetic wounds can easily develop into chronic non-healing wounds, which can impose a significant burden on healthcare. In diabetic condition, senescent cells accumulate in the wound area and suppress the wound healing process. AMPK, as a molecule related to metabolism, has a close relationship with aging and diabetes. The purpose of this study was to investigate the effects of AMPK activation on wound healing and explore the underlying mechanisms. METHODS: AMPK activator A769662 was topically applied in wound models of diabetic mice. Alterations in the wound site were observed and analyzed by immunohistochemistry. The markers related to autophagy and ferritinophagy were analyzed by western blotting and immunofluorescence staining. The role of AMPK activation and ferritinophagy were also analyzed by western blotting. RESULTS: Our results show that AMPK activation improved diabetic wound healing and reduced the accumulation of senescent cells. Intriguingly, we found that AMPK activation-induced ferroptosis is autophagy-dependent. We detected that the level of ferritin had deceased and NCOA4 was markedly increased after AMPK activation treatment. We further investigated that NCOA4-mediated ferritinophagy was involved in ferroptosis triggered by AMPK activation. Most importantly, AMPK activation can reverse the ferroptosis-insensitive of senescent fibroblast cells in diabetic mice wound area and promote wound healing. CONCLUSIONS: These results suggest that activating AMPK can promote diabetic wound healing by reversing the ferroptosis-insensitive of senescent fibroblast cells. AMPK may serve as a regulatory factor in senescent cells in the diabetic wound area, therefore AMPK activation can become a promising therapeutic method for diabetic non-healing wounds.

Application of postmortem MRI for identification of medulla oblongata contusion as a cause of death: a case report.

Whiplash injury is common in traffic accidents, and severe whiplash is characterized by cervical spinal cord injuries with cervical dislocation or fracture, that can be diagnosed by postmortem computed tomography (PMCT), postmortem magnetic resonance (PMMR), or conventional autopsy. However, for cervical spinal cord injury without fracture and dislocation, PMMR can be more informative because it provides higher resolution of soft tissues. We report the case of a 29-year-old male who died immediately following a traffic accident, in which the vehicle hit an obstacle at a high speed, causing deformation of the bumper and severe damage of the vehicle body. PMCT indicated no significant injuries or diseases related to death, but PMMR showed patchy abnormal signals in the medulla oblongata, and the lower edge of the cerebellar tonsil was herniated out of the foramen magnum. The subsequent pathological and histological results confirmed that death was caused by medulla oblongata contusion combined with cerebellar tonsillar herniation. Our description of this case of a rare but fatal whiplash injury in which there was no fracture or dislocation provides a better understanding of the potentially fatal consequences of cervical spinal cord whiplash injury without fracture or dislocation and of the underlying lethal mechanisms. Compared with PMCT, PMMR provides important diagnostic information in forensic practice for the identification of soft tissue injuries, and is therefore an important imaging modality for diagnosis of whiplash injury when there is no fracture or dislocation.

Application of Targeted Coronary Angiography in the Diagnosis of Sudden Cardiac Death.

OBJECTIVES: To diagnose coronary artery stenosis by using the postmortem computed tomography angiography (PMCTA), and to explore the diagnostic value of PMCTA in sudden cardiac death. METHODS: Six death cases were selected, and the contrast medium iohexol was injected under high pressure through femoral artery approach with 5F pigtail catheter to obtain coronary image data and then the data was analyzed. The results of targeted coronary imaging and coronary artery calcium score (CaS) were compared with the results of conventional autopsy and histopathological examination. RESULTS: The autopsy and histopathological examination of cases with coronary artery stenosis obtained similar results in targeted coronary angiography, with a diagnostic concordance rate of 83.3%. Targeted coronary angiography could effectively show coronary artery diseases, and the CaS was consistent with the results of conventional autopsy and histopathological examination. CONCLUSIONS: Targeted coronary angiography can be used as an effective auxiliary method for conventional autopsy in cases of sudden cardiac death.

Effects of Tang Luo Ning on diabetic peripheral neuropathy in rats revealed by LC-MS metabolomics approach.

Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes with limited therapies. Tang Luo Ning (TLN), a traditional Chinese medicine compound, has been proved to be effective in the treatment of DPN in clinical and experimental studies. However, the potential metabolic mechanism of TLN for the treatment of DPN is still unclear. Here the therapeutic effect of TLN on DPN was studied, and HPLC-IT-TOF/MS was used to explore the metabolic changes related to DPN and to explore the mechanism of TLN on DPN induced by high glucose. Furthermore, metabolic pathway analysis was used to explore the metabolic changes induced by DPN and TLN. As a result, TLN could improve the peripheral nerve function of DPN rats, and TLN could reduce the demyelination of the sciatic nerve in DPN rats. Metabolomics analysis showed that 14 potential biomarkers (citrate, creatine, fumarate, glyceric acid, glycine, succinate, etc.) of both DPN and TLN treatment were identified. Pathway analysis showed that the changes in these metabolites were mainly related to the citrate cycle (TCA cycle); glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism.

Effect of Wave Attenuation on Shear Wave Velocity Determination Using Bender Element Tests.

Wave attenuation is a widespread physical phenomenon in most acoustic tests, but there is a scarcity of quantitative investigations into the influence of wave attenuation on the determination of shear wave travel time in bender element tests. To ascertain this attenuation effect, a series of bender element tests were conducted on clay samples with different lengths under unconfined conditions. The experimental results suggest that the real first peak of the received signal attenuates gradually with the increase of the sample length and even becomes undistinguished when the sample length exceeds a limit. This phenomenon results in misinterpretation of the wave travel time using the time domain method. In this study, the shear wave travel time is misinterpreted when wave travel distance over approximately 80 mm, leading to underestimation of the VS by 17% for the peak-to-peak approach and 10% for the arrival-to-arrival method. Therefore, besides the near field effect and boundary reflection, the wave attenuation effect turned out to be an important factor influencing the determination of VS using the time domain method. Accordingly, it is advisable to predetermine the limit test distance for a specific testing system under conditions, particularly for long distance testing.

Discovery of natural 15-LOX small molecule inhibitors from Chinese herbal medicine using virtual Screening, biological evaluation and molecular dynamics studies.

Chinese herbal medicines (CHM) are frequently used to treat different types of inflammatory diseases and 15-Lipoxygenase (15-LOX) is a critical target enzyme for treating various inflammatory diseases. In this study, natural 15-LOX inhibitors were identified in CHM using an approach of virtual screening combined with the biological assays. First, an in-house Chinese medicine database containing 360 compounds was screened using a virtual screening approach based on pharmacophore and molecular docking to uncover several novel potential 15-LOX inhibitors. Secondly, the inhibitory effect of virtual screening hits against the 15-LOX enzyme was validated in an in vitro enzyme inhibition assay. Then, a tumor necrosis factor-α (TNF-α) release assay was carried out to explore the anti-inflammatory response of the active compounds. Furthermore, molecular dynamics (MD) simulation and binding free energy calculation were applied to analyze the process of inhibitors binding and also compared the mode of binding of the inhibitors by using the Molecular Mechanics-Generalized Born Surface Area (MM/GBSA) method. Finally, licochalcone B and eriodictyol were confirmed as inhibitors of the 15-LOX enzyme with IC50 values of 9.67 and 18.99 µM, respectively. In vitro cell-based assay showed that licochalcone B and eriodictyol inhibited the release of TNF-α factor in RAW264.7 cells stimulated by lipopolysaccharides (LPS) in a dose-dependent manner. Molecular dynamics and binding free energy analysis showed that the two 15-LOX-ligand systems immediately attained equilibrium with almost 1 Å fluctuation, the calculated binding free energies were found around -18.89 and -12.96 kcal/mol for licochalcone B and eriodictyol, respectively. Thr412, Arg415, Val420, Thr429, Ile602 and Trp606 were the main amino acid residues for the inhibition of 15-LOX enzyme activity. The current study confirms that licochalcone B and eriodictyol are 15-LOX inhibitors and can suppress the release of the TNF-α factor in RAW264.7 cells stimulated by LPS, thus providing a basis for the follow-up research and development for 15-LOX inhibitors.

IMU Data and GPS Position Information Direct Fusion Based on LSTM.

In recent years, the application of deep learning to the inertial navigation field has brought new vitality to inertial navigation technology. In this study, we propose a method using long short-term memory (LSTM) to estimate position information based on inertial measurement unit (IMU) data and Global Positioning System (GPS) position information. Simulations and experiments show the practicability of the proposed method in both static and dynamic cases. In static cases, vehicle stop data are simulated or recorded. In dynamic cases, uniform rectilinear motion data are simulated or recorded. The value range of LSTM hyperparameters is explored through both static and dynamic simulations. The simulations and experiments results are compared with the strapdown inertial navigation system (SINS)/GPS integrated navigation system based on kalman filter (KF). In a simulation, the LSTM method's computed position error Standard Deviation (STD) was 52.38% of what the SINS computed. The biggest simulation radial error estimated by the LSTM method was 0.57 m. In experiments, the LSTM method computed a position error STD of 23.08% using only SINSs. The biggest experimental radial error the LSTM method estimated was 1.31 m. The position estimated by the LSTM fusion method has no cumulative divergence error compared to SINS (computed). All in all, the trained LSTM is a dependable fusion method for combining IMU data and GPS position information to estimate position.

A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System.

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

In-Field Calibration of Triaxial Accelerometer Basedon Beetle Swarm Antenna Search Algorithm.

Traditional calibration method is usually performed with expensive equipments suchas three-axis turntable in a laboratory environment. However in practice, in order to ensure theaccuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibratethe inertial measurement unit (IMU) without external equipment in the field. In this paper, anew in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search(BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithmand its improvements based on basic beetle antennae search (BAS) algorithm are introduced indetail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established forhigher calibration accuracy, and then 24 optimal measurement positions are designed by theoreticalanalysis. In addition, the calibration procedures are improved according to the characteristics of BSASalgorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm.Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, whichshows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate thatthe proposed method can achieve high precision in-field calibration without any external equipment,and meet the accuracy requirements of the INS.

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm.

Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

Cell sheet technology: a promising strategy in regenerative medicine.

Regenerative medicine is a burgeoning field that is important to combat challenging diseases and functional impairments. Compared with traditional cell therapies with evident shortcomings (e.g., cell suspension injection or tissue engineering with scaffolds), scaffold-free cell sheet technology enables transplanted cells to be grafted and fully maintain their viability on target sites. Clinical and experimental studies have advanced the application of cell sheet technology to numerous tissues and organs (e.g., liver, cornea and bone). However, previous reviews have failed to discuss vital aspects of this rapidly developing technology, and many new challenges are gradually emerging. This review aims to provide a comprehensive introduction to cell sheet technology from cell selection to the ultimate applications of cell sheets, and challenges and future visions are also described.

Multistage Attitude Determination Alignment for Velocity-Aided In-Motion Strapdown Inertial Navigation System with Different Velocity Models.

A novel multistage attitude determination alignment algorithm with different velocity models is proposed to implement the alignment process of in-motion attitude determination alignment (IMADA) aided by the ground velocity expressed in body frame () in this paper. Normally, The-based IMADA is used to achieve the coarse alignment for strapdown inertial navigation system (SINS). The higher the coarse alignment accuracy, the better initial condition can be achieved to guarantee the performance of the subsequent fine alignment. Consider the influence of the principal model errors and the calculation errors on the alignment accuracy in traditional-based IMADA, this paper deals with a novel alignment algorithm by integrating two different velocity-based IMADAs and the multiple repeated alignment processes. The power of this novel alignment algorithm lies in eliminating the principal model errors and decreasing the calculation errors. Then, the higher alignment accuracy is achieved. Simulations and vehicle experiment are performed to demonstrate the validity of the proposed algorithm.

Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners.

Robot navigation is a fundamental problem in robotics and various approaches have been developed to cope with this problem. Despite the great success of previous approaches, learning-based methods are receiving growing interest in the research community. They have shown great efficiency in solving navigation tasks and offer considerable promise to build intelligent navigation systems. This paper presents a goal-directed robot navigation system that integrates global planning based on goal-directed end-to-end learning and local planning based on reinforcement learning (RL). The proposed system aims to navigate the robot to desired goal positions while also being adaptive to changes in the environment. The global planner is trained to imitate an expert's navigation between different positions by goal-directed end-to-end learning, where both the goal representations and local observations are incorporated to generate actions. However, it is trained in a supervised fashion and is weak in dealing with changes in the environment. To solve this problem, a local planner based on deep reinforcement learning (DRL) is designed. The local planner is first implemented in a simulator and then transferred to the real world. It works complementarily to deal with situations that have not been met during training the global planner and is able to generalize over different situations. The experimental results on a robot platform demonstrate the effectiveness of the proposed navigation system.

The Actor-Dueling-Critic Method for Reinforcement Learning.

Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state-action value (Q value), but it suffers from inaccurate Q estimation and results in poor performance in a stochastic environment. To mitigate this issue, we present an approach based on the actor-critic framework, and in the critic branch we modify the manner of estimating Q-value by introducing the advantage function, such as dueling network, which can estimate the action-advantage value. The action-advantage value is independent of state and environment noise, we use it as a fine-tuning factor to the estimated Q value. We refer to this approach as the actor-dueling-critic (ADC) network since the frame is inspired by the dueling network. Furthermore, we redesign the dueling network part in the critic branch to make it adapt to the continuous action space. The method was tested on gym classic control environments and an obstacle avoidance environment, and we design a noise environment to test the training stability. The results indicate the ADC approach is more stable and converges faster than the DDPG method in noise environments.

Vision-Based Robot Navigation through Combining Unsupervised Learning and Hierarchical Reinforcement Learning.

Extensive studies have shown that many animals' capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although there are numerous hippocampal modeling approaches, only a few span the wide functionalities ranging from processing raw sensory signals to planning and action generation. This paper presents a vision-based navigation system that involves generating place and HD cells through learning from visual images, building topological maps based on learned cell representations and performing navigation using hierarchical reinforcement learning. First, place and HD cells are trained from sequences of visual stimuli in an unsupervised learning fashion. A modified Slow Feature Analysis (SFA) algorithm is proposed to learn different cell types in an intentional way by restricting their learning to separate phases of the spatial exploration. Then, to extract the encoded metric information from these unsupervised learning representations, a self-organized learning algorithm is adopted to learn over the emerged cell activities and to generate topological maps that reveal the topology of the environment and information about a robot's head direction, respectively. This enables the robot to perform self-localization and orientation detection based on the generated maps. Finally, goal-directed navigation is performed using reinforcement learning in continuous state spaces which are represented by the population activities of place cells. In particular, considering that the topological map provides a natural hierarchical representation of the environment, hierarchical reinforcement learning (HRL) is used to exploit this hierarchy to accelerate learning. The HRL works on different spatial scales, where a high-level policy learns to select subgoals and a low-level policy learns over primitive actions to specialize on the selected subgoals. Experimental results demonstrate that our system is able to navigate a robot to the desired position effectively, and the HRL shows a much better learning performance than the standard RL in solving our navigation tasks.

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion.

As the restaurant industry is facing labor shortage issues, the use of meal delivery robots instead of waiters/waitresses not only allows the customers to experience the impact of robot technology but also benefits the restaurant business financially by reducing labor costs. Most existing meal delivery robots employ magnetic navigation technologies, which require magnetic strip installation and changes to the restaurant decor. Once the moving path is changed, the magnetic strips need to be re-laid. This study proposes multisource information fusion, i.e., the fusion of ultra-wide band positioning technology with an odometer and a low-cost gyroscope accelerometer, to achieve the positioning of a non-rail meal delivery robot with navigation. By using a low-cost electronic compass and gyroscope accelerometer, the delivery robot can move along a fixed orbit in a flexible and cost-effective manner with steering control. Ultra-wide band (UWB) and track estimation algorithm are combined by extended Kalman filter (EKF), and the positioning error after fusion is about 15 cm, which is accepted by restaurants. In summary, the proposed approach has some potential for commercial applications.

GAM-Based Mooring Alignment for SINS Based on An Improved CEEMD Denoising Method.

To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the attitude matrix between the body frame and the navigation frame is attributed to obtaining the matrix between the initial body frame and the current navigation frame using two gravitational apparent motion vectors at different moments. However, the accuracy and time of this alignment method always suffer from the measurement noise of sensors. Thus, a novel adaptive filter based on CEEMD using an l 2 -norm to calculate the similarity measure between the Probability Density Function (PDF) of each Intrinsic Mode Function (IMF) and the original signal is proposed to denoise the measurements of the accelerometer. Furthermore, the advantage of this filter is verified by comparing with other conventional denoising methods, such as PDF-based EMD (EMD-PDF) and the Finite Impulse Response (FIR) digital low-pass filter method. The results of the simulation and experiments indicate that the proposed method performs better than the conventional methods in both alignment time and alignment accuracy.

A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering.

Modern parking lots have gradually developed into underground garages to improve the efficient use of space. However, the complex design of parking lots also increases the demands on vehicle navigation. The traditional method of navigation switching only uses satellite signals. After the Position Dilution Of Precision (PDOP) of satellite signals is over the limit, vehicle navigation will enter indoor mode. It is not suitable for vehicles in underground garages to switch modes with a fast-response system. Therefore, this paper chooses satellite navigation, inertial navigation, and the car system to combine navigation. With the condition that the vehicle can freely travel through indoor and outdoor environments, high-precision outdoor environment navigation is used to provide the initial state of underground navigation. The position of the vehicle underground is calculated by the Dead Reckoning (DR) navigation system. This paper takes advantage of the Extended Kalman Filter (EKF) algorithm to provide two freely switchable navigation modes for vehicles in ground and underground garages. The continuity, robustness, fast response, and low cost of the indoor and outdoor switching navigation methods are verified in real-time systems.

[Expert consensus statement on treatment of type 2 diabetes with Xiaoke Pills in clinical practice].

Xiaoke Pills are Chinese and Western medicine compound preparations with effects of nourishing kidney and Yin,and supplementing Qi and promoting fluid. It is widely used in clinical treatment of type 2 diabetes( Qi and Yin deficiency syndrome),and continuously included in 2010,2013 and 2017 editions of Chinese prevention guide for type 2 diabetes. For the purpose of accurate positioning and rational use in clinic,it is necessary to further define the curative effect,indications,medication precautions and contraindications of Xiaoke Pills,in order to improve medication safety. This consensus was reached by reference of international clinical guidelines and expert consensus approach based on clinical evidence-based evidence,expert experience and standard specification. The evidence-based evaluation was oriented to clinical problems summarized by no less than 200 front-line clinical physicians in two rounds.GRADE system was adopted for quality classification and evaluation of the evidences,and then the nominal group method was used to form consensus recommendations or suggestions. This consensus defined the curative effect advantages,target users,dosage,administration method,contraindications and precautions of Xiaoke Pills,and provided valuable reference for the clinical use of the drug. Thisconsensus still needs to be updated and revised based on new clinical problems and evidence-based evidence in practical application in the future.

An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors.

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.