Search

Background Relationships between iron-dependent ferroptosis and nerve system diseases have been recently revealed. However, the role of ferroptosis in neuropathic pain (NeP) remains to be elucidated. Thus, we aimed to investigate whether ferroptosis in spinal cord contributes to NeP induced by a chronic constriction injury (CCI) of the sciatic nerve. Methods Forty Sprague-Dawley rats received CCI or sham surgery, and were randomly assigned to the following four groups: sham group; CCI + LIP group; CCI + Veh group; and CCI group. Liproxstatin-1 or corn oil were separately injected intraperitoneally for three consecutive days after surgery in the CCI + LIP or CCI + Veh group. The mechanical and thermal hypersensitivities were tested after surgery. Biochemical and morphological changes related to ferroptosis in the spinal cord were also assessed. These included iron content, glutathione peroxidase 4 (GPX4) and anti-acyl-CoA synthetase long-chain family member 4 (ACSL4) expression, lipid peroxidation assays, as well as mitochondrial morphology. Results CCI-induced NeP was followed by iron accumulation, increased lipid peroxidation and dysregulation of ACSL4 and GPX4. Moreover transmission electron microscopy confirmed the presence of aberrant morphological changes on mitochondrial, such as mitochondria shrinkage and membrane rupture. Furthermore, the administration of liproxstatin-1 on CCI rats attenuated hypersensitivities, lowered the iron level, decreased spinal lipid peroxidation, restored the dysregulations in GPX4 and ACSL4 levels, and protected against CCI induced morphological changes in mitochondria. Conclusions Our findings indicated the involvement of ferroptosis in CCI induced NeP, and point to ferroptosis inhibitors such as liproxstatin-1 as potential therapies for hypersensitivity induced by peripheral nerve injury. Significance The spinal ferroptosis-like cell death was involved in the development of neuropathic pain resulted from peripheral nerve injury, and inhibition of ferroptosis by liproxstatin-1 could alleviate mechanical and thermal hypersensitivities. This knowledge suggested that ferroptosis could represent a potential therapeutic target for neuropathic pain.

Jingyi Du,* Yifan Deng,* Zhuolin Qiu, Guoliang Sun, Yue Guo, Ziqing Hei, Xiang Li Department of Anesthesiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiang LiDepartment of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Number 600, Tianhe Road, Guangzhou, 510630, People’s Republic of ChinaTel/ Fax +86 20 8525 3132Email lixiang27@mail.sysu.edu.cnPurpose: Cognitive impairment is a complication that most frequently happens in patients with chronic neuropathic pain and has limited effective therapy. The aim of this study was to explore the effects of curcumin on the cognitive deficit in rats with peripheral nerve injury induced-neuropathic pain.Methods: The neuropathic pain rat model was constructed using chronic constriction injury (CCI). The curcumin (60 mg/kg) or vehicle was intraperitoneally administered once a day, beginning at 14th day after surgery and continued for 14 consecutive days. The nociceptive threshold tests were measured by paw mechanical withdraw threshold (PMWT) and paw thermal withdrawal latency (PTWL), while the spatial memory abilities were evaluated by the Morris water maze test. The mean counts of bromodeoxyuridine (Brdu)/neuronal nuclei (NeuN) as well as Brdu/doublecortin (DCX) co-labeled cells were used to evaluate neurogenesis in the dentate gyrus of hippocampus. The ultrastructure of the synapse in hippocampal region was visualized using transmission electron microscopy (TEM).Results: Increased PMWT and PTWL, as well as relieved memory deficits, were found in CCI rats under curcumin administration. Moreover, curcumin treatment increased the number of newly born immature (BrdU/NeuN) and newly generated mature neurons (BrdU/DCX). The TEM examination revealed increased PSD thickness and shorter active zone length as well as narrowed synaptic cleft width in the hippocampal region of CCI rats after curcumin injection.Conclusion: Curcumin can alleviate CCI induced nociceptive behaviors and memory deficit. This effect might be associated with hippocampal neurogenesis and synaptic plasticity improvements, which indicated curcumin as a potential strategy for the cognitive impairment restoration under prolonged neuropathic pain condition.Keywords: curcumin, peripheral nerve injury, cognitive impairment, hippocampus neurogenesis

A hybrid energy storage system (HESS) consists of two or more types of energy storage components and the power electronics circuit to connect them. Therefore, the real-time capacity of this system highly depends on the state of the system and cannot be simply evaluated with traditional battery models. To tackle this challenge, an equivalent state of charge (ESOC) which reflects the remaining capacity of a HESS unit in a specific operation mode, is proposed in this paper. Furthermore, the proposed ESOC is applied to the control of the distributed HESS which contains several units with their own local targets. To optimally distribute the overall power target among these units, a sparse communication network-based hierarchical control framework is proposed. This framework considers the distributed control and optimal power distribution in the HESS from two aspects - the power output capability and the ESOC balance. Based on the primary droop control, the total power is allocated according to the maximum output capacity of each unit, and the secondary control is used to adjust the power from the perspective of ESOC balance. Therefore, each energy storage unit can be controlled to meet the local power demand of the microgrid. Simulation results based on MATLAB/Simulink verify the effectiveness of the application of the proposed equivalent SOC. National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51877041] Published version This work was supported by the National Natural Science Foundation of China under Grant 51877041.

This article proposes a sliding mode observer based dynamic equivalent state of charge (ESOC) estimation method for hybrid energy storage system (HESS). Since different types of energy storage components and power electronics circuit are coupled in the HESS, the traditional SOC estimation method cannot reflect the real-time operation characteristics of the HESS. To tackle this problem, a sliding mode observer based on the model of the HESS is built in this article. By collecting the corresponding voltage and current signals, the internal parameters of energy storage elements can be observed accurately in real time. The dynamic ESOC is further defined with the idea of real-time charge balance to reflect the accurate available capacity of the HESS. Finally, the simulation results based on MATLAB/Simulink model are given to verify the feasibility of the proposed dynamic ESOC.

Disturbance-Free-Payload is a novel spacecraft architecture proposed to achieve perfect vibration isolation performance of space applications. In this paper, the object under study is a DFP system in which a flexible umbilical connection between the support module (SM) and the payload module (PM) is introduced for data, power and fluid transfer. Bead model accounting for properties of mass and flexibility, is utilized to model the flexible umbilical. With incorporation of bead model, six degree-of-freedom multibody rigid-flexible dynamics for the DFP system is established via Newtonian mechanics. Along with rotational coupling between solar panels and SM, translational coupling, in particular, is taken into consideration since there is a stringent requirement on relative position between PM and SM. Based on the accurate motion equations of the DFP system, simulation studies are conducted to gain insight of the impact of umbilical connection and translational coupling on pointing performance of the DFP system. Simulation results validate effectiveness of DFP configuration in isolating vibrations transmitted from SM to PM. Besides, umbilical connection plays an important part in degrading pointing performance of PM whereas it exerts little impact on SM. Moreover, translational coupling leads to significant pointing performance degradation for both PM and SM.

Diabetic neuropathic pain (DNP) is a highly prevalent complication of diabetes mellitus (DM) without effective treatments. The present study aimed to explore the effects of ferrostatin-1 (Fer-1) on mechanical hypersensitivity and spinal ferroptosis in rats with DM induced with streptozotocin (STZ). One week after STZ injection, the DM rats intraperitoneally received Fer-1 (2 μmol/kg) or a vehicle (1% DMSO) once daily for consecutive two weeks. The paw mechanical withdrawal thresholds (PMWT) were assessed weekly to investigate the nociceptive threshold. Biochemical and morphological alterations associated with spinal ferroptosis, including iron content, lipid peroxidation assays, the activities of anti-acyl-CoA synthetase long-chain family member 4 (ACSL4) and glutathione peroxidase 4 (GPX4), and mitochondrial morphology, were also evaluated. Decreased PMWT and significant changes in ferroptosis-associated markers, such as iron overload, lipid peroxidation, increased expression levels of ACSL4 and decreased expression levels of GPX4, and disrupted morphological features in mitochondria were found in rats with STZ-induced DM. These effects were significantly attenuated via intraperitoneal administration of the ferroptosis inhibitor Fer-1. Our findings indicated the involvement of iron-dependent ferroptosis in the spinal cords of rats with STZ-induced DM, and highlighted the effect of continuous administration of ferroptosis inhibitors such as Fer-1 on hypersensitivity in DM rats. These results suggest that inhibition of ferroptosis might be a potential therapeutic strategy in DNP treatment.

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.

Objectives: Pain and psychological disorders are the 2 most commonly occurring symptom clusters in patients with postherpetic neuralgia (PHN). This study aimed to investigate the risk factors for anxiety and depressive disorders in patients with PHN. Methods: Retrospectively, we examined the potential risk factors of anxiety and depression among patients with PHN from the clinic medical records of the Third Affiliated Hospital of Sun Yat-Sen University from 2017 to 2019. The Chinese version of the Hospital Anxiety and Depression Scale was used to assess anxiety and depression. Patients were retrospectively allocated to 2 groups – PHN with and without anxiety/depression – and compared to identify the differential patient characteristics. Results: Cases of 661 patients who were diagnosed with PHN were included. Anxiety and depression developed in 69.0% (456/661) and 65.8% (435/661) of the enrolled patients with PHN, respectively. Results of univariate regression analyses showed that female sex, magnitude of pain intensity, time from onset of rash and extent of spread of rashes were significantly associated with anxiety and depression in patients with PHN. Multivariate analysis revealed that both anxiety and depression states significantly correlated with female sex, magnitude of pain intensity, and extent of spread of rashes. Conclusions: Anxiety and depression were not uncommon in patients with PHN. Women with PHN who experience severe pain and develop extensive rashes have a high risk of developing anxiety and depressive disorders.

Neuropathic pain is clinically associated with the development of mental disorders. However, the early molecular changes possibly related to the late-set depressive consequence of neuropathic pain were obscure so far. In this genome-wide study, we aimed to characterize the molecular mechanisms at the early and late stages of neuropathic pain. The genetic data from anterior cingulate cortex (ACC) tissues of neuropathic pain mice in Gene Expression Omnibus database were analyzed by weighted gene co-expression network analysis. Modules with clinical significance were respectively distinguished for mice at two and eight weeks after operation. The genes that co-expressed in modules from two postoperative time points were obtained, and annotated by gene ontology and pathway enrichment analyses. Moreover, the hub genes were identified from the protein-protein interaction network, and their expression levels were validated by molecular biology experiments. Overall, two modules were respectively found to be associated with the neuropathic pain mice with and without depressive consequence. A total of 20 genes co-expressed in both modules, and MAPK signaling pathway was the most significant pathway for these genes. Furtherly, Dusp1, c-Fos and Gadd45β were identified as the hub genes. At two weeks after sciatic nerve cuffing, Gadd45β was significantly downregulated at both mRNA and protein levels in ACC and hippocampus, while the significant upregulation was only observed in mRNA and protein levels for c-Fos in ACC. This study firstly compared the gene expression profiles between neuropathic pain animals with and without depressive-like behavior, and we suggested the early changes in the activities of MAPK signaling pathway, c-Fos and Gadd45β might be related to late-onset depressive behavior induced by peripheral nerve injury.

An intelligent fault detection and classification for abnormal current data of transmission lines is proposed. According to the monthly report and the abnormal condition information stored in the system, four kinds of abnormal state data are chosen. By extracting three fault features of data, combining k-means algorithm and TSNE dimensionality reduction algorithm, fault categories can be clearly and intuitively distinguished, and the purpose of intelligent alarm for abnormal state current is realized.

Motivation Drug–drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. Results In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug–drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision–recall curve of 0.9208. Availability and implementation The source code and data are available at https://github.com/YifanDengWHU/DDIMDL. Supplementary information Supplementary data are available at Bioinformatics online.

A synchronous control of relative attitude and position is required in separated ultra-quiet spacecraft, such as drag-free, disturbance-free, and distributed spacecraft. Thus, a twistor-based synchronous sliding mode control is investigated in this paper to solve the control problem of relative attitude and position among separated spacecraft modules. The twistor-based control design and the stability proof are implemented using the Modified Rodrigues Parameter (MRP). To evaluate the effectiveness of the proposed control method, this paper presents a case study of separated spacecraft flying control considering the mass uncertainty and external disturbances. In addition, a simulation study of the Proportional-Derivative (PD) control is also presented for comparison. The results indicate that the twistor-based sliding mode controller can ensure global asymptotic stability. The states converge fast with ultra-precision and ultra-stability in both the attitude and position. Moreover, the proposed twistor-based sliding mode control system is robust to the mass uncertainty and external disturbances. Keywords: Distributed spacecraft, Gravitational wave detection, Synchronous control, Ultra-precision, Ultra-stability

In this work, a customized 3D ceramic printer was successfully designed and constructed. Three ceramic materials, alumina, Bioglass 45S5 (Bioglass), and zirconia were 3D printed into a C-ring shape by extruding the ceramic slurries. Once the 3D-printed specimens were in green state, a sintering process was performed. The microhardness values of the 3D-printed ceramic specimens were measured using a Vickers hardness tester. The hardness of the specimens increased from 37 ± 3 to 112 ± 5 HV0.2 for alumina, 23 ± 5 to 35 ± 1 HV0.2 for Bioglass, and 22 ± 5 to 31 ± 3 HV0.2 for zirconia, before and after the sintering process, respectively. These results, synthesis of slurries, the formation of specimens, and the mechanical properties, demonstrate the feasibility of 3D printing ceramic materials using a customized 3D ceramic printer.

Based on 5 requirements which are essential for stable OAM mode transmission, we propose an OAM fiber family based on a structure of circular photonic crystal fiber (C-PCF). The proposed C-PCF in the family is made of pure silica, with a big round air hole at the center, several rings of air-hole array as the cladding, and a ring shaped silica area in between as the core where the OAM modes propagate. We also provide a design strategy with which the optimized C-PCF can be obtained with optimum number of high quality OAM modes (up to 42 OAM modes), large effective index separation for corresponding vector modes over a wide bandwidth, relative small and flat dispersion, and low nonlinear coefficient compared with a conventional single mode fiber. The designed fiber can be used in MDM communications and other OAM applications in fibers.

An anonymous online voting system based on blockchain is proposed for the problem that the voting process details are not public, citizens do not trust, and the results cannot be verified. Using the characteristics of blockchain cryptography and distributed networks, it is possible to perfectly solve the problem of voting data security and server single point of failure. Recording by the id of the account for data verification in the future. The pricing mechanism of the Ethereum system can effectively prevent the situation of one vote and more investment, and truly realizes the openness and transparency of the voting process technically, and realizes the anonymity of the voter identity to a certain extent. In the long term, it is planned to study IPFS distributed storage in depth and distribute the voting data of the uplink to different nodes to achieve higher security and transparency.

Affective dimension prediction from multi-modal is becoming an increasingly attractive research field in artificial intelligence (AI) and human-computer interaction (HCI) . Previous works have shown that discriminative features from multiple modalities are of importance to accurately recognize emotional states. Recently, deep representations have proved to be effective for emotional state recognition. To investigate new deep spatial-temporal features and evaluate their effectiveness for affective dimension recognition, in this paper, we propose:~(1) combining a pre-trained 2D-CNN and a 1D-CNN for learning deep spatial-temporal features from video images and audio spectrograms; and~(2) a spatial-Temporal Graph Convolutional Networks (ST-GCN) adapted to facial landmarks graph. To evaluate the effectiveness of the proposed spatial-temporal features for affective dimension prediction, we propose Deep Bidirectional Long Short-Term Memory Networks (DBLSTM) model for single-modality prediction, early-fusion and late-fusion predictions. With respect to the liking dimension, we use the text modality for prediction. Experimental results, on the AVEC2019 CES dataset, show that our proposed spatial-temporal features and recognition model obtain promising results. On the development set, the obtained concordance correlation coefficient (CCC) is up to $0.724$ for arousal and $0.705$ for valence, and on the test set, the CCC is $0.513$ for arousal and $0.515$ for valence, which outperform the baseline system with corresponding CCC of $0.355$ and $0.468$ on arousal and valence, respectively.

Pointing accuracy and pointing stability play an important role in gravitational wave detection spacecraft. This paper presents dynamics model, pointing accuracy and stability analysis of a payload-spacecraft system connected using umbilical and non-contact actuators. Accurate model of payload-umbilical-spacecraft is proposed which takes into account the translational stiffness and damping effects, as well as the torsional stiffness effect. The Lagrangian approach is applied to derive the rigid-flexible coupling dynamics model. Both of the open-loop and closed-loop simulation studies are conducted to investigate the pointing accuracy and stability of the payload. The results indicate that the umbilical significantly degrades the vibration isolation performance of the payload at low frequencies between 0.01 Hz and 1 Hz. Simulation of two other cases, the payload without umbilical and the payload with umbilical considering only the translational stiffness effect, are also performed. The results of these three cases show that the umbilical greatly degrades the pointing accuracy of the payload and lowers the pointing stability. The torsional stiffness also has a slight effect on the pointing accuracy of the payload.

Background Renal cell carcinoma most commonly metastasizes to the lungs, skeleton or liver. Metastatic renal cell carcinoma to the breast is very rare, especially for clear cell carcinoma, and few cases regarding this condition have been reported. Case Presentation The case we presented was a 68-year-old Chinese female with metastatic renal clear cell carcinoma of the left breast 10 years after a nephrectomy. Identification of the metastatic renal clear cell carcinoma in the breast required multiple breast imaging modalities. Imaging showed a single, ovary-shaped, well-defined hypo-echoic mass, with abundant blood flow on ultrasound images. The mass was enhanced early on MRI, and it was hypointense on a T1-weighted image and hyperintense on a fat-saturated T2-weighted image. Following surgical excision of the tumor, a routine immunohistochemistry antibody panel on the tumor cells revealed negative staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (Her-2). Strong positive staining for the cluster of differentiation 10 (CD10) and vimentin was present. Conclusion This case is unusual because of the site of metastatic progression. It is important for physicians to be aware of this progression so early diagnoses can be made, and appropriate therapeutic planning can be initiated.

Design tools for circular PCF have been developed to analyze the vector modes by giving the modal map of the cutoff condition of eigenmodes and colormap of minimum effective refractive index separation between different eigenmodes. We also analyze the effect of fiber parameters for mode quality.

We propose a new orbital angular momentum (OAM) erbium-doped fiber amplifier based on a circular photonic crystal fiber, which can support a total of 18 modes (14 OAM modes) over C-band. A correction factor is proposed to modify the overlap factor, with the aim of evaluating the performance of the amplifier more accurately. We found that the confined doping profile can help optimize the differential model gain (DMG). Numerical simulations suggest that the proposed OAM fiber amplifier can provide a gain larger than 20 dB for all 14 OAM modes, with the small DMG less than 0.2 dB and the noise figure lower than 3.5 dB across the C-band.

The orbital angular momentum (OAM) of light can be another physical dimension that we exploit to make multiplexing in the spatial domain. The design of the OAM mode supporting fiber attracts many attentions in the field of the space division multiplexing (SDM) system. This paper reviews the recent progresses in photonic crystal fiber (PCF) supporting OAM modes, and summarizes why a PCF structure can be used to support stable OAM transmission modes. The emphasis is on the circular PCFs, which possess many excellent features of transmission performance, such as good-quality OAM modes, enough separation of the effective indices, low confinement loss, flat dispersion, a large effective area, and a low nonlinear coefficient. We also compare the transmission properties between the circular PCF and the ring core fiber, as well as the properties between the OAM EDFA based on circular PCF and the OAM EDFA based on the ring core fiber. At last, the challenges and prospects of OAM fibers based on the PCF structure are also discussed.