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1. Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies

Author

Xu, Mingkun, Yin, Huifeng, Wu, Yujie, Li, Guoqi, Liu, Faqiang, Pei, Jing, Zhong, Shuai, and Deng, Lei

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biological neurons. Despite this, the application of SNNs in graph representation learning, particularly for non-Euclidean data, remains underexplored, and the influence of spiking dynamics on graph learning is not yet fully understood. This work seeks to address these gaps by examining the unique properties and benefits of spiking dynamics in enhancing graph representation learning. We propose a spike-based graph neural network model that incorporates spiking dynamics, enhanced by a novel spatial-temporal feature normalization (STFN) technique, to improve training efficiency and model stability. Our detailed analysis explores the impact of rate coding and temporal coding on SNN performance, offering new insights into their advantages for deep graph networks and addressing challenges such as the oversmoothing problem. Experimental results demonstrate that our SNN models can achieve competitive performance with state-of-the-art graph neural networks (GNNs) while considerably reducing computational costs, highlighting the potential of SNNs for efficient neuromorphic computing applications in complex graph-based scenarios.

Published
2024

2. Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks

Author

Yin, Huifeng, Xu, Mingkun, Pei, Jing, and Deng, Lei

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spiking neural networks (SNNs) have recently emerged as a promising alternative to traditional neural networks for graph learning tasks, benefiting from their ability to efficiently encode and process temporal and spatial information. In this paper, we propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning. Specifically, we introduce an attention mechanism for SNN that can selectively focus on important nodes and corresponding features in a graph during the learning process. We evaluate our proposed method on several benchmark datasets and show that it achieves comparable performance compared to existing graph learning techniques.

Published
2024

3. Understanding the Functional Roles of Modelling Components in Spiking Neural Networks

Author

Yin, Huifeng, Zheng, Hanle, Mao, Jiayi, Ding, Siyuan, Liu, Xing, Xu, Mingkun, Hu, Yifan, Pei, Jing, and Deng, Lei

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models.

Published
2024

8. Natural variation in maize gene ZmSBR1 confers seedling resistance to Fusarium verticillioides

Author

Yunxia Song, Peipei Ma, Jingyang Gao, Chaopei Dong, Zhao Wang, Yifan Luan, Jiafa Chen, Doudou Sun, Pei Jing, Xuecai Zhang, Weibin Song, Zijian Zhou, and Jianyu Wu

Subjects
Seedling blight, Stalk rot, Multiple disease resistance, Agriculture, Agriculture (General), S1-972
Abstract

Maize seedling blight caused by Fusarium verticillioides is a widely occurring maize disease, but the genetics and mechanisms of resistance are not well understood. In this study, GWAS performed by MLM and 3VmrMLM identified 40 and 20 QTNs, associated with seedling blight resistance. These methods identified 49 and 36 genes, respectively. Functional verification of candidate gene ZmSBR1 identified by both methods showed that the resistance of a mutant line to seedling blight decreased by 0.37 grade points after inoculation with F. verticillioides, compared with the WT. The length of the stem rot lesion caused by F. verticillioides increased by 86% in mutant seedlings, and the relative length of the adult plant stalk rot increased by 35% in mutant plants compared to the wild type after inoculation with Fusarium graminearum. Transcriptome analysis showed that expression of defense-related genes after inoculation was down-regulated in the mutant compared to the wild type, synthesis of secondary metabolites associated with resistance was reduced, and the immune response triggered by PAMP decreased, resulting in decreased resistance of mutant maize seedlings. Candidate gene association analysis showed that most maize inbred lines carried the susceptible haplotype. A functional PCR marker was developed. The results demonstrated that ZmSBR1 conferred resistance to multiple Fusarium diseases at the seedling and adult growth stages and had important application value in breeding.

Published
2024
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9. A mempolar transistor made from tellurium

Author

Yang, Yifei, Xu, Lujie, Xu, Mingkun, Liu, Huan, Liu, Dameng, Duan, Wenrui, Pei, Jing, and Li, Huanglong

Subjects
Physics - Applied Physics
Abstract

The classic three-terminal electronic transistors and the emerging two-terminal ion-based memristors are complementary to each other in various nonconventional information processing systems in a heterogeneous integration approach, such as hybrid CMOS/memristive neuromorphic crossbar arrays. Recent attempts to introduce transitive functions into memristors have given rise to gate-tunable memristive functions, hetero-plasticity and mixed-plasticity functions. However, it remains elusive under what application scenarios and in what ways transistors can benefit from the incorporation of ion-based memristive effects. Here, we introduce a new type of transistor named 'mempolar transistor' to the transistor family. Its polarity can be converted reversibly, in a nonvolatile fashion, between n-type and p-type depending on the history of the applied electrical stimulus. This is achieved by the use of the emerging semiconducting tellurium as the electrochemically active source/drain contact material, in combination with monolayer two-dimensional MoS2 channel, which results in a gated lateral Te/MoS2/Te memristor, or from a different perspective, a transistor whose channel can be converted reversibly between n-type MoS2 and p-type Te. With this unique mempolar function, our transistor holds the promise for reconfigurable logic circuits and secure circuits. In addition, we propose and demonstrate experimentally, a ternary content-addressable memory made of only two mempolar transistors, which used to require a dozen normal transistors, and by simulations, a device-inspired and hardware matched regularization method 'FlipWeight' for training artificial neural networks, which can achieve comparable performance to that achieved by the prevalent 'Dropout' and 'DropConnect' methods. This work represents a major advance in diversifying the functionality of transistors.

Published
2023

10. Bio-realistic and versatile artificial dendrites made of anti-ambipolar transistors

Author

Yang, Yifei, Xu, Mingkun, Pei, Jing, Li, Peng, Li, Guoqi, Wu, Si, and Li, Huanglong

Subjects
Physics - Applied Physics
Abstract

The understanding of neural networks as neuron-synapse binaries has been the foundation of neuroscience, and therefore, the emerging neuromorphic computing technology that takes inspiration from the brain. This dogma, however, has been increasingly challenged by recent neuroscience research in which the downplayed dendrites were found to be active, dynamically unique and computationally powerful. To date, research on artificial dendrites is scarce and the few existing devices are still far from versatile or (and) bio-realistic. A breakthrough is hampered by the limited available physical mechanisms in mainstream device architectures. Here, we experimentally demonstrate a bio-realistic and versatile artificial dendrite made of WSe2/MoS2 heterojunction-channel anti-ambipolar transistor, which can closely mimic the experimentally recorded non-monotonic dendritic Ca2+ action potential that underpins various sophisticated computations. Spiking neural network simulations reveal that the incorporation of this nonconventional but bio-realistic dendritic activation enhances the robustness and representational capability of the network in non-stationary environments. By further exploiting the memristive effect, dendritic anti-ambipolar transistor can naturally mimic Ca2+-mediated regulation of synaptic plasticity (meta-plasticity) at dendritic spine, an important homeostatic phenomenon due to dendritic modulation. The invention of dendritic anti-ambipolar transistor completes the family of neuromorphic transistors and represents a major advance in diversifying the functionality of transistors.

Published
2022

13. Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Author

Xu, Mingkun, Wu, Yujie, Deng, Lei, Liu, Faqiang, Li, Guoqi, and Pei, Jing

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data represented by graph data, mainly due to the lack of effective modeling framework and useful training techniques. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning. Through spatial-temporal unfolding for spiking data flows of node features, we incorporate graph convolution filters into spiking dynamics and formalize a synergistic learning paradigm. Considering the unique features of spike representation and spiking dynamics, we propose a spatial-temporal feature normalization (STFN) technique suitable for SNN to accelerate convergence. We instantiate our methods into two spiking graph models, including graph convolution SNNs and graph attention SNNs, and validate their performance on three node-classification benchmarks, including Cora, Citeseer, and Pubmed. Our model can achieve comparable performance with the state-of-the-art graph neural network (GNN) models with much lower computation costs, demonstrating great benefits for the execution on neuromorphic hardware and prompting neuromorphic applications in graphical scenarios., Comment: Accepted by IJCAI-21

Published
2021

15. Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer’s disease transgenic mice

Author

Chieh-Wen Chen, Yam-Ting Kwok, Yu-Ting Cheng, Yu-Shan Huang, Terry B. J. Kuo, Cheng-Han Wu, Pei-Jing Du, Albert C. Yang, and Cheryl C. H. Yang

Subjects
Medicine, Science
Abstract

Abstract Occurrence of amyloid-β (Aβ) aggregation in brain begins before the clinical onset of Alzheimer’s disease (AD), as preclinical AD. Studies have reported that sleep problems and autonomic dysfunction associate closely with AD. However, whether they, especially the interaction between sleep and autonomic function, play critical roles in preclinical AD are unclear. Therefore, we investigated how sleep patterns and autonomic regulation at different sleep–wake stages changed and whether they were related to cognitive performance in pathogenesis of AD mice. Polysomnographic recordings in freely-moving APP/PS1 and wild-type (WT) littermates were collected to study sleep patterns and autonomic function at 4 (early disease stage) and 8 months of age (advanced disease stage), cognitive tasks including novel object recognition and Morris water maze were performed, and Aβ levels in brain were measured. APP/PS1 mice at early stage of AD pathology with Aβ aggregation but without significant differences in cognitive performance had frequent sleep–wake transitions, lower sleep-related delta power percentage, lower overall autonomic activity, and lower parasympathetic activity mainly during sleep compared with WT mice. The same phenomenon was observed in advanced-stage APP/PS1 mice with significant cognitive deficits. In mice at both disease stages, sleep-related delta power percentage correlated positively with memory performance. At early stage, memory performance correlated positively with sympathetic activity during wakefulness; at advanced stage, memory performance correlated positively with parasympathetic activity during both wakefulness and sleep. In conclusion, sleep quality and distinction between wake- and sleep-related autonomic function may be biomarkers for early AD detection.

Published
2023
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16. Brain-inspired global-local learning incorporated with neuromorphic computing

Author

Wu, Yujie, Zhao, Rong, Zhu, Jun, Chen, Feng, Xu, Mingkun, Li, Guoqi, Song, Sen, Deng, Lei, Wang, Guanrui, Zheng, Hao, Pei, Jing, Zhang, Youhui, Zhao, Mingguo, and Shi, Luping

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition
Abstract

Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs for exploiting the advantages. Here, we report a neuromorphic hybrid learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn local plasticity and receive top-down supervision information for multiscale synergic learning. We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods, and shows promise in empowering neuromorphic applications revolution. We further implemented the hybrid model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and proved that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm., Comment: 5 figures, 6 tables

Published
2020

17. Natural polyphenol-loaded cross-linked lipoic acid vesicles treat triple-negative breast cancer by cancer cell killing and metastasis inhibition

Author

Pei Jing, Hongli Luo, Jiangbing Tan, Chunyan Liao, and Shiyong Zhang

Subjects
Triple negative breast cancer, Nanodrug, Anticancer, Antimetastasis, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

The high incidence of metastases remains a major hurdle for triple negative breast cancer (TNBC) treatment. Herein, we developed a fairly safe anticancer nanodrug for TNBC treatment by loading Epigallocatechin-3-gallate (EGCG) in cross-linked lipoic acid vesicles (EGCG@cLAVs), which cannot only directly kill TNBC cells through inducing apoptosis, but also significantly suppress the TNBC metastasis by metalloproteinases (MMPs) inhibition, a key event in tumor extracellular matrix (ECM) remodeling. The cytotoxicity assay revealed that the ICEGCG 50 of EGCG@cLAVs (85.1 μM) fell in the range of common cytotoxic drugs. The in vitro antimetastasis results disclosed that the EGCG@cLAVs effectively suppressed the migration and invasion of TNBC cells (4T1) with low wound healing rate and relative invasion rate of 19.10 ± 2.12% and 15.0 ± 1.63%, 4.0 and 6.7 times lower than that of control, respectively. The outcomes of animal models revealed that EGCG@cLAVs not only achieved the comparable antitumor effect to that of first-line chemotherapeutic drug DOX, but also reduced the number of lung metastatic nodules from 31.4 (DOX) to 1.4. This nanodrug provides a promising candidate for TNBC therapy.

Published
2023
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18. Transfer Learning in General Lensless Imaging through Scattering Media

Author

Yang, Yukuan, Deng, Lei, Jiao, Peng, Chua, Yansong, Pei, Jing, Ma, Cheng, and Li, Guoqi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Recently deep neural networks (DNNs) have been successfully introduced to the field of lensless imaging through scattering media. By solving an inverse problem in computational imaging, DNNs can overcome several shortcomings in the conventional lensless imaging through scattering media methods, namely, high cost, poor quality, complex control, and poor anti-interference. However, for training, a large number of training samples on various datasets have to be collected, with a DNN trained on one dataset generally performing poorly for recovering images from another dataset. The underlying reason is that lensless imaging through scattering media is a high dimensional regression problem and it is difficult to obtain an analytical solution. In this work, transfer learning is proposed to address this issue. Our main idea is to train a DNN on a relatively complex dataset using a large number of training samples and fine-tune the last few layers using very few samples from other datasets. Instead of the thousands of samples required to train from scratch, transfer learning alleviates the problem of costly data acquisition. Specifically, considering the difference in sample sizes and similarity among datasets, we propose two DNN architectures, namely LISMU-FCN and LISMU-OCN, and a balance loss function designed for balancing smoothness and sharpness. LISMU-FCN, with much fewer parameters, can achieve imaging across similar datasets while LISMU-OCN can achieve imaging across significantly different datasets. What's more, we establish a set of simulation algorithms which are close to the real experiment, and it is of great significance and practical value in the research on lensless scattering imaging. In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs.

Published
2019

19. Adversarial symmetric GANs: bridging adversarial samples and adversarial networks

Author

Liu, Faqiang, Xu, Mingkun, Li, Guoqi, Pei, Jing, Shi, Luping, and Zhao, Rong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that adversarial training on fake samples is implemented in vanilla GANs, but adversarial training on real samples has long been overlooked. Consequently, the discriminator is extremely vulnerable to adversarial perturbation and the gradient given by the discriminator contains non-informative adversarial noises, which hinders the generator from catching the pattern of real samples. Here, we develop adversarial symmetric GANs (AS-GANs) that incorporate adversarial training of the discriminator on real samples into vanilla GANs, making adversarial training symmetrical. The discriminator is therefore more robust and provides more informative gradient with less adversarial noise, thereby stabilizing training and accelerating convergence. The effectiveness of the AS-GANs is verified on image generation on CIFAR-10 , CelebA, and LSUN with varied network architectures. Not only the training is more stabilized, but the FID scores of generated samples are consistently improved by a large margin compared to the baseline. The bridging of adversarial samples and adversarial networks provides a new approach to further develop adversarial networks.

Published
2019
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22. A new opportunity for two-dimensional van der Waals heterostructures: making steep-slope transistors

Author

Lv, Juan, Pei, Jing, Guo, Yuzheng, Gong, Jian, and Li, Huanglong

Subjects
Condensed Matter - Materials Science
Abstract

The use of a foreign metallic cold source (CS) has recently been proposed as a promising approach toward the steep-slope field-effect-transistor (FET). In addition to the selection of source material with desired density of states-energy relation (D(E)), engineering the source: channel interface for gate-tunable channel-barrier is crucial to a CS-FET. However, conventional metal: semiconductor (MS)-interfaces generally suffer from strong Fermi-level-pinning due to the inevitable chemical disorder and defect-induced gap states, precluding the gate-tunability of the barriers. By comprehensive materials and device modeling at the atomic-scale, we report that the two-dimensional (2D)-van der Waals (vdW)-MS-interfaces, with their atomic sharpness and cleanness, can be considered as general ingredients for CS-FETs. As test cases, InSe-based n-type FETs are studied. It is found that graphene can be spontaneously p-type doped along with slightly opened bandgap around the Dirac-point by interfacing with InSe, resulting in super-exponentially decaying hot carrier density with increasing n-type channel-barrier. Moreover, the D(E) relations suggest that 2D-transition-metal-dichalcogenides and 2D-transition-metal-carbides are rich libraries of CS materials. Both graphene and H-TaTe2 CSs lead to subthreshold swing below 60 mV/decade. This work broadens the application potentials of 2D-vdW-MS-heterostructures and serves as a springboard for more studies on low-power electronics based on 2D materials., Comment: Advanced Materials (2020)

Published
2019
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31. Association of early-onset breast cancer with body mass index, menarche, and menopause in Taiwan

Author

Pei-Jing Yang, Ming-Feng Hou, Fu Ou-Yang, Eing-Mei Tsai, and Tsu-Nai Wang

Subjects
Breast cancer, Early-onset, BMI, Menarche, Menopause, Interaction, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background The trend of women suffering from early-onset breast cancer is increasing in Taiwan. The association of early-onset breast cancer with body mass index (BMI), menarche, and menopausal status has focused interest on the field of cancer epidemiology; however, few studies have explored the interaction of these factors on early-onset risk. This study aimed to estimate the interaction effects of BMI, menarche, and menopausal status on 40-year-old early-onset breast cancer. Methods Breast cancer patients were recruited from Kaohsiung Medical University Chung-Ho Memorial Hospital from 2013 to 2020. Multivariable logistic regression was used to estimate odds ratios (ORs) for early-onset breast cancer risk associated with menarcheal age stratified by sociodemographic factors and for the interaction between BMI and menopausal status on early-onset risk. Results A total of 775 participants were divided into 131 early-onset cases (≤ 40 years) and 644 late-onset cases (> 40 years). Compared to the age of 13 years at menarche, the age ≤ 11 years was significantly positively associated (OR: 2.62, 95% CI: 1.38–4.97) and ≥ 16 years was negatively associated (OR: 0.13, 95% CI: 0.03–0.53) with 40-year-old early-onset breast cancer respectively. In an adjusted model, the status of BMI

Published
2022
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32. Association between recurrent breast cancer and phthalate exposure modified by hormone receptors and body mass index

Author

Pei-Jing Yang, Ming-Feng Hou, Fu Ou-Yang, Tsung-Hua Hsieh, Yen-Jung Lee, Eing-Mei Tsai, and Tsu-Nai Wang

Subjects
Medicine, Science
Abstract

Abstract The association between phthalate exposure and breast cancer remains controversial. We performed a prospective patient cohort design to explore the interaction between creatinine-corrected urinary phthalate metabolites and hormone receptors as well as body mass index (BMI) on recurrent breast cancer. In this follow-up study, 636 female breast cancer patients and 45 new recurrent cases diagnosed for a total of 1576.68 person-years of follow-up were recruited. Mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP) was negatively associated with breast cancer recurrence, with adjusted hazard ratio (aHR) 3rd vs. 1st quartile of 0.15 (95% CI 0.04–0.51). The MEOHP presented as a non-monotonic dose–response (NMDR) curve, being U-shaped. In the stratification of hormone receptors, MEOHP still exhibited a U-shaped dose–response curve. The third quartile of MEOHP showed significant lowest recurrent risk in the status of ER-positive (aHR 0.18, 95% CI 0.05–0.66), PR-negative (aHR 0.14, 95% CI 0.03–0.63), and HER2-negative (aHR 0.24, 95% CI 0.08–0.76). Whether in BMI

Published
2022
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33. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework

Author

Deng, Lei, Jiao, Peng, Pei, Jing, Wu, Zhenzhi, and Li, Guoqi

Subjects
Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms., Comment: 11 pages, 13 figures

Published
2017

34. The mechanisms through which auricular vagus nerve stimulation protects against cerebral ischemia/reperfusion injury

Author

Jing-Jun Zhao, Zheng-Hui Wang, Ying-Jie Zhang, Wen-Jing Wang, Ai-Fang Cheng, Pei-Jing Rong, and Chun-Lei Shan

Subjects
auricular vagus nerve stimulation, connexin 43, gap junction, inflammatory factor, ischemia/reperfusion injury, neurological function, neuroprotection, stroke, Neurology. Diseases of the nervous system, RC346-429
Abstract

Previous studies have shown that vagus nerve stimulation can improve patients’ locomotor function. The stimulation of the auricular vagus nerve, which is the only superficial branch of the vagus nerve, may have similar effects to vagus nerve stimulation. However, the precise mechanisms remain poorly understood. In this study, rat models of cerebral ischemia/reperfusion injury were established by modified Longa ligation. Twenty-four hours later, 7-day auricular vagus nerve stimulation was performed. The results showed that auricular vagus nerve stimulation promoted the secretion of acetylcholine, inhibited the secretion of interleukin-1β, interleukin-6, and tumor necrosis factor-α, and reduced connexin 43 phosphorylation in the ischemic penumbra and motor cortex, promoting locomotor function recovery in rats with cerebral ischemia/reperfusion injury. These findings suggested that auricular vagus nerve stimulation promotes the recovery of locomotor function in rats with cerebral ischemia/reperfusion injury by altering the secretion of acetylcholine and inflammatory factors and the phosphorylation of connexin 43. This study was approved by the Animal Use and Management Committee of Shanghai University of Traditional Chinese Medicine on November 8, 2019 (approval No. PZSHUTCM191108014).

Published
2022
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35. T1-2N1M0 nasopharyngeal carcinoma chemotherapy or not: A retrospective study

Author

Pei-Jing Li, Ming Chen, and Ye Tian

Subjects
Medicine, Science
Abstract

Background Radiotherapy (RT) combined with chemotherapy is the standard treatment for T1-2N1M0 nasopharyngeal carcinoma (NPC) based on conventional radiotherapy. However, intensity-modulated radiotherapy (IMRT) has narrowed the treatment gap between RT and chemoradiotherapy. Thus, this retrospective study aimed to compare the efficacy of RT and chemoradiotherapy (RT-chemo) in treating T1-2N1M0 NPC in the IMRT era. Materials and methods From January 2008 to December 2016, 343 consecutive patients with T1-2N1M0 NPC in two cancer centers were included. All patients received RT or RT-chemo, chemotherapy including induction chemotherapy (IC) + concurrent chemoradiotherapy (CCRT), CCRT, or CCRT + adjuvant chemotherapy (AC). The number of patients who received RT, CCRT, IC + CCRT, and CCRT + AC was 114, 101, 89, and 39. The survival rates were analyzed using the Kaplan-Meier method and compared using the log-rank test. Multivariable analysis was performed to identify valuable prognostic factors. Results The median follow-up time for survivors was 93 (range: 55–144) months. The 5-year overall survival (OS), progression-free survival (PFS), locoregional failure-free survival (LRFFS), and distant metastasis-free survival (DMFS) for the RT-chemo and RT groups were 93.7%, 88.5%, 93.8%, 93.8% and 93.0%, 87.7%, 91.9%, 91.2%, respectively (P>0.05 for all outcomes). No significant survival differences were found between the two groups. The T1N1M0 or T2N1M0 subgroup analysis showed that treatment outcomes had no significant differences between the RT and RT-chemo groups. After adjusting for various factors, treatment mode was not identified as an independent prognostic factor for all survival rates. Conclusions In this study, outcomes of T1-2N1M0 NPC patients treated by IMRT alone were comparable to chemoradiotherapy, supporting the omission or postponement of chemotherapy.

Published
2023

36. The pro-inflammatory factors contribute to the EEG microstate abnormalities in patients with major depressive disorder

Author

Ya-Nan Zhao, Jia-Kai He, Yu Wang, Shao-Yuan Li, Bao-Hui Jia, Shuai Zhang, Chun-Lei Guo, Jin-Ling Zhang, Guo-Lei Zhang, Bin Hu, Ji-Liang Fang, and Pei-jing Rong

Subjects
EEG microstates, Functional brain networks, Pro-inflammatory factors, Major depressive disorder, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Pro-inflammatory factors may be associated with abnormalities in functional brain networks, which may be a mechanism in the pathogenesis of major depressive disorder (MDD). Electroencephalogram (EEG) microstates reflect the functioning of brain networks. However, the relationship between pro-inflammatory factors and the microstate abnormalities in patients with MDD is poorly understood. 24 MDD patients and 24 age-and sex-matched healthy controls (HC) were recruited. Montgomery-Asberg Depression Rating Scale(MADRS) were assessed. Serum (interleukin- 2(IL- 2), tumor necrosis factor-α (TNF-α) and hs-C-reactive protein (CRP)and EEG data were collected. K-means clustering was performed to characterize different microstates. For each microstate, duration, occurrence and coverage were estimated. Four microstates (e.g. A, B, C, D) were characterized, MDD group showed lower duration, occurrence and coverage of microstate B and microstate D, while higher duration of microstate A and microstate C and levels of IL-2, TNF-α, hs-CRP than HC group. The duration, occurrence and coverage of microstate D were negatively correlated with levels of pro-inflammatory factors (IL-2, TNF- α and hs- CRP) (all P

Published
2022
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37. Does acupuncture therapy affect peripheral inflammatory cytokines of major depressive disorder? A protocol for the systematic review and meta-analysis

Author

Ya-Nan Zhao, Shuai Zhang, Yu Chen, Yu Wang, Hao Chen, Yu-Ting Duan, Shao-Yuan Li, Zi-Xuan Zhang, Yi-Fei Wang, Chen Xin, Liang Li, and Pei-Jing Rong

Subjects
major depressive disorder, acupuncture, peripheral inflammatory cytokines, anti-inflammatory, selective serotonergic reuptake inhibitors (SSRI), Neurology. Diseases of the nervous system, RC346-429
Abstract

BackgroundAcupuncture is widely used as adjuvant therapy for major depressive disorder (MDD). There is robust evidence that inflammation is closely associated with MDD. To date, only a few numbers of studies have investigated the potential relationship between acupuncture and the change of inflammatory biomarkers in patients with MDD. Additionally, the results are inconsistent among studies. The current study aims to provide a comprehensive, systematic review of the association between acupuncture and changes in peripheral inflammation of patients with MDD, and clarify the alterations of inflammatory cytokines before and after acupuncture treatment by meta-analysis.Methods and analysisThis study will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible randomized controlled trials (RCTs) reporting acupuncture, with inflammatory cytokines as the outcome measured before and after intervention in patients with MDD, were searched in electronic databases, such as PubMed, Embase, Cochrane, SINOMED, Wanfang, China national knowledge infrastructure (CNKI), and Chongqing VIP (CQVIP). Primary outcomes of interest will be validated to measure the levels of inflammatory cytokines before and after acupuncture treatment in patients with MDD.DiscussionAcupuncture can drive anti-inflammatory effects, as well as symptom changes in MDD, which may represent a viable, multi-faceted treatment approach in MDD.Systematic review registration[PROSPERO], identifier [CRD42021289207 on 04 December 2021].

Published
2022
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39. Electroacupuncture relieves hyperalgesia by regulating neuronal–glial interaction and glutamate transporters of spinal dorsal horns in rats with acute incisional neck pain

Author

Jun-ying Wang, Jin-ling Zhang, Shu-ping Chen, Yong-hui Gao, Jian-liang Zhang, Yu Chen, Yue Zhang, Pei-jing Rong, and Jun-ling Liu

Subjects
glial cells, electroacupuncture, incisional neck pain, glutamate transporter-1, glutamate–aspartate transporter, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

ObjectiveGlial cells are involved in the analgesic effect of electroacupuncture (EA) in rats with chronic neurological pain. The objective of this study was to observe the role of neuronal–glial interaction and glutamate (Glu) transporters in EA-induced acute neck pain relief in rats.Materials and methodsMale rats were placed into the following five groups: control, model, EA Futu (LI18), EA Hegu (LI4)-Neiguan (PC6), and EA Zusanli (ST36)-Yanglingquan (GB34). The incisional neck pain model was established by making a longitudinal incision along the midline of the neck. The thermal pain threshold (TPT) was measured using a radiation heat detector. The immunoactivities of glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule 1 (Iba-1), neurokinin-1 receptor (NK-1R), Glu aspartate transporter (GLAST), and Glu transporter-1 (GLT-1) in the dorsal horns (DHs) of the cervico-spinal cord (C2–C5) were detected using immunofluorescence histochemistry. The expression levels of GFAP, Iba-1, GLAST, and GLT-1 mRNAs were determined using quantitative real-time polymerase chain reaction (PCR).ResultsThe TPT and levels of mRNAs expression and immunoactivity of GLT-1 and GLAST were significantly decreased, and those of Iba-1 and GFAP were significantly increased in the model group than those of the control group (P < 0.05). The activated microgliacytes were gathered around the NK-1R positive neurons, and co-expression of NK-1R and astrocytes was observed in the model group. EA LI18 significantly increased the TPT and expression of GLAST and GLT-1 mRNAs (P < 0.05) and notably decreased the number of Iba-1 positive cells and Iba-l mRNA expression (P < 0.05), whereas GLAST and GLT-1 antagonists inhibited the analgesic effect of EA LI18. However, these effects, except for the downregulation of Iba-1 mRNA, were not observed in the EA ST36-GB34 group. Fewer NK-1R-positive neurons were visible in the spinal DHs in the EA LI18 group, and the co-expression of NK-1R and astrocytes was also lower than that in the three EA groups.ConclusionElectroacupuncture of LI18 had an analgesic effect in rats with neck incisions, which may be related to its functions in suppressing the neuronal–glial cell interaction through NK-1R and upregulating the expression of GLAST and GLT-1 in the spinal DHs.

Published
2022
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40. Effects of the COVID-19 pandemic on elderly patients with head and neck squamous cell carcinoma

Author

Pei-Jing Ye, Yan Xi, Chuan-Zheng Sun, Qian Lei, and Lei Li

Subjects
Head and neck squamous cell carcinoma (HNSCC), COVID-19, diagnosis delays, treatment interruptions, elderly patients, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

BackgroundThe 2019 novel coronavirus disease (COVID-19) strongly affects health care activities in countries around the world. The diagnosis and treatment of cancer have also been involved, and elderly head and neck squamous carcinoma is one of them. This study aimed to assess the impact of COVID-19 on elderly patients with head and neck squamous cell carcinoma (HNSCC) in our center.MethodsThis retrospective study analyzed the clinical characteristics of 400 HNSCC patients over 65 years of age, calculated their treatment interruption rates, and compared the time of delayed diagnosis.ResultsThe rate of elderly patients with HNSCC with a delayed diagnosis was higher in the “during COVID-19 pandemic” group (DCOV19 group) than in the “during COVID-19 pandemic” group (BCOV19 group), and the difference was statistically significant (p=0.0017). There was a substantial difference in the rate of treatment interruption between the two groups (p=0.002).ConclusionsThis is the first study to explore the effect of the COVID-19 pandemic on visits and treatment interruptions in elderly patients with HNSCC. The current impact of the COVID-19 pandemic on HNSCC treatment has resulted in reductions and delays in diagnosing cancer and providing treatment.

Published
2022
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44. A scientometric study of the top 100 most-cited publications based on Web-of-Science regarding robotic versus laparoscopic surgery

Author

Lu-Fang Feng, Pei-Jing Yan, Xia-Jing Chu, Na Zhang, Jie-Yun Li, Jing-Wen Li, Kang-Le Guo, Cun-Cun Lu, Mei-Xuan Li, Tian-Kang Guo, Xing-Rong Liu, and Ke-Hu Yang

Subjects
Robotic, Surgery, Laparoscopic, Cancer, Scientometric, RD1-811
Abstract

Minimally invasive surgery includes traditional laparoscopic and robot-assisted surgery. Although many studies related to robotic surgery and laparoscopic surgery have been published, when doing our search, scientometric studies that focus on related robotic surgery versus laparoscopic surgery were limited. In this study, we aimed to analyze and review the research hots and research status of robotic surgery versus laparoscopic surgery. We searched publications that involved robotic surgery versus laparoscopic surgery in the Web of Science database from 1980 to May 23, 2020. The top 100 publications were published in 2012 with the number of 17 and citations ranged from 618 to 64. Published across 34 different journals, namely European urology (n = 17) and others, the greatest contribution among 36 institutes was made by the Cleveland Clinic (n = 11). Of the top 100 publications, a total of 429 unique words were identified and the most frequently occurring keyword was laparoscopy (n = 33). The co-occurrence of keywords in the top 100 publications indicated that the study of diseases mainly focused on prostatectomy, complications, prostate cancer, retropubic prostatectomy, nephron-sparing surgery, lymph-node dissection, total mesenteric excision, sexual function, rectal cancer, and assisted distal gastrectomy. In recent years, comparative research on robot and laparoscopic surgery has decreased and most studies focus on cancer.

Published
2021
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45. Metabonomic Analysis of Macrobrachium rosenbergii with Iron Prawn Syndrome (IPS)

Author

Xi-Lian Li, Pei-Jing Shen, Wen-Ping Jiang, Ji-Lun Meng, Hai-Hua Cheng, and Qiang Gao

Subjects
Macrobrachium rosenbergii, metabonomic, transcriptome, LC–MS/MS, iron prawn syndrome, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

We previously reported on the comparison of proteomic data between seven tissue types of a novel “iron prawn” species. However, no transcripts or metabolic information are available for this species. We therefore performed shotgun LC–MS/MS metabonomic and RNA-seq analyses of the total protein from “iron prawns”. KEGG analysis revealed that the largest group consisted of a total of 114 KEGG pathway proteins, comparing the “iron prawns” with the normal prawns. A total of 423 peptides, corresponding to metabolic pathways, ABC transporters, starch and sucrose metabolism, insulin resistance/secretion, fatty digestion and absorption, and lipid metabolism, were identified. The pathways of carbohydrate and amino acid metabolism decreased in female iron prawns, while organic acid and its derivatives increased. However, the pathway of organic acid and its derivatives decreased and lipid metabolism increased in the male iron prawns. The pathways of choline metabolism in cancer and glycerophospholipid/histidine/propanoate metabolism have been significantly affected in iron prawns. Our work provides insight into the understanding of the formation mechanism of the “iron prawn”.

Published
2023
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46. A druggable cascade links methionine metabolism to epigenomic reprogramming in squamous cell carcinoma.

Author

Chehyun Nam, Li-Yan Li, Qian Yang, Ziman, Benjamin, Hua Zhao, Boyan Hu, Collet, Casey, Pei Jing, Qifang Lei, Li-Yan Xu, En-Min Li, Koeffler, H. Phillip, Sinha, Uttam K., and De-Chen Lin

Subjects
METHIONINE metabolism, SQUAMOUS cell carcinoma, AMINO acid metabolism, PROMOTERS
Abstract

Upper aerodigestive squamous cell carcinoma (UASCC) is a common and aggressive malignancy with few effective therapeutic options. Here, we investigate amino acid metabolism in this cancer, surprisingly noting that UASCC exhibits the highest methionine level across all human cancers, driven by its transporter LAT1. We show that LAT1 is also expressed at the highest level in UASCC, transcriptionally activated by UASCC-specific promoter and enhancers, which are directly coregulated by SCC master regulators TP63/KLF5/SREBF1. Unexpectedly, unbiased bioinformatic screen identifies EZH2 as the most significant target downstream of the LAT1-methionine pathway, directly linking methionine metabolism to epigenomic reprogramming. Importantly, this cascade is indispensable for the survival and proliferation of UASCC patient-derived tumor organoids. In addition, LAT1 expression is closely associated with cellular sensitivity to inhibition of the LAT1 -methionine-EZH2 axis. Notably, this unique LAT1-methionine-EZH2 cascade can be targeted effectively by either pharmacological approaches or dietary intervention in vivo. In summary, this work maps a unique mechanistic cross talk between epigenomic reprogramming with methionine metabolism, establishes its biological significance in the biology of UASCC, and identifies a unique tumor-specific vulnerability which can be exploited both pharmacologically and dietarily. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Transcutaneous Auricular Vagus Nerve Stimulation Modulates the Prefrontal Cortex in Chronic Insomnia Patients: fMRI Study in the First Session

Author

Jia-Kai He, Bao-Hui Jia, Yu Wang, Shao-Yuan Li, Bin Zhao, Zeng-Guang Zhou, Yan-Zhi Bi, Mo-Zheng Wu, Liang Li, Jin-Ling Zhang, Ji-Liang Fang, and Pei-Jing Rong

Subjects
chronic insomnia, transcutaneous auricular vagus nerve stimulation, functional magnetic resonance imaging (fMRI), biomarkers, prefrontal cortex, neuromodulation, Neurology. Diseases of the nervous system, RC346-429
Abstract

ObjectivesTranscutaneous auricular vagus nerve stimulation (taVNS) has been reported to be effective for chronic insomnia (CI). However, the appropriate population for taVNS to treat insomnia is unclear.MethodsTotal twenty-four patients with CI and eighteen health controls (HC) were recruited. Rest-state functional magnetic resonance imaging (Rs-fMRI) was performed before and after 30 min' taVNS at baseline. The activated and deactivated brain regions were revealed by different voxel-based analyses, then the seed-voxel functional connectivity analysis was calculated. In the CI group, 30 min of taVNS were applied twice daily for 4 weeks. Pittsburgh Sleep Quality Index (PSQI) and Flinders Fatigue Scale (FFS) were also assessed before and after 4 weeks of treatment in the CI group. The HC group did not receive any treatment. The correlations were estimated between the clinical scales' score and the brain changes.ResultsThe scores of PSQI (p < 0.01) and FFS (p < 0.05) decreased after 4 weeks in the CI group. Compared to the HC group, the first taVNS session up-regulated left dorsolateral prefrontal cortex (dlPFC) and decreased the functional connectivity (FCs) between dlPFC and bilateral medial prefrontal cortex in the CI group. The CI groups' baseline voxel wised fMRI value in the dlPFC were negatively correlated to the PSQI and the FFS score after 4 weeks treatment.ConclusionsIt manifests that taVNS has a modulatory effect on the prefrontal cortex in patients with CI. The initial state of dlPFC may predict the efficacy for taVNS on CI.

Published
2022
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50. Evaluation of intra- and post-operative outcomes to compare robot-assisted surgery and conventional laparoscopy for gynecologic oncology

Author

Liu-Li Wang, Pei-Jing Yan, Liang Yao, Rong Liu, Fan Hou, Xiao-Hong Chen, Liang-liang Han, Li-Yuan Xu, Hui Xu, Jing Li, Tian-Kang Guo, Ke-Hu Yang, and Hai-Lin Wang

Subjects
Surgery, RD1-811
Abstract

Summary: Objective: To compare robot-assisted surgery and conventional laparoscopy for gynecologic oncology regarding intra- and post-operative outcomes. Methods: A retrospective study was performed on consecutive patients with gynecologic oncology from February 2014 to October 2017 at Gansu Provincial Hospital, China. Multivariable linear and logistic regression models were performed to explore the difference between two surgeries in the surgical outcomes after adjusting for potential confounders. Results: 276 women were included in this study: 153 robot-assisted surgeries and 123 conventional laparoscopies. The multivariable linear regression model showed that robot-assisted surgery was longer operative time [coefficient (coef), 33.76; 95% CI, 12.47, 55.05; P = 0.002) ], higher lymph node yield (coef, 10.41; 95% CI, 7.47, 13.35; P 0.05). The multivariable logistic regression model showed that post-operative complications were similar between robot-assisted surgery and conventional laparoscopy (P > 0.05). Conclusion: Robot-assisted surgery was superior to conventional laparoscopy regarding intra- and post-operative outcomes for gynecologic oncology. Keywords: Early feeding, Gynecologic oncology, Laparoscopy, Minimally invasive surgery, Robotic-assisted surgery

Published
2020
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