FK506 contributes to peripheral nerve regeneration by inhibiting neuroinflammatory responses and promoting neuron survival

FK506(Tacrolimus)is a systemic immunosuppressant approved by the U.S.Food and Drug Administration.FK506 has been shown to promote peripheral nerve regeneration,however,its precise mechanism of action and its pathways remain unclear.In this study,we established a rat model of sciatic nerve injury and found that FK506 improved the morphology of the injured sciatic nerve,increased the numbers of motor and sensory neurons,reduced inflammatory responses,markedly improved the conduction function of the injured nerve,and promoted motor function recovery.These findings suggest that FK506 promotes peripheral nerve structure recovery and functional regeneration by reducing the intensity of inflammation after neuronal injury and increasing the number of surviving neurons.

Inhibitory gamma-aminobutyric acidergic neurons in the anterior cingulate cortex participate in the comorbidity of pain and emotion

Pain is often comorbid with emotional disorders such as anxiety and depression.Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairments in inhibitory gamma-aminobutyric acid neurotransmission.This review primarily aims to outline the main circuitry(including the input and output connectivity)of the anterior cingulate cortex and classification and functions of different gamma-aminobutyric acidergic neurons;it also describes the neurotransmitters/neuromodulators affecting these neurons,their intercommunication with other neurons,and their importance in mental comorbidities associated with chronic pain disorders.Improving understanding on their role in pain-related mental comorbidities may facilitate the development of more effective treatments for these conditions.However,the mechanisms that regulate gamma-aminobutyric acidergic systems remain elusive.It is also unclear as to whether the mechanisms are presynaptic or postsynaptic.Further exploration of the complexities of this system may reveal new pathways for research and drug development.

Many faces of neuronal activity manipulation in Drosophila

Animals exhibit complex responses to external and internal stimuli.The information is computed by interconnected neurons that express numerous ion channels,which modulate the neuronal membrane potential.How can neuronal activity orchestrate complex motor patterns or allow learning from previous experience?To answer such questions,we need the ability not only to record,but also to modulate neuronal activity in both space(e.g.,neuronal subsets)and time.

Small molecule inhibitor DDQ-treated hippocampal neuronal cells show improved neurite outgrowth and synaptic branching

The process of neurite outgrowth and branching is a crucial aspect of neuronal development and regeneration.Axons and dendrites,sometimes referred to as neurites,are extensions of a neuron's cellular body that are used to start networks.Here we explored the effects of diethyl(3,4-dihydroxyphenethylamino)(quinolin-4-yl)methylphosphonate(DDQ)on neurite developmental features in HT22 neuronal cells.In this work,we examined the protective effects of DDQ on neuronal processes and synaptic outgrowth in differentiated HT22cells expressing mutant Tau(mTau)cDNA.To investigate DDQ chara cteristics,cell viability,biochemical,molecular,western blotting,and immunocytochemistry were used.Neurite outgrowth is evaluated through the segmentation and measurement of neural processes.These neural processes can be seen and measured with a fluorescence microscope by manually tracing and measuring the length of the neurite growth.These neuronal processes can be observed and quantified with a fluorescent microscope by manually tracing and measuring the length of the neuronal HT22.DDQ-treated mTau-HT22 cells(HT22 cells transfected with cDNA mutant Tau)were seen to display increased levels of synaptophysin,MAP-2,andβ-tubulin.Additionally,we confirmed and noted reduced levels of both total and p-Tau,as well as elevated levels of microtubule-associated protein 2,β-tubulin,synaptophysin,vesicular acetylcholine transporter,and the mitochondrial biogenesis protein-pe roxisome prolife rator-activated receptor-gamma coactivator-1α.In mTa u-expressed HT22 neurons,we observed DDQ enhanced the neurite characteristics and improved neurite development through increased synaptic outgrowth.Our findings conclude that mTa u-HT22(Alzheimer's disease)cells treated with DDQ have functional neurite developmental chara cteristics.The key finding is that,in mTa u-HT22 cells,DDQ preserves neuronal structure and may even enhance nerve development function with mTa u inhibition.

Induced pluripotent stem cell-related approaches to generate dopaminergic neurons for Parkinson's disease

The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear,the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy.The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons,which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies.The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells.The benefits of induced pluripotent stem cell-based research are highlighted.Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared.The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated.Finally,limitations,challenges,and future directions of induced pluripotent stem cell–based approaches are analyzed and proposed,which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.

NOX4 exacerbates Parkinson's disease pathology by promoting neuronal ferroptosis and neuroinflammation

Parkinson's disease is primarily caused by the loss of dopaminergic neurons in the substantia nigra compacta.Ferroptosis,a novel form of regulated cell death characterized by iron accumulation and lipid peroxidation,plays a vital role in the death of dopaminergic neurons.However,the molecular mechanisms underlying ferroptosis in dopaminergic neurons have not yet been completely elucidated.NADPH oxidase 4 is related to oxidative stress,however,whether it regulates dopaminergic neuronal ferroptosis remains unknown.The aim of this study was to determine whether NADPH oxidase 4 is involved in dopaminergic neuronal ferroptosis,and if so,by what mechanism.We found that the transcriptional regulator activating transcription factor 3 increased NADPH oxidase 4 expression in dopaminergic neurons and astrocytes in an 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine-induced Parkinson's disease model.NADPH oxidase 4 inhibition improved the behavioral impairments observed in the Parkinson's disease model animals and reduced the death of dopaminergic neurons.Moreover,NADPH oxidase 4 inhibition reduced lipid peroxidation and iron accumulation in the substantia nigra of the Parkinson's disease model animals.Mechanistically,we found that NADPH oxidase 4 interacted with activated protein kinase Cαto prevent ferroptosis of dopaminergic neurons.Furthermore,by lowering the astrocytic lipocalin-2 expression,NADPH oxidase 4 inhibition reduced 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine-induced neuroinflammation.These findings demonstrate that NADPH oxidase 4 promotes ferroptosis of dopaminergic neurons and neuroinflammation,which contribute to dopaminergic neuron death,suggesting that NADPH oxidase 4 is a possible therapeutic target for Parkinson's disease.

Inhibition of the NLRP3 inflammasome attenuates spiral ganglion neuron degeneration in aminoglycoside-induced hearing loss

Aminoglycosides are a widely used class of antibacterials renowned for their effectiveness and broad antimicrobial spectrum.However,their use leads to irreversible hearing damage by causing apoptosis of hair cells as their direct target.In addition,the hearing damage caused by aminoglycosides involves damage of spiral ganglion neurons upon exposure.To investigate the mechanisms underlying spiral ganglion neuron degeneration induced by aminoglycosides,we used a C57BL/6J mouse model treated with kanamycin.We found that the mice exhibited auditory deficits following the acute loss of outer hair cells.Spiral ganglion neurons displayed hallmarks of pyroptosis and exhibited progressive degeneration over time.Transcriptomic profiling of these neurons showed significant upregulation of genes associated with inflammation and immune response,particularly those related to the NLRP3 inflammasome.Activation of the canonical pyroptotic pathway in spiral ganglion neurons was observed,accompanied by infiltration of macrophages and the release of proinflammatory cytokines.Pharmacological intervention targeting NLRP3 using Mcc950 and genetic intervention using NLRP3 knockout ameliorated spiral ganglion neuron degeneration in the injury model.These findings suggest that NLRP3 inflammasome-mediated pyroptosis plays a role in aminoglycoside-induced spiral ganglion neuron degeneration.Inhibition of this pathway may offer a potential therapeutic strategy for treating sensorineural hearing loss by reducing spiral ganglion neuron degeneration.

FUBP3 mediates the amyloid-β-induced neuronal NLRP3 expression

Alzheimer's disease is characterized by deposition of amyloid-β,which forms extracellular neuritic plaques,and accumulation of hyperphosphorylated tau,which aggregates to form intraneuronal neurofibrillary tangles,in the brain.The NLRP3 inflammasome may play a role in the transition from amyloid-βdeposition to tau phosphorylation and aggregation.Because NLRP3 is primarily found in brain microglia,and tau is predominantly located in neurons,it has been suggested that NLRP3 expressed by microglia indirectly triggers tau phosphorylation by upregulating the expression of pro-inflammatory cytokines.Here,we found that neurons also express NLRP3 in vitro and in vivo,and that neuronal NLRP3 regulates tau phosphorylation.Using biochemical methods,we mapped the minimal NLRP3 promoter and identified FUBP3 as a transcription factor regulating NLRP3 expression in neurons.In primary neurons and the neuroblastoma cell line Neuro2A,FUBP3 is required for endogenous NLRP3 expression and tau phosphorylation only when amyloid-βis present.In the brains of aged wild-type mice and a mouse model of Alzheimer's disease,FUBP3 expression was markedly increased in cortical neurons.Transcriptome analysis suggested that FUBP3 plays a role in neuron-mediated immune responses.We also found that FUBP3 trimmed the 5′end of DNA fragments that it bound,implying that FUBP3 functions in stress-induced responses.These findings suggest that neuronal NLRP3 may be more directly involved in the amyloid-β-to–phospho-tau transition than microglial NLRP3,and that amyloid-βfundamentally alters the regulatory mechanism of NLRP3 expression in neurons.Given that FUBP3 was only expressed at low levels in young wild-type mice and was strongly upregulated in the brains of aged mice and Alzheimer's disease mice,FUBP3 could be a safe therapeutic target for preventing Alzheimer's disease progression.

Transforming growth factor-beta 1 enhances discharge activity of cortical neurons

Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may depend on the pathological process and cell types involved.Voltage-gated sodium channels(VGSCs)are essential ion channels for the generation of action potentials in neurons,and are involved in various neuroexcitation-related diseases.However,the effects of TGF-β1 on the functional properties of VGSCs and firing properties in cortical neurons remain unclear.In this study,we investigated the effects of TGF-β1 on VGSC function and firing properties in primary cortical neurons from mice.We found that TGF-β1 increased VGSC current density in a dose-and time-dependent manner,which was attributable to the upregulation of Nav1.3 expression.Increased VGSC current density and Nav1.3 expression were significantly abolished by preincubation with inhibitors of mitogen-activated protein kinase kinase(PD98059),p38 mitogen-activated protein kinase(SB203580),and Jun NH2-terminal kinase 1/2 inhibitor(SP600125).Interestingly,TGF-β1 significantly increased the firing threshold of action potentials but did not change their firing rate in cortical neurons.These findings suggest that TGF-β1 can increase Nav1.3 expression through activation of the ERK1/2-JNK-MAPK pathway,which leads to a decrease in the firing threshold of action potentials in cortical neurons under pathological conditions.Thus,this contributes to the occurrence and progression of neuroexcitatory-related diseases of the central nervous system.

The burden of upper motor neuron involvement is correlated with the bilateral limb involvement interval in patients with amyotrophic lateral sclerosis:a retrospective observational study

Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives and may lead them to be confined to bed.However,the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear.To address this issue,we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022.A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis.We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients.Multiple factor analyses revealed that higher upper motor neuron scores(hazard ratio[HR]=1.05,95%confidence interval[CI]=1.01–1.09,P=0.018),onset in the left limb(HR=0.72,95%CI=0.58–0.89,P=0.002),and a horizontal pattern of progression(HR=0.46,95%CI=0.37–0.58,P<0.001)were risk factors for a shorter interval until bilateral limb involvement.The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients.These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.

DAMAGE DETECTION IN STRUCTURES USING MODIFIED BACK-PROPAGATION NEURAL NETWORKS

A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.

Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.

Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.

Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks

In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. Three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.

A hybrid model for short-term rainstorm forecasting based on a back-propagation neural network and synoptic diagnosis

Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network(BPNN)with synoptic diagnosis for predicting rainstorms,and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study.Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases,and the threat score(TS)of rainstorms was more than 0.75.However,the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%.The BPNN method efficiently forecasted these two rainstorm types;the TS and equitable threat score(ETS)of rainstorms were 0.80 and 0.79,respectively.The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception.This kind of hybrid model enhanced the forecasting accuracy of rainstorms.The findings of this study provide certain reference value for the future development of refined forecast models with local features.

Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.

Predict typhoon-induced storm surge deviation in a principal component back-propagation neural network model

To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.

Preparation of ZrB_2-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network

Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive（including CoSO4·7H2O,Y2O3 and TiO2）was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks（BP-ANNs）was adopted to establish a model for predicting the reaction extent（e g,the content of ZrB2-SiC in final product）in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment（with a mean square error in 10^（-3） degree）,verifying good effectiveness of the proposed strategy.

A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.