Pain management in animal experimentation is crucial for both ethical and scientific reasons, as unmanaged pain can distort physiological responses compromising data reliability. Current strategies are often invasive and pharmacology-based, introducing variability and confounding effects. Here, we present Light-Induced Analgesia, a drug-free, non-invasive method for pain relief in animals. We show that 365 nm illumination activates the pain-inhibitory TRAAK two-pore domain potassium (K2P) channel. This activation is driven by the oxidation of a native methionine at TRAAK’s regulatory fenestration site, triggering a conformational switch from its inactive (down) to active (up) state. We further demonstrate that this mechanism can be transferred to other related K2Ps via a single-point mutation, rendering them light-sensitive. In rodents, gentle skin exposure to 365 nm is sufficient to activate endogenous TRAAK, silence nociceptors, and produce potent, long-lasting analgesia that outperforms standard treatments. Light-Induced Analgesia thus offers an effective, drug-free alternative that can enhance animal welfare and experimental reliability in preclinical research. Membrane ion channels can be responsive to a variety of stimuli such as pressure, temperature, or pH. Here, the authors show that simply shining 365 nm light activates a native potassium channel in rodent pain-sensing neurons, delivering powerful analgesia without drugs or genetic manipulations.

BACKGROUND:Noxious stimuli are conveyed to and integrated in the dorsal horn of the spinal cord before being transmitted to supraspinal centres, where pain perception is generated. Descending pathways from the brainstem dynamically modulate this integration, either facilitating or inhibiting nociceptive information based on physiological, emotional, genetic and environmental factors. Serotonergic neurones in the nucleus raphe magnus (NRM), activating different spinal 5-hydroxytryptamine (5-HT) receptors, exert bidirectional control, both facilitatory and inhibitory, but the underlying mechanisms of this control remain unclear. METHODS:Serotonergic modulation by the NRM of nociception was investigated in adult mice using imaging, behavioural, pharmacological, electrophysiological, chemogenetic and optogenetic approaches. RESULTS:The action of serotonergic neurones in the NRM on spinal nociceptive transmission depends on their activation pattern, which targets different spinal 5-HT receptors likely associated with different spinal microcircuits. Serotonergic neurones of the NRM exert a tonic analgesic effect mediated by 5-HT2c receptor. Low increases in 5-HT activity led to increased analgesia through spinal inhibitory interneurones expressing 5-HT2c and 5-HT2A receptors. Prolonged stimulation of serotonergic neurones led to hyperalgesia mediated by 5-HT3 receptors. Comparison of 5-HT receptors in spinal tissue from mice and humans shows that 5-HT2c receptors have high expression level, comparable between both species. CONCLUSIONS:These results propose a bidirectional model of action by serotonin neurones of nociceptive transmission depending on their level of activity and show that 5-HT2c receptors mediate the serotonin-induced analgesia.

The subthalamic nucleus (STN), considered a primarily motor structure within the basal ganglia, is recognized as a key contributor to a wider set of behaviors. Among these, nociceptive processing has gained particular attention, especially given the high prevalence and early emergence of pain in Parkinson’s disease (PD). This review brings together anatomical, physiological and translational evidence to examine how the STN integrates motor and nociceptive information. The STN receives ascending afferents from brainstem nociceptive pathways and its activity tracks the intensity and salience of noxious stimuli, recruiting cortical and limbic networks that shape both sensory and affective dimensions of pain. In PD, the organization and dynamics of the STN are profoundly disrupted. Abnormal bursting and exaggerated beta oscillations, central to motor symptoms, also appear to promote central sensitization and enhanced pain responses. Studies from animal models consistently support the notion that parkinsonian states perturbate nociceptive signaling within the STN. Deep brain stimulation (DBS) of the STN, a well-established therapy for motor symptoms, further demonstrates the involvement of the nucleus in the pathophysiology of pain. Evidence from clinical and preclinical studies indicates that STN-DBS attenuates nociceptive hypersensitivity and modulates pain processing at spinal level, suggesting an intrinsic analgesic action rather than a secondary effect of motor improvement. Finally, recent optogenetic and chemogenetic approaches clarified how therapeutic interventions act on STN circuits, showing that symptom relief is linked to the suppression of pathological firing patterns. Together, these findings position the STN as a central node linking motor and pain networks in PD.

Chronic pain is a major public health issue, and despite advances in understanding its pathophysiology, current treatments remain insufficient, significantly affecting patients' quality of life. Existing therapies, including opioids, antidepressants and non-steroidal anti-inflammatory drugs, target specific mechanisms but fail to address the multifactorial nature of chronic pain, which is often accompanied by comorbidities like depression and anxiety. In cases like neuropathic pain, where pharmacological treatments are ineffective, alternatives such as deep brain stimulation (DBS) have gained attention. Although widely used for movement disorders, particularly in Parkinson's disease, DBS has the potential to treat pain by targeting identified deep brain structures while minimizing side effects. Neuropathic pain is linked to changes in several brain networks making up the so-called pain matrix, which includes the thalamus, the cornerstone of sensory, emotional and cognitive dimensions. This review focuses on the use of DBS of the thalamus and closely associated brain structures, such as the periaqueductal and periventricular gray, anterior cingulate cortex and insula, to treat pain.

Pain is a common non-motor symptom in Parkinson’s disease (PD), yet treatment options remain limited due to incomplete understanding of underlying mechanisms. Using the 6-hydroxydopamine (6-OHDA) rat model, we combined pharmacological, behavioural, chemogenetic, electrophysiological, and immunohistochemical approaches to investigate dopaminergic modulation of nociception by the hypothalamic A11 projecting to the dorsal horn of the spinal cord (DHSC). We demonstrate that A11 dopaminergic neurons are the sole source of dopamine in the DHSC. Activation of both D1 and D2 receptors alleviated mechanical allodynia, while only D2 receptor stimulation improved thermal hyperalgesia and normalized wide dynamic range (WDR) neuron hyperexcitability. Selective chemogenetic activation of the A11-DHSC pathway reduced nociceptive hypersensitivity and improved WDR neuronal function in 6-OHDA rats. These findings establish a critical role of spinal dopaminergic signaling in PD-related pain and highlight the A11 region as a potential therapeutic target for pain management in PD.

A robust inverse method for the complex wavenumber space (complex k-space) extraction is essential for structural vibration and damping analysis of two-dimensional structures. Most existing methods suffer from extracting the reliable complex k-space of plates in the presence of realistic uncertainties, especially for plates with low damping properties. To this end, this paper presents a new method for extracting the dispersion and damping characteristics of two-dimensional periodic structures using only the full-field displacement fields as input. The proposed method, the Algebraic K-Space Identification 2D technique (AKSI 2D), is an extension of the Algebraic Wavenumber Identification technique to solve two-dimensional problems. The optimised formulas are developed within the algebraic identification framework, which allows the extraction of all the properties of the complex k-space in a comprehensive way. The proposed method is validated numerically and experimentally, and its performances are compared with other popular k-space identification methods under different uncertainty conditions. The test cases cover analytically solved isotropic fields to numerically solve orthotropic fields and finally experimental measurements. The different cases show promising results and demonstrate that the proposed method is a robust tool to characterise the wave propagation of two-dimensional structures under stochastic structural and constitution conditions.

Brainstem vestibulospinal (VS) nuclei generate excitatory commands in response to multi-modality sensory integration, to activate specific spinal networks in order to generate adapted postural reflexes. Comparably organized in bilateral nuclei with both ipsi- and contralateral pathways in all species, excitatory VS projections alone fail to explain the mostly unilateral reflex responses typically observed. In the Xenopus laevis tadpole, we describe secondary vestibular neurons of inhibitory nature, and the synaptic contacts they make on VS neurons. Then, using a brainstem/spinal cord in vitro preparation we show that the spinal responses evoked by galvanic vestibular stimulation are shaped by both commissural and local inhibitory brainstem networks. We further show that a complex interaction between GABAergic and glycinergic inhibitory networks regulate VS neuron excitability and, consequently, the expression of the spinal response. Our data reveal that while excitatory VS neurons execute the neural score, inhibitory neurons in the central vestibular system coordinate and modulate the overall performance. ### Competing Interest Statement The authors have declared no competing interest.

The dataset presented contains the experimental structural response, in the frequency domain, of a suspended steel plate to a point force excitation. The plate is excited by a mechanical point force generated by a Brüel & kJær shaker with a white noise signal input from 3.125 Hz to 2000 Hz. The out-of-plate displacement fields on a 2D grid were measured using a Polytec PSV-400 Scanning Vibrometer. Finally, the displacement fields are acquired by a Fourier analyser connected to a sampler. The dataset provided is a useful resource for researchers to study the structural dynamic behaviour of large thin plates in the frequency domain and to validate the effectiveness of wavenumber identification methods. Its value has been illustrated in the research paper "Algebraic K-Space Identification 2D technique for the automatic extraction of complex k-space of 2D structures in presence of uncertainty" [1]. The data collection was carried out during three weeks in April 2022 at the Ecole Central de Lyon.

Parkinson’s disease arises from the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to motor symptoms such as akinesia, rigidity, and tremor at rest. The non-motor component of Parkinson’s disease includes increased neuropathic pain, the prevalence of which is 4 to 5 times higher than the general rate. By studying a mouse model of Parkinson’s disease induced by 6-hydroxydopamine, we assessed the impact of dopamine depletion on pain modulation. Mice exhibited mechanical hypersensitivity associated with hyperexcitability of neurons in the dorsal horn of the spinal cord (DHSC). Serotonin (5-HT) levels increased in the spinal cord, correlating with reduced tyrosine hydroxylase (TH) immunoreactivity in the nucleus raphe magnus (NRM) and increased excitability of 5-HT neurons. Selective optogenetic inhibition of 5-HT neurons attenuated mechanical hypersensitivity and reduced DHSC hyperexcitability. In addition, the blockade of 5-HT2A and 5-HT3 receptors reduced mechanical hypersensitivity. These results reveal, for the first time, that PD-like dopamine depletion triggers spinal-mediated mechanical hypersensitivity, associated with serotonergic hyperactivity in the NRM, opening up new therapeutic avenues for Parkinson’s disease-associated pain targeting the serotonergic systems.

The reliable estimation of the wavenumber space(k-space)of the plates remains a long-term concern for acoustic modeling and structural dynamic behavior characterization.Most current analyses of wavenumber identification methods are based on the deterministic hypothesis.To this end,an inverse method is proposed for identifying wave propagation characteristics of two-dimensional structures under stochastic conditions,such as wavenumber space,dispersion curves,and band gaps.The proposed method is developed based on an algebraic identification scheme in the polar coordinate system framework,thus named Algebraic K-Space Identification(AKSI)technique.Additionally,a model order estimation strategy and a wavenumber filter are proposed to ensure that AKSI is successfully applied.The main benefit of AKSI is that it is a reliable and fast method under four stochastic conditions:(A)High level of signal noise;(B)Small perturbation caused by uncertainties in measurement points'coordinates;(C)Non-periodic sampling;(D)Unknown structural periodicity.To validate the proposed method,we numerically benchmark AKSI and three other inverse methods to extract dispersion curves on three plates under stochastic conditions.One experiment is then performed on an isotropic steel plate.These investigations demonstrate that AKSI is a good in-situ k-space estimator under stochastic conditions.

In today's industrial landscape, the proactive implementation of predictive maintenance techniques is imperative, especially in the context of pipe systems, as companies increasingly embrace cutting-edge technologies such as artificial intelligence and the Internet of Things (IoT). Orano/La Hague, like many other industry leaders, recognizes the vital importance of integrating these technological advancements into their operations. One of the critical challenges they face relates to recurrent pipe-clogging incidents, leading to energy inefficiencies and financial losses. Addressing maintenance needs proactively is essential to mitigate risks and ensure the safety of both personnel and valuable assets. This research addresses these challenges by introducing an innovative hybrid approach that combines data-centric and model-centric methodologies for the continuous prognostics and monitoring of pipeline systems. Leveraging experimental passive acceleration measurements, this approach offers a reliable means to predict and assess the severity of clogs as they occur. To enhance the accuracy of predictions, a sliding window technique is employed to minimize noise and extract pertinent features from the data. The results of this study highlight the exceptional effectiveness of the proposed approach in accurately predicting clogging incidents and quantifying their severity, even in scenarios involving varying airflow rates within the pipes. This research marks a significant step forward in the domain of prognostics and health monitoring, with the potential for widespread applications across various industries. The integration of data-centric and model-centric approaches represents a promising solution to the complex challenge of predicting and preventing pipe-clogging incidents, ultimately contributing to enhanced operational efficiency and asset protection

Chronic pain is a pathological state defined as daily pain sensation over three consecutive months. It affects up to 30% of the general population. Although significant research efforts have been made in the past 30 years, only a few and relatively low effective molecules have emerged to treat chronic pain, with a considerable translational failure rate. Most preclinical models have focused on sensory neurotransmission, with particular emphasis on the dorsal horn of the spinal cord as the first relay of nociceptive information. Beyond impaired nociceptive transmission, chronic pain is also accompanied by numerous comorbidities, such as anxiety–depressive disorders, anhedonia and motor and cognitive deficits gathered under the term “pain matrix”. The emergence of cutting-edge techniques assessing specific neuronal circuits allow in-depth studies of the connections between “pain matrix” circuits and behavioural outputs. Pain behaviours are assessed not only by reflex-induced responses but also by various or more complex behaviours in order to obtain the most complete picture of an animal’s pain state. This review summarises the latest findings on pain modulation by brain component of the pain matrix and proposes new opportunities to unravel the mechanisms of chronic pain.

BACKGROUND:Pain is a non-motor symptom that impairs quality of life in Parkinson's patients. Pathological nociceptive hypersensitivity in patients could be due to changes in the processing of somatosensory information at the level of the basal ganglia, including the subthalamic nucleus (STN), but the underlying mechanisms are not yet defined. Here, we investigated the interaction between the STN and the dorsal horn of the spinal cord (DHSC), by first examining the nature of STN neurons that respond to peripheral nociceptive stimulation and the nature of their responses under normal and pathological conditions. Next, we studied the consequences of deep brain stimulation (DBS) of the STN on the electrical activity of DHSC neurons. Then, we investigated whether the therapeutic effect of STN-DBS would be mediated by the brainstem descending pathway involving the rostral ventromedial medulla (RVM). Finally, to better understand how the STN modulates allodynia, we used Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) expressed in the STN. METHODS:The study was carried out on the 6-OHDA rodent model of Parkinson's disease, obtained by stereotactic injection of the neurotoxin into the medial forebrain bundle of rats and mice. In these animals, we used motor and nociceptive behavioral tests, in vivo electrophysiology of STN and wide dynamic range (WDR) DHSC neurons in response to peripheral stimulation, deep brain stimulation of the STN and the selective DREADD approach. Vglut2-ires-cre mice were used to specifically target and inhibit STN glutamatergic neurons. RESULTS:STN neurons are able to detect nociceptive stimuli, encode their intensity and generate windup-like plasticity, like WDR neurons in the DHSC. These phenomena are impaired in dopamine-depleted animals, as the intensity response is altered in both spinal and subthalamic neurons. Furthermore, As with L-Dopa, STN-DBS in rats ameliorated 6-OHDA-induced allodynia, and this effect is mediated by descending brainstem projections leading to normalization of nociceptive integration in DHSC neurons. Furthermore, this therapeutic effect was reproduced by selective inhibition of STN glutamatergic neurons in Vglut2-ires-cre mice. CONCLUSION:Our study highlights the centrality of the STN in nociceptive circuits, its interaction with the DHSC and its key involvement in pain sensation in Parkinson's disease. Furthermore, our results provide for the first-time evidence that subthalamic DBS produces analgesia by normalizing the responses of spinal WDR neurons via descending brainstem pathways. These effects are due to direct inhibition, rather than activation of glutamatergic neurons in the STN or passage fibers, as shown in the DREADDs experiment.

This paper presents a method to identify wave equations’ parameters using wave dispersion characteristics (k-space) on two-dimensional domains. The proposed approach uses the minimization of the difference of an analytic formulation of the dispersion relation to wavenumbers calculated from solution fields. The implementation of partial differential equations (PDE) resolution on finite element software is explained and tested with analytic solutions in order to generate the test solution fields for the identification process. The coefficient identification is tested on solution fields generated by finite element solver for some 2nd- and 4th-order equations. In particular the test cases are the equations at different frequencies of deflection of isotropic and orthotropic membrane, flexion of isotropic and orthotropic plate and an original model of orthotropic plate equivalent to a bi-directional ribbed plate. In the limits of the spatial sampling rate and the domain size, the process allows an accurate retrieval of the wave equation parameters.

This work addresses the dynamic modeling of a negative stiffness absorber consisting of an assembly of curved beams. Design rules are derived from the orders of magnitude of stiffness and elastic energy stored by the negative stiffness elements. Although static and dynamic performances are widely documented using equivalent spring–mass system equations of motion, this paper presents a modeling approach based on beam dynamics to predict the behavior by incorporating the generation of negative stiffness with prestressed Euler beams. The static behavior is first recalled to feed the dynamic beam model with realistic orders of magnitude. The latter is derived from the beam balance instead of the spring–mass system and aims at solving the beam problem, which encompasses more realistic phenomena compared to introducing the equivalent stiffness in the spring–mass equation of motion. The consistency of the beam modeling is confirmed by comparison with available models in the literature and finite element simulations. A mock-up is built in which beam-type components are 3D-printed. Axial loading is introduced on the curved beams to evaluate its influence on the response of the isolator, and the observed softening trend complies with the theoretical predictions.

The dynamic characterization of complex structures raises the need for a robust experiment-based wavenumber identification method, especially when they are tested under complex conditions. To this end, this paper presents an optimized Algebraic Wavenumber Identification (AWI) method and describes the dynamic behavior of three complex structures under a series of complex conditions through AWI. The first novelty of this paper is to optimize AWI in terms of computation efficiency and accuracy by two solutions: (a) the multiple integral of AWI, which acts as a filter, is explicitly solved by the least squares fitting method, significantly reducing the computation cost, especially when multiple samples are used as input parameters; (b) a sampling strategy is provided to further improve the robustness of the AWI to measurement errors. On this basis, an AWI implementation procedure is proposed for experimental tests under complex conditions. The second novelty of this paper is on the applications of AWI in wave propagation parameters identification of complex structures, including the damping loss factor estimation of a viscoelastic beam and a honeycomb sandwich beam, the band gap identification of a meta-structure, and experimental dispersion curves extraction of these three complex structures. The third novelty of this paper is the experimental validation of AWI under a series of complex conditions, including signal noise, non-periodic sampling, and uncertainty of measuring points’ geometric coordinates. The experimental and numerical results have been compared to two popular inverse methods, namely, Inhomogeneous Wave Correlation (IWC) and INverse COnvolution MEthod (INCOME), demonstrating the validity of the AWI implementation procedure proposed in this paper.

This paper presents the practical implementation of passive tuned mass damper in the context of railway engineering and focuses on the vibrations experienced by the track equipment in the vicinity of switchers, specifically, the power switch machine that provides the force for opening and closing the switch.Such sensitive components are clamped to the sleepers and experience severe transverse displacement that damages the engine and results in the failure of the whole switcher.Finite element simulations were performed to identify kinematics likely to be responsible for damage and curative solutions were proposed and implemented.Particularly, a tuned mass damper was designed to provide an increased mitigation performance with respect to the targeted kinematic.This vibration absorber was designed according to the design rules available in the literature and its mechanical and geometrical properties were chosen to comply with in-situ constraints.The resulting device is a non-intrusive absorber, easily tunable depending on the installation site, and provides significant reduction the vibration level in the rather low frequency range.