Adjuvants to local anesthetics: Current understanding and future trends

Although beneficial in acute and chronic pain management, the use of local anaesthetics is limited by itsduration of action and the dose dependent adverse effects on the cardiac and central nervous system. Adjuvants or additives are often used with local anaesthetics for its synergistic effect by prolonging the duration of sensory-motor block and limiting the cumulative dose requirement of local anaesthetics. The armamentarium of local anesthetic adjuvants have evolved over time from classical opioids to a wide array of drugs spanning several groups and varying mechanisms of action. A large array of opioids ranging from morphine, fentanyl and sufentanyl to hydromorphone, buprenorphine and tramadol has been used with varying success. However, their use has been limited by their adverse effect like respiratory depression, nausea, vomiting and pruritus, especially with its neuraxial use. Epinephrine potentiates the local anesthetics by its antinociceptive properties mediated by alpha-2 adrenoreceptor activation along with its vasoconstrictive properties limiting the systemic absorption of local anesthetics. Alpha 2 adrenoreceptor antagonists like clonidine and dexmedetomidine are one of the most widely used class of local anesthetic adjuvants. Other drugs like steroids(dexamethasone), anti-inflammatory agents(parecoxib and lornoxicam), midazolam, ketamine, magnesium sulfate and neostigmine have also been used with mixed success. The concern regarding the safety profile of these adjuvants is due to its potential neurotoxicity and neurological complications which necessitate further research in this direction. Current research is directed towards a search for agents and techniques which would prolong local anaesthetic action without its deleterious effects. This includes novel approaches like use of charged molecules to produce local anaesthetic action(tonicaine and n butyl tetracaine), new age delivery mechanisms for prolonged bioavailability(liposomal, microspheres and cyclodextrin systems) and further studies with other drugs(adenosine, neuromuscular blockers, dextrans).

Why Labor Epidural Causes Fever and Why Lidocaine Burns on Injection? Role of TRPV 1 Receptor in Hyperthermia: Possible Explanation of Mechanism of Hyperthermia during Labor Epidural and Burning Sensation on Injection of Local Anesthetics

The mechanisms of epidural-associated fever remain incompletely understood [1-3]. We propose that action of local anesthetic on TPRV1. The transient receptor potential cation channel subfamily V member 1 (TRPV1), also known as the capsaicin receptor and the vanilloid receptor can explain this effect and explain mechanism of burning sensation on local anesthetic injected subcutaneously or intramuscular. Role of TRPV1 receptor was not discussed previously in Obstetric Anesthesia literature. Based on available data, we propose that Local Anesthetics work as agonist/antagonist on TPRV1 receptors. Antagonist action may cause hyperthermia through modifying thermoregulation [4], agonist action may cause hyperthermia thru release of IL-6 and other mediators of inflammation [5-10]. Agonist action may explain burning sensation on injection of Local Anesthetics. Burning sensation can be diminished by increasing pH of Local Anesthetic solution, because vanillin receptors are stimulated by acidification through lower pH [11,12].

Different Volumes of Local Anesthetics in Ultrasound-Guided Combined Interscalene-Supraclavicular Block for Traumatic Humeral Fracture

Background: Procedures that require anesthesia of entire length of the arm should have combined Interscalene block (ISB) and Supraclavicular block (SCB) to ensure adequate block. However, dual block requires high dose of local anesthetic (LA). We examined if Ultrasound (U/S) guided block helps to decrease the required volume of LA by visualizing trunks, so we only need injection of LA toward cervical nerve roots C5 and C6 aiming to block branches escaped from supraclavicular block. Patients and methods: Ninety-three patients were randomly allocated into one of three equal groups, each included 31 patients. All patients received SCB 20 ml (10 ml Bupivacaine 0.5% plus 10 ml lidocaine 2%) and ISB with different volumes of LA according to the group as follow: group A: 20 ml, group B: 15 ml, and group C: 10 ml. Result: No significant difference in onset of block (sensory and motor) and duration of block (motor only) between the three groups (P value > 0.05). The duration of sensory block was significantly longer in group A (760.65 ± 30.87 minutes) than in either group B (740.48 ± 21.15 minutes, P value P value P value P value P value P value < 0.001). Conclusion: A reduced volume of LA can be used in ultra sound guided ISB in combined with SCB to give satisfactory level of anesthesia to entire length of the arm, but the sensory block duration and duration of post-operative analgesia will be decreased significantly with decreasing the volume.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

Can repeated scorpion bite lead to development of resistance to the effect of local anesthetics? Maybe it does!

An 80 years old, bronchial asthmatic, male was posted for left cataract extraction with intra ocular lens implantation. He was administered peribulbar block/left facial nerve block. There was no sensory or motor block. Thereafter peribulbar block was repeated. Only partial akinesia was achieved, so under intermittent intra venous sedation, the surgery continued for 40 minutes. In the post-operative period, no signs of any residual/delayed block were noted. On specific enquiry, patient gave history of scorpion bite thrice, at the age of 27 years on his right foot, about 8-9 years back and again about 6-7 months back on his right hand. On 4th post-operative day after obtaining informed consent, local infiltration of the skin on the ventral aspect of the forearm, using, 6 mL, 2% lignocaine with adrenaline, was carried out. Confirming the suspicion, there was no sensory block after the injection, confirmed by pin prick method. Peribulbar block produces adequate intra-operative analgesia for cataract extraction. The cause of the failures may be due to technical inability to achieve block. However failure that occurs despite of technically correct injection of the correct drug can be mystifying. As the scorpion venom is known to affect the pumping mechanism of sodium channels in the nerve fibres, which are involved in the mechanism of action of local anaesthetic drugs, it may be responsible for the development of “resistance” to the action of local anaesthetic agents.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Anesthesia,Anesthetics,and Postoperative Cognitive Dysfunction in Elderly Patients

Postoperative cognitive dysfunction(POCD)remains a major issue that worsens the prognosis of elderly surgery patients.This article reviews the current research on the effect of different anesthesia methods and commonly utilized anesthetics on the incidence of POCD in elderly patients,aiming to provide an understanding of the underlying mechanisms contributing to this condition and facilitate the development of more reasonable anesthesia protocols,ultimately reducing the incidence of POCD in elderly surgery patients.

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Dynamical localization in a non-Hermitian Floquet synthetic system

We investigate the non-Hermitian effects on quantum diffusion in a kicked rotor model where the complex kicking potential is quasi-periodically modulated in the time domain.The synthetic space with arbitrary dimension can be created by incorporating incommensurate frequencies in the quasi-periodical modulation.In the Hermitian case,strong kicking induces the chaotic diffusion in the four-dimension momentum space characterized by linear growth of mean energy.We find that the quantum coherence in deep non-Hermitian regime can effectively suppress the chaotic diffusion and hence result in the emergence of dynamical localization.Moreover,the extent of dynamical localization is dramatically enhanced by increasing the non-Hermitian parameter.Interestingly,the quasi-energies become complex when the non-Hermitian parameter exceeds a certain threshold value.The quantum state will finally evolve to a quasi-eigenstate for which the imaginary part of its quasi-energy is large most.The exponential localization length decreases with the increase of the non-Hermitian parameter,unveiling the underlying mechanism of the enhancement of the dynamical localization by nonHermiticity.

Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Regulating Anderson localization with structural defect disorder

Localization due to disorder has been one of the most intriguing theoretical concepts that evolved in condensed matter physics.Here,we expand the theory of localization by considering two types of disorders at the same time,namely,the original Anderson’s disorder and the structural defect disorder,which has been suggested to be a key component in recently discovered two-dimensional amorphous materials.While increasing the degree of both disorders could induce localization of wavefunction in real space,we find that a small degree of structural defect disorder can significantly enhance the localization.As the degree of structural defect disorder increases,localized states quickly appear within the extended phase to enter a broad crossover region with mixed phases.We establish two-dimensional diagrams for the wavefunction localization and conductivity to highlight the interplay between the two types of disorders.Our theoretical model provides a comprehensive understanding of localization in two-dimensional amorphous materials and highlights the promising tunability of their transport properties.

Two-person device-free localization system based on ZigBee and transformer

Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indication(RSSI)and a Transformer network structure.The method aims to address the limited research and low accuracy of two-person device-free localization.This paper first describes the construction of the sensor network used for collecting ZigBee RSSI.It then examines the format and features of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the mapping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The proposed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.

Iterative Subregion Correction Preconditioners with Adaptive Tolerance for Problems with Geometrically Localized Stiffness

We present a class of preconditioners for the linear systems resulting from a finite element or discontinuous Galerkin discretizations of advection-dominated problems.These preconditioners are designed to treat the case of geometrically localized stiffness,where the convergence rates of iterative methods are degraded in a localized subregion of the mesh.Slower convergence may be caused by a number of factors,including the mesh size,anisotropy,highly variable coefficients,and more challenging physics.The approach taken in this work is to correct well-known preconditioners such as the block Jacobi and the block incomplete LU(ILU)with an adaptive inner subregion iteration.The goal of these preconditioners is to reduce the number of costly global iterations by accelerating the convergence in the stiff region by iterating on the less expensive reduced problem.The tolerance for the inner iteration is adaptively chosen to minimize subregion-local work while guaranteeing global convergence rates.We present analysis showing that the convergence of these preconditioners,even when combined with an adaptively selected tolerance,is independent of discretization parameters(e.g.,the mesh size and diffusion coefficient)in the subregion.We demonstrate significant performance improvements over black-box preconditioners when applied to several model convection-diffusion problems.Finally,we present performance results of several variations of iterative subregion correction preconditioners applied to the Reynolds number 2.25×10^(6)fluid flow over the NACA 0012 airfoil,as well as massively separated flow at 30°angle of attack.

Multiple Targets Localization Algorithm Based on Covariance Matrix Sparse Representation and Bayesian Learning

The multi-source passive localization problem is a problem of great interest in signal pro-cessing with many applications.In this paper,a sparse representation model based on covariance matrix is constructed for the long-range localization scenario,and a sparse Bayesian learning algo-rithm based on Laplace prior of signal covariance is developed for the base mismatch problem caused by target deviation from the initial point grid.An adaptive grid sparse Bayesian learning targets localization(AGSBL)algorithm is proposed.The AGSBL algorithm implements a covari-ance-based sparse signal reconstruction and grid adaptive localization dictionary learning.Simula-tion results show that the AGSBL algorithm outperforms the traditional compressed-aware localiza-tion algorithm for different signal-to-noise ratios and different number of targets in long-range scenes.

LFM Radar Source Passive Localization Algorithm Based on Range Migration

Traditional single-satellite passive localization algorithms are influenced by frequency and angle measurement accuracies,resulting in error estimation of emitter position on the order of kilometers.Subsequently,a single-satellite localization algorithm based on passive synthetic aper-ture(PSA)was introduced,enabling high-precision positioning.However,its estimation of azimuth and range distance is considerably affected by the residual frequency offset(RFO)of uncoopera-tive system transceivers.Furthermore,it requires data containing a satellite flying over the radia-tion source for RFO search.After estimating the RFO,an accurate estimation of azimuth and range distance can be carried out,which is difficult to achieve in practical situations.An LFM radar source passive localization algorithm based on range migration is proposed to address the dif-ficulty in estimating frequency offset.The algorithm first provides a rough estimate of the pulse repetition time(PRT).It processes intercepted signals through range compression,range interpola-tion,and polynomial fitting to obtain range migration observations.Subsequently,it uses the changing information of range migration and an accurate PRT to formulate a system of nonlinear equations,obtaining the emitter position and a more accurate PRT through a two-step localization algorithm.Frequency offset only induces a fixed offset in range migration,which does not affect the changing information.This algorithm can also achieve high-precision localization in squint scenar-ios.Finally,the effectiveness of this algorithm is verified through simulations.