bluuebunny/crossref_metadata_2025 huggingface dataset:
Approach for Uncertainty Quantification And Management Of Unmanned Aerial Vehicle Health
Authors: Corbetta Matteo , Kulkarni Chetan S.
Published: February 2021

The increasing interest in low-altitude unmanned aerial vehi- cle (UAV) operations is bringing along safety concerns. Per- formance of small, low-cost UAVs drastically changes with type, size and controller of the vehicle. Their reliability is sig- nificantly lower when compared to reliability of commercial aircraft, and the availability of on-board sensors for health and state awareness is extremely limited due to their size and propulsion capabilities. Uncertainty plays a dominant role in such a scenario, where a variety of UAVs of differ- ent size, propulsion systems, dynamic performance and reli- ability enters the low-altitude airspace. Unexpected failures could have dangerous consequences for both equipment and humans within that same airspace. As a result, a number of research tasks and methodologies are being proposed in the area of UAV dynamic modeling, health and safety monitor- ing, but uncertainty quantification is rarely addressed. Thus, this paper proposes a perspective towards uncertainty quan- tification for autonomous systems, giving special emphasis to UAV health monitoring application. A formal approach to classify uncertainty is presented; it is utilized to identify the uncertainty sources in UAVs health and operations, and then map uncertainty within a predictive process. To show the application of the methodology proposed here, the design of a model-based powertrain health monitoring algorithm for small-size UAVs is presented as case study. The example il- lustrates how the uncertainty quantification approach can help the modeling strategy, as well as the assessment of diagnostic and prognostic performance.
Design and In-Water Testing of a Fault-Detection System for Unmanned Underwater Vehicle Actuators
Authors: Kemp Matt , Erickson Jon , Jensen Scott , Lampoudi Sotiria , Martin Eric J.
Published: February 2021

We discuss the design of a fault-detection system for an unmanned underwater vehicle (UUV) actuator and present the results of in-water testing. We first discuss the design of the system, then its integration onto the UUV, the in-water testing sequence, and finally the analysis of the test results –- missed detection and false-alarm rate. This system serves as a platform for UUV fault detection and isolation research, enabling the development of system requirements, and providing the opportunity to compare the merits of the centralized vs decentralized fault-detection design approaches.
Evaluation of 1D CNN Autoencoders for Lithium-ion Battery Condition Assessment Using Synthetic Data
Authors: Valant Christopher J. , Wheaton Jay D. , Thurston Michael G. , McConky Sean P. , Nenadic Nenad G.
Published: February 2021

To access ground truth degradation information, we simulatedcharge and discharge cycles of automotive lithium ion batteriesin their healthy and degrading states and used this informationto determine performance of an autoencoder-basedanomaly detector. The simulated degradation mechanism wasan abrupt increase in the battery’s rate of time-dependent capacityfade. The neural network topology was based on onedimensionalconvolutional layers. The decision-support system,based on the sequential probability ratio test, interpretedthe anomaly generated by the autoencoder. Detection timeand time to failure were the metrics used for performanceevaluation. Anomaly detection was evaluated on five differentsimulated progressions of damage to examine the effectsof driving profile randomness on performance of the anomalydetector.
Multivariate Bernoulli Logit-Normal Model for Failure Prediction
Authors: Shao Huijuan , Deng Xinwei , Zhang Chi , Zheng Shuai , Khorasgani Hamed , Farahat Ahmed , Gupta Chetan
Published: February 2021

The failures among connected devices that are geographically close may have correlations and even propagate from one to another. However, there is little research to model this prob- lem due to the lacking of insights of the correlations in such devices. Most existing methods build one model for one de- vice independently so that they are not capable of captur- ing the underlying correlations, which can be important in- formation to leverage for failure prediction. To address this problem, we propose a multivariate Bernoulli Logit-Normal model (MBLN) to explicitly model the correlations of devices and predict failure probabilities of multiple devices simulta- neously. The proposed method is applied to a water tank data set where tanks are connected in a local area. The results indicate that our proposed method outperforms baseline ap- proaches in terms of the prediction performance such as ROC.
Probabilistic Risk Assessment and Mitigation for UAS Safety and Traffic Management
Authors: Hu Jueming
Published: February 2021

A probabilistic approach to help future UAS safety and traffic management is proposed. A probabilistic risk-based operational safety bound is first proposed. This will allow UAV to maintain the deviation from the trajectory plan within a ”buffer” zone, and keep away from obstacles. Uncertainty quantification, Monte Carlo simulation, and coordinate transformation techniques will be used. Additionally, an algorithm integrating reinforcement learning and the proposed operational safety bound will be developed which is used for collision avoidance and trajectory planning. This will provide a learning ability to UAVs in order to adapt their behavior to changing environments. Also, large-scale UAV management will be studied by using multi-agent generative adversarial imitation learning.
Vibration-based Condition Monitoring of Industrial Drivetrains Operating under Non-stationary Conditions
Authors: Choudhury Madhurjya Dev
Published: February 2021

This research is focused on advancing the state-of-the art in fault diagnostics of industrial drivetrains. It is proposed to develop a fault detection algorithm and fault severity prediction model for critical drivetrain components using measured vibration signals. The fault detection algorithm will be developed to overcome the challenge of extracting reliable fault information for drivetrains operating under speed and load fluctuations, whereas the severity prediction model will aid in predicting the degradation level of a faulty component. The results of this research can, therefore, help in improving the availability of industrial drivetrains, by providing a dependable platform to the maintenance personnel for proper decision-making.
Automatic detection of rare observations during production tests using statistical models
Authors: Mourer Alex , Lacaille Jérôme , Olteanu Madalina , Chavent Marie
Published: February 2021

Engines are verified through production tests before delivering them to customers. During those tests, lot of measures are taken on different parts of the engine, considering multiple physical parameters. Unexpected measures can be observed. For this very reason, it is important to assess if these unusual observations are statistically significant.

However, anomaly detection is a difficult problem in unsupervised learning. The obvious reason is that, unlike supervised classification, there is no ground truth against which we could evaluate results. Therefore, we propose a methodology based on two independent statistical algorithms to double check our results. One approach is the Isolation Forest (IF) model which is specific to anomaly detection and able to handle a large number of variables. The goal of the algorithm is to find rare items, events or observations which raise suspicions by differing significantly from the majority of the data and, at the same time, it discriminates non-informative variables to improve. One main issue of IF is its lack of interpretability. Within this scope, we extend the shapley values, interpretation indicators, to the unsupervised context to interpret the model outputs.

The second approach is the Self-Organizing Map (SOM) model which has nice properties for data mining by providing both clustering and visual representation. The performance of the method and its interpretability depends on the chosen subset of variables. In this respect, we first implement a sparse-weighted K-means to reduce the input space, allowing the SOM to give an interpretable discretized representation.

We apply the two methodologies on data on aircraft engines measurements. Both approaches show similar results which are easily interpretable and exploitable by the experts.
Text Classification and Tagging of United States Army Ground Vehicle Fault Descriptions in Support of Data-Driven Prognostics
Authors: Hansen Brandon , Coleman Cody , Zhang Yi , Seale Maria
Published: February 2021

The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conjunction with physics-based models. Historical maintenance data constitutes a critical element for the use of a data-driven approach to prognostics, such as supervised machine learning. The historical data is used to create training and testing data sets to develop the machine learning model. Categorical classes for prediction are required for machine learning methods; however, faults of interest in US Army Ground Vehicle Maintenance Records appear as natural language text descriptions rather than a finite set of discrete labels.

Transforming linguistically complex data into a set of prognostics classes is necessary for utilizing supervised machine learning approaches for prognostics. Manually labeling fault description instances is effective, but extremely time-consuming; thus, an automated approach to labelling is preferred. The approach described in this paper examines key aspects of the fault text relevant to enabling automatic labeling. A method was developed based on the hypothesis that a given fault description could be generalized into a category. This method uses various natural language processing (NLP) techniques and a priori knowledge of ground vehicle faults to assign classes to the maintenance fault descriptions.

The core component of the method used in this paper is a Word2Vec word-embedding model. Word embeddings are used in conjunction with a token-oriented rule-based data structure for document classification. This methodology tags text with user-provided classes using a corpus of similar text fields as its training set. With classes of faults reliably assigned to a given description, supervised machine learning with these classes can be applied using related maintenance information that preceded the fault.

This method was developed for labeling US Army Ground Vehicle Maintenance Records, but is general enough to be applied to any natural language data sets accompanied with a priori knowledge of its contents for consistent labeling. In addition to applications in machine learning, generated labels are also conducive to general summarization and case-by-case analysis of faults. The maintenance components of interest for this current application are alternators and gaskets, with future development directed towards determining the RUL of these components based on the labeled data.
WaveNet based Autoencoder Model: Vibration Analysis on Centrifugal Pump for Degradation Estimation.
Authors: Yen Fanyun , Katrib Ziad
Published: February 2021

Centrifugal pumps are versatile and have been used in a wide range of applications such as agricultural services, wastewater services, and other industrial services. The mechanism behind the pump is converting rotational kinetic energy to induce flow or raise pressure of liquid. Boiler feedewater pump (BFP) is an important piece of equipment in a thermal power generation plant. Generally, the cost of the pump itself only account less than 20% of its life cost and about 30% - 35% of the life cost spend on pump operation and maintenance. Therefore, it is important to understand the degradation status of the pumping system for optimizing the operational procedures and maintenance schedule to reduce the cost. Traditionally, engineers evaluate the performance and/or find faults by observing the vibrational signal on the pump, specifically, looking at the power spectrum density of the vibrational signal measured on different locations of the pump. However, such vibration analysis requires substantial domain knowledge and experience to accommodate all the variables caused by various conditions like different models, sizes in different plants, units and facilities. Often Vibration Analyst have to bin the vibration signal according to a predetermined frequency bins and potentially removing useful markers about vibration health.

This paper presents a novel way of conducting vibration analysis on pumps to determine the degradation trend, without requiring expert domain knowledge by extracting useful information using a WaveNet based autoencoder on the historical vibration data. WaveNet is known for processing raw audio data and building generative models. Unlike recurrent neural network (RNN), WaveNet is capable of handling much longer sequential data, which is very suitable for high frequency signals like sound and vibration signals. The autoencoder model extract essential information for reconstructing the input data. The embeddings from the autoencoders can represent the characteristics of the input data. Combining the two techniques, we were able to compress the vibration data 12x and extract the embeddings from raw vibration data and use them to estimate the degradation status of pumps. We pre-selected a collection of vibration data from pumps under “normal” condition.  The degradation trend is estimated by computing the distance of the embeddings from “normal” data to new inputs. Such model provides additional information on pump condition vis-a-vis vibration data with no prior domain knowledge. This technique can assist decision making and reduce costs from improper operation and maintenance.
Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis
Authors: Borba Evangelista Gustavo , Conceição Rocha Guilherme , Olivares Loesch Vianna Wlamir
Published: February 2021

The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result.

In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.
Deep anomaly detection for industrial systems: a case study
Authors: Xue Feng , Yan Weizhong , Wang Tianyi , Huang Hao , Feng Bojun
Published: February 2021

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation is used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are categorical in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.
A decision-making framework for safe operations of unmanned aerial vehicles in urban scenarios
Authors: Quiñones Marcos , Darrah Timothy , Biswas Gautam , Kulkarni Chetan
Published: February 2021

This paper presents a decision-making scheme at the level of individual unmanned aerial vehicles (UAVs) with the goal of maintaining safe operations for urban mobility. The decision-making approach for a single UAV will consider the risks associated with the current trajectory given the existing environmental conditions and the state of the vehicle. The proposed scheme combines the analysis of system performance, environmental conditions, and mission level parameters for contingency management, i.e., make a determination on: (1) to abort mission and land safely; (2) re-plan current mission in full or abbreviated form; and (3) change mission.  A path planning and trajectory optimization algorithm with the goal of minimizing the overall risk of mission failure by considering a number of factors such as the uncertainties in the environment and operating state of the vehicle is proposed. We will consider the mission failure as the loss of control of the vehicle resulting in a collision with other objects or a crash into the ground. An offline part of the framework generates an initial mission plan by considering the state of the vehicle, the environmental, conditions, and the static features of a map of the environment. Once the vehicle takes off, the risk of mission’ failure associated with the remaining trajectory is re-computed in an online framework to assess whether re-planning is required or not. A key challenge that we consider in this paper is to study the effects of multiple interacting subsystems of the UAV on system performance, especially under degraded conditions.
A low quiescent power wireless rotating machinery condition monitoring system
Authors: Chen Yan , Chaudhry Zaffir , Mantese Joseph
Published: February 2021

Vibration-based monitoring of rotating machinery is rapidly evolving within the aerospace industry with priority on detecting impending failures. The workhorse of such monitoring system remains a piezoelectric-based accelerometers which requires a wired-harnesses, connectors, significant power, and signal conditioning, etc. Raytheon Technologies Research Center (RTRC) along with Collins Aerospace and Sandia National Laboratory have jointly developed an Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE).       

This is a low power alternate for a conventional wired vibration-based monitoring system. This self-contained sensor system includes: (1) a low quiescent power sensing element with a wake-up module, (2) a wireless communication module, and (3) a coin-cell battery. Leveraging work performed under Defense Advanced Research Projects Agency (DARPA) N-Zero program. This wireless health monitoring system can operate in a quiescent low power mode (~10nW) for a period of several years without servicing. With an exceedance above a preset vibration level (at designate characteristic frequencies), the sensor wakes up and wirelessly sends a warning of a precursor-to-failure.

The ARISE sensor and wake-up module package has been validated with a replicated vibration environment acquired from a selected rotating machinery subject to progressive damage at the Structural Dynamics Laboratory at RTRC. The failure precursor is successfully detected by the sensor which triggers the wake-up module.

This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) Micro Technology Office (MTO), under Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE) program.
Fatigue Life Prognosis of a Light Aircraft Landing Gear Leg
Authors: Gerhardinger David , Domitrović Anita , Bazijanac Ernest
Published: February 2021

Aircraft components are subject to fatigue damage. The prediction of fatigue life has significant influence on maintenance and flight operations. Light aircraft, designed for recreational purposes, have vital components that are subject to a hard time maintenance approach. The focus of this article is on a simple method for predicting fatigue life. The method is applied to a light aircraft’s fixed landing gear leg. The landing gear leg is modelled in a computer aided design environment. The load spectrum is determined, based on a characteristic flight profile. Principal strains are determined with finite element analysis. Fatigue life is calculated with the Coffin-Manson low cycle fatigue relation. The Palmgren-Miner rule is applied, and cumulative damage is determined. The results are compared to actual landing gear leg fatigue damage and the hard time replacement interval which is given in the corresponding maintenance manual.
Digital Fleet Management: A Scalable Cloud Framework based on Data-driven Prediction Models
Authors: Thomas Sherin , Dubey Abhishek , Viassolo Daniel E , Zanette Magson
Published: February 2021

A scalable Digital Fleet Management system can be leveraged by most organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health becomes central, as it enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. End-to-end solutions, spanning all the way from gathering live tool data to the visual representation of tool health, are certainly of major interest.

Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes, and in turn it helps improve scheduling of procurement and distribution of the fleet.

This paper focuses on managing a fleet of thousands of downhole tools based on tool health condition and other variables – a very common use case in Oil & Gas Services.  An end-to-end automated scalable cloud framework is described in detail. This framework integrates failure prediction models for each single asset in the fleet of tools. Based on historical tool data, the models generate tool risk indices (one index per asset) which correlate to the probability of tool failure during near-future field jobs. These risk indices can be used for optimal asset-to-job mapping. They also help in de-risking field operations by identifying tools for overhaul or retirement. The proposed method integrates the tasks of: fetching data from 200,000+ tools, performing feature engineering, modeling via Machine Learning (ML) , and visualizing into a cloud pipeline. Framework scalability becomes a key requirement as fleet size increases or decreases over time to match market demands. The framework also allows for the addition of new ML models to the platform by citizen data scientists, who are not cloud experts. Finally, it is shown who this framework provides systematic steps for sustenance of such large cloud platform.
Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks
Authors: Kulkarni Chetan S , Corbetta Matteo , Robinson Elinirina
Published: February 2021

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles.The approach leverages the combination of failure mode and effect analysis (FMEA) and  Bayesian networks,

thus introducing dependability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded whitin FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric powertrain system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.
On the application of transfer learning in prognostics and health management
Authors: Moradi Ramin , Groth Katrina
Published: February 2021

Advancements in sensing and computing technologies, the development of human and computer interaction frameworks, big data storage capabilities, and the emergence of cloud storage and could computing have resulted in an abundance of data in modern industry. This data availability has encouraged researchers and industry practitioners to rely on data-based machine learning, specially deep learning, models for fault diagnostics and prognostics more than ever. These models provide unique advantages, however their performance is heavily dependent on the training data and how well that data represents the test data. This issue mandates fine-tuning and even training the models from scratch when there is a slight change in operating conditions or equipment. Transfer learning is an approach that can remedy this issue by keeping portions of what is learned from previous training and transferring them to the new application. In this paper, a unified definition for transfer learning and its different types is provided, Prognostics and Health Management (PHM) studies that have used transfer learning are reviewed in detail, and finally a discussion on TL application considerations and gaps is provided for improving the applicability of transfer learning in PHM.
Opioid Therapy in the Treatment of Chronic
Pain Conditions in Germany
Authors: Werber Andreas
Published: February 2021

Background: Primarily used for treating malignant pain, opioids are recently applied for
chronic non-tumor pain. For the lack of evidence based strategies from long-term studies,
opioids are discussed controversially, esp. considering cost-benefit.
Objectives: The purpose of this study is to evaluate characteristics in prescribing opioids for
tumor and non-tumor pain conditions.
Study design: Cost effectiveness study/observational study
Setting/Methods: Health insurance data of a German statutory health insurance company
(N = 6.800.000, data acquisition from 2006 to 2010) was evaluated by assigning opioid
prescriptions to certain pain related diagnoses using CART (Classification And Regression Tree)
segmentation analysis. Age- and gender-specific
characteristics of prescriptions were calculated.
Results: The number of prescriptions of opioid prescriptions increased linearly. Prescriptions
of mild opioids were decreasing for non-tumor pain, but increasing for tumor pain, while the
number of prescriptions of strong opioids was increasing both for tumor and nontumor pain.
Differences occurred in terms of duration and kind of the preferred substances, including
the considerations of common contraindications (e.g. somatoform disorders). The majority of
strong opioids being prescribed for non-tumor pain were fentanyl pain patches for 40 to 45
year old males with average annual costs of 1833 Euros per patient. Out of 21000 patients
with somatoform pain disorder, 44.4% were treated with opioids (20.7% with mild, 23.7%
with strong opioids).
Limitations: The results did not consider if the opioid medication was actually taken by
the patients. Another difference in terms of representativeness might occur since the gender
distribution varies between the official statistical data and data collected by the health insurance
company. Because of the acquisition of the data, no conclusions about possible correlation of
pain syndromes and educational and social classes are possible. Tumor patients who received
an opioid prescription for non-tumor pain could not be excluded.
Conclusions: While the overall expenditure of the health insurance companies increased,
it remains unknown which patient is receiving a particular opioid medication. Prescribing
behavior was often not consistent with common indications and contraindications.
Key words: Opioid therapy, chronic pain conditions
Pain Relief with Percutaneous
Trochanteroplasty in a Patient with Bilateral
Trochanteric Myelomatous Lytic Lesions
Authors: Wahezi Sayed E.
Published: February 2021

Multiple myeloma is a hematologic malignancy associated with destructive bone loss.
Lytic lesions, a hallmark of this cancer, can result in significant morbidity because of
associated pain and structural osseous compromise. Osteoplasty has demonstrated
efficacy in the treatment of myelomatous pain within the axial skeleton; however, there
is limited evidence supporting the utility of osteoplasty to treat extra-spinal lesions. We
describe a 67 year-old woman with stable IgA lambda multiple myeloma with sentinel
bilateral greater trochanteric lytic lesions that was referred to our interventional pain
management clinic for evaluation of bilateral lateral hip pain. Conservative treatment
options including physical therapy, non-steroidal anti-inflammatory drugs (NSAIDs),
oral opiates, and local corticosteroid injections to bilateral trochanteric bursae
failed to offer pain relief. The patient underwent minimally invasive percutaneous
trochanteroplasty with concomitant core biopsy of her bilateral trochanteric lytic
lesions. The intended goals of this novel procedure were to determine the cause of
the suspected lytic lesions, provide pain relief, and offer structural stability by safely
implanting bone cement as part of a fracture prevention strategy. At 12 month followup, the patient’s pain improved by 70% and she no longer required the use of pain
medication. The patient also displayed a significant improvement in her day-to-day
functioning and quality of life.
Key words: Pain, osteoplasty, trochanteroplasty, multiple myeloma, greater
trochanter, percutaneous
Comparative Effectiveness of Suprascapular Nerve
Block in the Relief of Acute Post-Operative Shoulder
Pain: A Systematic Review and Meta-analysis
Authors: Lin Chih-Peng
Published: February 2021

Background: The suprascapular nerve accounts for 70% of shoulder sensory innervations,
and suprascapular nerve block (SSNB) has been shown to be effective in the relief of chronic
shoulder pain including rotator cuff tendinitis, subdeltoid impingement syndrome, and
adhesive capsulitis. However, this remains inconclusive for patients undergoing surgery. The
present meta-analysis aimed to explore the effectiveness of SSNB for relieving acute postoperative shoulder pain.
Objective: To explore the effectiveness of SSNB for relieving acute post-operative shoulder
pain.
Study Design: A systematic review and meta-analysis.
Setting: Services of general surgery, orthopaedics, and anaesthesiology.
Methods: A systematic search of studies on SSNB for post-operative shoulder pain was
conducted mainly in PubMed and Scopus. The standardized mean difference (SMD) of postoperative pain scales of SSNB versus placebo was treated as the primary outcome, whereas the
odds ratio of nausea of SSNB versus placebo comprised the secondary outcome.
Results: The meta-analysis included 7 randomized controlled trials and 2 comparative studies
comprising 681 participants in total. The quantitative analysis showed a significantly lower
pain level of SSNB versus placebo in the shoulder surgery patient group (SMD: -0.33; 95%
confidence level [CI]: -0.51 to -0.15), but not in the non-shoulder surgery group (SMD: 0.28;
95% CI: -0.37 to 1.93). The pooled odds ratio of nausea in the SSNB arm compared with the
placebo arm was 0.20 (95% CI: 0.09 to 0.45), indicating a reduction in the incidence of nausea
following SSNB.
Limitations: Heterogeneity of included trials.
Conclusions: SSNB significantly reduced acute post-operative shoulder pain in the shoulder
surgery group but not in patients undergoing laparoscopic surgery or thoracotomy. This
suggests that SSNB can be used as a method of polymodal analgesia for patients undergoing
shoulder surgery; however, it is not recommended for the non-shoulder surgery patient
population.
Key words: Suprascapular nerve, shoulder surgery, thoracotomy, laparoscopic surgery
Effects of Stress and Relaxation on Central Pain
Modulation in Chronic Whiplash and Fibromyalgia
Patients Compared to Healthy Controls
Authors: Meeus Mira
Published: February 2021

Background: Compelling evidence has demonstrated that impaired central pain modulation
contributes to persistent pain in patients with chronic whiplash associated disorders (WAD) and
fibromyalgia (FM). However, there is limited research concerning the influence of stress and
relaxation on central pain modulation in patients with chronic WAD and FM.
Objectives: The present study aims to investigate the effects of acute cognitive stress and
relaxation on central pain modulation in chronic WAD and FM patients compared to healthy
individuals.
Study Design: A randomized crossover design was employed.
Setting: The present study took place at the University of Brussels, the University Hospital
Brussels, and the University of Antwerp.
Methods: Fifty-nine participants (16 chronic WAD patients, 21 FM, 22 pain-free controls)
were enrolled and subjected to various pain measurements. Temporal summation (TS) of pain
and conditioned pain modulation (CPM) were evaluated. Subsequently, participants were
randomly allocated to either a group that received progressive relaxation therapy or a group that
performed a battery of cognitive tests (= cognitive stressor). Afterwards, all pain measurements
were repeated. One week later participant groups were switched.
Results: A significant difference was found between the groups in the change in TS in response
to relaxation (P = 0.008) and cognitive stress (P = 0.003). TS decreased in response to relaxation
and cognitive stress in chronic WAD patients and controls. In contrast, TS increased after both
interventions in FM patients. CPM efficacy decreased in all 3 groups in response to relaxation (P
= 0.002) and cognitive stress (P = 0.001).
Limitations: The obtained results only apply for a single session of muscle relaxation therapy
and cognitive stress, whereby no conclusions can be made for effects on pain perception and
modulation of chronic cognitive stress and long-term relaxation therapies.
Conclusions: A single relaxation session as well as cognitive stress may have negative acute
effects on pain modulation in patients with FM, while cognitive stress and relaxation did not
worsen bottom-up sensitization in chronic WAD patients and healthy persons. However,
endogenous pain inhibition, assessed using a CPM paradigm, worsened in chronic WAD and
FM patients, as well as in healthy people following both interventions.
Key words: Chronic pain, central sensitization, endogenous pain inhibition, temporal
summation of pain, cognitive stressor, relaxation, fibromyalgia, whiplash-associated disorders
Patient Characteristics and Outcomes in
Unintentional, Non-fatal Prescription Opioid
Overdoses: A Systematic Review
Authors: Elzey Mark J.
Published: February 2021

Background: Opioid overdose continues to be a significant and growing cause of preventable
mortality and morbidity. Studies suggest that unintentional, non-fatal overdose from prescription
opioid analgesics constitutes a large portion of total overdose events. The societal burden
associated with these events is a frequently overlooked public health concern.
Objectives: To evaluate unintentional, non-fatal prescription opioid overdoses, including the
identification of risk factors, societal burden, and knowledge gaps where further study is warranted.
Study Design: Systematic review of the literature for unintentional, non-fatal opioid overdose.
Methods: Preferred reporting items for systematic reviews and meta-analyses guidelines were
used in constructing this systematic review. To determine the scope of the existing literature, a
systematic search was conducted using the MEDLINE, CINAHL, PsycINFO, and Web of Science
databases.
Results: This systematic review analyzes 24 articles (21 retrospective descriptive analyses, 2
prospective analyses, one phase III trial, and one meta-analysis). Articles were reviewed by authors
and relevant data examined. Results show that opioid overdose morbidity is significantly more
prevalent than mortality and sequelae of non-fatal events should be studied in more detail.
Limitations: The limitations of this systematic review include the range of study populations and
opioids discussed and the broad and variable definitions of “opioid overdose” in the literature.

Conclusion(s): Opioid overdose morbidity and mortality is seen across the entire spectrum of
inpatient and outpatient use with significant numbers of adverse events occurring in population
segments not identified by high risk indicators. Increased physician awareness and a multi-modal
approach could help mitigate the overdose epidemic while maintaining effective pain control for
patients.
Key words: Prescription, opioid, accidental drug overdose, unintentional overdose, drug
poisoning, fentanyl, oxycodone, hydrocodone, methadone, oxymorphone, hydromorphone
Digital Subtraction Angiography Use During
Epidural Steroid Injections Does Not Reliably
Distinguish Artery from Vein
Authors: Benfield Jonathan A.
Published: February 2021

Background: Epidural steroid injections (ESIs) are among the most common procedures
performed in an interventional pain management practice. It is well known that tragic
complications may arise from ESIs, most commonly those performed using a transforaminal
approach. Digital subtraction angiography (DSA) has been hailed as a fluoroscopic technique
that can be used to detect arterial placement of the injection needle, and therefore as a safety
measure that can decrease the incidence of catastrophic sequelae of these procedures.
Objective: The objective of this article was to review existing scientific pain literature to
determine if DSA can distinguish arterial vs. venous uptake.
Study Design: Narrative review.
Methods: The current narrative review of DSA in interventional spine was completed with
a PUBMED search using the key words: digital subtraction angiography, epidural, fluoroscopy,
intravascular injection, paraplegia, and quadriplegia in accordance with Preferred Reporting
Items for Systematic Reviews and Meta- Analyses (PRISMA) guidelines.
Results: After identification of duplicate articles, 383 articles were screened by title, abstract,
and/or full article review. Ten of these articles were deemed appropriate, after applying inclusion
and exclusion criteria, as they specifically looked at the use of digital subtraction angiography in
interventional spine epidural injections. This included 4 case reports, 3 prospective studies, one
retrospective analysis, one prospective cohort study, and one meta-analysis.
All of the available studies claiming that DSA was capable of detecting vascular spread are
likely accurate, but no significant detection of specifically arterial spread has been reported. The
known catastrophic complications related to ESIs are purported to be due to arterial injection
of insoluble steroids or local anesthetic and detection of arterial spread of contrast during
fluoroscopy would be of obvious benefit to the interventionalist.
Limitations: Small study size, non-randomized studies between DSA and real time
fluoroscopy.
Conclusion: Existing studies do not support that DSA can predict arterial spread. In fact, DSA
exposes the practitioner and the patient to higher levels of radiation without objective evidence
of any safety parameters.
Key words: Digital subtraction angiography, real-time fluoroscopy, transforaminal epidural
injection, particulate steroids, cervical radicular artery, lumbar radicular artery, spinal cord injury
Analgesic Effects and Neuropathology Changes
of Electroacupuncture on Curing a Rat Model of
Brachial Plexus Neuralgia Induced by Cobra Venom
Authors: An Jian-Xiong
Published: February 2021

Background: Electroacupuncture (EA) is widely applied to treat neuropathic pain. Brachial plexus
neuralgia (BPN) is a common form of chronic persistent pain. Few studies have evaluated the
analgesic effects and mechanism of EA using the novel animal model of BPN.
Objective: To observe the curative effects of repeated EA on curing BPN induced by administration
of cobra venom to the lower trunk of the right brachial plexus.
Study Design: Controlled animal study.
Setting: Department of Anesthesiology, Pain Medicine & Critical Care Medicine, Aviation General
Hospital of China Medical University.
Methods: Sixty-six adult male Sprague-Dawley rats were equally and randomly divided into
the following groups: normal control (NC), brachial plexus neuralgia (BPN), BPN with sham
EA stimulation, BPN with EA stimulation starting on postoperative day 1 (EA1), and BPN with
EA stimulation starting on postoperative day 12 (EA12). The BPN model was established by
administration of cobra venom to the lower trunk of the right brachial plexus. On postoperative day
1 or day 12, EA (constant aquare wave, 2 Hz and 100 Hz alternating frequencies, intensities ranging
from 1 – 1.5 – 2 mA) was applied to the right “Shousanli” (LI10) and “Quchi” (LI11) acupoints for
30 minutes, once every other day for 12 times in both groups. Mechanical withdrawal thresholds
(MWT) were tested with von Frey filaments. Video recordings were conducted to analyze the
spontaneous exploratory behaviors. Moreover, the organizational and structural alterations of the
right brachial plexus and cervical cord (C8-T1) were examined via light and electron microscopy.
Results: Following the production of the BPN model, the MWT of both ipsilateral and contralateral
paws demonstrated a profound decrease (P < 0.05). But after EA interventions, the MWT showed
a significant increase (P < 0.05). In comparison to the EA12 group, the analgesic effects of the EA1
group were more significant, and similar results were observed in exploratory behaviors. However,
grooming behaviors did not demonstrate significant differences. Meanwhile, on day 12 after
surgery it was observed under light microscopy that the inflammatory response in the right brachial
plexus and cervical cord (C8-T1) were significantly attenuated after EA stimulation. Furthermore,
the demyelination of the brachial plexus and cervical cord (C8-T1) were also reversed.
Limitations: Limitations include the fact that there was demyelination of the cervical cord (C8-
T1) in the control group because of inappropriate manipulation.
Conclusion: Repeated EA contributes significant analgesic effects in the treatment of BPN.
Key words: Electroacupuncture, brachial plexus neuralgia, cobra venom, cervical cord, ultrastructural alterations
Pentoxifylline Ameliorates Mechanical
Hyperalgesia in a Rat Model of ChemotherapyInduced Neuropathic Pain
Authors: Abdi Salahadin
Published: February 2021

Background: Chemotherapy-induced neuropathic pain is difficult to treat. Pentoxifylline inhibits
the production of inflammatory cytokines including tumor necrosis factor α (TNF-α) and interleukin
1β (IL-1β).
Objective: The aims of our study were to investigate the analgesic and preventive effects of
pentoxifylline on paclitaxel-induced neuropathic pain in rats and to identify its mechanisms of action.
Study Design: Controlled animal study.
Methods: Neuropathic pain was induced with intraperitoneally injected paclitaxel on 4 alternate
days in male Sprague-Dawley rats. Pentoxifylline was administered systemically as a single injection
and a continuous infusion before or after the injection of paclitaxel. The mechanical threshold for
allodynia was measured by using von Frey filaments. Protein levels and localization of inflammatory
cytokines were performed by using Western blotting and immunohistochemistry, respectively.
Results: After the rats developed neuropathic pain behavior, a single intraperitoneal injection
and continuous infusion of pentoxifylline ameliorated paclitaxel-induced mechanical allodynia.
In addition, systemic infusion of pentoxifylline in the early phase of the development of pain
behavior delayed the onset of paclitaxel-induced pain behavior. Paclitaxel increased the levels of
the catalytic subunit α of protein kinase A, phosphorylated nuclear factor κB, TNF-α, and IL-1β
in the lumbar dorsal root ganglia. Pentoxifylline decreased the paclitaxel-induced TNF-α and IL1β levels. In addition, IL-1β was expressed in neurons and satellite cells in the lumbar dorsal root
ganglia after paclitaxel.
Limitations: Although this study was performed in the animal model by well-designed manner,
clinical study will be needed to confirm the analgesic effect of pentoxifylline.
Conclusion: Pentoxifylline alleviated chemotherapy-induced neuropathic pain in rats by reducing
the levels of inflammatory cytokines in dorsal root ganglia and may be effective chemotherapyinduced neuropathic pain in patients.
Key words: Chemotherapy, chronic pain, inflammatory cytokines, neuropathic pain, paclitaxel,
pain behavior, pain treatment, pentoxifylline, phosphodiesterase inhibitor
Near Death Experience from Placement of an
Intrathecal Catheter
Authors: Patel Sagar
Published: February 2021

The management of pain due to cancer is challenging and often requires invasive therapy
in addition to medication management. Intrathecal drug delivery is a form of advanced
therapy that delivers medication locally in the intrathecal space while reducing systemic side
effects associated with high doses of opioids. Although risks associated with intrathecal
drug delivery are low, some common complications include dislodgement, kinking, or
fracture of the catheter, bleeding, neurological injury, infection, and cerebrospinal leaks.
We present a case of a 38-year-old woman with a medical history significant for stage IV
breast cancer, L2 metastatic lesion, opioid tolerance, and chronic neck and low back pain
who was admitted to the hospital for intractable pain. She had failed multiple interventional
procedures in the past including lumbar medial nerve radiofrequency ablation, epidural
steroid injection, and trigger point injections as well as a kyphoplasty at the L2 level. Failing
both oral and parenteral opioid treatments, the decision was made to place an intrathecal
pump in the patient. After placement of the intrathecal catheter and prior to any bolus
of medication being given, the patient became bradycardic with a heart rate in the 20s
and experienced a 10 second pause. The patient had intermittent bradycardia over the
following days and symptoms resolved only after removal of the intrathecal catheter itself.
To our knowledge, this is the first reported case with a complication of recurrent bradycardic
and asystolic episodes prior to the administration of intrathecal opioid but shortly after
placement of the intrathecal catheter itself.
Key words: Intrathecal drug delivery, complications, cancer pain, intrathecal analgesia,
bradycardia, opioids