Pain Detection Research Articles

Investigation of pain sensitivity following 3 nights of disrupted sleep in healthy individuals.

Poor quality sleep is a common complaint among people with chronic pain. The co-occurrence of poor sleep quality and chronic pain often comes with increased pain intensity, more disability and a higher cost of healthcare. Poor sleep has been suggested to affect measures of peripheral and central pain mechanisms. To date, sleep provocations are the only models proven to affect measures of central pain mechanisms in healthy subjects. However, there are limited studies investigating the effect of several nights of sleep disruption on measures of central pain mechanisms. The current study implemented three nights of sleep disruption with three planned awakenings per night in 30 healthy subjects sleeping at home. Pain testing was conducted at the same time of day at baseline and follow-up for each subject. Pressure pain thresholds were assessed bilaterally on the infraspinatus and gastrocnemius muscles. Using handheld pressure algometry, suprathreshold pressure pain sensitivity and area were also investigated on the dominant infraspinatus muscle. Cuff-pressure pain detection and tolerance thresholds, temporal summation of pain and conditioned pain modulation were investigated using cuff-pressure algometry. Temporal summation of pain was significantly facilitated (p= 0.022), suprathreshold pain areas (p= 0.005) and intensities (p< 0.05) were significantly increased, and all pressure pain thresholds were decreased (p< 0.005) after sleep disruption compared to baseline. The current study found that three consecutive nights of sleep disruption at home induced pressure hyperalgesia and increased measures of pain facilitation in healthy subjects, which is consistent with previous findings. Poor quality of sleep is often experienced by patients with chronic pain, with the most common complaint being nightly awakenings. This exploratory study is the first to investigate changes in measures of central and peripheral pain sensitivity in healthy subjects after sleep disruptions for three consecutive nights without any restrictions on total sleep time. The findings suggest that disruptions to sleep continuity in healthy individuals can induce increased sensitivity to measures of central and peripheral pain sensitization.

Serum Metabolome Analysis Identified Amino-Acid Metabolism Associated With Pain in People With Symptomatic Knee Osteoarthritis – A Cross-Sectional Study

Osteoarthritis (OA) is the most common arthritis affecting synovial joints such as knees and hips of millions of people globally. Usage-related joint pain and reduced function are the most common symptoms experienced by people with OA. To improve pain management, there is a need to identify validated biomarkers predicting therapeutic responses in targeted clinical trials. Our study aimed to identify the metabolic biomarkers for pain and pressure pain detection thresholds (PPTs) in participants with knee pain and symptomatic OA using metabolic phenotyping. Metabolite and cytokine measurements were done on serum samples using LC-MS/MS (liquid gas chromatography integrated magnetic resonance mass spectrometry) and Human Proinflammatory panel 1 kit respectively. Regression analysis was done in a test (n = 75) and replication study (n = 79) to investigate the metabolites associated with current knee pain scores and pressure pain detection thresholds (PPTs). Meta-analysis and correlation were done estimating precision of associated metabolites and identifying relationship between significant metabolites and cytokines respectively. Acyl ornithine, carnosine, cortisol, cortisone, cystine, DOPA, glycolithocholic acid sulphate (GLCAS), phenylethylamine (PEA) and succinic acid were found to be significantly (FDR <.1) associated with pain scores in meta-analysis of both studies. IL-10, IL-13, IL-1β, IL2, IL8 and TNF-α were also found to be associated with the significant metabolites. Significant associations of these metabolites and inflammatory markers with knee pain suggests that targeting relevant pathways of amino acid and cholesterol metabolism may modulate cytokines and these could be targeted as novel therapeutics development to improve knee pain and OA management. PerspectiveForeseeing the global burden of knee pain in Osteoarthritis (OA) and adverse effects of current pharmacological therapies, this study is envisaged to investigate serum metabolites and molecular pathways involved in knee pain. The replicated metabolites in this study suggests targeting amino-acid pathways for better management of OA knee pain.

Bioelectrical impedance analysis of thermal-induced cutaneous nociception

Objective:People perceive different levels of pain at the same stimulus intensity, hence overburdening objective nociception assessment. In the context of recently developed pain monitors, this work presents the analysis of the complex bioimpedance measured during cold-induced cutaneous nociception. Using the Anspec-PRO device, this analysis results in a more complex characterization of the acquired signal (i.e., 3-dimensional or image-based representations), rather than evaluations of one/multiple single-dimension signals (i.e., time-based index). Methods:The experimental protocol consists of thermal testing the nociception response of 13 participants, in terms of impedance changes. Several study cases are performed to evaluate the bioimpedance variability over one day, day to day, and at different stimulus intensities. Results:The measured bioimpedance fluctuations in time were validated against the self-reported pain level of the test subjects. Moreover, the bioimpedance, acting as the frequency response of cold-induced nociception, demonstrated the pain memory effect and the influence on the impedance of the liquid/fat in the body. Conclusion:The impedance analysis can delineate cold-induced nociception effects from nominal states, the impedance spectroscopy proving the potential to monitor the evoked pain. Significance:The implications of this work rely on advancing the state of art for pain detection by using time–frequency analysis and image-based information. Moreover, in closed-loop anesthesia control, the nociception surrogate monitoring provides direct feedback for analgesia and an input–output model to opioids.

Quantitative Sensory Changes Following Posterior Spinal Fusion to Treat Adolescent Idiopathic Scoliosis.

Sensory changes surrounding the incision frequently develop after posterior spinal fusion (PSF) to treat adolescent idiopathic scoliosis (AIS). Anecdotally, patients may experience sensory changes on the chest wall. Such postsurgical sensory changes are not well described quantitatively. This study aims to evaluate the presence, intensity, and duration of mechanical sensory changes in AIS patients postoperatively. A prospective cohort of AIS patients, 10 to 21 years old, was followed. Quantitative sensory testing (QST) included touch detection threshold [mechanical detection threshold (MDT)] and pain detection threshold (MPT), using VonFrey monofilaments and pinprick stimulators. QST was performed at 3 sites at T6: the right and left chest at the nipple line and adjacent to the incision below the inferior angle of the scapula. QST at the thenar eminence was the control. QST was collected at baseline, 3 days, 1, and 6 months postoperative. Thirty-four patients (21% males; mean age: 14.9 years old; median preoperative curve: 58 degrees) completed all testing. Mean deformity correction was 64% (SD: 10.4). Adjacent to the incision site, MDT was significantly higher compared with baseline at 3 days and 1 month ( P < 0.001) but not at 6 months ( P = 0.19), whereas MPT was significantly higher at 3 days, ( P < 0.001), 1 month ( P < 0.001), and 6 months ( P = 0.001). For the chest wall in all patients, MPT was higher on the left chest at 3 days ( P = 0.04) and on the right chest at 3 days ( P = 0.022) and 1 month ( P = 0.05). For patients with right-sided curves, MDT ( P = 0.01) and MPT ( P = 0.015) overall were significantly higher on the concave side (left) chest postoperatively. PSF is associated with sensory disturbances that are detectable within days, persist at 1 month, and improve at 6 months postoperatively adjacent to the incision and on the chest wall. We suspect that these sensory changes are transient. Describing postoperative sensory changes will help us better set postoperative expectations for patients undergoing PSF. Level I.

Ensemble of multimodal deep learning autoencoder for infant cry and pain detection

Background: Babies cannot communicate their pain properly. Several pain scores are developed, but they are subjective and have high variability inter-observer agreement. The aim of this study was to construct models that use both facial expression and infant voice in classifying pain levels and cry detection. Methods: The study included a total of 23 infants below 12-months who were treated at Dr Soetomo General Hospital. The the Face Leg Activity Cry and Consolability (FLACC) pain scale and recordings of the baby's cries were taken in the video format. A machine-learning-based system was created to detect infant cries and pain levels. Spectrograms with the Short-Time Fourier Transform were used to convert the audio data into a time-frequency representation. Facial features combined with voice features extracted by using the Deep Learning Autoencoders was used for the classification of infant pain levels. Two types of autoencoders: Convolutional Autoencoder and Variational Autoencoder were used for both faces and voices. Result: The goal of the autoencoder was to produce a latent-vector with much smaller dimensions that was still able to recreate the data with minor losses. From the latent-vectors, a multimodal data representation for Convolutional Neural Network (CNN) was used for producing a relatively high F1 score, higher than single data modal such as the voice or facial expressions alone. Two major parts of the experiment were: 1. Building the three autoencoder models, which were autoencoder for the infant’s face, amplitude spectrogram, and dB-scaled spectrogram of infant’s voices. 2. Utilising the latent-vector result from the autoencoders to build the cry detection and pain classification models. Conclusion: In this paper, four pain classifier models with a relatively good F1 score was developed. These models were combined by using ensemble methods to improve performance, which resulted in a better F1 score.

Extensive Sensorimotor Training Predetermines Central Pain Changes During the Development of Prolonged Muscle Pain

Repetitive movements (RM) are a main risk factor for musculoskeletal pain, which is partly explained by the overloading of musculoskeletal structures. However, RM may also drive brain plasticity, leading to maladaptive changes in sensorimotor areas and altered pain processing. This study aimed to understand whether individuals performing extensive RM (musicians) exhibit altered brain processing to prolonged experimental muscle pain. Nineteen healthy musicians and 20 healthy nontrained controls attended 3 sessions (Day 1–Day 3–Day 8). In each session, event-related potentials (ERPs) to non-nociceptive superficial and nociceptive intraepidermal electrical stimulation, reaction times, electrical detection thresholds, and pressure pain thresholds (PPTs) were recorded. In all participants, prolonged muscle pain was induced by intramuscular injection of nerve growth factor (NGF) into the right first dorsal interosseous muscle at the end of Day1. Pain intensity was assessed on a numerical rating scale (NRS) and was lower in musicians compared to non-musicians (P < .007). Moreover, in musicians, the higher amount of weekly training was associated with lower NRS pain scores on Day 3 to Day 8 (P < .037). Compared with Day1, NGF reduced PPTs on Day 3 to Day 8 (P < .001) and non-nociceptive P200 and P300 ERP amplitudes on Day 8 (P < .044) in both groups. Musicians compared to controls showed secondary hyperalgesia to electrical stimulation on Day 3 to Day 8 (P < .004) and reduced nociceptive P200 ERP amplitudes on Day 8 (P < .005). Across participants, ERP components correlated with pain detection reaction times, sensitivity (PPTs and electrical detection thresholds), and severity (NRS), (all P < .043). These results show that repetitive sensorimotor training leads to brain changes in the processing of prolonged pain, biasing the cortical response to nociceptive inputs. PerspectiveRepetitive sensorimotor training may increase the responsiveness of nociceptive inputs during the development of prolonged muscle pain. These novel data highlight the role of repetitive sensorimotor practice as a source for interindividual variability in central pain processing.

Neural empathic response to disability: An ERP study of prejudice

While social neuroscience has already provided evidence for a deficit of affective empathy in racial prejudice, little is known about other less visible social categories when considered as an outgroup. We studied the process of empathy through event-related brain potentials (ERPs). We focused on the group "people with disabilities" as they are the target of a large amount of prejudice. Twenty-six participants performed a pain decision task. The mean amplitudes of N1, P2, N2–N3 and P3 components were recorded. Our results are consistent with previous work on prejudice, showing that the pain detection is modulated by group membership (with disabilities vs. without disabilities) on N2–N3, suggesting a better neural decoding of pain vs. non-pain in the without-disability condition. Critically, no effect of early sensory components (N1, P2) was found, and P3 was not moderated by disability. These findings indicate a different time course of empathic responses depending on the condition, suggesting that people with disabilities trigger specific empathic responses. Our results contribute to disentangling perceptual processes from affective empathy reactions.

Human Facial Emotion Recognition using Deep Neural Networks

Humans experience a plethora of different, confusing and nearly indiscernible emotions. Interpreting and understanding these emotions is a rewarding challenge as they play an important role in developing and maintaining interpersonal relationships. Hence, extracting and understanding emotions is paramount to the interaction and communication between human and machine. This understanding aids applications such as brand humanization, sales, advertising and marketing by helping to gauge the response of existing or prospective clients, medical industry, e-learning, law enforcement, automatic counseling system, drunk-driving detection, pain or stress detection, brand value analysis from consumer reactions and the entertainment industry, social interactions and facilitate rational decision making and perception. The recognition of facial emotion is quite difficult problem since there will be a variation of same emotion of single person owing to occlusion, illumination, aging, pose, gender etc. This paper proposes a deep learning model to detect the seven basic emotion classes. The dataset used are CK+, JAFFE and it achieves higher accuracy when compared to the existing approaches.

Prevalence and interference of neuropathic pain in the quality of life in patients with knee osteoarthritis

ObjectiveWith the aging of the population, more patients have complained of pain due to knee Osteoarthritis (OA), and the number of arthroplasties has also increased. The objective of this study is to evaluate the prevalence of the neuropathic pain component in candidates for Total Knee Replacement and the effects of this component on their quality of life. MethodsIn this cross-sectional study, patients with OA candidates for knee arthroplasty in the present institution were evaluated using the pain detection questionnaire and the Visual Analog Pain (VAS) scale to measure the pain index and the presence of associated neuropathic pain. In addition, evaluation of the quality of life and functionality using the EQ5D and SF12 questionnaires and their relationship with cases of neuropathic pain were performed. ResultsOne hundred twenty-six patients were evaluated, and 71.4 % were female. The age ranged from 46 to 85 years, and about 70 % of the patients had some associated clinical comorbidity. Neuropathic pain was present in 28.6 % of the patients evaluated. Patients with neuropathic pain presented worse results in the VAS evaluation, in the care, pain, and anxiety domains of the EQ5D, and in the physical and mental scores of the SF12. ConclusionNeuropathic pain was present in 28.6 % of the patients with knee OA who are candidates for arthroplasty. Patients with associated neuropathic pain present a higher level of pain and worse quality of life scores. Recognizing this type of pathology is extremely important in fully monitoring gonarthrosis.

Work from home-related musculoskeletal pain during the COVID-19 pandemic: A rapid review

ObjectiveThis rapid review explores the prevalence of musculoskeletal pain symptoms associated with work from home conditions during the COVID-19 pandemic. MethodsWe conducted a rapid review across three databases (i.e., PubMed, Medline, and CINAHL) for observational studies that report on the musculoskeletal functions among individuals placed in a work from home setup due to the COVID-19 pandemic, published between December 2019–August 2021. Two independent review authors searched, appraised, and extracted data from the articles included in the final review. A descriptive approach was used to synthesize the narrative evidence. ResultsForty-four articles were initially identified. A total of six (n = 6) studies met the full inclusion criteria and were included. Among them, there were five cross-sectional studies and one case-control study. The highest prevalence reported were neck pain (20.3–76.9%), low back pain (19.5–74.1%), and shoulder pain (3.0–72.9%). The most common instrument used was the Nordic Musculoskeletal Questionnaire. One of the common professions that report musculoskeletal pain symptoms associated with work from home conditions were individuals working in the academic sector. ConclusionThe increased prevalence of musculoskeletal pain symptoms associated with work from home conditions during the COVID-19 pandemic is a concern that should be addressed to prevent negative neuromusculoskeletal outcomes. Systematic review registrationThis review is in the Open Science Framework registry (osf.io/vxs4w) and the PROSPERO database (CRD42021266097). Implications for practice•A system in the workplace should be developed for the early detection of musculoskeletal pain.•Apart from standard occupational safety and proper ergonomic, sustainable policies and programs that address the mental health issues of employees should also be addressed.•Programs addressing musculoskeletal pain should be available online for employees to address accessibility and ubiquity.

Application of Kinetic Recovery Programs in Performance Sports Dance Pathologies

"For the detection of the flat foot and ligament laxity, the method of visual observation of the anatomical landmarks, the planogram on paper and the test of bearing body weight were used. At the same time, to test the stability of the ankle joint, we used the unipodal test on Bosu ball, where it was measured in seconds how much the subjects managed to maintain the unipodal position on Bosu ball. For the detection of low back pain, the method of individual interviewing of each athlete, the VAS scale of pain, the DLLT test (Double Leg Lowering Test) and the visual evaluation of any malalignments present in the bone structures was used. Applying the protocol for flat foot pathology, we obtained an improvement of the plantar arch in all subjects, as evidenced by the improvement of paper planograms where the decrease of the plantar footprint on the medial part of the foot was observed in all subjects of the experiment group. Compared to the standard protocol applied to the control group, we obtained by applying our protocol an improvement of the Achilles tendon line in orthostatic in 9 out of 10 subjects, while when applying the standard protocol, only 7 out of 10 subjects were observed. In conclusion, applying the kinetic protocol to recover post-training low back pain has improved low back pain, proving to be more beneficial than the standardized protocol for dancers, especially for female subjects. Keywords: kinetic recovery, sport performance, dancers pathologies, sport traumatology "

Inventor and innovator: cardiac troponin T.

When I started my research fellowship in 1978 in Edgar Haber’s Lab at the MGH in Boston, blood testing for acute myocardial infraction (AMI) commonly employed CK- and CKMB-activity measurements, which were neither sensitive nor specific for AMI nor very useful for risk prediction. Research in Edgar Haber’s Lab was focused on antibody diversity and the use of antibodies as diagnostic and therapeutic tools. In this thrilling environment, we hypothesized in 1979 that cardiac sarcomere proteins, due to their high intracellular mass and their expression as tissue specific isoforms, may improve diagnosis of AMI if detected in blood by highly sensitive assays. The advent of the hybridoma technology for production of monoclonal antibodies (first reported by Köhler and Milstein in Nature 1975) opened new avenues for the predictive engineering of immuno-assays according to the affinity and epitope specificity of selected antibody molecules. Using preselected monoclonal antibodies, we engineered in 1980 a novel assay format (double sandwich assay) and used this sandwich assay modality to measure cardiac myosin light chains.1 Using this cardiac myosin light chain assay, we were the first to show that sarcomere biomarker outperform CKMB in risk prediction of chest pain patients and detection of AMI.2

Automatic detection of pain using machine learning.

Pain is one of the most common symptoms reported by individuals presenting to hospitals and clinics and is associated with significant disability and economic impacts; however, the ability to quantify and monitor pain is modest and typically accomplished through subjective self-report. Since pain is associated with stereotypical physiological alterations, there is potential for non-invasive, objective pain measurements through biosensors coupled with machine learning algorithms. In the current study, a physiological dataset associated with acute pain induction in healthy adults was leveraged to develop an algorithm capable of detecting pain in real-time and in natural field environments. Forty-one human subjects were exposed to acute pain through the cold pressor test while being monitored using electrocardiography. A series of respiratory and heart rate variability features in the time, frequency, and nonlinear domains were calculated and used to develop logistic regression classifiers of pain for two scenarios: (1) laboratory/clinical use with an F1 score of 81.9% and (2) field/ambulatory use with an F1 score of 79.4%. The resulting pain algorithms could be leveraged to quantify acute pain using data from a range of sources, such as ECG data in clinical settings or pulse plethysmography data in a growing number of consumer wearables. Given the high prevalence of pain worldwide and the lack of objective methods to quantify it, this approach has the potential to identify and better mitigate individual pain.

Carotid atherosclerosis: An independent risk factor for small fiber nerve dysfunction in patients with type 2 diabetes mellitus.

To explore whether carotid atherosclerosis is an independent risk factor for small fiber nerve dysfunction in type 2 diabetes mellitus patients. A total of 247 type 2 diabetes patients from Nanjing Drum Tower Hospital received carotid ultrasonography and quantitative sensory testing, including cold and warm detection thresholds, and some patients received cold and heat pain detection thresholds, respectively. According to the results of quantitative sensory testing, patients were divided into normal small fiber nerve function (NSF)and small fiber nerve dysfunction (SFD) group. Meanwhile, patients were divided into the non-carotid atherosclerosis group, carotid intimal thickening, unilateral carotid atherosclerosis and bilateral carotid atherosclerosis group. The correlation between carotid ultrasonography with quantitative sensory testing parameters was analyzed by SPSS 26.0. First, the incidence rate of SFD increased significantly in patients with carotid atherosclerosis (72.2%, P < 0.001) especially in bilateral carotid atherosclerosis (81.7%, P < 0.001). Second, compared with the NS group, the carotid intima-media thickness in SFD was thicker (P=0.018) and the size of atherosclerotic plaque was larger (P < 0.001). In addition, the cold detection threshold decreased (P < 0.001), whereas the warm detection threshold (P < 0.001) and heat pain detection threshold (P < 0.001) increased as aggravation of carotid atherosclerosis. In the correlation analysis, the size of atherosclerotic plaque presented a positive correlation with the warm detection threshold (r=0.476, P < 0.001) and heat pain detection threshold (r=0.213, P < 0.001), but presented a negative correlation with the cold detection threshold (r=-0.239, P < 0.01). Furthermore, carotid atherosclerosis (odds ratio 2.326, P=0.017), especially bilateral carotid atherosclerosis (odds ratio 5.042, P=0.001), was an independent risk factor for SFD (P < 0.05). Carotid atherosclerosis was significantly associated with quantitative sensory testing and found to be an independent risk factor for small fiber nerve dysfunction in type 2 diabetes patients.

Imbalanced regression for intensity series of pain expression from videos by regularizing spatio-temporal face nets

• Addressing limited data with expression intensity labels. • Pushing the pain assessment performance by a large margin. • Proposing to add a center-loss-based regularizer. • Proposing a more sensible evaluation metric . Obtaining accurate patient-reported pain intensity is essential for effective pain management. An automatic pain recognition system can simplify the pain reporting process and reduce the strain on manual efforts. Limited and imbalanced labeled data are available for the research of estimating the intensity of pain based on facial expressions. However, the ability to train deep networks for automated pain assessment is limited by small datasets with imbalanced labels of patient-reported pain levels. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification or recognition, can alleviate this problem to some extent. In this paper, we propose a network which fine-tunes a face verification or recognition network using a regularized regression loss and additional data with pain-intensity labels. The expression intensity regression task can benefit from the rich feature representations trained on a large number of data for face analysis tasks. In order to explore the temporal information between frames, we combine CNN with LSTM to obtain a better prediction result of each frame in videos. A weighted evaluation metric and re-sampling technique are also proposed to address the imbalance issue of different pain levels. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset and BioVid Heat Pain dataset, achieving the state-of-the-art performance. As pain detection is a form of micro facial expression recognition, we also apply the transferred deep regressor to estimate the intensity of facial action units, obtaining high quality performance.

Role of smartphone devices in precision oncology.

To improve the care for cancer patients, personalized treatment including monitoring and managing Quality of life (QoL) data collection of patients in his/her home environment, its integration and its analysis is necessary. Advanced technologies have been used to develop smartphone devices to support cancer patients and clinicians by integrating all patient-relevant data, helping with Patient Reported Outcomes (PRO), side effect management, appointments, and nutritional support. In this review the role and challenges of using smartphone applications for precision oncology is discussed. The methodology section includes the data collection, data integration and predictive modelling approaches. The design, development and evaluation of (AI/ML) algorithms of these apps need intended purpose of these algorithms, description of used mepthods, validity and appropriateness of the datasets, design of the algorithms, evaluation, implementation of these (AI/ML) algorithms and post treatment monitoring. Though Artificial intelligence (AI) based results showed higher diagnostic classification accuracy in most of the results, the advancement of these mobile apps technologies has a few limitations. ML techniques and DL are used to discover novel biomarkers for early detection and diagnostics, and AI are used to accelerate drug discovery, exploit biomarkers to accurately match patients to clinical trials, and personalize cancer therapy based only on patient's own data. AI based smartphone apps cannot be treated as autonomous rather used as an integrative tool for patient-relevant data, PRO, side effect management, appointments, nutritional support, emotional and social support, severity of pain detection and correct diagnosis at higher level. It should encourage the clinicians and care givers to support and establish patient-physician relationships with the help of these apps.

Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images

Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases—University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database—which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.

Effects of Self-Efficacy and Outcome Expectations on Motor Imagery-Induced Thermal and Mechanical Hypoalgesia: A Single-Blind Randomised Controlled Trial.

The main aim of this study was to assess whether self-efficacy (SE) and outcome expectations (OEs) modulate the hypoalgesic effect induced by motor imagery (MI). A total of 75 asymptomatic participants were randomly assigned to the positive (SE+, OE+), negative (SE−, OE−) or non-expectation (CG) groups. Heat pain threshold (HPT) and pain pressure threshold (PPT) were the main variables. Cold detection threshold (CDT), warm detection threshold (WDT), heart rate (HR) and perceived fatigue were the secondary variables. The variables were assessed preintervention, immediately postintervention and 10 min postintervention, except for HR, which was measured continuously during the intervention. Regarding HPT, significant within-group pre-post differences were found in the OE+ group, with a low effect size (p = 0.01, d = −0.39). With regard to ΔPPT, significant intergroup differences were found in Δpost-pre between the SE+ and CG groups (p = 0.012, d = 1.04) and also between SE+ and OE− (p = 0.006, d = 1.08), both with a large effect size. CG, SE−, and OE− groups had poorer CDT and WDT. Regarding HR, significant intergroup differences were found in the postintervention measurement between OE+ and SE−, with a large effect size (p = 0.016, d = 1.34). Lastly, no between-group differences were found regarding perceived fatigue (p > 0.05). The results obtained showed that positive expectations have a slight influence on the increase in heat and mechanical pain detection thresholds. Positive and non-expectancy groups showed an autonomic activation. The results also showed that negative expectations led to poorer perceptual processes.

Audio Feature Analysis for Acoustic Pain Detection in Term Newborns

Introduction: Crying newborns signal a need or discomfort as part of the innate communication system. Exposure to pain is related to infants’ unfavorable neurodevelopmental outcomes. There is a tremendous need for more objective methods to assess neonatal pain. An audio analysis of acoustic utterances could provide specific information on the patient’s pain level. Methods: We analyzed 67 videos of 33 term-born newborns recorded during a planned capillary blood sample, including the stimuli, non-noxious thermal stimulus, short noxious stimulus, and prolonged unpleasant stimulus, between December 2020 and March 2021. Two expert raters evaluated the infants’ pain responses using the Neonatal Facial Coding System (NFCS). The mean values of 123 timbre features of the recorded audio data were analyzed by using specific toolboxes and libraries from the following programming environments: MIRtoolbox (MATLAB), MiningSuite (MATLAB), Essentia (Python), AudioCommons timbral models (Python), and Librosa (Python). Results: The NFCS values were significantly higher during the short noxious stimulus (p < 0.001) and prolonged unpleasant stimulus (p < 0.001) than during the non-noxious thermal stimulus, whereas NFCS values during the short noxious stimulus and prolonged unpleasant stimulus were similar (p = 0.79). Brightness, roughness, percussive energy, and attack times were identified as the features having the highest impact on the NFCS. Conclusion: This hypothesis-generating study identified several salient acoustic features highly associated with pain responses in term newborns. Our analysis is an encouraging starting point for the targeted analysis of pain-specific acoustic features of neonatal cries and vocalizations from the perspective of real-time acoustic processing.

Retracted: Effect of Sham Acupuncture on Chronic Pain: A Bayesian Network Meta-Analysis.

Along with increasing research on acupuncture for chronic pain, the validity of sham acupuncture (SA) has also been argued. Nine databases were searched for randomized controlled trials (RCTs) from the inception dates of the databases to July 5, 2022. With Markov Chain Monte Carlo methods, a Bayesian multiple-treatment network meta-analysis (NMA) with random-effects model was conducted. A total of 62 RCTs with 6,806 patients and four kinds of treatments (real acupuncture [RA], non-acupuncture [NA], penetrative SA [PSA], and non-penetrative SA [NPSA]) were included. The results indicated that both NPSA and PSA were not superior to NA in improving chronic pain (NPSA: mean difference [MD]= -4.77, 95% confidence interval [CI] -11.09 to 1.52; PSA: MD= -4.96, 95% CI -10.38 to 0.48). After NPSA and PSA were combined into the SA group, the weak trend of pain relief from SA was still not statistically significant (MD= -4.91, 95% CI -9.93 to 0.05). NPSA and PSA had similar effects (MD= 0.18, 95% CI -5.45 to 5.81). RA was significantly associated with pain relief, compared with NPSA and PSA (NPSA: MD= -12.03, 95% CI -16.62 to -7.41; PSA: MD= -11.85, 95% CI -15.48 to -8.23). The results were generally consistent regardless of pain phenotype, frequency, duration, acupuncture methods, analgesic intake, or detection bias. These results suggested that acupuncture was significantly associated with reduced chronic pain. The two kinds of placebo acupuncture, NPSA and PSA, have similar effects. Both NPSA and PSA, with a weak but not significant effect, are appropriate to be inert placebo controls in RCTs for chronic pain.