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1. Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare

   https://doi.org/10.1371/journal. pone.0155001  

   Larry Carbone, Jamie Austin ( May 2016 , PloS one)

   Scientists who perform major survival surgery on laboratory animals face a dual welfare and methodological challenge: how to choose surgical anesthetics and post-operative analgesics that will best control animal suffering, knowing that both pain and the drugs that manage pain can all affect research outcomes. Scientists who publish full descriptions of animal procedures allow critical and systematic reviews of data, demonstrate their adherence to animal welfare norms, and guide other scientists on how to conduct their own studies in the field. We investigated what information on animal pain management a reasonably diligent scientist might find in planning for a successful experiment. To explore how scientists in a range of fields describe their management of this ethical and methodological concern, we scored 400 scientific articles that included major animal survival surgeries as part of their experimental methods, for the completeness of information on anesthesia and analgesia. The 400 articles (250 accepted for publication pre-2011, and 150 in 2014–15, along with 174 articles they reference) included thoracotomies, craniotomies, gonadectomies, organ transplants, peripheral nerve injuries, spinal laminectomies and orthopedic procedures in dogs, primates, swine, mice, rats and other rodents. We scored articles for Publication Completeness (PC), which was any mention of use ofmore »anesthetics or analgesics; Analgesia Use (AU) which was any use of post-surgical analgesics, and Analgesia Completeness (a composite score comprising intra-operative analgesia, extended post-surgical analgesia, and use of multimodal analgesia). 338 of 400 articles were PC. 98 of these 338 were AU, with some mention of analgesia, while 240 of 338 mentioned anesthesia only but not postsurgical analgesia. Journals’ caliber, as measured by their 2013 Impact Factor, had no effect on PC or AU. We found no effect of whether a journal instructs authors to consult the ARRIVE publishing guidelines published in 2010 on PC or AC for the 150 mouse and rat articles in our 2014–15 dataset. None of the 302 articles that were silent about analgesic use included an explicit statement that analgesics were withheld, or a discussion of how pain management or untreated pain might affect results. We conclude that current scientific literature cannot be trusted to present full detail on use of animal anesthetics and analgesics. We report that publication guidelines focus more on other potential sources of bias in experimental results, under-appreciate the potential for pain and pain drugs to skew data, PLOS ONE | DOI:10.1371/journal.pone.0155001 May 12, 2016 1 / 24 a11111 OPEN ACCESS Citation: Carbone L, Austin J (2016) Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare. PLoS ONE 11(5): e0155001. doi:10.1371/journal. pone.0155001 Editor: Chang-Qing Gao, Central South University, CHINA Received: December 29, 2015 Accepted: April 22, 2016 Published: May 12, 2016 Copyright: © 2016 Carbone, Austin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Authors may be contacted for further information. Funding: This study was funded by the United States National Science Foundation Division of Social and Economic Sciences. Award #1455838. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. and thus mostly treat pain management as solely an animal welfare concern, in the jurisdiction of animal care and use committees. At the same time, animal welfare regulations do not include guidance on publishing animal data, even though publication is an integral part of the cycle of research and can affect the welfare of animals in studies building on published work, leaving it to journals and authors to voluntarily decide what details of animal use to publish. We suggest that journals, scientists and animal welfare regulators should revise current guidelines and regulations, on treatment of pain and on transparent reporting of treatment of pain, to improve this dual welfare and data-quality deficiency.« less
2. Pain and Physical Activity Association in Critically Ill Patients ^*

   https://doi.org/10.1109/EMBC44109.2020.9176227  

   Davoudi, Anis ; Ozrazgat-Baslanti, Tezcan ; Tighe, Patrick J. ; Bihorac, Azra ; Rashidi, Parisa ( July 2020 , 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group.
3. Sleep Problems Mediate the Relationship Between Psychosocial Stress and Pain Facilitation in Native Americans: A Structural Equation Modeling Analysis from the Oklahoma Study of Native American Pain Risk

   https://doi.org/10.1093/abm/kaac034  

   Kell, Parker A. ; Huber, Felicitas A. ; Street, Erin N. ; Shadlow, Joanna O. ; Rhudy, Jamie L. ( July 2022 , Annals of Behavioral Medicine)

   Native Americans (NAs) are more likely to experience chronic pain than non-Hispanic Whites (NHWs); however, the proximate causes predisposing NAs to chronic pain remain elusive. Likely due to centuries of adversity, discrimination, and marginalization, NAs report greater psychological stress than NHWs, which may place them at risk for sleep problems, a well-established risk factor for chronic pain onset.

   This study examined the effects of psychological stress and sleep problems on subjective and physiological measures of pain processing in NAs and NHWs.

   Structural equation modeling was used to determine whether ethnicity (NA or NHW) was associated with psychological stress or sleep problems and whether these variables were related to conditioned pain modulation of pain perception (CPM-pain) and the nociceptive flexion reflex (CPM-NFR), temporal summation of pain (TS-pain) and NFR (TS-NFR), and pain tolerance in a sample of 302 (153 NAs) pain-free participants.

   NAs experienced more psychological stress (Estimate = 0.027, p = .009) and sleep problems (Estimate = 1.375, p = .015) than NHWs. When controlling for age, sex, physical activity, BMI, and general health, NA ethnicity was no longer related to greater sleep problems. Psychological stress was also related to sleep problems (Estimate = 30.173, p = <.001) andmore »psychological stress promoted sleep problems in NAs (indirect effect = 0.802, p = .014). In turn, sleep problems were associated with greater TS-pain (Estimate = 0.714, p = .004), but not other pain measures.

   Sleep problems may contribute to chronic pain risk by facilitating pain perception without affecting facilitation of spinal neurons or endogenous inhibition of nociceptive processes. Since psychological stress promoted pain facilitation via enhanced sleep problems, efforts to reduce psychological stress and sleep problems among NAs may improve health outcomes.

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4. Black Racial Phenotypicality Shapes Social Pain and Support Judgments

   https://doi.org/https://doi.org/10.1016/j.jesp.2020.103998  

   Jason C. Deska, Jonathan W. ( September 2020 , Journal of experimental social psychology)

   Social pain, defined as responses to aversive interpersonal experiences (e.g., ostracism, unfairness, disrespect), has profound effects on health and well-being. Yet, research indicates that race biases judgments of social pain, leading people to believe that Black individuals experience less social pain than White individuals. The current work extends this research, testing whether characteristics associated with Black racial phenotypicality shapes this social pain effect. Five studies tested the hypothesis that people would judge targets high in Black racial phenotypicality as less sensitive to social pain and consequently requiring fewer coping resources than targets low in racial phenotypicality. The results of these studies reveals a consistent effect of Black racial phenotypicality on social pain judgments (Studies 1-5; Ncumulative=1,064). Moreover, this phenotypicality effect shaped judgments of social pain for both Black and White targets, suggesting effects are driven by stereotype-related characteristics rather than activation of the Black racial category. Study 3 links this bias with judgments of toughness independent of other plausible mechanisms and Studies 4-5 provide evidence that phenotypic biases in social pain undermine social support judgments. Perceivers believed Black individuals high in phenotypicality experienced less social pain and, consequently, required fewer coping resources to manage distress compared to individuals low inmore »Black phenotypicality. These results provide evidence for a target-level bias in social pain judgments.« less
5. Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

   https://doi.org/10.1371/journal.pone.0254108  

   Pouromran, Fatemeh ; Radhakrishnan, Srinivasan ; Kamarthi, Sagar ( July 2021 , PLOS ONE)

   Le, Khanh N.Q. (Ed.)

   In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achievedmore »by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.« less