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TailTimer: A device for automating data collection in the rodent tail immersion assay.

ABSTRACT: The tail immersion assay is a widely used method for measuring acute thermal pain in a way which is quantifiable and reproducible. It is non-invasive and measures response to a stimulus that may be encountered by an animal in its natural environment. However, quantification of tail withdrawal latency relies on manual timing of tail flick using a stopwatch, and precise temperatures of the water at the time of measurement are most often not recorded. These two factors greatly reduce the reproducibility of tail immersion assay data and likely contribute to some of the discrepancies present among relevant literature. We designed a device, TailTimer, which uses a Raspberry Pi single-board computer, a digital temperature sensor, and two electrical wires, to automatically record tail withdrawal latency and water temperature. We programmed TailTimer to continuously display and record water temperature and to only permit the assay to be conducted when the water is within ± 0.25°C of the target temperature. Our software also records the identification of the animals using a radio frequency identification (RFID) system. We further adapted the RFID system to recognize several specific keys as user interface commands, allowing TailTimer to be operated via RFID fobs for increased usability. Data recorded using the TailTimer device showed a negative linear relationship between tail withdrawal latency and water temperature when tested between 47-50°C. We also observed a previously unreported, yet profound, effect of water mixing speed on latency. In one experiment using TailTimer, we observed significantly longer latencies following administration of oral oxycodone versus a distilled water control when measured after 15 mins or 1 h, but not after 4 h. TailTimer also detected significant strain differences in baseline latency. These findings valorize TailTimer in its sensitivity and reliability for measuring thermal pain thresholds.

SUBMITTER: Udell ME

PROVIDER: S-EPMC8375991 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionChronic pain is highly prevalent, and the ability to routinely measure patients' pain and treatment response using validated patient-reported outcome (PRO) assessments is important to clinical care. Despite this recognition, systematic use in everyday clinical care is rare.AimsThe aims of this study were to (1) describe infrastructure designed to automate PRO data collection, (2) compare study-enhanced PRO completion rates to those in clinical care, and (3) evaluate patient response rates by method of PRO administration and sociodemographic and/or clinical characteristics.SettingThe Pain Program for Active Coping and Training (PPACT) is a pragmatic clinical trial conducted within three regions of the Kaiser Permanente health care system.Program descriptionPPACT evaluates the effect of integrative primary care-based pain management services on outcomes for chronic pain patients on long-term opioid treatment. We implemented a tiered process for quarterly assessment of PROs to supplement clinical collection and ensure adequate trial data using three methods: web-based personal health records (PHR), automated interactive voice response (IVR) calls, and live outreach.Program evaluationAmong a subset of PPACT participants examined (n = 632), the tiered study-enhanced PRO completion rates were higher than in clinical care: 96% completed ≥ 1 study-administered PRO with mean of 3.46 (SD = 0.85) vs. 74% completed in clinical care with a mean of 2.43 (SD = 2.08). Among all PPACT participants at 3 months (n = 831), PRO completion was 86% and analyses of response by key characteristics found only that participant age predicted an increased likelihood of responding to PHR and IVR outreach.DiscussionAdherence to pain-related PRO data collection using our enhanced tiered approach was high. No demographic or clinical identifiers other than age were associated with differential response by modality. Successful ancillary support should employ multimodal electronic health record functionalities for PRO administration. Using automated modalities is feasible and may facilitate better sustainability for regular PRO administration within health care systems. Clinical Trials Registration Number: NCT02113592.

Project description:Global-scale energy flow throughout Earth’s magnetosphere is catalyzed by processes that occur at Earth’s magnetopause (MP). Magnetic reconnection is one process responsible for solar wind entry into and global convection within the magnetosphere, and the MP location, orientation, and motion have an impact on the dynamics. Statistical studies that focus on these and other MP phenomena and characteristics inherently require MP identification in their event search criteria, a task that can be automated using machine learning so that more man hours can be spent on research and analysis. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network model to detect MP crossings and assist studies of energy transfer into the magnetosphere. As its first application, the LSTM has been implemented into the operational data stream of the Magnetospheric Multiscale (MMS) mission. MMS focuses on the electron diffusion region of reconnection, where electron dynamics break magnetic field lines and plasma is energized. MMS employs automated burst triggers onboard the spacecraft and a Scientist-in-the-Loop (SITL) on the ground to select intervals likely to contain diffusion regions. Only low-resolution survey data is available to the SITL, which is insufficient to resolve electron dynamics. A strategy for the SITL, then, is to select all MP crossings. Of all 219 SITL selections classified as MP crossings during the first five months of model operations, the model predicted 166 (76%) of them, and of all 360 model predictions, 257 (71%) were selected by the SITL. Most predictions that were not classified as MP crossings by the SITL were still MP-like, in that the intervals contained mixed magnetosheath and magnetospheric plasmas. The LSTM model and its predictions are public to ease the burden of arduous event searches involving the MP, including those for EDRs. For MMS, this helps free up mission operation costs by consolidating manual classification processes into automated routines.

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TailTimer: A device for automating data collection in the rodent tail immersion assay.

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