Physiological Recordings Research Articles

Symmetry engineering in 2D bioelectronics facilitating augmented biosensing interfaces

Symmetry lies at the heart of two-dimensional (2D) bioelectronics, determining material properties at the fundamental level. Breaking the symmetry allows emergent functionalities and effects. However, symmetry modulation in 2D bioelectronics and the resultant applications have been largely overlooked. Here, we devise an oxidized architectural MXene, referred to as oxidized MXene (OXene), that couples orbit symmetric breaking with inverse symmetric breaking to entitle the optimized interfacial impedance and Schottky-induced piezoelectric effects. The resulting OXene validates applications ranging from microelectrode arrays, gait analysis, active transistor matrix, and wireless signaling transmission, which enables high-fidelity signal transmission and reconfigurable logic gates. Furthermore, OXene interfaces were investigated in both rodent and porcine myocardium, featuring high-quality and spatiotemporally resolved physiological recordings, while accurate differentiated predictions, enabled via various machine learning pipelines.

Abstract Su806: Enhancing Cardiac Arrest Registries and Research – A Framework for Handling Defibrillator Data

Background: Cardiac arrest is the most time-sensitive medical condition, and survival probability is highly dependent on early, high-quality cardiopulmonary resuscitation (CPR) measures. Resuscitation registries are established tools for continuous quality improvement and serve as a source for research. Yet they lack comprehensive information on the quality of CPR measures themselves—such as the depth, rate, and release of chest compressions—and are limited in the temporal resolution of these measures. However, data on both are contained in the recordings of defibrillators. Aims: To develop a framework for healthcare providers to gain immediate case-specific feedback, reduce documentation efforts and make fine-grained information accessible for research. Approach: An open-source Python module named CPRDAT (Kern et al. Zenodo, https://doi.org/10.5281/zenodo.10057964) was designed. This module is capable of importing and extracting data from various defibrillators as well as data recorded by VitalRecorder ( VitalDB , Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea) into a uniform data object. Methods were implemented to automatically process, analyze, and aggregate these time-series of physiological signals, with particular attention to CPR. To reduce manual annotation workload, classification algorithms were developed. Plotting routines were developed to display the signals interactively in web applications, allowing healthcare providers to interact with the data through zooming, scaling, shifting, concatenating, merging and labelling. Results: The module was implemented into the German Resuscitation Registry, offering users a manufacturer-agnostic, data-driven, case-specific debriefing tool. Extracted information on CPR measures is stored in the registry. The framework, able to manage multiple time-series data sources, was further implemented in other settings, such as the analysis of laboratory data of a CPR swine model. Conclusions: The new module dramatically enhances the accessibility of physiologic time-series recordings. More fine-grained data will provide deeper insights into processes and CPR physiology. Finally, the open-source Python module abolishes barriers in research.

Decoding episodic autobiographical memory in naturalistic virtual reality

Episodic autobiographical memory (EAM) is a long-term memory system of personally experienced events with their context – what, where, when – and subjective elements, e.g., emotions, thoughts, or self-reference. EAM formation has rarely been studied in a controlled, real-life-like paradigm, and there is no predictive model of long-term retrieval from self-rated subjective experience at encoding. The present longitudinal study, with three surprise free recall memory tests immediately, one-week and one-month after encoding, investigated incidental encoding of EAM in an immersive virtual environment where 30 participants either interacted with or observed specific events of varying emotional valences with simultaneous physiological recordings. The predictive analyses highlight the temporal dynamics of the predictors of EAM from subjective ratings at encoding: common characteristics related to sense of remembering and infrequency of real-life encounter of the event were identified over time, but different variables become relevant at different time points, such as the emotion and mental imagery or prospective aspects. This dynamic and time-dependent role of memory predictors challenges traditional views of a uniform influence of encoding factors over time. Current evidence for the multiphasic nature of memory formation points to the role of different mechanisms at play during encoding but also consolidation and subsequent retrieval.

Seizure forecasting based on AI-supported analysis of multidien and circadian cycles in EEG and non-EEG long-term datasets

AbstractLong-term datasets in epilepsy encompassing weeks to months of continuous physiological signal recordings along with novel data analysis techniques have recently advanced the understanding of epilepsy in several aspects. Patterns of seizures, interictal discharges, and autonomous nervous system activity were observed to often exhibit long, multidien cycles that are often correlated with each other. These observations have provided the basis for new approaches to forecast seizure risk from electroencephalographic (EEG) and non-EEG data.

Carbon-based Wearable Pressure Sensor System for Real-time Step Frequency Monitoring

Abstract Wearable pressure sensors have attracted great attention in human motion and health monitoring due to their sensitivity, flexibility and portability that can provide continuous physiological information recording. However, current pressure sensors used for wearable motion monitoring suffer from low sensitivity, poor resistance to interference, and the inability to be mass-produced. In this study, we proposed the utilization of multi-walled carbon nanotubes (MWNTs) as sensing materials, fabric as substrates, and screen printing process for fabricating flexible pressure sensors. The fabricated sensors exhibited a high sensitivity at low pressure, and good stability of more than 3000 cycles. Moreover, we designed a wearable step frequency monitoring system for real-time recording of footstep frequency during human walking activities. The results demonstrated that the system accurately monitors frequency under different walking speeds and footstep activities during various movements, providing a promising strategy to develop wearable and wireless sensing system for real-time motion monitoring.

Uncertainty and information in physiological signals: Explicit physical trade-off with log-normal wavelets

Physiological recordings contain a great deal of information about the underlying dynamics of Life. The practical statistical treatment of these single-trial measurements is often hampered by the inadequacy of overly strong assumptions. Heisenberg’s uncertainty principle allows for more parsimony, trading off statistical significance for localization. By decomposing signals into time–frequency atoms and recomposing them into local quadratic estimates, we propose a concise and expressive implementation of these fundamental concepts based on the choice of a geometric paradigm and two physical parameters. Starting from the spectrogram based on two fixed timescales and Gabor’s normal window, we then build its scale-invariant analogue, the scalogram based on two quality factors and Grossmann’s log-normal wavelet. These canonical estimators provide a minimal and flexible framework for single trial time–frequency statistics, which we apply to polysomnographic signals: EEG representations, HRV extraction from ECG, coherence and mutual information between heart rate and respiration.

Mouse Exploratory Behaviour in the Open Field with and without NAT-1 EEG Device: Effects of MK801 and Scopolamine.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with onboard memory for direct high-resolution recording of brain activity for >48 h. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). The mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between the drug treatments. Hypoactivity was globally observed as a consistent outcome in the MK801-treated mice and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological, and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in, e.g., drug discovery and models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

Electroacupuncture alleviates myocardial ischemia-reperfusion injury by inhibiting hypothalamic paraventricular nucleus neurons projecting to the rostral ventrolateral medulla.

Accumulating evidence suggests that electroacupuncture (EA) has obvious therapeutic effects and unique advantages in alleviating myocardial ischemia-reperfusion injury (MIRI), while the underlying neuromolecular mechanisms of EA intervention for MIRI have not been fully elucidated. The aim of the study is to investigate the role of the neural pathway of hypothalamic paraventricular nucleus (PVN) neurons projecting to the rostral ventrolateral medulla (RVLM) in the alleviation of MIRI rats by EA preconditioning. MIRI models were established by ligating the left anterior descending coronary artery for 30 min followed by reperfusion for 2h. Electrocardiogram recording, chemogenetics, enzyme-linked immunosorbent assay, multichannel physiology recording and haematoxylin-eosin and immunofluorescence staining methods were conducted to demonstrate that the firing frequencies of neurons in the PVN and the expression of c-Fos decreased by EA pretreatment. Meanwhile, EA preconditioning significantly reduced the levels of creatine kinase isoenzymes (CK-MB), cardiac troponin I (cTnI) and lactic dehydrogenase (LDH). Virus tracing showed a projection connection between PVN and RVLM. The inhibition of the PVN-RVLM neural pathway could replicate the protective effect of EA pretreatment on MIRI rats. However, the activation of the pathway weakened the effect of EA preconditioning. EA pretreatment alleviated MIRI by regulating PVN neurons projecting to RVLM. This work provides novel evidence of EA pretreatment for alleviating MIRI.

Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

AbstractBioelectronic implantable devices are adept at facilitating continuous monitoring of health and enabling the early detection of diseases, offering insights into the physiological conditions of various bodily organs. Furthermore, these advanced systems have therapeutic capabilities in neuromodulation, demonstrating their efficacy in addressing diverse medical conditions through the precise delivery of stimuli directly to specific targets. This comprehensive review explores developments and applications of bioelectronic devices within the biomedical field. Special emphasis is placed on the evolution of closed‐loop systems, which stand out for their dynamic treatment adjustments based on real‐time physiological feedback. The integration of Artificial Intelligence (AI) and edge computing technologies is discussed, which significantly bolster the diagnostic and therapeutic functions of these devices. By addressing elemental analyses, current challenges, and future directions in implantable devices, the review aims to guide the pathway for advances in bioelectronic devices.

Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features.

The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA's video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson's correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions.

Cortical and subcortical brain networks predict prevailing heart rate.

Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstem's autonomic control over heart rate is well established, less is known about the regulatory role of higher level cortical and subcortical brain regions, especially in humans. This study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, high-dimensional data sets, to predict variation in instantaneous heart period (the inter-heartbeat-interval) from whole-brain hemodynamic signals measured by fMRI. Task-based and resting-state fMRI, as well as peripheral physiological recordings, were taken from two data sets that included extensive repeated measurements within individuals. Our models reliably predicted instantaneous heart period from whole-brain fMRI data both within and across individuals, with prediction accuracies being highest when measured within-participants. We found that a network of cortical and subcortical brain regions, many linked to visceral motor and visceral sensory processes, were reliable predictors of variation in heart period. This adds to evidence on brain-heart interactions and constitutes an incremental step toward developing clinically applicable biomarkers of brain contributions to CVD risk.

Development of a patient health monitoring system based on a service-oriented architecture using artificial intelligence

The object of the study is a patient health monitoring system that uses service-oriented architecture (SOA) and artificial intelligence (AI) to integrate and analyze medical data. Such a system integrates data from a variety of sources, including medical devices, health apps, electronic health records, and wearables and physiological performance recorders, providing a comprehensive approach to health monitoring. Thanks to SOA, the system is able to process large arrays of data in real time, providing the opportunity to quickly process and analyze them. This allows medical professionals to get a comprehensive picture of patients' health, taking into account both long-term trends and real-time indicators. One of the most challenging areas is ensuring effective integration and processing of disparate data from various medical devices and applications for accurate diagnosis and prognosis of diseases. It is also important to create a system that is easily scalable and can be adapted to the needs of different medical facilities and various monitoring systems. As a result of the research, it is concluded that the use of SOA allows creating flexible and scalable systems capable of integrating a wide range of medical devices and applications. The use of AI in these systems makes it possible to automatically detect deviations in health indicators, recognize pathologies in the early stages and predict the possible development of diseases. This is due to the fact that the proposed architecture has a number of features, in particular, the ability to collect, process and analyze large volumes of medical data in real time. Artificial intelligence algorithms provide high accuracy of diagnosis and forecasting thanks to the ability to quickly process complex data and find hidden patterns. Thanks to this, it is possible to obtain accurate and reliable indicators of the state of health of patients. Compared to similar known systems, it provides such advantages as increased efficiency of medical care, reduced risk of complications, early detection of diseases and a personalized approach to patient treatment, as well as the concentration of all data in one system.

Pupillometry indexes ocular dominance plasticity

Short-term monocular deprivation in normally sighted adult humans produces a transient shift of ocular dominance, boosting the deprived eye. This effect has been documented with both perceptual tests and through physiological recordings, but no previous study simultaneously measured physiological responses and the perceptual effects of deprivation. Here we propose an integrated experimental paradigm that combines binocular rivalry with pupillometry, to introduce an objective physiological index of ocular dominance plasticity, acquired concurrently with perceptual testing. Ten participants reported the perceptual dynamics of binocular rivalry, while we measured pupil diameter. Stimuli were a white and a black disk, each presented monocularly. Rivalry dynamics and pupil-size traces were compared before and after 2 h of monocular deprivation, achieved by applying a translucent patch over the dominant eye. Consistent with prior research, we observed that monocular deprivation boosts the deprived-eye signal and consequently increases ocular dominance. In line with previous studies, we also observed subtle but systematic modulations of pupil size that tracked alternations between exclusive dominance phases of the black or white disk. Following monocular deprivation, the amplitude of these pupil-size modulations increased, which is consistent with the post-deprivation boost of the deprived eye and the increase of ocular dominance. This provides evidence that deprivation impacts the effective strength of monocular visual stimuli, coherently affecting perceptual reports and the automatic and unconscious regulation of pupil diameter. Our results show that a combined paradigm of binocular rivalry and pupillometry gives new insights into the physiological mechanisms underlying deprivation effects.

Noradrenergic tuning of arousal is coupled to coordinated movements.

Matching arousal level to the motor activity of an animal is important for efficiently allocating cognitive resources and metabolic supply in response to behavioral demands, but how the brain coordinates changes in arousal and wakefulness in response to motor activity remains an unclear phenomenon. We hypothesized that the locus coeruleus (LC), as the primary source of cortical norepinephrine (NE) and promoter of cortical and sympathetic arousal, is well-positioned to mediate movement-arousal coupling. Here, using a combination of physiological recordings, fiber photometry, optogenetics, and behavioral tracking, we show that the LCNE activation is tightly coupled to the return of organized movements during waking from an anesthetized state. Moreover, in an awake animal, movement initiations are coupled to LCNE activation, while movement arrests, to LCNE deactivation. We also report that LCNE activity covaries with the depth of anesthesia and that LCNE photoactivation leads to sympathetic activation, consistent with its role in mediating increased arousal. Together, these studies reveal a more nuanced, modulatory role that LCNE plays in coordinating movement and arousal.

Advances in microneedles for transdermal diagnostics and sensing applications.

Microneedles, the miniaturized needles, which can pierce the skin with minimal invasiveness open up newpossibilities for constructing personalized Point-of-Care (POC) diagnostic platforms.Recent advances in microneedle-based POC diagnostic systems, especially their successful implementation with wearable technologies, enable biochemical detection and physiological recordings in a user-friendly manner. This review presents an overview of the current advances in microneedle-based sensor devices, with emphasis on thebiological basis of transdermal sensing, fabrication, and application of different types of microneedles, and a summary of microneedle devices based on various sensing strategies. It concludes with the challenges and future prospects of this swiftly growing field. The aim is to present a critical and thorough analysis of the state-of-the-art development of transdermal diagnostics and sensing devices based on microneedles, and to bridge the gap between microneedle technology and pragmatic applications.

Open cranium model for the study of cerebrovascular dynamics in intracranial hypertension

BackgroundSignificant research has been devoted to developing noninvasive approaches to neuromonitoring. Clinical validation of such approaches is often limited, with minimal data available in the clinically relevant elevated ICP range. New MethodTo allow ultrasound-guided placement of an intraventricular catheter and to perform simultaneous long-duration ICP and ultrasound recordings of cerebral blood flow, we developed a large unilateral craniectomy in a swine model. We also used a microprocessor-controlled actuator for intraventricular saline infusion to reliably and reversibly manipulate ICP according to pre-determined profiles. ResultsThe model was reproducible, resulting in over 80 hours of high-fidelity, multi-parameter physiological waveform recordings in twelve animals, with ICP ranging from 2 to 78 mmHg. ICP elevations were reversible and reproducible according to two predetermined profiles: a stepwise elevation up to an ICP of 30–35 mmHg and return to normotension, and a clinically significant plateau wave. Finally, ICP was elevated to extreme levels of greater than 60 mmHg, simulating extreme clinical emergency. Comparison with existing methodsExisting methods for ICP monitoring in large animals typically relied on burr-hole approaches for catheter placement. Accurate catheter placement can be difficult in pigs, given the thickness of their skull. Additionally, ultrasound is significantly attenuated by the skull. The open cranium model overcomes these limitations. ConclusionsThe hemicraniectomy model allowed for verified placement of the intraventricular catheter, and reversible and reliable ICP manipulation over a wide range. The large dural window additionally allowed for long-duration recording of cerebral blood flow velocity from the middle cerebral artery.

A Cocaine-Activated Ensemble Exerts Increased Control Over Behavior While Decreasing in Size

BackgroundSubstance use disorder is characterized by long-lasting changes in reward-related brain regions, such as the nucleus accumbens. Previous work has shown that cocaine exposure induces plasticity in broad, genetically defined cell types in the nucleus accumbens; however, in response to a stimulus, only a small percentage of neurons are transcriptionally active—termed an ensemble. Here, we identify an Arc-expressing neuronal ensemble that has a unique trajectory of recruitment and causally controls drug self-administration after repeated, but not acute, cocaine exposure. MethodsUsing Arc-CreERT2 transgenic mice, we expressed transgenes in Arc+ ensembles activated by cocaine exposure (either acute [1 × 10 mg/kg intraperitoneally] or repeated [10 × 10 mg/kg intraperitoneally]). Using genetic, optical, and physiological recording and manipulation strategies, we assessed the contribution of these ensembles to behaviors associated with substance use disorder. ResultsRepeated cocaine exposure reduced the size of the ensemble while simultaneously increasing its control over behavior. Neurons within the repeated cocaine ensemble were hyperexcitable, and their optogenetic excitation was sufficient for reinforcement. Finally, lesioning the repeated cocaine, but not the acute cocaine, ensemble blunted cocaine self-administration. Thus, repeated cocaine exposure reduced the size of the ensemble while simultaneously increasing its contributions to drug reinforcement. ConclusionsWe showed that repeated, but not acute, cocaine exposure induced a physiologically distinct ensemble characterized by the expression of the immediate early gene Arc, which was uniquely capable of modulating reinforcement behavior.

MiR-92b-3p Protects against Myocardial Ischemia-Reperfusion Injury by Inhibiting MAP3K2 in a Mouse Model.

MicroRNAs are well-known RNA regulators modulating biological functions in complex signaling networks. This work aims to explore the impact of microRNA-92b-3p (miR-92b-3p) on myocardial ischemia-reperfusion (I/R) injury. The I/R model was established by left anterior descending coronary artery ligation in mice. The hemodynamic parameters were detected through a multichannel physiological recorder. Myocardial injury markers: serum cardiac troponin I, myocardial kinase isoenzyme (creatine kinase-MB), and serum inflammatory factors (tumor necrosis factor-α, interleukin [IL]-1β, and IL-6) were evaluated by enzyme-linked immunosorbent assay. Cardiac tissue oxidative stress-related factors (malondialdehyde, glutathione peroxidase, total antioxidation capability, and superoxide dismutase) were assessed by colorimetry, myocardial pathology was observed by hematoxylin-eosin staining, and cardiomyocyte apoptosis was measured by triphosphate nick end-labeling staining, as well as the expression of miR-92b-3p and mitogen-activated protein kinase kinase kinase 2 (MAP3K2) in cardiac tissues were determined by reverse transcription quantitative polymerase chain reaction or western blot assay. The targeting relationship between miR-92b-3p and MAP3K2 was verified by bioinformatics, RNA immunoprecipitation, and luciferase reporter assays. miR-92b-3p was lowly expressed and MAP3K2 was highly expressed in myocardial I/R injury mice. Upregulation of miR-92b-3p improved hemodynamic indices, decreased serum levels of myocardial injury biomarkers, inhibited serum inflammatory response, alleviated cardiac tissue oxidative stress, relieved myocardial pathology, and reduced cardiomyocyte apoptosis during the myocardial I/R injury in mice. MAP3K2 was a direct target gene of miR-92b-3p. This research suggests that miR-92b-3p protects against myocardial I/R injury by inhibiting MAP3K2, which may provide novel candidates for treatment of myocardial I/R injury.

The trepidatious return to in-person instruction during the COVID-19 pandemic: valuable lessons applied from online teaching using Lt in the face-to-face classroom

The speed at which educators have embraced new technologies during the COVID-19 pandemic has been remarkable. This pivot to virtual instruction has been particularly diffcult in courses where hands-on experiences are the norm, such as in anatomy and physiology laboratory courses. Now with the transition back to mostly in-person instruction, the anatomy and physiology lab environment has faced a new set of challenges where the want to return to a strictly hands-on experience is being met with the need to still maintain flexibility and accessibility for students placed in quarantine. The anatomy and physiology lab curriculum at the University of the Incarnate Word (UIW) has adapted to this varied set of needs by adopting the use of the web-based laboratory software platform, Lt. Students in introductory anatomy and physiology labs surveyed during strictly virtual semesters reported a high level of satisfaction with the Lt lab software. Ratings regarding ease of use, support of learning, and overall ratings increased each semester with the highest ratings occurring during the recent semesters where instruction returned to mostly in-person and hands-on (Fall 2021 – Spring 2022). Interestingly, beginning in 2022 and continuing into fall of 2023, ratings of overall satisfaction began to decrease. Although it is currently diffcult to determine the root cause of this decrease in satisfaction, we speculate that many students are becoming fatigued with the continued use of seemingly virtual labs. Although most lab exercises are hands-on and include dissections and live physiological recordings, we think that the continued use of a virtual interface is too reminiscent of learning under quarantine conditions. Even with variations in ratings regarding Lt use in our introductory anatomy and physiology laboratories, our data still help to support the widely held belief that students perform best, and that instruction is more effective with face-to-face laboratory courses. With our continued use of Lt, we have found an effective combination of virtual and in-person instruction that best fits our course outcomes while still supporting the flexibility and accessibility our students require. Research reported in this poster was supported by the University of the Incarnate Word Offce of Research and Graduate Studies. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Enhancing Physiology Education Through Physiological Simulation

Advances in Physiology Education began publishing papers in 2005, using classic papers in physiology teaching [1]. This approach has several valuable outcomes: Classic experiments, conclusion of which form the foundation of much of our undergraduate teaching textbooks, require less specialized knowledge to understand and interpret the data and conclusions. It demonstrates the scientific method, demonstrating both "what do we know" and "how do we know." It promotes inquiry-based learning. It fosters data literacy, teaching students to interpret graphs and provides historical context for physiology [1]. Traditional practical classes achieve many of these outcomes while also providing a tactile experience, and have added benefits to students’ data literacy — in as much as the students perform the protocols themselves. Students will gain insight to what “squiggly lines” on a screen are measuring and the individual variability in what raw physiological recordings look like. Students will gain a greater appreciation for analytical techniques, and the manipulations and analysis that needs to be performed to interpret data. Students also gain an appreciation of the difference between textbook vs real data. Nevertheless, in person practical classes have limitations due to resource intensity, including space, materials, and curriculum time. HumMod (hummod.org) is a freely available mathematical model of Human Physiology, a "time-dependent physiologic simulation engine" [2]. JustPhysiology (justphysiology.com) is an education-focused web interface for HumMod [3]. We have written four physiology-based workshops utilizing simulated data from JustPhysiology. The JustPhysiology workshops were setup as a structured inquiry activity where students are expected to apply their content knowledge to interpret data, and students submitted their reports after each workshop for evaluation and feedback. In preparing the workshop activities for deployment, we trialed the activities with students who had previously completed our second year Human Physiology subject (and were thus familiar with the content). We then engaged students in focus group interviews —ascertaining the students’ perceptions of the learning experience. The outcome of the focus group interview suggests that students engaging with the workshop tasks enter into a positive learning cycle: [Formula: see text] The spontaneous emergence of a learning cycle, promoting deeper understanding of the physiology suggests that these data driven workshops will have improve student learning in Human Physiology.