Shravan Guruprasad Aras

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Journals/Publications

• Aras, S. G., Grant, A. D., & Konhilas, J. P. (2025). Clustering of > 145,000 symptom logs reveals distinct pre, peri, and menopausal phenotypes. Scientific Reports, 15(Issue 1). doi:10.1038/s41598-024-84208-3
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  The transition to menopause is associated with disappearance of menstrual cycle symptoms and emergence of vasomotor symptoms. Although menopausal women report a variety of additional symptoms, it remains unclear which emerge prior to menopause, which occur in predictable clusters, how clusters change across the menopausal transition, or if distinct phenotypes are present within each life stage. We present an analysis of symptoms in premenopausal to menopausal women using the MenoLife app, which includes 4789 individuals (23% premenopausal, 29% perimenopausal, 48% menopausal) and 147,501 symptom logs (19% premenopausal, 39% perimenopausal, 42% menopausal). Clusters generated from logs of 45 different symptoms were assessed for similarities across methods: hierarchical clustering analysis (HCA), K-Means clustering of principal components of symptom reports, and binomial network analysis. Participants were further evaluated based on menstrual cycle regularity or natural versus medically induced menopause. Menstrual cycle-associated symptoms (e.g., cramps, breast swelling), digestive, mood, and integumentary symptoms were characteristic of premenopause. Vasomotor symptoms, pain, mood, and cognitive symptoms were characteristic of menopause. Perimenopausal women exhibited both menstrual cycle-associated and vasomotor symptoms. Subpopulations across life stages presented with additional correlated mood and cognitive, integumentary, digestive, nervous, or sexual complaints. Symptoms also differed among women depending on the reported regularity of their menstrual cycles or the way in which they entered menopause. Notably, we identified a set of symptoms that were very common across life stages: fatigue, headache, anxiety, and brain fog. Finally, we identified a lack of predictive power of hot flashes for any symptom except night sweats. Together, premenopausal women exhibit menstrual cycle-associated symptoms and menopausal women reported vasomotor symptoms, while perimenopausal women report both. All report high rates of fatigue, headache, anxiety, and brain fog. Limiting focus of menopausal treatment to vasomotor symptoms, or to premenstrual syndrome in premenopausal women, neglects a large proportion of overall symptom burden. Future interventions targeting mood and cognition, digestion, and the integumentary system are needed across stages of female reproductive life.
• Sepanloo, K., Shevelev, D., Islam, M. T., Son, Y. J., Aras, S., & Hinton, J. E. (2025). Improving nursing education through an AI-enhanced mixed reality training platform: development and pilot evaluation. Educational Technology Research and Development, 73(Issue). doi:10.1007/s11423-025-10473-2
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  Integrating Mixed Reality (MR) into nursing education and professional practice has recently captured significant interest as a transformative approach. This paper presents a comprehensive exploration and practical insights into designing and implementing an advanced MR training platform to provide nursing students with immersive experiences across various patient care scenarios. Further enhancing the platform’s utility is the incorporation of a unique conversational artificial intelligence (AI) module. This innovation breathes life into digital patients, enabling dynamic and realistic interactions that challenge nursing students to develop clinical reasoning skills in a controlled yet flexible MR environment. The AI’s capacity to understand and contextually react to the learner’s' verbal and behavioral inputs simulates authentic patient interactions. A total of 7 nursing students and 3 nursing faculty engaged in the pilot study, which served as a proving ground for the MR training system’s effectiveness. The study involved in-depth analysis, employing performance metrics, and evaluating situational awareness alongside cognitive workload using NASA Task Load Index (TLX) and learner’s thought verbalizations. The primary objective was to create a system that enhances nursing students' competencies and readiness for clinical healthcare practice. This system can potentially elevate the preparedness of new graduate nurses by providing a rich, interactive learning environment that mirrors the complexity of real-life clinical settings.
• Sepanloo, K., Shevelev, D., Son, Y. J., Aras, S., & Hinton, J. E. (2025). Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training. Sensors, 25(Issue 10). doi:10.3390/s25103222
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  This study explores nursing students’ stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal activity, and skin temperature, the study assesses the level of stress when the students interact with digital patients whose vital signs and symptoms interact dynamically to respond to student inputs. The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. Among the models tested, the Stacking Classifier demonstrated the highest classification accuracy of 96.4%, outperforming both Random Forest (96.18%) and Gradient Boosting (95.35%). The results showed clear patterns of stress during the simulation segments. Statistical analysis also found significant differences in stress responses and identified key physiological markers linked to each stress level. This pioneering study demonstrates the effectiveness of MR as a training tool for healthcare professionals in high-pressured scenarios and lays the groundwork for further studies on stress management, adaptive training procedures, and real-time detection and intervention in MR-based nursing training.
• Sepanloo, K., Shevelev, D., Son, Y., Aras, S. G., & Hinton, J. (2025).

  Assessing physiological stress responses in student nurses using mixed reality training

  . Sensors.
• Aras, S. G., Runyon, J. R., Kazman, J. B., Thayer, J. F., Sternberg, E. M., & Deuster, P. A. (2024). Is Greener Better? Quantifying the Impact of a Nature Walk on Stress Reduction Using HRV and Saliva Cortisol Biomarkers. International Journal of Environmental Research and Public Health, 21(Issue 11). doi:10.3390/ijerph21111491
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  The physiological impact of walking in nature was quantified via continuous heart rate variability (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N = 17 participants who walked “The Green Road” at Walter Reed National Military Medical Center in Bethesda, Maryland. For N = 15 of the participants, HRV analysis revealed two main groups: group one individuals had a 104% increase (mean) in the root mean square standard deviation (RMSSD) and a 47% increase (mean) in the standard deviation of NN values (SDNN), indicating an overall reduction in physiological stress from walking the Green Road, and group two individuals had a decrease (mean) of 42% and 31% in these respective HRV metrics, signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturbance were more robust for the Green Road than for a comparable urban road corridor and showed that a higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants, and it showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53%, on average, when compared with 37% of walking along an urban road. It was also observed that the order in which individuals walked the Green Road and urban road also impacted their cortisol responses, with those walking the urban road before the Green Road showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the Green Road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.
• Basavaraj, C., Grant, A. D., Aras, S. G., & Erickson, E. N. (2024). Deep learning model using continuous skin temperature data predicts labor onset. BMC Pregnancy and Childbirth, 24(Issue 1). doi:10.1186/s12884-024-06862-9
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  Background: Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset. Methods: We evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset. Results: Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true – predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor. Conclusion: Continuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.
• Skaria, R. S., Lopez‐Pier, M. A., Kathuria, B. S., Leber, C. J., Langlais, P. R., Aras, S. G., Khalpey, Z. I., Hitscherich, P. G., Chnari, E., Long, M., Churko, J. M., Runyan, R. B., & Konhilas, J. P. (2023). Epicardial placement of human placental membrane protects from heart injury in a swine model of myocardial infarction. Physiological Reports, 11(20). doi:10.14814/phy2.15838
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  Cardiac ischemic reperfusion injury (IRI) is paradoxically instigated by reestablishing blood-flow to ischemic myocardium typically from a myocardial infarction (MI). Although revascularization following MI remains the standard of care, effective strategies remain limited to prevent or attenuate IRI. We hypothesized that epicardial placement of human placental amnion/chorion (HPAC) grafts will protect against IRI. Using a clinically relevant model of IRI, swine were subjected to 45 min percutaneous ischemia followed with (MI + HPAC, n = 3) or without (MI only, n = 3) HPAC. Cardiac function was assessed by echocardiography, and regional punch biopsies were collected 14 days post-operatively. A deep phenotyping approach was implemented by using histological interrogation and incorporating global proteomics and transcriptomics in nonischemic, ischemic, and border zone biopsies. Our results established HPAC limited the extent of cardiac injury by 50% (11.0 ± 2.0% vs. 22.0 ± 3.0%, p = 0.039) and preserved ejection fraction in HPAC-treated swine (46.8 ± 2.7% vs. 35.8 ± 4.5%, p = 0.014). We present comprehensive transcriptome and proteome profiles of infarct (IZ), border (BZ), and remote (RZ) zone punch biopsies from swine myocardium during the proliferative cardiac repair phase 14 days post-MI. Both HPAC-treated and untreated tissues showed regional dynamic responses, whereas only HPAC-treated IZ revealed active immune and extracellular matrix remodeling. Decreased endoplasmic reticulum (ER)-dependent protein secretion and increased antiapoptotic and anti-inflammatory responses were measured in HPAC-treated biopsies. We provide quantitative evidence HPAC reduced cardiac injury from MI in a preclinical swine model, establishing a potential new therapeutic strategy for IRI. Minimizing the impact of MI remains a central clinical challenge. We present a new strategy to attenuate post-MI cardiac injury using HPAC in a swine model of IRI. Placement of HPAC membrane on the heart following MI minimizes ischemic damage, preserves cardiac function, and promotes anti-inflammatory signaling pathways.
• Lopez-Pier, M. A., Koppinger, M. P., Harris, P. R., Cannon, D. K., Skaria, R. S., Hurwitz, B. L., Watts, G., Aras, S., Slepian, M. J., & Konhilas, J. P. (2021). An adaptable and non-invasive method for tracking Bifidobacterium animalis subspecies lactis 420 in the mouse gut. Journal of Microbiological Methods, 189(Issue). doi:10.1016/j.mimet.2021.106302
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  Probiotic strains from the Bifidobacterium or Lactobacillus genera improve health outcomes in models of metabolic and cardiovascular disease. Yet, underlying mechanisms governing these improved health outcomes are rooted in the interaction of gut microbiota, intestinal interface, and probiotic strain. Central to defining the underlying mechanisms governing these improved health outcomes is the development of adaptable and non-invasive tools to study probiotic localization and colonization within the host gut microbiome. The objective of this study was to test labeling and tracking efficacy of Bifidobacterium animalis subspecies lactis 420 (B420) using a common clinical imaging agent, indocyanine green (ICG). ICG was an effective in situ labeling agent visualized in either intact mouse or excised gastrointestinal (GI) tract at different time intervals. Quantitative PCR was used to validate ICG visualization of B420, which also demonstrated that B420 transit time matched normal murine GI motility (~8 hours). Contrary to previous thoughts, B420 did not colonize any region of the GI tract whether following a single bolus or daily administration for up to 10 days. We conclude that ICG may provide a useful tool to visualize and track probiotic species such as B420 without implementing complex molecular and genetic tools. Proof-of-concept studies indicate that B420 did not colonize and establish residency align the murine GI tract.
• Aras, S., Johnson, T., Gniady, C., Skaria, R., & Khalpey, Z. (2018). InDetector – Automatic detection of infected driveline regions. Smart Health, 9-10(Issue). doi:10.1016/j.smhl.2018.07.016
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  Although there have been significant advancements in Left Ventricular Assist Device(LVAD) technology and improvements in mortality rates, infection remains one of the major complications associated with LVAD therapy with an incidence of 25-80% cases annually. Amongst such infections driveline infections are the most common and account for 14-28% according to the RE-MATCH trial of the total of LVAD related infections. If a patient is diagnosed with LVAD infection, it is important to initiate antibiotic therapy as early as possible. If left untreated it can lead to sepsis with multi-organ failure, longer hospital stay, delay heart transplant or early mortality. To improve infection detection and monitoring we propose InDetector, a driveline infection detection system that allows at-home patients to use a smartphone to capture images of their driveline regions to check for infections. InDetector uses a Convolutional Neural Network along with image augmentation techniques for inferring infected images and achieves an overall classification accuracy of 93.75% on our validation dataset.

Proceedings Publications

• Aras, S., Dao, A., Gniady, C., Skaria, R., & Khalpey, Z. (2019). MCATSS-end-to-end mobile cardiopulmonary tolerance score system. In 11th Annual Conference of the Prognostics and Health Management Society, PHM 2019, 11.
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  Recently hospital re-admission rates have received national attention due to the rise in healthcare costs and poor clinical outcomes specially in case cardiac surgery patients. While there exists risk prediction model for discharging patients, few are utilized due several limitations such as poor discriminative ability, lack of discharge assessment and inadequate measurement of functional status. Further none of the current models present real-time actionable information to facilitate early identification and risk-stratification for patients. Hence in this paper we propose a Mobile Cardiopulmonary Tolerance Score System (MCATSS), which is a system that facilitates data collection using a series of automated tests that incorporate data acquisition from external sensors placed on the patient's body. MCATSS has been designed to reduce erroneous input collection, increase the ease of use for clinicians and provide detailed statistics for medical personals to assist them in determining the safe discharge of patients. Overall, MCATSS offers a platform for digital cross-talk of continuous, sensor- and input-driven data to clinicians at their fingertips and will improve patient care, communication and evidence-based practice while decreasing costs and readmission rates.
• Aras, S., Gniady, C., & Venugopalan, H. (2019). MultiLock: Biometric-based graded authentication for mobile devices. In 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2019.
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  While traditionally smartphones have relied on methods such as a passcode or pattern-based authentication, biometric authentication techniques are gaining popularity. However current biometric methods are heavily dependent on various environmental factors. For example, face authentication methods depend on lighting conditions, camera shake and picture framing, while fingerprint scanning relies on finger placement. All of these variables can result in these systems becoming time-consuming for the user to use. To remedy these problems, we propose MultiLock, a passive, graded authentication system, which uses face authentication as a case study to propose a system that gives users access to their devices without requiring them to manually interact with the lock screen. MultiLock allows a user to categorize applications into various security bins based on their sensitivity. By doing so MultiLock can grant users access to different sensitivity applications, based on varying degrees of sureness that the device is being used by its rightful owner. Thus, allowing the device to be used even in adverse lighting conditions without hampering user experience. In our tests, MultiLock was able to grant access to users for 88% of the interactions on average, while passively running in the background. While we use face authentication as an example to demonstrate and propose MultiLock, our system can be used with any confidence based biometric system.
• Aras, S., & Gniady, C. (2016). GreenTouch: Transparent energy management for cellular data radios. In 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016.
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  Smartphones come equipped with multiple radios for cellular data communication such as 4G LTE, 3G, and 2G, that offer different bandwidths and power profiles. 4G LTE offers the highest bandwidth and is desired by users as it offers quick response while browsing the Internet, streaming media, or utilizing numerous network aware applications available to users. However, majority of the time this high bandwidth level is unnecessary, and the bandwidth demand can be easily met by 3G radios at a reduced power level. While 2G radios demand even lower power, they do not offer adequate bandwidth to meet the demand of interactive applications; however, the 2G radio may be utilized to provide connectivity when the phone is in the standby mode. To address different demands for bandwidth, we propose GreenTouch, a system that dynamically adapts to the bandwidth demand and system state by switching between 4G LTE, 3G, and 2G with the goal of minimizing delays and maximizing energy efficiency. GreenTouch associates users' behavior to network activity through capturing and correlating user interactions with the touch display. We have used top applications on the Google play store to show the potential of GreenTouch to reduce energy consumption of the radios by 10%, on average, compared to running the applications in the standard Android. This translates to an overall energy savings of 7.5% for the entire smartphone.
• Aras, S., Johnson, T., Cabulong, K., & Gniady, C. (2015). GreenMonitor: Extending battery life for continuous heart rate monitoring in smartwatches. In 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015.
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  Health monitoring applications using smart devices are becoming increasingly popular due to an expanding number of new devices available and growing affordability of such devices. Smartwatches provide a new way to acquire data for heart rate and activity levels via accelerometers, gyroscopes, and other builtin sensors, which can enable a full range of health applications to improve users' lives. However, continuous heart rate monitoring can significantly reduce the operating time of a smartwatch, reducing the applicability for continuous monitoring. We propose GreenMonitor to extend the operating time of a smartwatch while maintaining accuracy of heart rate monitoring by leveraging the correlation between heart rate and activity level changes indicated by the accelerometer data. Through detailed implementation and evaluation we show that GreenMonitor can save 26% energy, on average for our traces, while maintaining accurate physical activity tracking and evaluation.

Presentations

• Hinton, J., Sepanloo, K., Son, Y., & Aras, S. G. (2025, October).

  Innovation and humanity: AI, XR, and biosensor technologies to advance competency and compassion in simulation-based education

  . 15th Annual Arizona Simulation Network Conference. Mesa, AZ: Arizona Simulation Network.

Poster Presentations

• Newton, T., Aras, S. G., Son, Y., Shevelev, D., Sepanloo, K., & Hinton, J. (2025, January).

  Intelligent simulation environment: extended reality and conversational artificial intelligence

  . IMSH2025. Orlando, FL: Society for Simulation in Healthcare.