Frontiers in Physiology

Heart rate (HR) and its variability (HRV), mediated by the autonomic nervous system are key indicators of diving physiology and behavioral state, in vertebrates. However, these indicators remain understudied in cetaceans due to the technical challenges of recording electrocardiograms (ECGs) underwater. To overcome these challenges, we developed a waterproof device based on an all-in-one suction-cup that integrate an ECG-accelerometer logger with bipolar electrodes. Using this device, we obtained high-resolution ECG signals in bottlenose dolphins (Tursiops truncatus, n=8), belugas (Delphinapterus leucas, n=2), and orcas (Orcinus orca, n=1) during breathing and apnea. This approach allowed us to highlight species-specific features of the ECG waveform, consistent with a biphasic T wave in the three species of cetaceans and a bifid P wave unique to belugas, which were independent from the respiration state. Resting surface HRs were 70 {+/-} 4 bpm in dolphins, 51 {+/-} 1 bpm in belugas, and 50 {+/-} 2 bpm in the orca and exhibited pronounced oscillation related to the mechanism of respiratory sinus arrhythmia. As expected, short apneas ([~] 1 min) induced bradycardia in all three species (53 {+/-} 5, 33 {+/-} 3, and 37 {+/-} 2 bpm, respectively). In dolphins this bradycardia was coupled with a significant decrease of the coefficient of variability of RR intervals, one of the indices of HRV. Moreover, we were surprised to observe HR oscillations throughout apnea, suggesting a persistent fluctuation of autonomic modulation. Thus, to better understand autonomic modulation in cetaceans we employed food (fishes, squids, gelatin, etc.) as a strong rewarding stimulus. For that we compared HR and HRV during 2-min of food deprivation versus continuous feeding periods. In dolphins, food deprivation produced no significant change in HR or HRV from resting surface values, whereas continuous feeding decreased HR of about 20 % and increased HRV metrics (StDRR, CVRR, RMSSD). Belugas showed similar responses, with a HR decline of about 40 % and an increase HRV indices. These findings established baseline HR and HRV parameters during breathing or apnea for three cetacean species and demonstrate that autonomic responses to appetitive stimuli can be non-invasively quantified, validating a novel tool to investigate cetacean cardiovascular physiology and environmental perception.

Neural temporal point processes (NTPPs) are powerful tools for modeling sequences of timestamped events with statistical temporal structure. Density-based NTPPs, in particular, are an interesting opportunity to merge the universal function approximation capability of neural networks with a defined statistical model in a way that has many potential applications. We demonstrate one such application to heartbeat dynamics, a physiologic point process. We specifically apply a lognormal mixture NTPP to compute instantaneous estimates of the mean and standard deviation of beat-to-beat intervals. We compare our results to the state of art (Barbieri et al.) point process model for heartbeat dynamics, which uses a more physiologically rigorous inverse Gaussian model. We find that the NTPP model maintains reasonable accuracy while improving upon robustness to noise.

The successful management of type-1 diabetes (T1D) and insulin-dependent type-2 diabetes depends on the ability to accurately calibrate bolus and basal insulin doses, and minimize the time spent with high postprandial blood glucose (BG) levels while avoiding dangerously low hypoglycemic excursions. Precise calibration of the insulin pump helps with successful disease management; however, people with T1D may still experience prolonged, potentially damaging BG levels due to postprandial highs. Insulin pumps offer large, currently underexploited degrees of freedom in insulin delivery, which can have a dramatic impact on average postprandial BG levels. Building on existing models of the glucose-insulin system, we first propose a simple, automated, and individualized insulin-pump calibration system based on a series of measures taken before and a few hours after carbohydrate ingestion. We then modulate the shape of pre-meal insulin dosing to explicitly reduce postprandial BG levels while minimizing the likelihood of dangerously low BG. Such an optimal insulin delivery time course can potentially improve postprandial BG levels and rapidly bring BG to the target level. We evaluated our methods in 20 patients over 4 days, with BG levels sampled frequently.

BACKGROUND: Atrial fibrillation (AF) remains difficult to explain using a single focal driver or rotor-centered mechanism across disease stages. We tested whether progressive atrial substrate remodeling can drive a critical transition toward turbulence-like, decentralized multi wavelet electrical activity. METHODS: We constructed a controlled two-dimensional atrial reaction-diffusion model with six graded substrate-remodeling stages. We evaluated effective wavelength, theoretical wavelet capacity, AF inducibility, vulnerable-window dynamics, spatial randomness, temporal memory, spectral dispersion, nonlinear indices, virtual ablation response and ERP-prolongation reverse mechanistic testing. RESULTS: Progressive remodeling shortened effective wavelength from 12.0 to 2.4 cm and increased theoretical wavelet capacity from 0.69 to 17.36. Inducibility rose sigmoidally as wavelength shortened, with a model-derived transition near lambda50=4.5 cm. Advanced substrates showed increased wavebreak, spatial randomness, short-memory dynamics, broad spectral dispersion, positive nonlinear indices and resistance to random local ablation. Culprit atrial premature beats within the vulnerable window efficiently triggered AF, whereas counter pacing at 20 to 35 ms reduced inducibility from 52% to 11% in stage 2. CONCLUSIONS: In this controlled model, AF initiation and maintenance were linked to substrate-dependent wavelength, wavelet capacity and vulnerable-window triggering. The model-derived transition provides a testable framework for future high-density mapping, patient30 specific modeling and device-based studies. Key Words atrial fibrillation; turbulence-like electrical activity; substrate remodeling; critical wavelength; multi-wavelet re-entry; vulnerable window; culprit premature atrial beat; counter pacing

Fetal-monitoring biomarkers for neonatal hypoxic-ischemic brain injury face a structural gap: the mechanistic ground truth that would label a training set -- perfusion pressure, the moment of decompensation, the injury time course -- cannot be measured at scale or ethically in human pregnancy or labor, and generative synthetic data carry no mechanistic labels. We address this with a mechanistic computational model of the fetal cardiovascular, autonomic, and metabolic response to controlled hypoxic stress, and use it to test how beat detection and acquisition fidelity alter the interpretation of fetal-heart-rate-variability (HRV) biomarkers. The model integrates these systems forward in time across antepartum development (gestational-age growth scaling) and intrapartum stress (umbilical-cord occlusions), emitting synthetic monitoring signals (fetal heart rate, RR intervals) co-registered with model-computed latent labels (pH, base deficit, lactate, perfusion pressure, decompensation and injury states). In a fetal-sheep-derived autonomic-loop configuration we report three results. First, a phase-accumulator beat detector shows that the apparently physiologic baseline HRV of an earlier build was largely a detector artifact, and a noise-off control shows beat-to-beat variability requires an explicit stochastic driver rather than self-sustained autonomic oscillation. Second, a sampling-fidelity sweep yields a fidelity-matched selection rule: a deceleration-area biomarker is preserved at CTG-grade 4 Hz whereas RMSSD is corrupted there (inflated about 8-fold by timing quantization) and recovers only at fetal-ECG rates. Third, autonomic modulation alone does not reproduce the published RMSSD rise-then-collapse -- a negative result that motivates, but does not prove, an intrinsic sinoatrial-pacemaker contribution as a testable hypothesis. This is an in-silico, hypothesis-generating study: the model is not validated for individual fetal prediction, clinical risk estimation, or clinical decision-making. The model is implemented as Fetal Twin (engine fetaltwin), a source-available research instrument released under a noncommercial license, together with all figure configurations, so that these controlled experiments are reproducible. Key PointsO_LIProgress on fetal-monitoring biomarkers for neonatal brain-injury risk is constrained by a structural gap: the mechanistic ground truth that would label a training set -- perfusion pressure, the moment of cardiovascular decompensation, the time course of injury -- cannot be measured at scale or ethically during human pregnancy or labor. C_LIO_LIWe present Fetal Twin (source-available engine fetaltwin), a publicly available, noncommercially-licensed mechanistic testbed for fetal physiological development. It integrates the fetal cardiovascular, metabolic, and autonomic systems forward in time across antepartum development (gestational-age growth scaling) and intrapartum stress (umbilical-cord occlusions), and emits synthetic monitoring signals (fetal heart rate, RR intervals) co-registered with model-computed latent labels (pH, lactate, perfusion pressure, decompensation and injury states). C_LIO_LIThe names digital-twin connotation is deliberate but bounded: Fetal Twin is a mechanistic, population-level twin of fetal physiology used as a research instrument -- not a validated, patient-specific clinical digital twin or predictor. Its purpose is to interrogate what candidate biomarkers can and cannot mean, via in-silico controls impossible in vivo -- swapping the beat detector, turning a noise source off, ablating a reflex, or quantizing the signal to a monitors sampling grid. C_LIO_LIDemonstrations in a fetal-sheep-derived autonomic-loop configuration show that beat-to-beat HRV amplitude can be a numerical artifact of the beat detector, and that in this model class beat-to-beat variability requires an explicit stochastic driver -- it does not arise as a self-sustained oscillation of the deterministic autonomic loop. C_LIO_LIA further demonstration establishes a fidelity-matched biomarker-selection rule -- a deceleration-area biomarker survives CTG-grade 4 Hz sampling whereas RMSSD is corrupted at that rate and needs fetal-ECG timing -- and a negative result shows the published RMSSD "rise-then-collapse" is not reproducible from autonomic modulation in this model class, motivating (but not proving) an intrinsic-pacemaker hypothesis. C_LI

Background and Objective: Indications for right ventricular assist device (RVAD) insertion include right heart failure after implantation of a left ventricular assist device or early graft failure following heart transplantation. This study aimed to investigate how the upstream and downstream circulatory network interacts with the Impella RP(R) device. Methods: A numerical model of the Impella RP(R) was implemented within CARDIOSIM(C) software platform for this study. In the numerical configuration, the RVAD aspirated blood from either the right atrium (RA-PA connection) or the right ventricle (RV-PA connection) and delivered it to the pulmonary artery. Only RA-PA connection is the currently used setting for Impella RP(R) in clinical practice. Based on right ventricular (RV) decompression and total flow, our study may help define the need for a direct RV-unloading Impella RP(R). Results: The simulations showed that activating the RVAD in RA-PA mode, regardless of its rotational speed, the mean pulmonary artery pressure (PAP) percentage change was higher than the unsupported condition when the mean systemic venous pressure (SVP) and the pulmonary artery wedge pressure (PAWP) were both set to 20 mmHg. When RV-PA connection was applied, a similar trend was observed although the PAP percentage changes were about halved compared to the RA-PA connection. Conclusions: The Impella RP(R) has the potential to become a valid option for RV support based on current experimental and simulation data. Although already in use, further evaluation in the clinical setting will likely confirm its potential and lead to a more routinely application for RV support.

Background and ObjectiveAccurately parameterizing dynamic, time-varying interactions in physiological systems is a methodological challenge, as global causal discovery methods may obscure transient, local fluctuations. This study introduces tempord, an open-source Python library designed to estimate local temporal orders and evaluate the short-term stability, directionality, and strength of causal links in non-stationary biological signals. MethodsThe algorithm estimates temporal relationships by keeping one signal stationary while iteratively shifting another one within a sliding window. To parameterize optimal inter-signal shifts (causal vector, CV), the framework utilizes linear modeling or time series distance metrics. The methodology was validated through a simulation study on synthetic bivariate signals with mathematically imposed dynamic phase delays, under both deterministic and noisy conditions. Furthermore, in-vivo capabilities were demonstrated by evaluating cardiorespiratory coupling dynamics across spontaneous and music-induced relaxation breathing states. ResultsThe simulation study demonstrated that the extracted CV trajectories precisely aligned with ground-truth temporal delays, assessed using mean absolute error and root mean square error for both noise-free and noisy synthetic data. In-vivo application demonstrated dynamic temporal stability and the detection of minor step changes during autonomic nervous system state transitions. ConclusionsThe tempord Python package bridges the gap between global causal discovery and local beat-by-beat statistical parameterization. It provides a robust "bottom-up" analytical instrument for investigating the transient mechanisms governing complex biological networks.

Cardiac electrophysiology adapts throughout pediatric development, driven by factors including age-associated ion channel expression changes and decreasing heart rate. Our prior transcriptomic-guided simulations of human atrial cardiomyocytes predicted developmental-associated changes in electrophysiology biomarkers at a fixed pacing rate, leaving the contribution of age- and patient-specific heart rate unresolved. In this study, we incorporated intrinsic heart rate into gene expression-guided computational models to predict the interaction between developmental maturation and pacing rate to shape atrial electrophysiology. Virtual patient-specific populations of atrial cardiomyocytes were generated from the right atrial cardiomyocyte gene expression data from 117 patients, spanning neonates to young adults. We simulated each population at pacing rates corresponding to each patients intrinsic ECG-based heart rate and at fixed rates corresponding to the patient cohort minimum, median, and maximum. Action potential and calcium transient biomarkers were quantified, and partial least squares regression assessed key biomarker dependencies. For intrinsic-rate pacing conditions, action potential duration at 50% and 90% repolarization increased with age, whereas early repolarization shortened; maximum upstroke velocity increased, resting membrane potential became more negative, and alternans prevalence decreased. Developmental differences persisted during fixed-rate pacing conditions, indicating that differences were not explained solely by the faster heart rates of younger patients. Notably, intrinsic-rate simulations exhibited stronger age associations for upstroke velocity and alternans than fixed-rate simulations. Sensitivity analyses indicated that electrophysiological phenotypes arose from interactions among ionic conductances, calcium handling, age, and heart rate. Collectively, we find that pediatric atrial electrophysiology reflects both intrinsic developmental remodeling and rate-dependent modulation.

The middle cerebral artery (MCA) is critical for cerebral blood flow autoregulation and a primary site of cerebrovascular pathology in stroke, Alzheimers disease, and vascular dementia. Pressure myography enables precise ex vivo quantification of MCA structure and function, but requires accurate anatomical identification and careful vessel handling to ensure reproducibility across diverse rat genetic models. This chapter provides a comprehensive, step-by-step protocol for isolating and cannulating the rat MCA M2 segment for pressure myography. We detail precise anatomical landmarks to ensure consistent vessel selection across strains. The protocol includes optimized solutions, cannulation techniques, and pressure protocols validated across multiple rat models, including transgenic (TgF344-AD), diabetic (T2DN), consomic (SS.5BN, FHH.1BN), and genome-edited strains. Extensive troubleshooting notes address common technical challenges, including vessel viability assessment, pressure integrity, and strain-specific autoregulatory ranges. This methodology bridges molecular genetic findings with fundamental cerebrovascular physiology, enabling researchers to characterize myogenic reactivity, passive mechanical properties, and structural remodeling in rat models of cerebrovascular disease.

Accurate prediction of both mechanical and metabolic behavior of muscle remains an important challenge in biomechanics. The typically employed Hill-type muscle models have shown limited success in this regard. Therefore, Huxley-type models, in which mechanical and metabolic behavior is linked through cross-bridge cycling, have gained renewed attention. Previous studies fitted the cross-bridge cycling rate parameter values of such models on mechanical behavior only. It seems reasonable to assume that accurate predictions of mechanical behavior in a Huxley-type model will also result in accurate predictions of metabolic behavior as both depend on the cross-bridge dynamics. Here, we show that this assumption does not hold. We simulated previously collected mechanical and metabolic data from an experiment where participants performed either isometric or dynamic knee extensions in the gravitational field. We modeled this experiment with a musculoskeletal model consisting of two segments driven by one Huxley-type muscle model. We obtained 10 sets of cross-bridge rate parameter values by systematically varying the value of one of the rate parameters and optimizing the values of the remaining rate parameters with respect to the mechanical behavior. We then compared the predicted mechanical and metabolic behavior between the 10 sets. The predicted mechanical behavior was similar for all 10 sets. However, the accuracy of the predicted metabolic behavior differed substantially between the 10 sets. Our findings illustrate that different sets of cross-bridge rate parameter values may lead to similar mechanical behavior. We conclude that this should be exploited to obtain accurate predictions of mechanical and metabolic behavior simultaneously in Huxley-type muscle models.

The features of the PPG (photoplethysmography) morphology are known to reflect age-related cardiac and vascular changes. In most contemporary wearables, PPG signals are acquired from distal sites such as the wrist and finger. The superficial temporal artery (STA), accessible at the temple region, is reached via a shorter arterial path from the aortic root than the radial circulation, and may therefore carry hemodynamic and aging information with less distance-dependent attenuation. We hypothesized that the morphology of the PPG at temple region (STA) would show stronger and more numerous age correlates than the PPG at the wrist. To test this, we extracted a common set of 89 pulse-morphology features, spanning raw-waveform timing/amplitude/area measures, ratios among them, derivative-based ratios, and spectral harmonic-ratio features. We compared an in-house temple-worn device which has PPG as one of the sensors, with a publicly available Microsoft Aurora-BP wrist-worn PPG dataset, and tested each feature's association with age. We identified 14 robust age correlates at the temple region, compared to 3 at the wrist. The temple's correlates spanned multiple morphological categories and showed a larger age-association than at the wrist. These results support the hypothesis that the temple region may be a more robust PPG measurement site than the wrist to extract age-related cardiovascular information, which motivates further investigation of temple-based cardiovascular sensing.

Length and time constants are foundational to the study of conduction in neurons and other biological cables but are exactly defined only for passive membranes. Here we define and derive exact length and time constants for propagating action potentials in unmyelinated axons. This derivation exploits specific instants during action potential conduction when the net transmembrane ionic current is zero, but axial current remains non-zero. At these instants, we define a curvature parameter,{kappa} , explore its determinants using computer modelling, demonstrate that it is the local real Laplace exponent of the action potential upstroke, and suggest practical approaches for its experimental measurement. From{kappa} , we define action potential length and time constants, {lambda}AP = 1/{surd}({kappa}racm) and {tau}AP = 1/{kappa}, and show that action potential propagation velocity is exactly {lambda}AP/{tau}AP.

Neuromechanical simulations provide a powerful framework for investigating how neural control architectures generate and regulate human locomotion. Numerous biologically inspired locomotion controllers have been proposed, including reflex-based, central pattern generator (CPG)-based, and muscle synergy-based models. However, direct comparison across studies remains difficult because of differences in musculoskeletal models, optimization methods, and evaluation protocols. Here, we implemented four representative locomotion control architectures, reflex-based, CPG-reflex-based, muscle synergy-based, and CPG-reflex-synergy-based controllers, within a unified neuromechanical simulation framework to enable controlled comparisons under shared biomechanical and computational conditions. Performance was assessed in terms of (1) agreement with experimentally observed gait characteristics, including kinematics, kinetics, muscle activations, and biomechanical trends across speeds and slopes, and (2) locomotor versatility across speed-slope conditions. The reflex-based and CPG-reflex-synergy-based controllers most closely reproduced experimentally observed gait characteristics, while the CPG-reflex-synergy controller achieved the broadest range of stable walking behaviors across speeds and slopes, followed closely by the reflex-based controller. These findings should be interpreted as comparisons of specific model implementations rather than definitive evaluations of the underlying biological hypotheses. Moreover, because the investigated controllers primarily focused on spinal-level mechanisms for nominal steady-state locomotion, the limited versatility observed in some of the models across broader speed and slope conditions suggests the importance of integrating spinal locomotor mechanisms with supraspinal modulation when modeling locomotion beyond nominal steady gait. To facilitate further investigation, we publicly share the simulation framework and controller implementations. Key pointsO_LIExisting neuromechanical locomotion controllers have been difficult to compare directly because of differences in simulation frameworks. C_LIO_LIWe implemented four representative spinal locomotion control models (reflex-based, central pattern generator (CPG)-reflex-based, muscle synergy-based, and CPG-reflex-synergy-based) within a unified simulation framework and compared their human-likeness and versatility. C_LIO_LIThe reflex-based and CPG-reflex-synergy-based controllers best reproduced human-like gait characteristics, while the CPG-reflex-synergy-based controller demonstrated the greatest locomotor versatility across speed-slope conditions, followed closely by the reflex-based controller. C_LIO_LIBecause the investigated controllers primarily modeled spinal-level mechanisms associated with steady-state locomotion, their reduced adaptability across broader speed and slope conditions highlights the importance of incorporating supraspinal modulation when modeling locomotion beyond nominal gait. C_LIO_LIWe publicly share the simulation framework and controller implementations to support further investigation of human locomotion control. C_LI

Modeling the circadian impact of seasonal clock changing requires precise synchronization between solar and social time. This report critiques a recent study that associated disease prevalence in the United States with seasonal clock exposure. We identify a fundamental computational error in which a sign reversal of the longitudinal offset effectively inverted the US East-West axis, cross-correlating local health data with the circadian burden of hypothetical locations on the opposite side of a time zone. We outline the methodology for a correct modelization of the circadian process in the context of US geography.

Globally, hypercholesterolemia affects over 20% of the population; and while many studies have examined its impact on cardiovascular health, little is known about its effects on the lymphatic system. In mice, hypercholesterolemia has been linked to multiple aspects of lymphatic dysfunction; and a recent study demonstrated that cholesterol depletion by cyclodextrins promoted lymphatic vessel regeneration and restored lymphatic drainage in mouse models of lymphedema. Collecting lymphatic vessels rely on the spontaneous and highly entrained contractions of lymphatic muscle cells (LMCs) and competent unidirectional on-way valves to propel lymph forward. Critical to lymphatic pacemaking and contractility is the proper functioning of ion channels, which are known to be modulated by the cholesterol content in the plasma membrane. Therefore, we sought to understand the role cholesterol plays in regulating lymphatic contractility. The effects of cholesterol depletion by the cyclodextrins M{beta}CD and HP{beta}CD were assessed in cannulated and pressurized inguinal-axillary collecting lymphatic vessels (CLVs) from C57BL6/J (WT) mice. Noteworthy, studies have shown that HP{beta}CD is safe for human use, and in fact, it is commonly used as a drug excipient. Acute treatment with both cyclodextrins significantly increased the pumping capacity of CLVs, as demonstrated by the increased contraction amplitudes by [~]50{+/-}12% and calculated fluid volume displacement by each contraction by [~]35{+/-}11%. Calcium imaging demonstrated that HP{beta}CD increased the amplitude and duration of the large Cav1.2-mediated calcium events (termed calcium flashes. In contrast, cholesterol supplementation by incubation with BODIPY-cholesterol, which presumably incorporates cholesterol into the cell membrane, significantly impaired the contractile activity of CLVs compared to controls by decreasing contraction amplitude (control: 42{+/-}2 {micro}m versus BODIPY-cholesterol: 20{+/-}7{micro}m) and calculated fluid volume displacement (control: 9.2{+/-}3.9nL versus BODIPY cholesterol: 3.3{+/-}1.2nL) which were significantly restored with subsequent cholesterol depletion using HP{beta}CD (amplitude: 36{+/-}11{micro}m, volume displacement: 5.5{+/-}2.4nL). Similarly, treatment with HP{beta}CD significantly improved the contractile capacity of dysfunctional CLVs isolated from hypercholesterolemic ApoEKO mice. In conclusion, changes to cell membrane cholesterol content acutely and significantly altered CLV contractility with depletion improving contractility associated with recruitment of voltage-gated Cav1.2 channels in lymphatic muscle cells (LMCs). Future studies from our lab will determine whether pharmacological depletion of membrane cholesterol can be therapeutic strategy to improve and/or restore lymphatic contractile function in secondary lymphedema, including obesity/hypercholesterolemia-induced and cancer-related lymphedemas.

Predictions of skeletal muscle energy consumption under a diverse range of muscle contractile conditions are critical for improving our understanding of locomotion. Existing mathematical models, while capturing the mechanical dependence of energy consuming processes, neglect the time-dependent behaviour and recovery costs associated with regenerating ATP. This time-dependence is important for predicting the energetic response of muscles during repetitive or cyclical tasks like locomotion, where muscle undergoes many contraction cycles. This study presents a novel model to predict energetic rates based on physiological processes: Ca2+ transport costs, cross-bridge cycling costs, and ATP regeneration. Previous mathematical models include the dependence on Ca2+ transport and cross-bridge cycling, but neglect the time-dependent response and the subsequent recovery of ATP following the contraction. Model parameters were obtained from existing data on isolated muscle preparations, and predicted energetic rates were validated on separate datasets across a range of contractile conditions including dynamic, sub-maximal, and twitch contractions. The time-dependent model was able to capture the influence of contraction frequency on peak energetic rates and the time-course of energetic recovery observed experimentally. The model captures key physiological processes while maintaining a minimal number of free parameters and low computational cost. This enables generalisability across muscles and species, and implementation into larger scale musculoskeletal models.

Niemann-Pick disease type C (NPC) is a rare autosomal recessive neurodegenerative lysosomal storage disease caused by pathogenic variants in NPC1 or NPC2. Sudden death can occur due to seizures, but cardiac involvement has not been well defined. We performed 12-lead electrocardiograms (ECG) in 14 adult NPC patients (8 male, 6 female). Cardiac structure and function were examined in Npc1-/- adult mouse hearts, alongside wild-type controls. Glycosphingolipid accumulation was quantified by high-performance liquid chromatography, fibrosis and collagen deposition were quantified using Massons Trichrome (M&T) and Picrosirius Red (PR) staining. Whole-heart morphology, including chamber size and wall thickness, was assessed. Ex vivo ECG recordings assessed conduction abnormalities and arrhythmias. RNA-seq transcriptomics characterised molecular pathways altered in Npc1-/- hearts. 8/14 patients showed ECG abnormalities including abnormal QRS transitions (N=8), increased QRS amplitude (N=4), fascicular block (N=2), and abnormal T wave inversion (N=1). 13 patients also had transthoracic echocardiograms identifying mildly impaired LV systolic function (N=2) and increased wall thickness/LV mass (N=4). In Npc1-/- mice, age-related glycosphingolipid accumulation was associated with pronounced ventricular fibrotic remodelling. There was a significant increase in stained connective tissue area and connective tissue to cardiac tissue ratio in both MT and PR staining. ECG from Langendorff-perfused Npc1-/- hearts showed QT prolongation and atrioventricular conduction abnormalities under isoprenaline stress. Transcriptomics revealed major changes in Npc1-/- hearts, consistent with histological fibrosis and linking NPC to inflammation-driven remodelling and arrhythmogenesis. These findings support routine cardiac screening in NPC patients and highlight the need for further studies to improve management and treatment.

Blood pressure (BP) is a vital sign which is measured to diagnose and manage hypertension. However, current methods to measure BP use inflatable cuffs which cause discomfort and limit the frequency at which measurements can be made, or intra-arterial catheters which are invasive and pose infection risks. Here, we propose and evaluate the use of Diffuse Speckle Pulsatile Flowmetry (DSPF) as a cuffless BP measurement method to address these limitations. DSPF is a laser speckle-based technique which simultaneously records blood flow rate and blood volume (i.e. photoplethysmography or PPG) signals from relatively deep vascular tissue. Using information from these signals, we studied DSPFs effectiveness in measuring systolic BP (SBP) and diastolic BP (DBP) through an outpatient study in which 133 patients were recruited, and in measuring beat-to-beat BP waveforms through an inpatient study in which two patients were recruited. In the outpatient study, the DSPF method was able to achieve mean absolute errors (MAEs) of 4.17 mmHg and 2.42 mmHg for SBP and DBP respectively compared to conventional cuff-based methods. It was also able to fulfil the requirements of the AAMI/ESH/ISO 81060-2:2018 standard for BP measurement devices and attain an "A" grade according to the British Hypertension Society grading scheme. For the inpatient study, it produced BP waveforms which had MAEs of 2.35 mmHg and 3.06 mmHg compared to arterial-line measurements for the two patients, respectively. Compared to PPG which has been studied more extensively as a cuffless BP measurement method, we found through ablation studies that DSPF was able to reach significantly lower MAEs and hence better accuracies. DSPF augments the performance of PPG-only methods by leveraging additional information from the blood flow rate signal, and we therefore find it to be a superior cuffless BP measurement method which can potentially be used in outpatient, inpatient, and remote settings.

Cardiac tissue is primarily made up of cardiomyocytes, which are regulated by the autonomic nervous system. We have used and developed approaches such as patch clamping and electrical stimulation-combined calcium imaging, computer modeling, optogenetics and chemogenetics combining with video-based Short-Time Fourier transformation (STFT) method to study the physiological activities of cardiomyocytes. The action potential of cardiomyocytes was found to be synchronized with calcium signals, which can be grouped into two categories by STFT. A mathematical model was developed to simulate the changes in electrical activities within cardiomyocytes caused by energy depletion, especially for 2-deoxy-D-glucose (2DG) treatment. Optogenetic and chemogenetics tools, such as ChR2(H134R), OptoXR-{beta}2AR and hM3Dq accelerated beating, while GR, ACR1 and hM4Di inhibited cardiomyocytes beating. A video-based STFT method was developed to visualize the beating frequency during these manipulations. An in vitro co-culture method was developed to study the relationship between sympathetic neuronal firing and calcium dynamics in cardiomyocytes. In vivo, electrocardiograph (ECG) measurements showed that Clozapine N-oxide (CNO) caused heart rates increasement in cTnT-hM3Dq virus injected mouse. However, it had no impact on cTnT-hM4Di virus injected mouse. This study provides comprehensive methodologies for studying cardiomyocyte physiology and manipulating heart rates in vitro and in vivo.

Background: Spinal cord injury (SCI) is frequently associated with orthostatic hypotension, defined by a sustained decrease in blood pressure upon assuming an upright posture due to impaired autonomic regulation. Cardiovascular spinal cord epidural stimulation (CV-scES) can regulate systolic blood pressure (SBP) in people with SCI, but stimulation paradigms are highly individualized. To make this treatment available to more patients, we developed an algorithm to tailor individualized CV-scES paradigms that closely mimic researcher-developed paradigms. Methods: We performed an offline analysis using datasets collected from eight individuals with SCI with epidural stimulators implanted over the lumbosacral spinal segments. During data collection, researchers modulated stimulation parameters with the goal of maintaining SBP between 110-120 mmHg. Each two-hour dataset included synchronized SBP and stimulation recordings. We ran optimization analyses offline to determine temporal requirements before modifying stimulation amplitude to mitigate out-of-range SBP. Results: The algorithm parameters that best matched researcher-selected stimulation changed relatively quickly during the first 12 min (one every ~40 sec), and more slowly thereafter (one every ~79 sec). Overall, algorithmic stimulation closely tracked researcher-controlled stimulation, with a mean correlation coefficient of 0.94. To evaluate online performance, we tested the algorithm in real time with a single participant. We found that a faster approach was needed to respond to changes in SBP caused by rapid, unpredictable events, such as postural changes. We implemented a sigmoid-based paradigm that determined the time to wait before changing stimulation as a function of the current SBP, with worse SBP values requiring faster responses. The new paradigm outperformed the original algorithm and researcher-controlled stimulation across measures of SBP stability, though recovery from a postural tilt maneuver remained slower than with researcher control. Conclusions: Our results indicate that algorithmic stimulation may minimize assistance required from researchers and participants, making CV-scES more feasible for clinical translation.