Inhibitory and stimulatory micropeptides preferentially bind to different conformations of the cardiac calcium pump.

The ATP-dependent ion pump sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) sequesters Ca2+ in the endoplasmic reticulum to establish a reservoir for cell signaling. Because of its central importance in physiology, the activity of this transporter is tightly controlled via direct interactions with tissue-specific regulatory micropeptides that tune SERCA function to match changing physiological conditions. In the heart, the micropeptide phospholamban (PLB) inhibits SERCA, while dwarf open reading frame (DWORF) stimulates SERCA. These competing interactions determine cardiac performance by modulating the amplitude of Ca2+ signals that drive the contraction/relaxation cycle. We hypothesized that the functions of these peptides may relate to their reciprocal preferences for SERCA binding; SERCA binds PLB more avidly at low cytoplasmic [Ca2+] but binds DWORF better when [Ca2+] is high. In the present study, we demonstrated this opposing Ca2+ sensitivity is due to preferential binding of DWORF and PLB to different intermediate states that SERCA samples during the Ca2+ transport cycle. We show PLB binds best to the SERCA E1-ATP state, which prevails at low [Ca2+]. In contrast, DWORF binds most avidly to E1P and E2P states that are more populated when Ca2+ is elevated. Moreover, FRET microscopy revealed dynamic shifts in SERCA-micropeptide binding equilibria during cellular Ca2+ elevations. A computational model showed that DWORF exaggerates changes in PLB-SERCA binding during the cardiac cycle. These results suggest a mechanistic basis for inhibitory versus stimulatory micropeptide function, as well as a new role for DWORF as a modulator of dynamic oscillations of PLB-SERCA regulatory interactions.

Computational physiological models for individualised mechanical ventilation: a systematic literature review focussing on quality, availability, and clinical readiness.

BACKGROUND: Individualised optimisation of mechanical ventilation (MV) remains cumbersome in modern intensive care medicine. Computerised, model-based support systems could help in tailoring MV settings to the complex interactions between MV and the individual patient's pathophysiology. Therefore, we critically appraised the current literature on computational physiological models (CPMs) for individualised MV in the ICU with a focus on quality, availability, and clinical readiness. METHODS: A systematic literature search was conducted on 13 February 2023 in MEDLINE ALL, Embase, Scopus and Web of Science to identify original research articles describing CPMs for individualised MV in the ICU. The modelled physiological phenomena, clinical applications, and level of readiness were extracted. The quality of model design reporting and validation was assessed based on American Society of Mechanical Engineers (ASME) standards. RESULTS: Out of 6,333 unique publications, 149 publications were included. CPMs emerged since the 1970s with increasing levels of readiness. A total of 131 articles (88%) modelled lung mechanics, mainly for lung-protective ventilation. Gas exchange (n = 38, 26%) and gas homeostasis (n = 36, 24%) models had mainly applications in controlling oxygenation and ventilation. Respiratory muscle function models for diaphragm-protective ventilation emerged recently (n = 3, 2%). Three randomised controlled trials were initiated, applying the Beacon and CURE Soft models for gas exchange and PEEP optimisation. Overall, model design and quality were reported unsatisfactory in 93% and 21% of the articles, respectively. CONCLUSION: CPMs are advancing towards clinical application as an explainable tool to optimise individualised MV. To promote clinical application, dedicated standards for quality assessment and model reporting are essential. Trial registration number PROSPERO- CRD42022301715 . Registered 05 February, 2022.

Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data.

OBJECTIVE: Prediction of physiological mechanics are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because medicine is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes. Computational methods can increase prediction accuracy, but prediction with clinical data is difficult because the data are sparse, noisy and nonstationary. This paper focuses on predicting physiological processes given sparse, non-stationary, electronic health record data in the intensive care unit using data assimilation (DA), a broad collection of methods that pair mechanistic models with inference methods. METHODS: A methodological pipeline embedding a glucose-insulin model into a new DA framework, the constrained ensemble Kalman filter (CEnKF) to forecast blood glucose was developed. The data include tube-fed patients whose nutrition, blood glucose, administered insulins and medications were extracted by hand due to their complexity and to ensure accuracy. The model was estimated using an individual's data as if they arrived in real-time, and the estimated model was run forward producing a forecast. Both constrained and unconstrained ensemble Kalman filters were estimated to compare the impact of constraints. Constraint boundaries, model parameter sets estimated, and data used to estimate the models were varied to investigate their influence on forecasting accuracy. Forecasting accuracy was evaluated according to mean squared error between the model-forecasted glucose and the measurements and by comparing distributions of measured glucose and forecast ensemble means. RESULTS: The novel CEnKF produced substantial gains in robustness and accuracy while minimizing the data requirements compared to the unconstrained ensemble Kalman filters. Administered insulin and tube-nutrition were important for accurate forecasting, but including glucose in IV medication delivery did not increase forecast accuracy. Model flexibility, controlled by constraint boundaries and estimated parameters, did influence forecasting accuracy. CONCLUSION: Accurate and robust physiological forecasting with sparse clinical data is possible with DA. Introducing constrained inference, particularly on unmeasured states and parameters, reduced forecast error and data requirements. The results are not particularly sensitive to model flexibility such as constraint boundaries, but over or under constraining increased forecasting errors.

Modeling the physiological roles of the heart and kidney in heart failure with preserved ejection fraction during baroreflex activation therapy.

Heart failure (HF) is a leading cause of death and is increasing in prevalence. Unfortunately, therapies that have been efficacious in patients with HF with reduced ejection fraction (HFrEF) have not convincingly shown a reduction in cardiovascular mortality in patients with HF with preserved ejection fraction (HFpEF). It is thought that high sympathetic nerve activity (SNA) in the heart plays a role in HF progression. Clinical trials demonstrate that baroreflex activation therapy reduces left ventricular (LV) mass and blood pressure (BP) in patients with HFpEF and hypertension; however, the mechanisms are unclear. In the present study, we used HumMod, a large physiology model to simulate HFpEF and predict the time-dependent changes in systemic and cardiac hemodynamics, SNA, and cardiac stresses during baroreflex activation. The baseline HFpEF model was associated with elevations in systolic BP, diastolic dysfunction, and LV hypertrophy and stiffness similar to clinical HFpEF. Simulating 12 mo of baroreflex activation resulted in reduced systolic BP (-25 mmHg) and LV mass (-15%) similar to clinical evidence. Baroreflex activation also resulted in sustained decreases in cardiac and renal SNA (-22%) and improvement in LV ß1-adrenergic function. However, the baroreflex-induced reductions in BP and improvements in cardiac stresses, mass, and function were mostly attenuated when renal SNA was clamped at baseline levels. These simulations suggest that the suppression of renal SNA could be a primary determinant of the cardioprotective effects from baroreflex activation in HFpEF.NEW & NOTEWORTHY Treatments that are efficacious in patients with HFrEF have not shown a significant impact on cardiovascular mortality in patients with HFpEF. We believe these simulations offer novel insight into the important roles of the cardiac and renal nerves in HFpEF and the potential mechanisms of how baroreflex activation alleviates HFpEF disease progression.

Modelling and experimental study of thermo-physiological responses of human exercising in cold environments.

A numerical human thermo-physiological model is developed with the consideration of characteristics of exercising people in cold environments. The developed model is characterized by: 1) the concept of net exercise efficiency which is used to correct the calculation of metabolic heat production by excluding mechanical energy; 2) the effects of low temperature on basal metabolic rate and basal blood flow rate; 3) the integration with a multi-layer clothing model to calculate the heat and moisture transfer through the clothing system, which takes into account the air gaps between the clothing layers to reflect the ventilation and air penetration effect from the ambient wind. Human subject experiment is conducted in a climate chamber to validate the proposed model. The human subject experiment is also carried out in a cold environment (-5 °C) combined with different air velocity conditions (still air, 2 m/s), taking into account the activities of different intensities (standing statically, 2 km/h walking and 7 km/h running). Thermo-physiological parameters including the core temperature, 8-point local skin temperatures and the clothing layer temperatures, are measured during the experiment. Comparison between the predicted and experimental results gives the root mean squared error (RMSE) of core temperature and mean skin temperature of 0.06-0.10 °C and 0.17-0.27 °C, respectively. RMSE values for local skin and clothing layer temperatures are no higher than 1.5 °C and most within 0.8 °C. The model is also validated with published data under various ambient temperature and activity intensity conditions. The proposed model is shown to be capable of predict the thermo-physiological responses of people exposed and exercising in cold environments.

Plant CSR types in the north: comparing the morphological and morpho-physiological approaches.

Grime's competition-stress-ruderal (CSR) theory is widely used to study plant species' responses to multiple environmental factors. We compared two models to allocate CSR types the global "StrateFy" model (Pierce et al. Funct Ecol, 31:444-457, 2017) and a locally developed morpho-physiological model (Novakovskiy et al. Int J Ecol, p e1323614, 2016). The "StrateFy" model is based on three morphological leaf traits: leaf area (LA), leaf dry matter content (LDMC) and specific leaf area (SLA). The morpho-physiological model additionally uses plant height (PH), leaf dry weight (LDW), photosynthetic capacity (PN) and respiration rate (RD), leaf nitrogen, and carbon concentration (LNC, LCC). We applied both models to 74 plant species, the traits of which were measured at mountain (Northern Urals) and plane (Komi Republic, Russia) landscapes of European Northeast. The comparison of the calculated C, S, and R scores showed two groups of species with large and unidirectional differences. The first group consists of species with a shift from S (morpho-physiological model) to CR (StrateFy model) strategy. Species of this group are typical for deep shaded habitats and characterized by low LDMC (10-25%) and high SLA (30-60 mm2 mg-1). The second group consists of C species (morpho-physiological model) which were classified as S (StrateFy model) strategy. This group includes mainly tall shrubs, graminoids, and forbs with relatively small leaves (300-2000 mm2). In our opinion, the CSR strategies obtained by the morpho-physiological model showed better agreement with the basic principles underlying Grime's theory. The use of a limited number of morphological traits (LA, LDMC, SLA) in the StrateFy model does not always allow to determine the life strategy correctly. For example, these traits are insufficient for a clear separation of deeply shaded stress-tolerant species and ruderals. On the other hand, the use of the morpho-physiological model requires a large number of field measurements, which makes it difficult to use this model to allocate CSR strategies for a large number of species. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-00973-9.

Inflection point in climatic suitability of insect pest species in Europe suggests non-linear responses to climate change.

Climate change and globalization affect the suitable conditions for agricultural crops and insect pests, threatening future food security. It remains unknown whether shifts in species' climatic suitability will be linear or rather non-linear, with crop exposure to pests suddenly increasing when a critical temperature threshold is crossed. Moreover, uncertainty of forecasts can arise because of the modelling approach based either on species distribution data or on physiological measurements. Here, we compared the predictions of two modelling approaches (physiological models and species distribution models) for forecasting the potential distribution of agricultural insect pests in Europe. Despite conceptual differences, we found good agreement overall between the two approaches. We further identified a potential regime change in pest pressure along a temperature gradient. With both modelling approaches, we found an inflection point in the number of pest species with suitable climatic conditions around a minimum temperature of the coldest month of -3°C. Our results could help decision-makers anticipate the onset of rising pest pressure and provide support for intensifying surveillance measures, particularly in regions where temperatures are already beyond the inflection point.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (fu,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Organ-on-a-chip: recent breakthroughs and future prospects.

The organ-on-a-chip (OOAC) is in the list of top 10 emerging technologies and refers to a physiological organ biomimetic system built on a microfluidic chip. Through a combination of cell biology, engineering, and biomaterial technology, the microenvironment of the chip simulates that of the organ in terms of tissue interfaces and mechanical stimulation. This reflects the structural and functional characteristics of human tissue and can predict response to an array of stimuli including drug responses and environmental effects. OOAC has broad applications in precision medicine and biological defense strategies. Here, we introduce the concepts of OOAC and review its application to the construction of physiological models, drug development, and toxicology from the perspective of different organs. We further discuss existing challenges and provide future perspectives for its application.

Host thermoregulatory constraints predict growth of an amphibian chytrid pathogen (Batrachochytrium dendrobatidis).

1. The course and outcome of many wildlife diseases are context-dependent, and therefore change depending on the behaviour of hosts and environmental response of the pathogen. 2. Contemporary declines in amphibian populations are widely attributed to chytridiomycosis, caused by the pathogenic fungus Batrachochytrium dendrobatidis. Despite the thermal sensitivity of the pathogen and its amphibian hosts, we do not understand how host thermal regimes experienced by frogs in the wild directly influence pathogen growth. 3. We tested how thermal regimes experienced by the rainforest frog Litoria rheocola in the wild influence pathogen growth in the laboratory, and whether these responses differ from pathogen growth under available environmental thermal regimes. 4. Frog thermal regimes mimicked in the laboratory accelerated pathogen growth during conditions representative of winter at high elevations more so than if temperatures matched air or stream water temperatures. By contrast, winter frog thermal regimes at low elevations slowed pathogen growth relative to air temperatures, but not water temperatures. 5. The growth pattern of the fungus under frog thermal regimes matches field prevalence and intensity of infections for this species (high elevation winter > high elevation summer > low elevation winter > low elevation summer), whereas pathogen growth trajectories under environmental temperatures did not match these patterns. 6. If these laboratory results translate into field responses, tropical frogs may be able to reduce disease impacts by regulating their body temperatures to limit pathogen growth (e.g., by using microhabitats that facilitate basking to reach high temperatures); in other cases, the environment may limit the ability of frogs to thermoregulate such that individuals are more vulnerable to this pathogen (e.g., in dense forests at high elevations). 7. Species-specific thermoregulatory behaviour, and interactions with and constraints imposed by the environment, are therefore essential to understanding and predicting the spatial and temporal impacts of this global disease.

In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation, and Precipitation of Posaconazole.

The aim of this study was to evaluate gastrointestinal (GI) dissolution, supersaturation, and precipitation of posaconazole, formulated as an acidified (pH 1.6) and neutral (pH 7.1) suspension. A physiologically based pharmacokinetic (PBPK) modeling and simulation tool was applied to simulate GI and systemic concentration-time profiles of posaconazole, which were directly compared with intraluminal and systemic data measured in humans. The Advanced Dissolution Absorption and Metabolism (ADAM) model of the Simcyp Simulator correctly simulated incomplete gastric dissolution and saturated duodenal concentrations of posaconazole in the duodenal fluids following administration of the neutral suspension. In contrast, gastric dissolution was approximately 2-fold higher after administration of the acidified suspension, which resulted in supersaturated concentrations of posaconazole upon transfer to the upper small intestine. The precipitation kinetics of posaconazole were described by two precipitation rate constants, extracted by semimechanistic modeling of a two-stage medium change in vitro dissolution test. The 2-fold difference in exposure in the duodenal compartment for the two formulations corresponded with a 2-fold difference in systemic exposure. This study demonstrated for the first time predictive in silico simulations of GI dissolution, supersaturation, and precipitation for a weakly basic compound in part informed by modeling of in vitro dissolution experiments and validated via clinical measurements in both GI fluids and plasma. Sensitivity analysis with the PBPK model indicated that the critical supersaturation ratio (CSR) and second precipitation rate constant (sPRC) are important parameters of the model. Due to the limitations of the two-stage medium change experiment the CSR was extracted directly from the clinical data. However, in vitro experiments with the BioGIT transfer system performed after completion of the in silico modeling provided an almost identical CSR to the clinical study value; this had no significant impact on the PBPK model predictions.

Human responses in heat - comparison of the Predicted Heat Strain and the Fiala multi-node model for a case of intermittent work.

Two mathematical models of human thermal regulation include the rational Predicted Heat Strain (PHS) and the thermophysiological model by Fiala. The approaches of the models are different, however, they both aim at providing predictions of the thermophysiological responses to thermal environments of an average person. The aim of this study was to compare and analyze predictions of the two models against experimental data. The analysis also includes a gender comparison. The experimental data comprised of ten participants (5 males, 5 females, average anthropometric values were used as input) conducting an intermittent protocol of rotating tasks (cycling, stacking, stepping and arm crank) of moderate metabolic activities (134-291W/m2) with breaks in-between in a controlled environmental condition (34°C, 60% RH). The validation consisted of the predictions' comparison against experimental data from 2.5h of data of rectal temperature and mean skin temperature based on contact thermometry from four body locations. The PHS model over-predicted rectal temperatures during the first activity for males and the cooling effectiveness of sweat in the recovery periods, for both males and females. As a result, the PHS simulation underestimated the thermal strain in this context. The Fiala model accurately predicted the rectal temperature throughout the exposure. The fluctuation of the experimental mean skin temperature was not reflected in any of the models. However, the PHS simulation model showed better agreement than the Fiala model. As both models predicted responses more accurately for males than females, we suggest that in future development of the models it is important to take this result into account. The paper further discusses possible sources of the observed discrepancies and concludes with some suggestions for modifications.

Gastrointestinal Motility Variation and Implications for Plasma Level Variation: Oral Drug Products.

The oral route of administration is still by far the most ubiquitous method of drug delivery. Development in this area still faces many challenges due to the complexity and inhomogeneity of the gastrointestinal environment. In particular, dosing unpredictably relative to motility phase means the gastrointestinal environment is a random variable within a defined range. Here, we present a mass balance analysis that captures this variation and highlights the effects of gastrointestinal motility, exploring what impacts it ultimately has on plasma levels and the relationship to bioequivalence for high solubility products with both high and low permeability (BCS I and III). Motility-dependent compartmental absorption and transit (MDCAT) mechanistic analysis is developed to describe the underlying fasted state cyclical motility and how the contents of the gastrointestinal tract are propelled.

Recursive Model Identification for the Evaluation of Baroreflex Sensitivity.

A method for the recursive identification of physiological models of the cardiovascular baroreflex is proposed and applied to the time-varying analysis of vagal and sympathetic activities. The proposed method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictors and the results show a close match between experimental and simulated signals. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and the obtained estimators of vagal and sympathetic activities were compared to traditional markers associated with baroreflex sensitivity. High correlations were observed between traditional markers and model-based indices.

Toward systems-level analysis of agricultural production from crassulacean acid metabolism (CAM): scaling from cell to commercial production.

Systems-level analyses have become prominent tools for assessing the yield, viability, economic consequences and environmental impacts of agricultural production. Such analyses are well-developed for many commodity crops that are used for food and biofuel, but have not been developed for agricultural production systems based on drought-tolerant plants that use crassulacean acid metabolism (CAM). We review the components of systems-level evaluations, and identify the information available for completing such analyses for CAM cropping systems. Specific needs for developing systems-level evaluations of CAM agricultural production include: improvement of physiological models; assessment of product processing after leaving the farm gate; and application of newly available genetic tools to the optimization of CAM species for commercial production.

Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662.

AIMS: Previously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice. METHODS: One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens. RESULTS: Sunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy. CONCLUSIONS: A semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects.

Ex vivo gut-hepato-biliary organ perfusion model to characterize oral absorption, gut-wall metabolism, pre-systemic hepatic metabolism and biliary excretion; application to midazolam.

To date, characterization of the first-pass effect of orally administered drugs consisting of local intestinal absorption and metabolism, portal vein transport and hepatobiliary processes remains challenging. Aim of this study was to explore the applicability of a porcine ex-vivo perfusion model to study oral absorption, gut-hepatobiliary metabolism and biliary excretion of midazolam. Slaughterhouse procured porcine en bloc organs (n = 4), were perfused via the aorta and portal vein. After 120 min of perfusion, midazolam, atenolol, antipyrine and FD4 were dosed via the duodenum and samples were taken from the systemic- and portal vein perfusate, intestinal faecal effluent and bile to determine drug and metabolite concentrations. Stable arterial and portal vein flow was obtained and viability of the perfused organs was confirmed. After intraduodenal administration, midazolam was rapidly detected in the portal vein together with 1-OH midazolam (EG-pv of 0.16±0.1) resulting from gut wall metabolism through oxidation. In the intestinal faecal effluent, 1-OH midazolam and 1-OH midazolam glucuronide (EG-intestine 0.051±0.03) was observed resulting from local gut glucuronidation. Biliary elimination of midazolam (0.04±0.01 %) and its glucuronide (0.01±0.01 %) only minimally contributed to the enterohepatic circulation. More extensive hepatic metabolism (FH 0.35±0.07) over intestinal metabolism (FG 0.78±0.11) was shown, resulting in oral bioavailability of 0.27±0.05. Ex vivo perfusion demonstrated to be a novel approach to characterize pre-systemic extraction of midazolam by measuring intestinal as well as hepatic extraction. The model can generate valuable insights into the absorption and metabolism of new drugs.

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Real-time tracking of fibrinolysis under constant wall shear and various pulsatile flows in an in-vitro thrombolysis model.

A great need exists for the development of a more representative in-vitro model to efficiently screen novel thrombolytic therapies. We herein report the design, validation, and characterization of a highly reproducible, physiological scale, flowing clot lysis platform with real-time fibrinolysis monitoring to screen thrombolytic drugs utilizing a fluorescein isothiocyanate (FITC)-labeled clot analog. Using this Real-Time Fluorometric Flowing Fibrinolysis assay (RT-FluFF assay), a tPa-dependent degree of thrombolysis was observed both via clot mass loss as well as fluorometrically monitored release of FITC-labeled fibrin degradation products. Percent clot mass loss ranged from 33.6% to 85.9% with fluorescence release rates of 0.53 to 1.17 RFU/min in 40 and 1000 ng/mL tPa conditions, respectively. The platform is easily adapted to produce pulsatile flows. Hemodynamics of human main pulmonary artery were mimicked through matching dimensionless flow parameters calculated using clinical data. Increasing pressure amplitude range (4-40 mmHg) results in a 20% increase of fibrinolysis at 1000 ng/mL tPA. Increasing shear flow rate (205-913 s-1) significantly increases fibrinolysis and mechanical digestion. These findings suggest pulsatile level affects thrombolytic drug activities and the proposed in-vitro clot model offers a versatile testing platform for thrombolytic drug screening.