Correlation Analysis Between Echinocytosis Stages and Blood Viscosity During Oxygenator Perfusion: An In Vitro Study.

The study aimed to investigate the effect of red blood cell (RBC) morphology on oxygenator perfusion, focusing on stages of echinocytosis and their correlation with blood viscosity. A test circuit with an oxygenator and human RBC mixtures was used to induce changes in RBC shape by increasing sodium salicylate concentrations (0, 10, 20, 30, 60, and 120 mmol/L), while hematocrit, blood temperature, and anticoagulation were maintained. Blood viscosity was measured using a continuous blood viscosity monitoring system based on pressure-flow characteristics. Under a scanning electron microscope, the percentages of discocytes, echinocytes I-III, spheroechinocytes, and spherocytes were determined from approximately 400 cells per RBC sample. Early echinocytes, mainly discocytes and echinocytes I and II in the range of 0-30 mmol/L were predominant, resulting in a gradual increase in blood viscosity from 1.78 ± 0.12 to 1.94 ± 0.12 mPa s. At 60 mmol/L spherocytes emerged, and at 120 mmol/L, spheroidal RBCs constituted 50% of the population, and blood viscosity sharply rose to 2.50 ± 0.15 mPa s, indicating a 40% overall increase. In conclusion, the presence of spherocytes significantly increases blood viscosity, which may affect oxygenator perfusion.

The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature.

GOAL: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature. METHODS: Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters. RESULTS: Bland-Altman analysis indicated a high estimation accuracy (R2 > 0.95, p < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases (R2 = 0.8576) was achieved between measured and estimated MB count rates. CONCLUSIONS: Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk.

Neuroimaging-based evidence for sympathetic correlation between brain activity and peripheral vasomotion during pain anticipation.

Anticipation of pain engenders anxiety and fear, potentially shaping pain perception and governing bodily responses such as peripheral vasomotion through the sympathetic nervous system (SNS). Sympathetic innervation of vascular tone during pain perception has been quantified using a peripheral arterial stiffness index; however, its innervation role during pain anticipation remains unclear. This paper reports on a neuroimaging-based study designed to investigate the responsivity and attribution of the index at different levels of anticipatory anxiety and pain perception. The index was measured in a functional magnetic resonance imaging experiment that randomly combined three visual anticipation cues and painful stimuli of two intensities. The peripheral and cerebral responses to pain anticipation and perception were quantified to corroborate bodily responsivity, and their temporal correlation was also assessed to identify the response attribution of the index. Contrasting with the high responsivity across levels of pain sensation, a low responsivity of the index across levels of anticipatory anxiety revealed its specificity across pain experiences. Discrepancies between the effects of perception and anticipation were validated across regions and levels of brain activity, providing a brain basis for peripheral response specificity. The index was also characterized by a 1-s lag in both anticipation and perception of pain, implying top-down innervation of the periphery. Our findings suggest that the SNS responds to pain in an emotion-specific and sensation-unbiased manner, thus enabling an early assessment of individual pain perception using this index. This study integrates peripheral and cerebral hemodynamic responses toward a comprehensive understanding of bodily responses to pain.

Sound-based cough peak flow estimation in patients with neuromuscular disorders.

INTRODUCTION/AIMS: Cough impairment is common in individuals with neuromuscular disorders and is associated with respiratory infections and shorter survival. Cough strength is assessed by measuring cough peak flow (CPF) using a flow meter, but this method requires a complex device setup and trained staff. The aim of the study is to evaluate the reliability of a smartphone app to estimate CPF based on cough sounds in a cohort of individuals with neuromuscular disorders. METHODS: Individuals with neuromuscular disorders underwent CPF measurement with a flow meter and a smartphone app. A CPF <270 L/min was considered abnormal. RESULTS: Of the 50 patients studied, 26 had amyotrophic lateral sclerosis (52%), 15 had hereditary myopathies (30%), and 9 had myasthenia gravis (18%). The intraclass correlation coefficient (ICC) between the CPF measured with a flow meter and CPF estimated with cough sounds was 0.774 (p < .001) even if the patients had orofacial weakness (ICC = 0.806, p < .001). The smartphone app had 94.4% sensitivity and 100% specificity to detect patients with CPF of less than 270 L/min. DISCUSSION: Our findings suggest that sounds measured with a smartphone app provide a reliable estimate of CPF in patients with neuromuscular disorders, even in the presence of with orofacial weakness. This may be a convenient way to monitor respiratory involvement in patients with neuromuscular disorders, but larger studies of more diverse patient cohorts are needed.

Vertical dynamic visual acuity is significantly lower than horizontal dynamic visual acuity.

Dynamic visual acuity (DVA) is crucial for the perception of moving objects. While traditional DVA assessment tools predominantly focus on horizontal movements, the evaluation of vertical DVA remains unstandardized. Consequently, the disparities between vertical and horizontal DVAs are yet to be thoroughly investigated. Therefore, we designed a system capable of conducting multidirectional DVA tests and eye movement measurements. During the experiments, the participants identified the gap direction of the Landolt-C ring moving either horizontally or vertically. The speed of movement decelerated from its maximum as a high-speed infrared camera captured the pupil movements of the left eye at 500 fps. We conducted tests on 15 healthy university students (aged [Formula: see text] years) and measured vertical and horizontal DVAs five times each. DVA was deduced from the Landolt-C ring speed with accurate gap direction responses, and eye movement was assessed based on the total gaze movement distance. The results revealed superior DVA and eye movement in the horizontal direction compared with the vertical direction ([Formula: see text]). This highlights the anisotropic characteristics of DVA and eye movement. The proposed system has the potential for multidirectional dynamic vision evaluation and training in clinical scenarios.

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis.

BACKGROUND: People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been elucidated. METHODS: In this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving aptitude. RESULTS: The experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving aptitude. CONCLUSIONS: The proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.

Simultaneous Control of Venous Reservoir Level and Arterial Flow Rate in Cardiopulmonary Bypass With a Centrifugal Pump.

Cardiopulmonary bypass (CPB) is an indispensable technique in cardiac surgery, providing the ability to temporarily replace cardiopulmonary function and create a bloodless surgical field. Traditionally, the operation of CPB systems has depended on the expertise and experience of skilled perfusionists. In particular, simultaneously controlling the arterial and venous occluders is difficult because the blood flow rate and reservoir level both change, and failure may put the patient's life at risk. This study proposes an automatic control system with a two-degree-of-freedom model matching controller nested in an I-PD feedback controller to simultaneously regulate the blood flow rate and reservoir level. CPB operations were performed using glycerin and bovine blood as perfusate to simulate flow-up and flow-down phases. The results confirmed that the arterial blood flow rate followed the manually adjusted target venous blood flow rate, with an error of less than 5.32%, and the reservoir level was maintained, with an error of less than 3.44% from the target reservoir level. Then, we assessed the robustness of the control system against disturbances caused by venting/suction of blood. The resulting flow rate error was 5.95%, and the reservoir level error 2.02%. The accuracy of the proposed system is clinically satisfactory and within the allowable error range of 10% or less, meeting the standards set for perfusionists. Moreover, because of the system's simple configuration, consisting of a camera and notebook PC, the system can easily be integrated with general CPB equipment. This practical design enables seamless adoption in clinical settings. With these advancements, the proposed system represents a significant step towards the automation of CPB.

Cough sound-based estimation of vital capacity via cough peak flow using artificial neural network analysis.

This study presents a novel approach for estimating vital capacity using cough sounds and proposes a neural network-based model that utilizes the reference vital capacity computed using the lambda-mu-sigma method, a conventional approach, and the cough peak flow computed based on the cough sound pressure level as inputs. Additionally, a simplified cough sound input model is developed, with the cough sound pressure level used directly as the input instead of the computed cough peak flow. A total of 56 samples of cough sounds and vital capacities were collected from 31 young and 25 elderly participants. Model performance was evaluated using squared errors, and statistical tests including the Friedman and Holm tests were conducted to compare the squared errors of the different models. The proposed model achieved a significantly smaller squared error (0.052 L2, p < 0.001) than the other models. Subsequently, the proposed model and the cough sound-based estimation model were used to detect whether a participant's vital capacity was lower than the typical lower limit. The proposed model demonstrated a significantly higher area under the receiver operating characteristic curve (0.831, p < 0.001) than the other models. These results highlight the effectiveness of the proposed model for screening decreased vital capacity.

Noninvasive characterization of peripheral sympathetic activation across sensory stimuli using a peripheral arterial stiffness index.

Introduction: The peripheral arterial stiffness index has been proposed and validated as a noninvasive measure quantifying stimulus intensity based on amplitude changes induced by sympathetic innervation of vascular tone. However, its temporal response characteristics remain unclear, thus hindering continuous and accurate monitoring of the dynamic process of sympathetic activation. This paper presents a study aimed at modeling the transient response of the index across sensory stimuli to characterize the corresponding peripheral sympathetic activation. Methods: The index was measured using a continuous arterial pressure monitor and a pulse oximeter during experiments with local pain and local cooling stimuli designed to elicit different patterns of sympathetic activation. The corresponding response of the index was modeled to clarify its transient response characteristics across stimuli. Results: The constructed transfer function accurately depicted the transient response of the index to local pain and local cooling stimuli (Fit percentage: 78.4% ± 11.00% and 79.92% ± 8.79%). Differences in dead time (1.17 ± 0.67 and 0.99 ± 0.56 s, p = 0.082), peak time (2.89 ± 0.81 and 2.64 ± 0.68 s, p = 0.006), and rise time (1.81 ± 0.50 and 1.65 ± 0.48 s, p = 0.020) revealed different response patterns of the index across stimuli. The index also accurately characterized similar vasomotor velocities at different normalized peak amplitudes (0.19 ± 0.16 and 0.16 ± 0.19 a.u., p = 0.007). Discussion: Our findings flesh out the characterization of peripheral arterial stiffness index responses to different sensory stimuli and demonstrate its validity in characterizing peripheral sympathetic activation. This study valorizes a noninvasive method to characterize peripheral sympathetic activation, with the potential to use this index to continuously and accurately track sympathetic activators.

Pharmacological effects of caffeine on ventilation in adult zebrafish under free-swimming conditions.

The zebrafish is widely used as a model in biological studies. In particular, the heart rate and cortisol levels of zebrafish are commonly measured to elucidate the pharmacological effects of chemical substances. Meanwhile, although ventilation is also an important physiological index reflecting emotion-like states, few studies have evaluated the effects of chemicals on ventilation in adult zebrafish. In this study, we assessed whether it is possible to evaluate the pharmacological effects elicited by caffeine in adult zebrafish under free-swimming conditions. We measured the ventilation in adult zebrafish exposed to multiple concentrations of caffeine under restraint and free-swimming conditions and evaluated the pharmacological effects of caffeine using linear mixed model analysis. In addition, results of electrocardiogram analysis and swimming speeds were compared with those in previous reports to ensure that an appropriate dose of caffeine was administered. Under restraint conditions, caffeine significantly decreased heart rate and increased ventilation in a concentration-dependent manner. Under free-swimming conditions, the ventilation rate significantly increased with increasing caffeine concentration. These results indicate that the pharmacological effects elicited by chemicals on ventilation can be evaluated in free-swimming zebrafish.

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants.

Early intervention is now considered the core treatment strategy for autism spectrum disorders (ASD). Thus, it is of significant clinical importance to establish a screening tool for the early detection of ASD in infants. To achieve this goal, in a longitudinal design, we analyzed spontaneous bodily movements of 4-month-old infants from general population and assessed their ASD-like behaviors at 18 months of age. A total of 26 movement features were calculated from video-recorded bodily movements of infants at 4 months of age. Their risk of ASD was assessed at 18 months of age with the Modified Checklist for Autism in Toddlerhood, a widely used screening questionnaire. Infants at high risk for ASD at 18 months of age exhibited less rhythmic and weaker bodily movement patterns at 4 months of age than low-risk infants. When the observed bodily movement patterns were submitted to a machine learning-based analysis, linear and non-linear classifiers successfully predicted ASD-like behavior at 18 months of age based on the bodily movement patterns at 4 months of age, at the level acceptable for practical use. This study analyzed the relationship between spontaneous bodily movements at 4 months of age and the ASD risk at 18 months of age. Experimental results suggested the utility of the proposed method for the early screening of infants at risk for ASD. We revealed that the signs of ASD risk could be detected as early as 4 months after birth, by focusing on the infant's spontaneous bodily movements.

Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application Study.

The cardiopulmonary bypass system used in cardiac surgery can generate microbubbles (MBs) that may cause complications, such as neurocognitive dysfunction, when delivered into the blood vessel. Estimating the number of MBs generated, thus, is necessary to enable the surgeons to deal with it. To this end, we previously proposed a neural network-based model for estimating the number of MBs from four factors measurable from the cardiopulmonary bypass system: suction flow rate, venous reservoir level, blood viscosity, and perfusion flow rate. However, the model has not been adapted to the data collected from actual surgery cases. In this study, the accuracy of MBs estimated by the proposed model was examined in four clinical cases. The results showed that the coefficient of determination between estimated MBs and the measured MBs throughout the surgeries was R2=0.558 (p<0.001). We found that the surgical treatments, such as administration of drugs, fluids and blood transfusions, increased the number of measured MBs. The coefficient of determination increased to R2= 0.8762 (p<0.001) by excluding the duration of these treatments. This result indicates that the model can estimate the number of MBs with high accuracy under the clinical environment.

Automatic control of blood flow rate on the arterial-line side during cardiopulmonary bypass.

The operation of the cardiopulmonary bypass (CPB) system requires skilled techniques and experience. Intraoperatively, the perfusionist needs to intermittently manipulate both of the occluders on the venous- and arterial-line sides to achieve the desired blood flow rates. To facilitate the occluder operation, we propose an automatic control system for the arterial-line side blood flow rate based on a dynamic model that addresses the relationship between the occluder operation and blood flow rate in the CPB system. The simulation results demonstrated that the proposed system was able to control the blood flow rate even when the estimation error of the model parameters was presented. Then, we implemented this control system in the CPB system and conducted an experiment to automatically control the arterial-line side blood flow rate. We confirmed that the blood flow rate on the arterial-line side could follow the manually operated venous-line side blood flow rate.Clinical Relevance--- The automatic blood flow rate control system for a cardiopulmonary bypass system, proposed in this paper, contributes to reducing the burden of occluder operation on a perfusionist.

Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation.

Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.

Cardiorespiratory synchronisation and systolic blood pressure correlation of peripheral arterial stiffness during endoscopic thoracic sympathectomy.

Muscle sympathetic nerve activity (MSNA) is known as an effective measure to evaluate peripheral sympathetic activity; however, it requires invasive measurement with the microneurography method. In contrast, peripheral arterial stiffness affected by MSNA is a measure that allows non-invasive evaluation of mechanical changes of arterial elasticity. This paper aims to clarify the features of peripheral arterial stiffness to determine whether it inherits MSNA features towards non-invasive evaluation of its activity. To this end, we propose a method to estimate peripheral arterial stiffness [Formula: see text] at a high sampling rate. Power spectral analysis of the estimated [Formula: see text] was then performed on data acquired from 15 patients ([Formula: see text] years) who underwent endoscopic thoracic sympathectomy. We examined whether [Formula: see text] exhibited the features of MSNA where its frequency components synchronise with heart and respiration rates and correlates with the low-frequency component of systolic blood pressure. Regression analysis revealed that the local peak frequency in the range of heartbeat frequency highly correlate with the heart rate ([Formula: see text], [Formula: see text]) where the regression slope was approximately 1 and intercept was approximately 0. Frequency analysis then found spectral peaks of [Formula: see text] approximately 0.2 Hz that correspond to the respiratory cycle. Finally, cross power spectral analysis showed a significant magnitude squared coherence between [Formula: see text] and systolic blood pressure in the frequency band from 0.04 to 0.2 Hz. These results indicate that [Formula: see text] inherits the features observed in MSNA that require invasive measurements, and thus [Formula: see text] can be an effective non-invasive substitution for MSNA measure.

Peripheral arterial stiffness during electrocutaneous stimulation is positively correlated with pain-related brain activity and subjective pain intensity: an fMRI study.

Brain activity associated with pain perception has been revealed by numerous PET and fMRI studies over the past few decades. These findings helped to establish the concept of the pain matrix, which is the distributed brain networks that demonstrate pain-specific cortical activities. We previously found that peripheral arterial stiffness [Formula: see text] responds to pain intensity, which is estimated from electrocardiography, continuous sphygmomanometer, and photo-plethysmography. However, it remains unclear whether and to what extent [Formula: see text] aligns with pain matrix brain activity. In this fMRI study, 22 participants received different intensities of pain stimuli. We identified brain regions in which the blood oxygen level-dependent signal covaried with [Formula: see text] using parametric modulation analysis. Among the identified brain regions, the lateral and medial prefrontal cortex and ventral and dorsal anterior cingulate cortex were consistent with the pain matrix. We found moderate correlations between the average activities in these regions and [Formula: see text] (r = 0.47, p < 0.001). [Formula: see text] was also significantly correlated with self-reported pain intensity (r = 0.44, p < 0.001) and applied pain intensity (r = 0.43, p < 0.001). Our results indicate that [Formula: see text] is positively correlated with pain-related brain activity and subjective pain intensity. This study may thus represent a basis for adopting peripheral arterial stiffness as an objective pain evaluation metric.

Measurement of emotional states of zebrafish through integrated analysis of motion and respiration using bioelectric signals.

Fear, anxiety, and preference in fish are generally evaluated by video-based behavioural analyses. We previously proposed a system that can measure bioelectrical signals, called ventilatory signals, using a 126-electrode array placed at the bottom of an aquarium and achieved cameraless real-time analysis of motion and ventilation. In this paper, we propose a method to evaluate the emotional state of fish by combining the motion and ventilatory indices obtained with the proposed system. In the experiments, fear/anxiety and appetitive behaviour were induced using alarm pheromone and ethanol, respectively. We also found that the emotional state of the zebrafish can be expressed on the principal component (PC) space extracted from the defined indices. The three emotional states were discriminated using a model-based machine learning method by feeding the PCs. Based on discrimination performed every 5 s, the F-score between the three emotional states were as follows: 0.84 for the normal state, 0.76 for the fear/anxiety state, and 0.59 for the appetitive behaviour. These results indicate the effectiveness of combining physiological and motional indices to discriminate the emotional states of zebrafish.

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass.

The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

The right hemisphere is important for driving-related cognitive function after stroke.

Considering quality of life (QOL) after stroke, car driving is one of the most important abilities for returning to the community. In this study, directed attention and sustained attention, which are thought to be crucial for driving, were examined. Identification of specific brain structure abnormalities associated with post-stroke cognitive dysfunction related to driving ability would help in determining fitness for car driving after stroke. Magnetic resonance imaging was performed in 57 post-stroke patients (51 men; mean age, 63 ± 11 years) who were assessed for attention deficit using a standardized test (the Clinical Assessment for Attention, CAT), which includes a Continuous Performance Test (CPT)-simple version (CPT-SRT), the Behavioral Inattention Test (BIT), and a driving simulator (handle task for dividing attention, and simple and selective reaction times for sustained attention). A statistical non-parametric map (SnPM) that displayed the association between lesion location and cognitive function for car driving was created. From the SnPM analysis, the overlay plots were localized to the right hemisphere during handling the hit task for bilateral sides (left hemisphere damage related to right-side neglect and right hemisphere damage related to left-side neglect) and during simple and selective reaction times (false recognition was related to damage of both hemispheres). A stepwise multiple linear regression analysis confirmed the importance of both hemispheres, especially the right hemisphere, for cognitive function and car driving ability. The present study demonstrated that the right hemisphere has a crucial role for maintaining directed attention and sustained attention, which maintain car driving ability, improving QOL for stroke survivors.