General movements (GMs), a type of spontaneous movement, have been used for the early diagnosis of infant disorders. In clinical practice, GMs are visually assessed by qualified licensees; however, this presents a difficulty in terms of quantitative evaluation. Various measurement systems for the quantitative evaluation of GMs track target markers attached to infants; however, these markers may disturb infants' spontaneous movements. this paper proposes a markerless movement measurement and evaluation system for GMs in infants. The proposed system calculates 25 indices related to GMs, including the magnitude and rhythm of movements, by video analysis, that is, by calculating background subtractions and frame differences. Movement classification is performed based on the clinical definition of GMs by using an artificial neural network with a stochastic structure. This supports the assessment of GMs and early diagnoses of disabilities in infants. in a series of experiments, the proposed system is applied to movement evaluation and classification in full-term infants and lowbirth-weight infants. The experimental results confirm that the average agreement between four GMs classified by the proposed system and those identified by a licensee reaches up to 83.1 ± 1.84%. In addition, the classification accuracy of normal and abnormal movements reaches 90.2 ± 0.94%.

Cough peak flow (CPF) is a measurement for evaluating the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on both patients and their caregivers. Therefore, this study develops a novel cough strength evaluation method using cough sounds. This paper presents an exponential model to estimate CPF from the cough peak sound pressure level (CPSL). We investigated the relationship between cough sounds and cough flows and the effects of a measurement condition of cough sound, microphone type and participant’s height and gender on CPF estimation accuracy. The results confirmed that the proposed model estimated CPF with a high accuracy. The absolute error between CPFs and estimated CPFs were significantly lower when the microphone distance from the participant’s mouth was within 30 cm than when the distance exceeded 30 cm. Analysis of the model parameters showed that the estimation accuracy was not affected by participant’s height or gender. These results indicate that the proposed model has the potential to improve the feasibility of measuring and assessing CPF.

Although cough peak flow (CPF) is an important measurement for evaluating the risk of cough dysfunction, some patients cannot use conventional measurement instruments, such as spirometers, because of the configurational burden of the instruments. Therefore, we previously developed a cough strength estimation method using cough sounds based on a simple acoustic and aerodynamic model. However, the previous model did not consider age or have a user interface for practical application. This study clarifies the cough strength prediction accuracy using an improved model in young and elderly participants. Additionally, a user interface for mobile devices was developed to record cough sounds and estimate cough strength using the proposed method. We then performed experiments on 33 young participants (21.3 ± 0.4 years) and 25 elderly participants (80.4 ± 6.1 years) to test the effect of age on the CPF estimation accuracy. The percentage error between the measured and estimated CPFs was approximately 6.19%. In addition, among the elderly participants, the current model improved the estimation accuracy of the previous model by a percentage error of approximately 6.5% (p < 0.001). Furthermore, Bland-Altman analysis demonstrated no systematic error between the measured and estimated CPFs. These results suggest that the developed device can be applied for daily CPF measurements in clinical practice.

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To clarify the tissue responsible for a biological function, that function can be experimentally perturbed by an external stimulus, such as radiation. Radiation can be precisely and finely administered and any subsequent change in function examined. To investigate the involvement of the central nervous system (CNS) in Caenorhabditis elegans’ locomotion, we irradiated a limited 20-µm-diameter area of the CNS with a single dose and evaluated the resulting effects on motility. However, whether irradiated area (beam size)-dependent or dose-dependent effects on motility occur via targeted irradiation remain unknown. In the present study, we examined the irradiated area- and dose-dependent effects of CNS-targeted irradiation on the motility of C. elegans using a collimating microbeam system and confirmed the involvement of the CNS and body-wall muscle cells around the CNS in motility. After CNS-targeted microbeam irradiation, C. elegans’ motility was assayed. The results demonstrated a dose-dependent effect of CNS-targeted irradiation on motility reflecting direct effects on the irradiated CNS. In addition, when irradiated with 1000-Gy irradiation, irradiated area (beam size)-dependent effects were observed. This method has two technical advantages: Performing a series of on-chip imaging analyses before and after irradiation and targeted irradiation using a distinct ion-beam size.

In clinical practice, subjective pain evaluations, e.g., the visual analogue scale and the numeric rating scale, are generally employed, but these are limited in terms of their ability to detect inaccurate reports, and are unsuitable for use in anesthetized patients or those with dementia. We focused on the peripheral sympathetic nerve activity that responds to pain, and propose a method for evaluating pain sensation, including intensity, sharpness, and dullness, using the arterial stiffness index. In the experiment, electrocardiogram, blood pressure, and photoplethysmograms were obtained, and an arterial viscoelastic model was applied to estimate arterial stiffness. The relationships among the stiffness index, self-reported pain sensation, and electrocutaneous stimuli were examined and modelled. The relationship between the stiffness index and pain sensation could be modelled using a sigmoid function with high determination coefficients, where R2 ≥ 0.88, p < 0.01 for intensity, R2 ≥ 0.89, p < 0.01 for sharpness, and R2 ≥ 0.84, p < 0.01 for dullness when the stimuli could appropriately evoke dull pain.

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 $${\beta }_{\text{art}}$$ β art responds to pain intensity, which is estimated from electrocardiography, continuous sphygmomanometer, and photo-plethysmography. However, it remains unclear whether and to what extent $${\beta }_{\text{art}}$$ β art 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 $${\beta }_{\text{art}}$$ β art 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 $${\beta }_{\text{art}}$$ β art (r = 0.47, p < 0.001). $${\beta }_{\text{art}}$$ β art 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 $${\beta }_{\text{art}}$$ β art 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.

This paper proposes an algorithm that estimates blood viscosity during cardiopulmonary bypass (CPB) and validates its application in clinical cases. The proposed algorithm involves adjustable parameters based on the oxygenator and fluid types and estimates blood viscosity based on pressure-flow characteristics of the fluid perfusing through the oxygenator. This novel nonlinear model requires four parameters that were derived by in vitro experiments. The results estimated by the proposed method were then compared with a conventional linear model to demonstrate the former's optimal curve fitting. The viscosity (η) estimated using the proposed algorithm and the viscosity (η) measured using a viscometer were compared for 20 patients who underwent mildly hypothermic CPB. The developed system was applied to ten patients, and η was recorded for comparisons with hematocrit and blood temperature. The residual sum of squares between the two curve fittings confirmed the significant difference, with p < 0.001. η and η showed a very strong correlation with R = 0.9537 and p < 0.001. Regarding the mean coefficient of determination for all cases, the hematocrit and temperature showed weak correlations at 0.33 ± 0.14 and 0.22 ± 0.21, respectively. For CPB measurements of all cases, η was more than 98% distributed in the range from 1 to 3 mPa⋅s. This new system for estimating viscosity may be useful for detecting various viscosity-related effects that may occur during CPB.