Associations between Aging and Vitamin D Status with Whole-Body Nitric Oxide Production and Markers of Endothelial Function.

BACKGROUND: Aging and vitamin D deficiency have been associated with reduced nitric oxide (NO) synthesis and impaired endothelial function (EF) but the evidence in humans remains weak. OBJECTIVES: Two independent cross-sectional studies were designed to evaluate the association between age, sex, and plasma vitamin D concentrations with physiological and biochemical biomarkers of NO synthesis and EF in young and older healthy participants (Study 1) and in overweight and obese postmenopausal females (Study 2). METHODS: In Study 1, 40 young (20-49 y) and older (50-75 y) males and females (10 participants per age and sex group) were included. Resting blood pressure and ear-to-finger peripheral pulse wave velocity (PWV) were measured. A stable-isotopic method was used to determine whole-body NO production. Plasma 25-hydroxyvitamin D (25(OH)D), nitrate, nitrite, and asymmetric dimethylarginine (ADMA) concentrations were determined. In Study 2, 80 older overweight and obese females (age 61.2 ± 6.2 y, body mass index 29.5 ± 4.4 kg/m2) were recruited. Postocclusion reactive hyperemia (PORH) and peripheral PWV were measured. Plasma concentrations of 25(OH)D, nitrate, cyclic guanosine monophosphate, 3-nitrotyrosine (3-NT), endothelin-1, vascular endothelial growth factor, and ADMA were determined. RESULTS: In Study 1, whole-body NO production was significantly greater in young compared with older participants (0.61 ± 0.30 µmol·h-1·kg-1 compared with 0.39 ± 0.10 µmol·h-1·kg-1, P = 0.01) but there was no evidence of a sex difference (P = 0.81). Plasma 25(OH)D concentration was not associated with PWV (r = 0.18, P = 0.28) or whole-body NO production (r = -0.20, P = 0.22). Plasma ADMA concentration was associated positively with age (r = 0.35, P = 0.03) and negatively with whole-body NO production (r = -0.33, P = 0.04). In Study 2, age was associated with lower PORH (r = -0.28, P = 0.02) and greater ADMA concentrations (r = 0.22, P = 0.04). Plasma 25(OH)D concentration was inversely associated with 3-NT concentrations (r = -0.31, P = 0.004). CONCLUSIONS: Older age was associated with lower whole-body NO production. Plasma vitamin D concentrations were not associated with NO production or markers of EF but showed a weak, significant correlation with oxidative stress in postmenopausal overweight females.

Design and implementation of an automatic nursing assessment system based on CDSS technology.

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones. METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation. RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s. CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.

Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task.

The black-box nature of machine learning models hinders the deployment of some high-accuracy medical diagnosis algorithms. It is risky to put one's life in the hands of models that medical researchers do not fully understand or trust. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai, China, to identify biomarkers indicative of infection severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, partial dependence plot, individual conditional expectation, accumulated local effects, local interpretable model-agnostic explanations, and Shapley additive explanation, we identify an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, a decrease in lymphocyte is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

Multi-constraint point set registration with redundant point removal for the registration of coronary arteries.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge. METHODS: Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance. RESULTS: The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries. CONCLUSION: This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions.

Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement.

Background: Pulse transit time (PTT) is a key parameter in cuffless blood pressure measurement based on photoplethysmography (PPG) signals. In wearable PPG sensors, raw PPG signals are filtered, which can change the timing of PPG waveform feature points, leading to inaccurate PTT estimation. There is a lack of comprehensive investigation of filtering-induced PTT changes in subjects with different ages. Objective: This study aimed to quantitatively investigate the effects of aging and PTT definition on the infinite impulse response (IIR) filtering-induced PTT changes. Methods: One hundred healthy subjects in five different ranges of age (i.e., 20-29, 30-39, 40-49, 50-59, and over 60 years old, 20 subjects in each) were recruited. Electrocardiogram (ECG) and PPG signals were recorded simultaneously for 120 s. PTT was calculated from the R wave of ECG and PPG waveform features. Eight PTT definitions were developed from different PPG waveform feature points. The raw PPG signals were preprocessed then further low-pass filtered. The difference between PTTs derived from preprocessed and filtered PPG signals, and the relative difference, were calculated and compared among five age groups and eight PTT definitions using the analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. Linear regression analysis was used to investigate the relationship between age and filtering-induced PTT changes. Results: Filtering-induced PTT difference and the relative difference were significantly influenced by age and PTT definition (p < 0.001 for both). Aging effect on filtering-induced PTT changes was consecutive with a monotonous trend under all PTT definitions. The age groups with maximum and minimum filtering-induced PTT changes depended on the definition. In all subjects, the PTT defined by maximum peak of PPG had the minimum filtering-induced PTT changes (mean: 16.16 ms and 5.65% for PTT difference and relative difference). The changes of PTT defined by maximum first PPG derivative had the strongest linear relationship with age (R-squared: 0.47 and 0.46 for PTT difference relative difference). Conclusion: The filtering-induced PTT changes are significantly influenced by age and PTT definition. These factors deserve further consideration to improve the accuracy of PPG-based cuffless blood pressure measurement using wearable sensors.

Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm.

PURPOSE: Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD. AIM: To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD. METHODS: Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. RESULTS: ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. CONCLUSIONS: Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Age-Related Changes in Blood Volume Pulse Wave at Fingers and Ears.

OBJECTIVE: The decline in vascular elasticity with aging can be manifested in the shape of pulse wave. The study investigated the pulse wave features that are sensitive to age and the pattern of these features change with increasing age were examined. METHODS: Five features were proposed and extracted from the photoplethysmography (PPG)-based pulse wave or its first derivative wave. The correlation between these PPG features and ages was studied in 100 healthy subjects with a wide range of ages (20-71 years). Piecewise regression coefficients were calculated to examine the rates of change of the PPG features with age at different age stages. RESULTS: The proposed PPG features obtained from the finger showed a strong and significant correlation with age (with r = 0.76 - 0.77, p < 0.01), indicating higher sensitivity to age changes compared to the PPG features reported in previous studies (with r = 0.66 - 0.75). The correlation remained significant even after correcting for other clinical variables. The rate of change of the PPG feature values was found to be significantly faster in subjects aged ≥40 years compared to those aged < 40 years in the healthy population. This rate of change was similar to the age-related progression of arterial stiffness evaluated by pulse wave velocity (PWV), which is considered a gold standard for evaluating vascular stiffness. CONCLUSIONS: The proposed PPG features showed a high correlation with chronological age in healthy subjects and exhibited a similar age-related change trend as PWV. SIGNIFICANCE: With the convenience of PPG measures, the proposed age-related features have the potential to be used as biomarkers for vascular aging and estimating the risk of cardiovascular disease.

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study.

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals. Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories. Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR). Results: The simulated mean ICPs in normal condition were in the normal range (8.87-11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (>20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean. Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.

Dual-Stream Contrastive Learning for Channel State Information Based Human Activity Recognition.

WiFi-based human activity recognition (HAR) has been extensively studied due to its far-reaching applications in health domains, including elderly monitoring, exercise supervision and rehabilitation monitoring, etc. Although existing supervised deep learning techniques have achieved remarkable performances for these tasks, they are however data-hungry and hence are notoriously difficult due to the privacy and incomprehensibility of WiFi-based HAR data. Existing contrastive learning models, mainly designed for computer vision, cannot guarantee their performance on channel state information (CSI) data. To this end, we propose a new dual-stream contrastive learning model that can process and learn the raw WiFi CSI data in a self-supervised manner. More specifically, our proposed method, coined as DualConFi, takes raw WiFI CSI data as input and incorporates channel and temporal streams to learn highly-discriminative spatiotemporal features under a mutual information constraint using unlabeled data. We exhibit the effectiveness of our model on three publicly available CSI data sets in various experiment settings, including linear evaluation, semi-supervised, and transfer learning. We show that DualConFi is able to perform favourably against challenging baselines in each setting. Moreover, by studying the effects of different transform functions on CSI data, we finally verify the effectiveness of highly-discriminative features.

Changes of oscillogram envelope maximum with blood pressure and aging: a quantitative observation.

Objective.The oscillometric blood pressure (BP) measurement technique estimates BPs from analyzing the oscillometric cuff pressure waveform (oscillogram) envelope. The oscillogram envelope maximum is associated with physiological changes and influences BP measurement accuracy. We aim to quantitatively investigate the effect of BP and aging on the changes of oscillogram envelope maximum.Approach.Four hundred and sixty-two subjects (214 female, 248 male) were recruited. The cuff pressure was digitally recorded during linear cuff deflation to derive oscillogram envelopes and their maximums. Moderation analysis was performed to investigate whether the relationship between BP and envelope maximum was moderated by age. Subjects were divided into five age categories and three BP groups. The envelope maximums were compared between different BP and age categories to qualify their changes with increased BP and aging.Main results.Age has a significant moderating effect on the relationship between BP and envelope maximum (P < 0.05). The oscillogram envelope maximums increased significantly with increased BPs (P < 0.05 between each BP groups) and aging (P < 0.05 for > 60 years old groups in comparison with younger groups).Significance.This study experientially and theoretically concluded the BPs and aging are two important factors that influence the maximum value of the oscillogram envelope.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning.

BACKGROUND: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

A Wearable Swallowing Recognition System Based on Motion and Dual Photoplethysmography Sensing of Laryngeal Movements.

Swallowing recognition is the leading step in the evaluation of dysphagia which seriously affects people's life. Current medical swallowing monitoring methods require an in-hospital environment and overly rely on professional knowledge of the medical staff. In this study, we developed a wearable swallowing recognition system that consists of an on-neck wearable swallowing sensing device and a data processing module on a host computer. The wearable device collects inertial signals including acceleration and angular velocity, as well as dual photoplethysmography (PPG) signals based on infrared and green light from the neck. A novel processing framework for dual PPG signals is proposed to extract and enhance the laryngeal motion component introduced by swallowing activities in the data processing module. The laryngeal motion component of dual PPG signals together with the preprocessed inertial signals are further used for feature extraction to proceed swallowing recognition based on random forest classifier. We collected data from 32 healthy subjects in the center and side positions on the neck using our system to analyze their swallowing activities. As a result, we achieved a high average area under curve (AUC) of the swallowing recognition by 86.6%. We also find the sensing position has a significant impact on gender-specific swallowing recognition performance, as the center position was better for females (92.9%), while the side position was better for males (87.6%). The results indicate that the proposed system could achieve high integrity and good performance, which is helpful for the future swallowing research.

Validation of Computational Simulation for Tumor-treating Fields with Homogeneous Phantom.

Tumor-treating Fields (TTFields) is a promising cancer therapy technique in clinical application. Computational simulation of TTFields has been used to predict the electric field (EF) distribution in the human body and to optimize the treatment parameters. However, there are only a few studies to validate the accuracy of the simulation model. Here we propose a measurement platform with technical details for validating the simulation model of TTFields. We further constructed homogeneous agar phantoms with different conductivity for voltage measurement. With the measured voltages from six equidistance recording points in the cylinder phantom, we calculated the EF intensity in the phantoms at different frequencies. Comparing the measured values with the simulated values obtained from two types of source simulation, we found that the current source simulation model of TTFields is a reliable method for evaluating the EF intensity distribution.

Neural Entrainment to Rhythms of Imagined Syllables.

Imagined speech based brain-computer interface (BCI) is of great interest due to its efficiency and user-friendliness for patients with speech impairment. The aim of this work was to study whether different rhythms of imagined syllables could elicit corresponding frequency components on EEG amplitude spectra. Seventeen participants were recruited to take part in the experiments, and performed a control task and four imagery tasks with the presence of periodic pure tones while their EEG signals were recorded. The four imagery tasks included imagining the syllable' /a/' every time, every two times, and every three times the periodic pure tones occurred, and imagined twice every three times the periodic pure tones occurred. The experimental results analyzed by Fourier transform indicated that neural entrainment to rhythmic speech imagery can be notably reflected on the EEG amplitude spectra. Clinical Relevance- This work manifested that different rhythms of imagined syllables could be identified from EEG amplitude spectra, which may be beneficial to the development of imagined speech based BCIs.

Calcium Activation of Parvalbumin Neurons Induced by Electrical Motor Cortex Stimulation.

Electrical motor cortex stimulation (EMCS) has been used for Parkinson's Disease (PD) treatment. Some studies found that distinct cell types might lead to selective effects. As the largest subgroup of interneurons, Parvalbumin (PV) neurons have been reported to be involved in the mechanisms of therapeutic efficacy for PD treatment. However, little is known about their responses to the EMCS. In this study, we used in-vivo two-photon imaging to record calcium activities of PV neurons (specific type) and all neurons (non-specific type) in layer 2/3 primary motor cortex (MI) during EMCS with various stimulus parameters. We found PV neurons displayed different profiles of activation property compared to all neurons. The cathodal polarity preference of PV neurons decreased at a high-frequency stimulus. The calcium transients of PV neurons generated by EMCS trended to be with large amplitude and short active duration. The optimal activation frequency of PV neurons is higher than that of all neurons. These results improved our understanding of the selective effects of EMCS on specific cell types, which could bring more effective stimulation protocols for PD treatment.

Sources of automatic office blood pressure measurement error: a systematic review.

Objective: Accurate and reliable blood pressure (BP) measurement is important for the prevention and treatment of hypertension. The oscillometric-based automatic office blood pressure measurement (AOBPM) is widely used in hospitals and clinics, but measurement errors are common in BP measurements. There is a lack of systematic review of the sources of measurement errors.Approach: A systematic review of all existing research on sources of AOBPM errors. A search strategy was designed in six online databases, and all the literature published before October 2021 was selected. Those studies that used the AOBPM device to measure BP from the upper arm of subjects were included.Main results: A total of 1365 studies were screened, and 224 studies were included in this final review. They investigated 22 common error sources with clinical AOBPM. Regarding the causes of BP errors, this review divided them into the following categories: the activities before measurement, patient's factors, measurement environment, measurement procedure, and device settings. 13 sources caused increased systolic and diastolic BP (SBP and DBP), 2 sources caused the decrease in SBP and DBP, only 1 source had no significant effect on BPs, and the other errors had a non-uniform effect (either increase or decrease in BPs). The error ranges for SBP and DBP were -14 to 33 mmHg and -6 to 19 mmHg, respectively.Significance: The measurement accuracy of AOBPM is susceptible to the influence of measurement factors. Interpreting BP readings need to be treated with caution in clinical measurements. This review made comprehensive evidence for the need for standardized BP measurements and provided guidance for clinical practitioners when measuring BP with AOBPM devices.