The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study.

BACKGROUND: Patient-ventilator asynchrony commonly occurs during pressure support ventilation (PSV). IntelliSync + software (Hamilton Medical AG, Bonaduz, Switzerland) is a new ventilation technology that continuously analyzes ventilator waveforms to detect the beginning and end of patient inspiration in real time. This study aimed to evaluate the physiological effect of IntelliSync + software on inspiratory trigger delay time, delta airway (Paw) and esophageal (Pes) pressure drop during the trigger phase, airway occlusion pressure at 0.1 s (P0.1), and hemodynamic variables. METHODS: A randomized crossover physiologic study was conducted in 14 mechanically ventilated patients under PSV. Patients were randomly assigned to receive conventional flow trigger and cycling, inspiratory trigger synchronization (I-sync), cycle synchronization (C-sync), and inspiratory trigger and cycle synchronization (I/C-sync) for 15 min at each step. Other ventilator settings were kept constant. Paw, Pes, airflow, P0.1, respiratory rate, SpO2, and hemodynamic variables were recorded. The primary outcome was inspiratory trigger and cycle delay time between each intervention. Secondary outcomes were delta Paw and Pes drop during the trigger phase, P0.1, SpO2, and hemodynamic variables. RESULTS: The time to initiate the trigger was significantly shorter with I-sync compared to baseline (208.9±91.7 vs. 301.4±131.7 msec; P = 0.002) and I/C-sync compared to baseline (222.8±94.0 vs. 301.4±131.7 msec; P = 0.005). The I/C-sync group had significantly lower delta Paw and Pes drop during the trigger phase compared to C-sync group (-0.7±0.4 vs. -1.2±0.8 cmH2O; P = 0.028 and - 1.8±2.2 vs. -2.8±3.2 cmH2O; P = 0.011, respectively). No statistically significant differences were found in cycle delay time, P0.1 and other physiological variables between the groups. CONCLUSIONS: IntelliSync + software reduced inspiratory trigger delay time compared to the conventional flow trigger system during PSV mode. However, no significant improvements in cycle delay time and other physiological variables were observed with IntelliSync + software. TRIAL REGISTRATION: This study was registered in the Thai Clinical Trial Registry (TCTR20200528003; date of registration 28/05/2020).

Respiratory Rate Oxygenation (ROX) index as predictor of high flow nasal cannula in pediatric patients in pediatric intensive care unit.

BACKGROUND: High-flow nasal cannula (HFNC) is often used in pediatric populations with respiratory distress. In adults, the respiratory-rate oxygenation (ROX) index is used as a predictor of HFNC therapy; however, children have age-associated differences in respiratory rate, thus may not be applicable to children. This study aims to find the reliability of ROX index and modified P-ROX index as predictors of HFNC therapy failure in pediatric patients. METHODS: Subjects in this analytical cross-sectional study were taken from January 2023 until November 2023 in Cipto Mangunkusumo Hospital. Inclusion criteria are children aged 1 month to 18 years with respiratory distress and got HFNC therapy. Receiver operating characteristics (ROC) analysis was used to find mP-ROX index cutoff value as a predictor of HFNC failure. The area under curve (AUC) score of mP-ROX index was assessed at different time point. RESULTS: A total of 102 patients, with 70% of the population with pneumonia, were included in this study. There are significant differences in the ROX index between the successful and failed HFNC group therapy (p < 0.05). This study suggests that mP-ROX index is not useful as predictor of HFNC therapy in pediatrics. While ROX index < 5.52 at 60 min and < 5.68 at 90 min after HFNC initiation have a sensitivity of 90% and specificity of 71%, sensitivity of 78% and specificity of 76%, respectively. CONCLUSION: mP-ROX index is not useful as a predictor of HFNC therapy in pediatrics. Meanwhile, ROX index at 60 min and 90 min after initiation of HFNC is useful as a predictor of HFNC failure.

Respiration and Heart Rate Monitoring in Smart Homes: An Angular-Free Approach with an FMCW Radar.

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform (CWT) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of -44.8 to +44.8 deg. In the Bland-Altman plot, the measured results indicate the average difference of -1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively.

Aluminum Nitride Thin Film Piezoelectric Pressure Sensor for Respiratory Rate Detection.

In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15-30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R2 = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way.

Unobtrusive Sensors for Synchronous Monitoring of Different Breathing Parameters in Care Environments.

Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to simultaneously monitor two different breathing parameters: respiratory rate, and exhaled breath. Experiments were carried out with two subjects undergoing three breathing cases in breaths per minute (BPM): (1) slow breathing (12 BPM), (2) moderate breathing (20 BPM), and (3) fast breathing (28 BPM). Respiratory rates were captured by Wi-Fi sensors, and the data were processed to extract the respiration rates and compared with a metronome that controlled the subjects' breathing. On the other hand, exhaled breath data were captured by a UWB antenna using a vector network analyser (VNA). Corresponding reflection coefficient data (S11) were obtained from the subjects at the time of exhalation and compared with S11 in free space. The exhaled breath data from the UWB antenna were compared with relative humidity, which was measured with a digital psychrometer during the breathing exercises to determine whether a correlation existed between the exhaled breath's water vapour content and recorded S11 data. Finally, captured respiratory rate and exhaled breath data from the antenna sensors were compared to determine whether a correlation existed between the two parameters. The results showed that the antenna sensors were capable of capturing both parameters simultaneously. However, it was found that the two parameters were uncorrelated and independent of one another.

Multidisciplinary Approach to the Diagnosis of Idiopathic Interstitial Pneumonias: Focus on the Pathologist's Key Role.

Idiopathic Interstitial Pneumonias (IIPs) are a heterogeneous group of the broader category of Interstitial Lung Diseases (ILDs), pathologically characterized by the distortion of lung parenchyma by interstitial inflammation and/or fibrosis. The American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary consensus classification of the IIPs was published in 2002 and then updated in 2013, with the authors emphasizing the need for a multidisciplinary approach to the diagnosis of IIPs. The histological evaluation of IIPs is challenging, and different types of IIPs are classically associated with specific histopathological patterns. However, morphological overlaps can be observed, and the same histopathological features can be seen in totally different clinical settings. Therefore, the pathologist's aim is to recognize the pathologic-morphologic pattern of disease in this clinical setting, and only after multi-disciplinary evaluation, if there is concordance between clinical and radiological findings, a definitive diagnosis of specific IIP can be established, allowing the optimal clinical-therapeutic management of the patient.

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals.

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

[Design of Pulse and Respiratory Signals Detection System].

A pulse and respiration synchronous detection system is designed to explore the changes of physiological signals in different situations. The system obtains the corresponding signal through STM32 control pulse and respiratory acquisition circuit, calculates and displays real-time parameters such as heart rate and respiratory rate, and transmits the data to the upper computer for storage in the database. The experimental test results show that the system can monitor pulse and respiratory waveform in different situations, and the waveform is in good condition. Compared with medical pulse oximeter, the error of measured heart rate and blood oxygen concentration is less than 3%, and the error of respiratory rate is less than 5% compared with the actual value, which verifies the accuracy of system signal acquisition. The system is small in size, low in cost, and comfortable to wear, and can be applied in experimental research related to pulse and respiratory signals.

Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human.

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

Time to Clinical Stability in Children With Community-Acquired Pneumonia.

BACKGROUND AND OBJECTIVES: Time to clinical stability (TCS) is a commonly used outcome in adults with community-acquired pneumonia (CAP), yet few studies have evaluated TCS in children. Our objective was to determine the association between TCS and disease severity in children with suspected CAP, as well as factors associated with reaching early stability. METHODS: This is a prospective cohort study of children (aged 3 months to 18 years) hospitalized with suspected CAP. TCS parameters included temperature, heart rate, respiratory rate, and hypoxemia with the use of supplemental oxygen. TCS was defined as time from admission to parameter normalization. The association of TCS with severity and clinical factors associated with earlier TCS were evaluated. RESULTS: Of 571 children, 187 (32.7%) had at least 1 abnormal parameter at discharge, and none had ≥3 abnormal discharge parameters. A greater proportion of infants (90 [93%]) had all 4 parameters stable at discharge compared with 12- to 18-year-old youths (21 [49%]). The median TCS for each parameter was <24 hours. Younger age, absence of vomiting, diffusely decreased breath sounds, and normal capillary refill were associated with earlier TCS. Children who did not reach stability were not more likely to revisit after discharge. CONCLUSIONS: A TCS outcome consisting of physiologic variables may be useful for objectively assessing disease recovery and clinical readiness for discharge among children hospitalized with CAP. TCS may decrease length of stay if implemented to guide discharge decisions. Clinicians can consider factors associated with earlier TCS for management decisions.

Comparison of physiological markers, behavior monitoring, and clinical illness scoring as indicators of an inflammatory response in beef cattle.

Clinical illness (CI) scoring using visual observation is the most widely applied method of detecting respiratory disease in cattle but has limited effectiveness in practice. In contrast, body-mounted sensor technology effectively facilitates disease detection. To evaluate whether a combination of movement behavior and CI scoring is effective for disease detection, cattle were vaccinated to induce a temporary inflammatory immune response. Cattle were evaluated before and after vaccination to identify the CI variables that are most indicative of sick cattle. Respiratory rate (H2 = 43.08, P < 0.0001), nasal discharge (H2 = 8.35, P = 0.015), and ocular discharge (H2 = 16.38, P = 0.0003) increased after vaccination, and rumen fill decreased (H2 = 20.10, P < 0.0001). Locomotor activity was measured via leg-mounted sensors for the four days preceding and seven days following vaccination. A statistical model that included temperature, steps, lying time, respiratory rate, rumen fill, head position, and excess saliva was developed to distinguish between scores from before and after vaccination with a sensitivity of 0.898 and specificity of 0.915. Several clinical illness signs were difficult to measure in practice. Binoculars were required for scoring respiratory rate and eye-related metrics, and cattle had to be fitted with colored collars for individual identification. Scoring each animal took up to three minutes in a small research pen; therefore, technologies that can automate both behavior monitoring and identification of clinical illness signs are key to improving capacity for BRD detection and treatment.

Respiratory and locomotor muscle blood flow measurements using near-infrared spectroscopy and indocyanine green dye in health and disease.

Measuring respiratory and locomotor muscle blood flow during exercise is pivotal for understanding the factors limiting exercise tolerance in health and disease. Traditional methods to measure muscle blood flow present limitations for exercise testing. This article reviews a method utilising near-infrared spectroscopy (NIRS) in combination with the light-absorbing tracer indocyanine green dye (ICG) to simultaneously assess respiratory and locomotor muscle blood flow during exercise in health and disease. NIRS provides high spatiotemporal resolution and can detect chromophore concentrations. Intravenously administered ICG binds to albumin and undergoes rapid metabolism, making it suitable for repeated measurements. NIRS-ICG allows calculation of local muscle blood flow based on the rate of ICG accumulation in the muscle over time. Studies presented in this review provide evidence of the technical and clinical validity of the NIRS-ICG method in quantifying respiratory and locomotor muscle blood flow. Over the past decade, use of this method during exercise has provided insights into respiratory and locomotor muscle blood flow competition theory and the effect of ergogenic aids and pharmacological agents on local muscle blood flow distribution in COPD. Originally, arterial blood sampling was required via a photodensitometer, though the method has subsequently been adapted to provide a local muscle blood flow index using venous cannulation. In summary, the significance of the NIRS-ICG method is that it provides a minimally invasive tool to simultaneously assess respiratory and locomotor muscle blood flow at rest and during exercise in health and disease to better appreciate the impact of ergogenic aids or pharmacological treatments.

The metabolic effects of habitual leg shaking: A randomized crossover trial.

AIMS: The adverse effects of sedentary behavior on obesity and chronic diseases are well established. However, the prevalence of sedentary behavior has increased, with only a minority of individuals meeting the recommended physical activity guidelines. This study aimed to investigate whether habitual leg shaking, a behavior traditionally considered unfavorable, could serve as an effective strategy to improve energy metabolism. MATERIALS AND METHODS: A randomized crossover study was conducted, involving 15 participants (mean [SD] age, 25.4 [3.6]; mean [SD] body mass index, 22 [3]; 7 women [46.7%]). The study design involved a randomized sequence of sitting and leg shaking conditions, with each condition lasting for 20 min. Energy expenditure, respiratory rate, oxygen saturation, and other relevant variables were measured during each condition. RESULTS: Compared to sitting, leg shaking significantly increased total energy expenditure [1.088 kj/min, 95% confidence interval, 0.69-1.487 kj/min], primarily through elevated carbohydrate oxidation. The average metabolic equivalent during leg shaking exhibited a significant increase from 1.5 to 1.8. Leg shaking also raised respiratory rate, minute ventilation, and blood oxygen saturation levels, while having no obvious impact on heart rate or blood pressure. Electromyography data confirmed predominant activation of lower leg muscles and without increased muscle fatigue. Intriguingly, a significant correlation was observed between the increased energy expenditure and both the frequency of leg shaking and the muscle mass of the legs. CONCLUSIONS: Our study provides evidence that habitual leg shaking can boost overall energy expenditure by approximately 16.3%. This simple and feasible approach offers a convenient way to enhance physical activity levels.

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection.

OBJECTIVES: To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery. METHODS: We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method. RESULTS: In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2. CONCLUSIONS: CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.

Growth, filtration and respiration characteristics of small single-osculum demosponge Halichondria panicea explants.

Filter-feeding demosponges are modular organisms that consist of modules each with one water-exit osculum. Once a mature module has been formed, the weight-specific filtration and respiration rates do not change. Sponge modules only grow to a certain size and for a sponge to increase in size, new modules must be formed. However, the growth characteristics of a small single-osculum module sponge are fundamentally different from those of multi-modular sponges, and a theoretically derived volume-specific filtration rate scales as F/V=V-1/3, indicating a decrease with increasing total module volume (V, cm3). Here, we studied filtration rate (F, l h-1), respiration rate (R, ml O2 h-1), volume-specific (F/V) and weight-specific (F/W) filtration rates, and the ratios F/R and F/W along with growth rates of small single-osculum demosponge Halichondria panicea explants of various sizes exposed to various concentrations of algal cells. The following relationships were found: F/V=7.08V-0.24, F=a1W1.05, and R=a2W0.68 where W is the dry weight (mg). The F/R and F/W ratios were constant and essentially independent of W, and other data indicate exponential growth. It is concluded that the experimental data support the theoretical F/V∝V-1/3.

Relationship between anaerobic capacity and changes in speed in the 100-m breaststroke stroke in short-distance breaststroke swimmers: the mediating role of technical change

Objective:By analyzing and summarizing the relationship between anaerobic capacity, technical changes of 100m breaststroke en route and speed changes of short distance breaststroke athletes, the interrelationship and internal pathways between the three are revealed to provide reference for improving athletic performance of short distance breaststroke athletes and provide theoretical basis for anaerobic capacity training.Method:Fifteen male short-distance breaststroke athletes (age 19.67±2.61 years, height 178.4±7.04 cm, weight 71.6±7.79 kg) were selected to perform anaerobic power cycling and 100 m breaststroke tests on the upper and lower extremities. The correlations and intrinsic linkage pathways between the three were explored by calculating Pearson correlation coefficients and using a mediating effects model.Result:Significant differences existed in speed, stroke rate, cycle time per stroke, and swim efficiency index in the 100 m breaststroke all-out test. There were significant correlations between the rate of anaerobic power decrease in the upper limb and the changes in stroke amplitude, cycle time per stroke, and speed. There were significant correlations between the change in mean stroke rate, the change in cycle time per stroke, the change in swim efficiency index and the change in speed. Anaerobic power indirectly influenced the speed variation during the en-route swim, which was mediated by the technical variation in cycle time per stroke.Conclusion:The upper limb anaerobic fatigue resistance of short distance breaststroke athletes is a key factor affecting the technique and speed stability of the 100m breaststroke en route, and the rate of decline in upper limb anaerobic power leads to a decrease in speed by affecting the change in time per stroke cycle. (AU)

Flexible Textile Sensors-Based Smart T-Shirt for Respiratory Monitoring: Design, Development, and Preliminary Validation.

Respiratory rate (fR) monitoring through wearable devices is crucial in several scenarios, providing insights into well-being and sports performance while minimizing interference with daily activities. Strain sensors embedded into garments stand out but require thorough investigation for optimal deployment. Optimal sensor positioning is often overlooked, and when addressed, the quality of the respiratory signal is neglected. Additionally, sensor metrological characterization after sensor integration is often omitted. In this study, we present the design, development, and feasibility assessment of a smart t-shirt embedded with two flexible sensors for fR monitoring. Guided by a motion capture system, optimal sensor design and position on the chest wall were defined, considering both signal magnitude and quality. The sensors were developed, embedded into the wearable system, and metrologically characterized, demonstrating a remarkable response to both static (sensitivity 9.4 Ω⋅%-1 and 9.1 Ω⋅%-1 for sensor A and sensor B, respectively) and cyclic loads (min. hysteresis span 20.4% at 36 bpm obtained for sensor A). The feasibility of the wearable system was assessed on healthy volunteers both under static and dynamic conditions (such as running, walking, and climbing stairs). A mean absolute error of 0.32 bpm was obtained by averaging all subjects and tests using the combination of the two sensors. This value was lower than that obtained using both sensor A (0.53 bpm) and sensor B (0.78 bpm) individually. Our study highlights the importance of signal amplitude and quality in optimal sensor placement evaluation, as well as the characterization of the embedded sensors for metrological assessment.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Parametric analysis of an integrated cardio-respiratory model in preterm newborns during apnea.

The analysis of the complex interactions involved in the acute physiological response to apnea-bradycardia events in preterm newborns remains a challenging task. This paper presents a novel integrated model of cardio-respiratory interactions, adapted to preterm newborns. A sensitivity analysis, based Morris' screening method, was applied to study the effects of physiological parameters on heart rate and desaturation, during the simulation of a 15-seconds apnea-bradycardia episode. The most sensitive parameters are associated with fundamental, integrative physiological mechanisms involving: (i) respiratory mechanics (intermediate airways and lung compliance), (ii) fraction of inspired oxygen, (iii) metabolic rates (oxygen consumption rate), (iv) heart rate regulation and (v) chemoreflex (gain). Results highlight the relevant influence of physiological variables, involved in preterm apnea-bradycardia events.

MousePZT: A simple, reliable, low-cost device for vital sign monitoring and respiratory gating in mice under anesthesia.

Small animal studies in biomedical research often require anesthesia to reduce pain or stress experienced by research animals and to minimize motion artifact during imaging or other measurements. Anesthetized animals must be closely monitored for the safety of the animals and to prevent unintended effects of altered physiology on experimental outcomes. Many currently available monitoring devices are expensive, invasive, or interfere with experimental design. Here, we present MousePZT, a low-cost device based on a simple piezoelectric sensor, with a custom circuit and computer software that allows for measurements of both respiratory rate and heart rate in a non-invasive, minimal contact manner. We find the accuracy of the MousePZT device in measuring respiratory and heart rate matches those of commercial systems. Using the widely-used gas isoflurane and injectable ketamine/xylazine combination, we also demonstrate that changes in respiratory rate are more easily detected and can precede changes in heart rate associated with variations in anesthetic depth. Additional circuitry on the device outputs a respiration-locked trigger signal for respiratory-gating of imaging or other data acquisition and has high sensitivity and specificity for detecting respiratory cycles. We provide detailed instruction documents and all necessary microcontroller and computer software, enabling straightforward construction and utilization of this device.