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The fungal genus Armillaria contains necrotrophic pathogens and some of the largest terrestrial organisms that cause tremendous losses in diverse ecosystems, yet how they evolved pathogenicity in a clade of dominantly non-pathogenic wood degraders remains elusive. Here we show that Armillaria species, in addition to gene duplications and de novo gene origins, acquired at least 1,025 genes via 124 horizontal gene transfer events, primarily from Ascomycota. Horizontal gene transfer might have affected plant biomass degrading and virulence abilities of Armillaria, and provides an explanation for their unusual, soft rot-like wood decay strategy. Combined multi-species expression data revealed extensive regulation of horizontally acquired and wood-decay related genes, putative virulence factors and two novel conserved pathogenicity-induced small secreted proteins, which induced necrosis in planta. Overall, this study details how evolution knitted together horizontally and vertically inherited genes in complex adaptive traits of plant biomass degradation and pathogenicity in important fungal pathogens.

IntroductionThough the nature of breastfeeding is critical, scant information is available on how the action of the milk transfer from mother to infant is regulated in humans, where the points of dysfunction are, and what can be done to optimize breastfeeding outcomes. While better therapeutic strategies are needed, before they can be devised, a basic scientific understanding of the biomechanical mechanisms that regulate human milk transfer from breast to stomach must first be identified, defined, and understood.MethodsCombining systems biology and systems medicine into a conceptual framework, using engineering design principles, this work investigates the use of biosensors to characterize human milk flow from the breast to the infant's stomach to identify points of regulation. This exploratory study used this framework to characterize Maternal/Infant Lactation physioKinetics (MILK) utilizing a Biosensor ARray (BAR) as a data collection method.ResultsParticipants tolerated the MILKBAR well during data collection. Changes in breast turgor and temperature were significant and related to the volume of milk transferred from the breast. The total milk volume transferred was evaluated in relation to contact force, oral pressure, and jaw movement. Contact force was correlated with milk flow. Oral pressure appears to be a redundant measure and reflective of jaw movements.DiscussionNipple and breast turgor, jaw movement, and swallowing were associated with the mass of milk transferred to the infant's stomach. More investigation is needed to better quantify the mass of milk transferred in relation to each variable and understand how each variable regulates milk transfer.

This study presents a novel approach for the production of cellulosic fibers from scots pine wood and their subsequent surface modification with greenly synthesized silver nanoparticles (AgNPs), leading to remarkable color effects and improved functionalities, particularly enhanced thermal stability. The size distribution of the scots pine fiber (SPF)materials was determined through sieve analysis, revealing a conversion of scots pine particles from 1.25 mm to 0.8 mm in size. The AgNPs were biosynthesized in situ on the wood fibers using the same fiber as a sustainable stabilizing and reducing agent. Scanning electron microscopy (SEM) was employed to investigate the morphologies of the control and AgNP-deposited samples, indicating a uniform distribution of nanosilver on the coated surfaces of the SPF material. The concentration of synthesized AgNPs on SPF was quantitatively measured using X-ray fluorescence (XRF) tests, ranging from 4149 ± 43 to 6329 ± 55 parts per million (PPM) depending on the AgNP concentration. The weight percentage of nanoparticles was determined by SEM-mediated energy-disruptive X-ray (EDX) analysis. Additional analyses, Fourier transform infrared spectroscopy (FTIR) test was also conducted. Thermal stability tests demonstrated an improving trend correlated with increasing nanosilver loading in the modified SPF materials. The coefficients of variation (R2) exhibited a significant relationship between nanosilver loading and most color parameters. Overall, this research presents a pioneering approach for the eco-friendly production of cellulosic fibers from scots pine wood and sustainable synthesis of AgNPs onto defibrated fibers, thus establishing a new benchmark for sustainable manufacturing processes.

Genes involved in mycoremediation were identified by comparative genomics analysis in 10 armillarioid species and selected groups of white-rot Basidiomycota (14) and soft-rot Ascomycota (12) species to confine the distinctive bioremediation capabilities of the armillarioids. The genomes were explored using phylogenetic principal component analysis (pPCA), searching for genes already documented in a biocatalysis/biodegradation database. The results underlined a distinct, increased potential of aromatics-degrading genes/enzymes in armillarioids, with particular emphasis on a high copy number and diverse spectrum of benzoate 4-monooxygenase [EC:1.14.14.92] homologs. In addition, other enzymes involved in the degradation of various monocyclic aromatics were more abundant in the armillarioids than in the other white-rot basidiomycetes, and enzymes involved in the degradation of polycyclic aromatic hydrocarbons (PAHs) were more prevailing in armillarioids and other white-rot species than in soft-rot Ascomycetes. Transcriptome profiling of A. ostoyae and A. borealis isolates confirmed that several genes involved in the degradation of benzoates and other monocyclic aromatics were distinctively expressed in the wood-invading fungal mycelia. Data were consistent with armillarioid species offering a more powerful potential in degrading aromatics. Our results provide a reliable, practical solution for screening the likely fungal candidates for their full biodegradation potential, applicability, and possible specialization based on their genomics data.

ABSTRACT Armillaria ostoyae, a species among the destructive forest pathogens from the genus Armillaria, causes root rot disease on woody plants worldwide. Efficient control measures to limit the growth and impact of this severe underground pathogen are under investigation. In a previous study, a new soilborne fungal isolate, Trichoderma atroviride SZMC 24276 (TA), exhibited high antagonistic efficacy, which suggested that it could be utilized as a biocontrol agent. The dual culture assay results indicated that the haploid A. ostoyae-derivative SZMC 23085 (AO) (C18/9) is highly susceptible to the mycelial invasion of TA. In the present study, we analyzed the transcriptome of AO and that of TA in in vitro dual culture assays to test the molecular arsenal of Trichoderma antagonism and the defense mechanisms of Armillaria. We conducted time-course analysis and functional annotation and analyzed enriched pathways and differentially expressed genes including biocontrol-related candidate genes from TA and defense-related candidate genes from AO. The results indicated that TA deployed several biocontrol mechanisms when confronted with AO. In response, AO initiated multiple defense mechanisms to protect against the fungal attack. To our knowledge, the present study offers the first transcriptome analysis of a biocontrol fungus attacking AO. Overall, this study provides insights that aid the further exploration of plant pathogen-biocontrol agent interaction mechanisms. IMPORTANCE Armillaria species can survive for decades in the soil on dead woody debris, develop rapidly under favorable conditions, and harmfully infect newly planted forests. Our previous study found Trichoderma atroviride to be highly effective in controlling Armillaria growth; therefore, our current work explored the molecular mechanisms that might play a key role in Trichoderma-Armillaria interactions. Direct confrontation assays combined with time course-based dual transcriptome analysis provided a reliable system for uncovering the interactive molecular dynamics between the fungal plant pathogen and its mycoparasitic partner. Furthermore, using a haploid Armillaria isolate allowed us to survey the deadly prey-invading activities of the mycoparasite and the ultimate defensive strategies of its prey. Our current study provides detailed insights into the essential genes and mechanisms involved in Armillaria defense against Trichoderma and the genes potentially involved in the efficiency of Trichoderma to control Armillaria. In addition, using a sensitive haploid Armillaria strain (C18/9), with its complete genome data already available, also offers the opportunity to test possible variable molecular responses of Armillaria ostoyae toward diverse Trichoderma isolates with various biocontrol abilities. Initial molecular tests of the dual interactions may soon help to develop a targeted biocontrol intervention with mycoparasites against plant pathogens.

BackgroundOxygen supplementation is commonly used to maintain oxygen saturation (SpO2) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring.Subjects and methodsSpO2 data from 25 infants with gestational age 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring.ConclusionAnalysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care.

Armillarioids, including the genera Armillaria, Desarmillaria and Guyanagaster, represent white-rot specific fungal saprotrophs with soilborne pathogenic potentials on woody hosts. They propagate in the soil by root-like rhizomorphs, connecting between susceptible root sections of their hosts, and often forming extended colonies in native forests. Pathogenic abilities of Armillaria and Desarmillaria genets can readily manifest in compromised hosts, or hosts with full vigour can be invaded by virulent mycelia when exposed to a larger number of newly formed genets. Armillaria root rot-related symptoms are indicators of ecological imbalances in native forests and plantations at the rhizosphere levels, often related to abiotic environmental threats, and most likely unfavourable changes in the microbiome compositions in the interactive zone of the roots. The less-studied biotic impacts that contribute to armillarioid host infection include fungi and insects, as well as forest conditions. On the other hand, negative biotic impactors, like bacterial communities, antagonistic fungi, nematodes and plant-derived substances may find applications in the environment-friendly, biological control of armillarioid root diseases, which can be used instead of, or in combination with the classical, but frequently problematic silvicultural and chemical control measures.

Local synchronization, both prolonged and transient, of oscillatory neuronal behavior in cortical networks plays a fundamental role in many aspects of perception and cognition. Here we study networks of Hindmarsh-Rose neurons with a new type of adaptive coupling, and show that these networks naturally produce both permanent and transient synchronization of local clusters of neurons. These deterministic systems exhibit complex dynamics with 1/fη power spectra, which appears to be a consequence of a novel form of self-organized criticality.

[Display omitted] • Pulmonary hypertension (PH) complicates the clinical course and outcomes in preterm infants. • Early screening for the condition can inform medical decision and neonatal care. • Information based similarity analysis of oxygen saturation patterns identified infants with PH. • Our method using short data sets detected PH prior to diagnostic echocardiography. • Noninvasive signal-based screening can be a continuous marker of disease development. Pulmonary hypertension (PH) is a complex cardiovascular condition associated with multiple morbidities and mortality risk in preterm infants. PH often complicates the clinical course of infants who have bronchopulmonary dysplasia (BPD), a more common lung disease in these neonates, causing respiratory deterioration and an even higher risk of mortality. While risk factors and prevalence of PH are not yet well defined, early screening and management of PH in infants with BPD are recommended by consensus guidelines from the American Heart Association. In this study, we propose a screening method for PH by applying a signal analysis technique to oxygen saturation in infants. Oxygen saturation data from infant groups with BPD (41 with and 60 without PH), recorded prior to their clinical PH diagnosis were analyzed in this study. An information-based similarity approach was applied to quantify the regularity of SpO 2 fluctuations represented as binary words between adjacent five-minute segments. Similarity indices (SI) were observed to be lower in subjects with PH compared to those with BPD alone (p < 0.001). These measures were also assessed for performance in screening for PH. SI of 7-bit words, exhibited 80 % detection accuracy, 76 % sensitivity and specificity of 83 %. This index also exhibited a cross-validated mean (SD) F1-score of 0.80 (0.08) ensuring that sensitivity and recall of the screening were balanced. Similarity analysis of oxygen saturation patterns is a novel technique that can be potentially developed into a signal based early PH detection method to support clinical decision and care in this vulnerable population. [ABSTRACT FROM AUTHOR]

Objective: To test the hypothesis that environmental compared with nasal cannula oxygen decreases episodes of intermittent hypoxemia (oxygen saturations <85% for ≥10 seconds) in preterm infants on supplemental oxygen by providing a more stable hypopharyngeal oxygen concentration.Study Design: This was a single center randomized crossover trial with a 1:1 parallel allocation to order of testing. Preterm infants on supplemental oxygen via oxygen environment maintained by a servo-controlled system or nasal cannula with flow rates ≤1.0 L per kg per minute were crossed over every 24 hours for 96 hours. Data were collected electronically to capture real time numeric and waveform data from patient monitors.Results: Twenty-five infants with gestational age of 27 ± 2 weeks (mean ± SD) and a birth weight of 933 ± 328 g were studied at postnatal day 36 ± 26. The number of episodes of intermittent hypoxemia per 24 hours was 117 ± 77 (median, 98; range, 4-335) with oxygen environment vs 130 ± 63 (median, 136; range, 16-252) with nasal cannula (P = .002). Infants on oxygen environment compared with nasal cannula also had decreased episodes of severe intermittent hypoxemia (P = .005). Infants on oxygen environment compared with nasal cannula had a lower proportion of time with oxygen saturations <85% (.05 ± .03 vs .06 ± .03, P < .001), and a lower coefficient of variation of oxygen saturation (P = .02).Conclusions: In preterm infants receiving supplemental oxygen, servo-controlled oxygen environment decreases hypoxemia compared with nasal cannula.Trial Registration: ClinicalTrials.gov: NCT02794662. [ABSTRACT FROM AUTHOR]

In this paper, determination of medicinal plants was performed within eight departments in the area of the Šuma Košutnjak Natural Monument. Overall, a total of 101 medicinal plants were established in the whole research area. The identification of medicinal plants was conducted according to appropriate references. In addition to this, taxonomy analysis, definining of floral elements and finally determining of life forms for the recorded species were also performed. As for the taxonomy analysis, the greatest abundance in the whole research area was found for families Lamiaceae (12 representatives) and Rosaceae (10 representatives), while all other families are not so much present. There are overall 27 floral elements in the whole research area and among them, most of them belong to submideuropean (18 individuals) and european (16 individuals) floral elements, while the share of all other floral elements is much smaller. As for the life forms, the most dominant are hemicryptophytes (45 representatives), then phanerophytes (23 representatives) and finally therophytes (10 representatives). Differences between some departments have been established just in terms of the number of medicinal plants.

Local synchronization, both prolonged and transient, of oscillatory neuronal behavior in cortical networks plays a fundamental role in many aspects of perception and cognition. Here we study networks of Hindmarsh-Rose neurons with a new type of adaptive coupling, and show that these networks naturally produce both permanent and transient synchronization of local clusters of neurons. These deterministic systems exhibit complex dynamics with 1/fη power spectra, which appears to be a consequence of a novel form of self-organized criticality.

Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson’s disease (PD).Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD.Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26–30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis.Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage.Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.

To examine the therapeutic potential of stochastic vibrotactile stimulation (SVS) as a complementary non-pharmacological intervention for withdrawal in opioid-exposed newborns.A prospective, within-subjects single-center study was conducted in 26 opioid-exposed newborns (>37 weeks; 16 male) hospitalized since birth and treated pharmacologically for Neonatal Abstinence Syndrome. A specially-constructed mattress delivered low-level SVS (30-60Hz, 10-12μm RMS), alternated in 30-min intervals between continuous vibration (ON) and no vibration (OFF) over a 6-8 hr session. Movement activity, heart rate, respiratory rate, axillary temperature and blood-oxygen saturation were calculated separately for ON and OFF.There was a 35% reduction in movement activity with SVS (p30 sec duration for ON than OFF (p = 0.003). Incidents of tachypneic breaths and tachycardic heart beats were each significantly reduced with SVS, whereas incidents of eupneic breaths and eucardic heart beats each significantly increased with SVS (p

Background: Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups., Methods: Using a longitudinal observational study design, continuous physiologic data were collected on participants with acute pain receiving opioid analgesia. Monitoring continued throughout hospitalization and for up to 7 days posthospital discharge. Opioid administration data were obtained from electronic health record (EHR) and participant self-report. Participants were classified as belonging to 1 of 3 categories based on opioid use history: naïve, occasional, or chronic use. Thirty features were derived from sensor data, and an additional 9 features were derived from participant demographic and treatment characteristics. Physiologic feature behavior immediately postopioid use was compared among naïve and chronic participants, and subsequently features were used to generate machine learning models which were validated using cross-validation and holdout data., Results: Forty-one participants with a combined total of 169 opioid administrations were ultimately included in the final analysis. Four interpretable decision tree-based machine learning models with 14 sensor-based and 5 clinical features were developed to predict class membership on the level of a given observation (dose) and on the participant level. Ranges for model metrics on the participant level were as follows: accuracy 70% to 90%, sensitivity 67% to 100%, and specificity 67% to 100%., Conclusions: Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure., Competing Interests: Conflicts of Interest, Funding: Please see DISCLOSURES at the end of this article., (Copyright © 2024 International Anesthesia Research Society.)

Background: Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery., Methods: This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection., Results: Sixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected., Conclusions: Future work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact., Competing Interests: Declaration of Competing Interest JS is employed by RAE Health. SC and PI and are academic partners with RAE Health on two Small Business Innovation Research awards., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The prevalence of e-cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de-noise beat-to-beat interval (BBI) data from smartwatches worn by 12 young adult regular e-cigarette users for 7 days; and (2) summarize the de-noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de-noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention., (© 2024 John Wiley & Sons Ltd.)

Objective. Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes. Approach . We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90&#95;DPE (duration per event of intermittent hypoxemia events below 90%). Main Results. The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90&#95;DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90&#95;DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). Significance . These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90&#95;DPE as an optimal predictor of respiratory outcomes., (Creative Commons Attribution license.)

Background: In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life., Methods: The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation. Waveforms were downloaded from bedside monitors with a novel sharing analytics strategy utilized to run software locally, with summary data sent to the Data Coordinating Center for compilation., Results: Apnea, periodic breathing, and intermittent hypoxemia events rose from day 3 of life then fell to near-resolution by 8-12 weeks of age. Apnea/intermittent hypoxemia were inversely correlated with gestational age, peaking at 3-4 weeks of age. Periodic breathing was positively correlated with gestational age peaking at 31-33 weeks postmenstrual age. Females had more periodic breathing but less intermittent hypoxemia/bradycardia. White infants had more apnea/periodic breathing/intermittent hypoxemia. Infants never receiving mechanical ventilation followed similar postnatal trajectories but with less apnea and intermittent hypoxemia, and more periodic breathing., Conclusions: Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation., Impact: Physiologic curves of cardiorespiratory events in extremely preterm-born infants offer (1) objective measures to assess individual patient courses and (2) guides for research into control of ventilation, biomarkers and outcomes. Presented are updated maturational trajectories of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in 717 infants born <29 weeks gestation from the multi-site NHLBI-funded Pre-Vent study. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Different time courses for apnea and periodic breathing suggest different maturational mechanisms., (© 2023. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Background: Identifying suicidal risk based on clinical assessment is challenging. Suicidal ideation fluctuates, can be downplayed or denied, and seems stigmatizing if divulged. In contrast, vitality is foundational to subjectivity in being immediately conscious before reflection. Including its assessment may improve detection of suicidal risk compared to relying on suicidal ideation alone. We hypothesized that objective motility measures would be associated with vitality and enhance assessment of suicidal risk., Methods: We evaluated 83 adult-psychiatric outpatients with a DSM-5 bipolar (BD) or major depressive disorder (MDD): BD-I (n = 48), BD-II (20), and MDD (15) during a major depressive episode. They were actigraphically monitored continuously over 3 weekdays and self-rated their subjective states at regular intervals. We applied cosinor analysis to actigraphic data and analyzed associations of subjective psychopathology measures with circadian activity parameters., Results: Actigraphic circadian mesor, amplitude, day- and nighttime activity were lower with BD versus MDD. Self-rated vitality (wish-to-live) was significantly lower, self-rated suicidality (wish-to-die) was higher, and their difference was lower, with BD versus MDD. There were no other significant diagnostic differences in actigraphic sleep parameters or in self-rated depression, dysphoria, or anxiety. By linear regression, the difference between vitality and passive suicidal ideation was strongly positively correlated with mesor (p &lt; 0.0001), daytime activity (p &lt; 0.0001), and amplitude (p = 0.001)., Conclusions: Higher circadian activity measures reflected enhanced levels of subjective vitality and were associated with lesser suicidal ideation. Current suicidal-risk assessment might usefully include monitoring of motility and vitality in addition to examining negative affects and suicidal thinking., (© 2023 S. Karger AG, Basel.)

Introduction: Though the nature of breastfeeding is critical, scant information is available on how the action of the milk transfer from mother to infant is regulated in humans, where the points of dysfunction are, and what can be done to optimize breastfeeding outcomes. While better therapeutic strategies are needed, before they can be devised, a basic scientific understanding of the biomechanical mechanisms that regulate human milk transfer from breast to stomach must first be identified, defined, and understood., Methods: Combining systems biology and systems medicine into a conceptual framework, using engineering design principles, this work investigates the use of biosensors to characterize human milk flow from the breast to the infant's stomach to identify points of regulation. This exploratory study used this framework to characterize Maternal/Infant Lactation physioKinetics (MILK) utilizing a Biosensor ARray (BAR) as a data collection method., Results: Participants tolerated the MILKBAR well during data collection. Changes in breast turgor and temperature were significant and related to the volume of milk transferred from the breast. The total milk volume transferred was evaluated in relation to contact force, oral pressure, and jaw movement. Contact force was correlated with milk flow. Oral pressure appears to be a redundant measure and reflective of jaw movements., Discussion: Nipple and breast turgor, jaw movement, and swallowing were associated with the mass of milk transferred to the infant's stomach. More investigation is needed to better quantify the mass of milk transferred in relation to each variable and understand how each variable regulates milk transfer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Francis, Flynn, Naowar, Indic and Dickton.)

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O 2 /support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O 2 /support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Rationale: Bedside biomarkers that allow early identification of infants with bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) are critically important, given the higher risk of death in these infants. Objectives: We hypothesized that infants with BPD-PH have patterns of intermittent hypoxemia (IH) that differ from infants with BPD without PH. Methods: We conducted a matched case-control study of extremely preterm infants from 22 weeks 0 days to 28 weeks 6 days born between 2018 and 2020 at the University of Alabama at Birmingham. BPD-PH status was determined using echocardiographic data performed after postnatal Day 28. Physiologic data were compared between infants with BPD-PH (cases) and BPD alone (control subjects). Receiver operating characteristic (ROC) analysis estimated the predictive ability of cumulative hypoxemia, desaturation frequency, and duration of intermittent hypoxemic events in the week preceding echocardiography to discriminate between cases and control subjects. Measurements and Main Results: Forty infants with BPD-PH were compared with 40 infants with BPD alone. Infants with and without PH had a similar frequency of IH events, but infants with PH had more prolonged hypoxemic events for desaturations below 80% (7 s vs. 6 s; P  = 0.03) and 70% (105 s vs. 58 s; P  = 0.008). Among infants with BPD-PH, infants who died had longer hypoxemic events below 70% (145 s vs. 72 s; P  = 0.01). Using the duration of hypoxemic events below 70%, the areas under the ROC curves for diagnosis of BPD-PH and death in BPD-PH infants were 0.71 and 0.77, respectively. Conclusions: Longer duration of intermittent hypoxemic events was associated both with a diagnosis of BPD-PH and with death among infants with BPD-PH.

Background: Oxygen supplementation is commonly used to maintain oxygen saturation (SpO 2 ) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO 2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring., Subjects and Methods: SpO 2 data from 25 infants with gestational age <32 weeks and birthweight <2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores., Results: While most SpO 2 measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO 2 variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC > 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring., Conclusion: Analysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Ramanand, Indic, Travers and Ambalavanan.)

Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored.

Hypoxemia, characterized by low blood oxygen levels is pervasive in preterm infants and is associated with development of multiple adverse cardiovascular morbidities. In clinical practice, it is often quantified using frequency, pattern and time spent in it. A predictive tool of hypoxemia occurrence will aid clinicians in risk stratifying infant oxygenation patterns and improving personalized care. As a first step towards this goal in characterizing the underlying temporal processes, we studied inter-hypoxemia interval distributions in preterm infants on oxygen supplementation. We derived regression relationships of characterizing parameters of the distributions with gestational age and birth weight of infants. The modeling and goodness of fit tests of pooled and individual inter-hypoxemia intervals indicated that the inverse Gaussian and Birnbaum Saunders distributions fit well over short time scales and the lognormal at longer time scales. Information from distribution modeling may provide insights into hypoxemia recurrence times and be helpful in developing models to predict severe hypoxemic events that may be translated to personalized care in clinical settings. Clinical relevance - Understanding the stochastic nature of temporal processes underlying hypoxemia in preterm infants is a critical step towards developing predictive models for their occurrence. This may potentially aid in the neonatal care and treatment of these vulnerable infants.

Abstract: Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment., (Copyright © 2021 International Association for the Study of Pain.)

Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson's disease (PD). Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD. Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls ( N = 20 each). PD subjects with normal (PDN, MoCA 26-30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis. Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status ( p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status ( p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage. Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Novak, Chase, Narayanan, Indic and Markopoulou.)

Background: Substance use disorders are a highly prevalent group of chronic diseases with devastating individual and public health consequences. Current treatment strategies suffer from high rates of relapse, or return to drug use, and novel solutions are desperately needed. Realize Analyze Engage (RAE) is a digital, mHealth intervention that focusses on real time, objective detection of high-risk events (stress and drug craving) to deploy just-in-time supportive interventions. The present study aims to (1) evaluate the accuracy and usability of the RAE system and (2) evaluate the impact of RAE on patient centered outcomes., Methods: The first phase of the study will be an observational trial of N = 50 participants in outpatient treatment for SUD using the RAE system for 30 days. Accuracy of craving and stress detection algorithms will be evaluated, and usability of RAE will be explored via semi-structured interviews with participants and focus groups with SUD treatment clinicians. The second phase of the study will be a randomized controlled trial of RAE vs usual care to evaluate rates of return to use, retention in treatment, and quality of life., Anticipated Findings and Future Directions: The RAE platform is a potentially powerful tool to de-escalate stress and craving outside of the clinical milieu, and to connect with a support system needed most. RAE also aims to provide clinicians with actionable insight to understand patients' level of risk, and contextual clues for their triggers in order to provide more personalized recovery support., Competing Interests: CONFLICTS OF INTEREST Megan Reinhardt is the CEO of RAE Health and Nicole Gilbertson is the COO of RAE Health. They represent the small business partners in this small business innovation research award.

The possible mechanisms for the synchronization of rest-activity rhythms of individual animals living in groups is a relatively understudied question; synchronized rhythms could occur by entrainment of individuals to a common external force and/or by social synchronization between individuals. To gain insight into this question, we explored the synchronization dynamics of populations of globally coupled Kuramoto oscillators and analyzed the effects of a finite oscillator number (N) and the variable strengths of their periodic forcing (F) and mutual coupling (K). We found that increasing N promotes entrainment to a decreasing value of F, but that F could not be reduced below a certain level determined by the number of oscillators and the distribution width of their intrinsic frequencies. Our analysis prompts some simple predictions of ecologically optimal animal group sizes under differing natural conditions.

Aims: To determine the accuracy of a wearable sensor to detect and differentiate episodes of self-reported craving and stress in individuals with substance use disorders, and to assess acceptability, barriers, and facilitators to sensor-based monitoring in this population., Methods: This was an observational mixed methods pilot study. Adults enrolled in an outpatient treatment program for a substance use disorder wore a non-invasive wrist-mounted sensor for four days and self-reported episodes of stress and craving. Continuous physiologic data (accelerometry, skin conductance, skin temperature, and heart rate) were extracted from the sensors and analyzed via various machine learning algorithms. Semi-structured interviews were conducted upon study completion, and thematic analysis was conducted on qualitative data from semi-structured interviews., Results: Thirty individuals completed the protocol, and 43 % (N = 13) were female. A total of 41 craving and 104 stress events were analyzed. The differentiation accuracies of the top performing models were as follows: stress vs. non-stress states 74.5 % (AUC 0.82), craving vs. no-craving 75.7 % (AUC 0.82), and craving vs. stress 76.8 % (AUC 0.8). Overall participant perception was positive, and acceptability was high. Emergent themes from the exit interviews included a perception of connectedness and increased mindfulness related to wearing the sensor, both of which were reported as helpful to recovery. Barriers to engagement included interference with other daily wear items, and perceived stigma., Conclusions: Wearable sensors can be used to objectively differentiate episodes of craving and stress, and individuals in recovery from substance use disorder are accepting of continuous monitoring with these devices., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to disclose., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data were continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals.

Current understanding of the pathogenesis of the familial form of amyotrophic lateral sclerosis has been aided by the study of transgenic mice that over-express mutated forms of the human CuZn-superoxide dismutase (SOD1) gene. While mutant SOD1 in motor neurons determines disease onset, other non-cell autonomous factors are critical for disease progression, and altered energy metabolism has been implicated as a contributing factor. Since most energy expended by laboratory mice is utilized to defend body temperature (T b ), we analyzed thermoregulation in transgenic mice carrying the G93A mutation of the human SOD1 gene, using implantable temperature data loggers to continuously record T b for up to 85 days. At room (22 °C) ambient temperature, G93A mice exhibited a diminished amplitude of the daily T b rhythm compared to C57BL/6J controls, secondary to decreased T b values during the dark (behaviorally active) phase of the light-dark cycle. The defect arose at 85-99 days of age, around the age of symptom onset (as assessed by grip strength), well before observable weakness and weight loss, and could not be accounted for by decreased levels of locomotor activity or food consumption. Housing under thermoneutral (29 °C) ambient temperature partially rescued the defect, but age-dependently (only in animals >100 days of age), suggesting that the deficit in older mice was due in part to inadequate thermogenesis by "peripheral" thermogenic organs as the disease progressed. In younger mice, we found that cold-induced thermogenesis and energy expenditure were intact, hinting that an initial "central" defect might localize to the subparaventricular zone, involving neural output pathways from the circadian clock in the hypothalamic suprachiasmatic nucleus to forebrain thermoregulatory circuitry., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Sociality has beneficial effects on fitness, and timing the activities of animals may be critical. Social cues could influence daily rhythmic activities via direct effects on the circadian clock or on processes that bypass it (masking), but these possibilities remain incompletely addressed. We investigated the effects of social cues on the circadian body temperature (Tb) rhythms in pairs of co-housed and isolated grass rats, Arvicanthis niloticus (a social species), in constant darkness (DD). Cohabitation did not induce synchronization of circadian Tb rhythms. However, socio-sexual history did affect circadian properties: accelerating the clock in sexually experienced males and females in DD and advancing rhythm phase in the females in a light-dark cycle. To address whether synchronization occurs at an ultradian scale, we analyzed Tb and activity rhythms in pairs of co-housed sisters or couples in DD. Regardless of pair type, co-housing doubled the percentage of time individuals were simultaneously active without increasing individual activity levels, suggesting that activity bouts were synchronized by redistribution over 24 h. Together, our laboratory findings show that social cues affect individual "time allocation" budgets via mechanisms at multiple levels of biological organization. We speculate that in natural settings these effects could be adaptive, especially for group-living animals.

Opioid abuse is a rapidly escalating problem in the United States. Effective opioid reversal is achieved with the antidote naloxone, but often does not last as long as the offending opioid, necessitating in-hospital observation. Continuous physiologic monitoring using wearable biosensors represents a potential option to extend monitoring capability outside the clinical setting across the spectrum of opioid abuse including post- naloxone administration. The present study aims to identify the physiologic change that marks the cessation of naloxone's effect. Eleven participants were recruited in the Emergency Department after naloxone administration for an opioid overdose and continuously monitored using a wearable biosensor measuring heart rate, temperature, electrodermal activity and accelerometry. Hilbert transform was used to evaluate a 90- minute post naloxone time point. Physiologic changes were consistent with the onset of opioid drug effect across parameters, but only changes in heart rate and skin temperature research statistical significance.

Objective: Episodes of bradycardia are common and recur sporadically in preterm infants, posing a threat to the developing brain and other vital organs. We hypothesize that bradycardias are a result of transient temporal destabilization of the cardiac autonomic control system and that fluctuations in the heart rate signal might contain information that precedes bradycardia. We investigate infant heart rate fluctuations with a novel application of point process theory., Methods: In ten preterm infants, we estimate instantaneous linear measures of the heart rate signal, use these measures to extract statistical features of bradycardia, and propose a simplistic framework for prediction of bradycardia., Results: We present the performance of a prediction algorithm using instantaneous linear measures (mean area under the curve = 0.79 ± 0.018) for over 440 bradycardia events. The algorithm achieves an average forecast time of 116 s prior to bradycardia onset (FPR = 0.15). Our analysis reveals that increased variance in the heart rate signal is a precursor of severe bradycardia. This increase in variance is associated with an increase in power from low content dynamics in the LF band (0.04-0.2 Hz) and lower multiscale entropy values prior to bradycardia., Conclusion: Point process analysis of the heartbeat time series reveals instantaneous measures that can be used to predict infant bradycardia prior to onset., Significance: Our findings are relevant to risk stratification, predictive monitoring, and implementation of preventative strategies for reducing morbidity and mortality associated with bradycardia in neonatal intensive care units.

Objective: To examine the therapeutic potential of stochastic vibrotactile stimulation (SVS) as a complementary non-pharmacological intervention for withdrawal in opioid-exposed newborns., Study Design: A prospective, within-subjects single-center study was conducted in 26 opioid-exposed newborns (>37 weeks; 16 male) hospitalized since birth and treated pharmacologically for Neonatal Abstinence Syndrome. A specially-constructed mattress delivered low-level SVS (30-60Hz, 10-12μm RMS), alternated in 30-min intervals between continuous vibration (ON) and no vibration (OFF) over a 6-8 hr session. Movement activity, heart rate, respiratory rate, axillary temperature and blood-oxygen saturation were calculated separately for ON and OFF., Results: There was a 35% reduction in movement activity with SVS (p<0.001), with significantly fewer movement periods >30 sec duration for ON than OFF (p = 0.003). Incidents of tachypneic breaths and tachycardic heart beats were each significantly reduced with SVS, whereas incidents of eupneic breaths and eucardic heart beats each significantly increased with SVS (p<0.03). Infants maintained body temperature and arterial-blood oxygen level independent of stimulation condition., Conclusions: SVS reduced hyperirritability and pathophysiological instabilities commonly observed in pharmacologically-managed opioid-exposed newborns. SVS may provide an effective complementary therapeutic intervention for improving autonomic function in newborns with Neonatal Abstinence Syndrome.

Accurate estimation of heart rate dynamics in preterm infants is important for predicting recurrent episodes of severe bradycardia. We hypothesize that estimation of heart rate can be improved by including respiration as a state variable, based on mechanisms that underlie cardio-respiratory coherence. For ten preterm infants, we demonstrate that including respiration as a covariate improves estimation accuracy by an average of 11% across bradycardia severity, and reduces the maximum error by 8%. We also find that cardio-respiratory coherence increases in low frequency content just prior to severe bradycardia. Thus, incorporating respiratory information may improve models of heart rate dynamics and narrow potential features for bradycardia prediction.

Premature infants are susceptible to a variety of life-threatening events. Underdeveloped cardiovascular control due to an immature autonomic nervous system can lead to recurrent bradycardias that reduce blood flow and oxygen to critical organs, and result in long-term developmental disabilities or sudden death. In this study, we investigate the use of a novel point process framework to model heart rate dynamics in premature infants, including the full range of bradycardia severity. We find that the lognormal distribution accurately models the R-R interval time series, due to the long-tail nature of the distribution. We also find that the degree of bradycardia severity is correlated with distinct clustering features of the point-process indices in regions encompassing and adjacent to bradycardias. This underlying property in heart rate dynamics may provide valuable statistical information for quantifying the vulnerability of premature infants to develop bradycardia.