IntroductionObstructive sleep apnea (OSA) is a prevalent sleep disorder with a high rate of undiagnosed patients, primarily due to the complexity of its diagnosis made by polysomnography (PSG). Considering the severe comorbidities associated with OSA, especially in the cardiovascular system, the development of early screening tools for this disease is imperative. Heart rate variability (HRV) is a simple and non-invasive approach used as a probe to evaluate cardiac autonomic modulation, with a variety of newly developed indices lacking studies with OSA patients.ObjectivesWe aimed to evaluate numerous HRV indices, derived from linear but mainly nonlinear indices, combined or not with oxygen saturation indices, for detecting the presence and severity of OSA using machine learning models.MethodsECG waveforms were collected from 291 PSG recordings to calculate 34 HRV indices. Minimum oxygen saturation value during sleep (SatMin), the percentage of total sleep time the patient spent with oxygen saturation below 90% (T90), and patient anthropometric data were also considered as inputs to the models. The Apnea-Hypopnea Index (AHI) was used to categorize into severity classes of OSA (normal, mild, moderate, severe) to train multiclass or binary (normal-to-mild and moderate-to-severe) classification models, using the Random Forest (RF) algorithm. Since the OSA severity groups were unbalanced, we used the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority classes.ResultsMulticlass models achieved a mean area under the ROC curve (AUROC) of 0.92 and 0.86 in classifying normal individuals and severe OSA patients, respectively, when using all attributes. When the groups were dichotomized into normal-to-mild OSA vs. moderate-to-severe OSA, an AUROC of 0.83 was obtained. As revealed by RF, the importance of features indicates that all feature modalities (HRV, SpO2, and anthropometric variables) contribute to the top 10 ranks.ConclusionThe present study demonstrates the feasibility of using classification models to detect the presence and severity of OSA using these indices. Our findings have the potential to contribute to the development of rapid screening tools aimed at assisting individuals affected by this condition, to expedite diagnosis and initiate timely treatment.

We present preliminary measurements of the change in oxidized cytochrome c oxidase concentration in cerebral tissue during asphyxia and cardiopulmonary resuscitation in a pediatric swine model of asphyxia-associated cardiac arrest.

Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal datasets. Here, we present a generalizable data framework that was implemented for large animal research at the Children’s Hospital of Philadelphia to address this technological gap. The presented framework culminates in a custom, interactive dashboard for exploratory analysis and filtered dataset download. Compared with existing clinical and preclinical data management solutions, the presented framework better enables management of various data types (single measure, repeated measures, time series, and imaging), integration of datasets for comparison across experimental models, cohorts, and groups, and facilitation of predictive modeling from integrated datasets. Further, a predictive model development use case demonstrated utilization and value of the data framework. The general outline of a preclinical data framework presented here can serve as a template for other translational research labs that generate heterogeneous datasets and require a dynamic platform that can easily evolve alongside their research.

Cardiovascular autonomic neuropathy (CAN) alters cardiovascular autonomic control in type 2 diabetes (T2DM). We evaluated the heart rate fragmentation (HRF) in T2DM with and without CAN and relate it to heart period (HP) and systolic arterial pressure (SAP). HP and SAP series were collected in T2DM with CAN (n = 28) and without CAN ($n=36$) at supine and during active standing for 15 min. The number of words with $\boldsymbol{n}$ inflection points ($\mathbf{W n}$), the percentage of inflection points (PIP), and the coherence ($\mathrm{K}^{2}{ }_{\text {HP-SAP }}$) at low frequency (LF) and high frequency (HF) were quantified. T2DM with CAN showed higher W 3 in supine with an increase during STAND. At REST, PIP, W 1 and W 3 were linearly correlated with $\mathrm{K}^{2}{ }_{\text {HP-SAP }}$ at HF. During STAND, PIP was linearly correlated with $\mathrm{K}^{2}{ }_{\mathrm{HP}-\mathrm{SAP}}$ at LF. CAN increases HRF and a higher HRF was associated with a lower $\mathrm{K}^{2}{ }_{\text {HP-sap }}$ indicating a less efficient baroreflex especially during STAND.

A parada cardiorrespiratória (PCR) é um evento médico crítico caracterizado pela súbita interrupção das atividades cardíacas e respiratórias. Em um contexto global marcado por desafios na saúde pública, a PCR assume destaque, especialmente em pacientes com COVID-19. Desde sua identificação em 2019 e a declaração de pandemia pela Organização Mundial da Saúde (OMS) em 2020, a COVID-19 impacta significativamente o mundo, não se limitando ao sistema respiratório, afetando também o sistema cardiovascular. Portanto, esta pesquisa tem como objetivo compreender melhor a PCR em pacientes com COVID-19 e desenvolver estratégias de prevenção e tratamento mais eficazes.

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate ultra‐rapid variability of heart rate. HRF has been shown to be increased in the aging process and in conditions such as coronary artery disease. Moreover, it proved to be a strong biomarker of adverse cardiovascular events, mortality and was an independent predictor of atrial fibrillation. Type 2 diabetes mellitus (DM) is a disease known to affect heart rate variability (HRV) indices markedly; however, HRF in diabetic patients is to be studied. We aimed to evaluate HRF in patients with DM, without autonomic cardiovascular neuropathy, compared to their healthy counterparts. Electrocardiographic (ECG) recordings collected from patients with diabetes (n=102) and healthy subjects (n=96), aged 44 to 59 years, were retrospectively analyzed from the Cardiovascular Physical Therapy Laboratory database. ECG were recorded at rest (supine) for 10 to 15 minutes. RR interval (RRi) series were processed by the customized software PyBios. RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. The percentage of inflection points (PI) was quantified and sequences of 4 consecutive symbols, so‐called “words”, were analyzed computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (‐1 to 1: WH), soft (‐1 to 0 or 0 to 1: WS), or mixed (WM) (i.e., both H and S) transitions were also quantified. Patients with diabetes presented higher PIP (58±1 vs 56±1, p=0.033), W3(17±1 vs14±1, p<0.001), WS(25±2 vs16±1, p<0.001) and WM(22±1 vs 17±1, p<0.001). On the other hand, patients with diabetes showed a lower percentage of W1 (31±1 vs 35±1, p=0.024) and WH (47±2 vs 61±2, p<0.001). The occurrence of W0 and W2 were similar between groups. In conclusion, diabetics patients without autonomic cardiovascular neuropathy have augmented fragmentation of heart rate as compared to healthy subjects. Clinical applicability: In the future, studies with this approach might be useful to assess heart rate dynamic abnormalities in order to detect cardiac risks in patients with diabetes mellitus.

Background:Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets.Methods:Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download.Results:Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome.Conclusions:The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.

introdução alimentar precoce; (7,9%) problema nas mamas; (10,5%) mitos e crenças; (15,8%) bicos artificiais e (31,6%) mais de uma causa relacionada.Conclusão: A presente pesquisa foi desenvolvida com intuito de investigar mais a fundo e possibilitou responder o objetivo do estudo de identificar os principais fatores evitáveis do desmame precoce.

Monitoring physiological waveforms, specifically hemodynamic variables (e.g., blood pressure waveforms) and end-tidal CO 2 (EtCO 2 ), during pediatric cardiopulmonary resuscitation (CPR) has been demonstrated to improve survival rates and outcomes when compared to standard depth-guided CPR. However, waveform guidance has largely been based on thresholds for single parameters and therefore does not leverage all the information contained in multimodal data. We hypothesize that the combination of multimodal physiological features improves the prediction of the return of spontaneous circulation (ROSC), the clinical indicator of short-term CPR success. We used machine learning algorithms to evaluate features extracted from eight low-resolution (4 samples per minute) physiological waveforms to predict ROSC. The waveforms were acquired from the 2nd to 10th minute of CPR in pediatric swine models of cardiac arrest (N = 89, 8–12 kg). The waveforms were divided into segments with increasing length (both forward and backward) for feature extraction, and machine learning algorithms were trained for ROSC prediction. For the full CPR period (2nd to 10th minute), the area under the receiver operating characteristics curve (AUC) was 0.93 (95% CI: 0.87–0.99) for the multivariate model, 0.70 (0.55–0.85) for EtCO 2 and 0.80 (0.67–0.93) for coronary perfusion pressure. The best prediction performances were achieved when the period from the 6th to the 10th minute was included. Poor predictions were observed for some individual waveforms, e.g., right atrial pressure. In conclusion, multimodal waveform features carry relevant information for ROSC prediction. Using multimodal waveform features in CPR guidance has the potential to improve resuscitation success and reduce mortality.

Although rapid oscillations of the heart rate (HR) are attributed to vagal modulation of the heart, a pattern of ultra-rapid HR variation has been named heart rate fragmentation (HRF). It is a recently proposed approach to evaluate sino-atrial instability characterized by the presence of numerous inflections points in a series of successive values of cardiac intervals. HRF is increased in aging and coronary diseases. Hypertension markedly alters HRV indices and might affect HRF as well. This study aimed to investigate the HRF in renovascular hypertension in rats. Wistar rats were anesthetized, and hypertension was surgically induced by partial constriction of the left renal artery with a silver clip with a 0.2 mm gap. In one kidney one clip (1K1C) hypertension model, the contralateral kidney was removed. In two kidneys one clip (2K1C) model, the contralateral kidney was kept intact. Sham-operated control rats underwent to same surgical procedures without receiving the clip around the renal artery. After 45 days (2K1C) or 60 days (1K1C), animals were instrumented with a catheter into the femoral artery, and on the following day, their arterial pressure (AP) was directly recorded without the effect of anesthesia. For HRF analysis, pulse interval (PI) series were generated and transformed into a sequence of symbols “-1,” “0,” or “1” when the difference between successive PI values (transitions) was negative, zero, or positive, respectively. Following, sequences of 4 consecutive symbols were classified according to the number of transitions. The total percentage of inflection points (PIP) and the percentage of sequences with zero (W0), one (W1), two (W2), or three (W3) inflection points were quantified. As expected, the arterial pressure increased in both groups of hypertension rats compared to control counterparts. All HRF indices were found similar in 1K1C hypertensive rats compared to their control counterparts. Nevertheless, in 2K1C hypertensive rats HRF was found to be lower than normotensive controls (PIP: 64±7 vs. 79±1% and W2: 30±1 vs. 39±1% in 2K1C and normotensive controls, respectively). In conclusion, high blood pressure itself does not affect HRF, although, depending on the model of hypertension used, HRF can be markedly impaired. The interpretation of these findings is challenging, and the high levels of angiotensin in the 2K1C model may affect cardiac pacemaker cells, making their action potentials more stable than unstable. However, the specific mechanisms by which this could happen still need to be studied. Funding Sources: FAPESP, CAPES and CNPq This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Background and PurposeMetabolic and vascular dysfunction are common features of obesity. Aryl hydrocarbon receptor (AhR) regulates lipid metabolism and vascular homeostasis, but whether vascular AhR are activated in obesity or have a protective and/or harmful effects on vascular function in obesity are unknown. Our study addresses whether AhR activation contributes to obesity‐associated vascular dysfunction and the mechanisms involved in these AhR effects.Experimental ApproachMale AhR KO (Ahr−/−) and WT mice were fed either control or a HF (high‐fat) diet for 10 weeks. Metabolic and inflammatory parameters were measured in serum and adipose tissue. Vascular reactivity (isometric force) was evaluated using a myography. Endothelial NOS (eNOS) and AhR protein expression was determined by western blot, Cyp1A1 and Nos3 gene expression by RT‐PCR and.NO production was quantified by DAF fluorescence.Key ResultsHF diet increased total serum HDL and LDL, as well as vascular AhR protein expression and proinflammatory cytokines in the adipose tissue. HF diet decreased endothelium‐dependent vasodilation. AhR deletion protected mice from HF diet‐induced dyslipidaemia, weight gain and inflammatory processes. HF diet‐induced endothelial dysfunction was attenuated in Ahr−/− mice. Vessels from Ahr−/− mice exhibited a greater NO reserve. In cultured endothelial cells, lysophosphatidylcholine (LPC) a major component of LDL and oxidized LDL [oxLDL]) reduced Nos3 gene expression and NO production. Antagonism of the AhR inhibited LPC effects on endothelial cells and induced decreased endothelium‐dependent vasodilation.Conclusion and ImplicationsAhR deletion attenuates HF diet‐induced dyslipidaemia and vascular dysfunction by improving eNOS/NO signalling. Targeting AhRs may prevent obesity‐associated vascular dysfunction.

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate sino‐atrial instability characterized by the presence of numerous inflections points in a series of successive values of RR intervals (RRi). HRF has been shown to be increased in aging and coronary diseases and seems to be linked to the risk of life‐threatening cardiovascular events and death. We recently demonstrated that cardiac autonomic modulation in involved in HRF in rats. Moreover, HRF is increased in a rat model of heart failure. Diabetes mellitus (DM) markedly alters HRV indices and might affect HRF as well. Therefore, we hypothesize that HRF is altered in streptozotocin‐ (STZ) induced diabetic rats. Male Sprague‐Dawley rats were injected with STZ (50 mg/kg), or vehicle (citrate buffer) into penile vein. Development of diabetes was confirmed 72h after STZ by the presence of hyperglycemia (> 350 mg/dL). Electrocardiographic (ECG) recordings were performed in conscious rats, 7 days (acute) or 4 weeks (chronic) after STZ or vehicle administration. Series of RR intervals were generated and processed by the customized software PyBios as follows: RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. Transitions between symbols “‐1” and “1” were labeled as “hard” (H) inflection points, while those between “‐1” (or “1”) and “0” were labeled as “soft” (S) inflection points. The percentage of inflection points (PIP) was quantified, as well as sequences of 4 consecutive symbols, so‐called “words”, computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (WH) or only soft transitions (WS) were also quantified. Both groups of diabetic rats were bradycardic as compared to their control counterparts. Data of HRF are shown in the table below. Occurrences (%, mean ± SEM) of PIP, words with 0 to 3 inflection points, and words with only soft or hard transitions of symbols. As hypothesized, diabetes affects HRF, increasing fragmented indices (PIP, W3 and WH) and decreasing fluent ones (W1 and WS). Moreover, our results showed that changes in HRF due to diabetes in this model is time‐dependent. In addition to traditional methods of analyzing heart rate variability, HRF is a new method that can contribute to the diagnosis and prognosis of cardiovascular diseases in diabetic patients. Further studies are necessary to understand better the mechanisms involved in our findings.

Nonlinear techniques have found an increasing interest in the dynamical analysis of various kinds of systems. Among these techniques, entropy-based metrics have emerged as practical alternatives to classical techniques due to their wide applicability in different scenarios, specially to short and noisy processes. Issued from information theory, entropy approaches are of great interest to evaluate the degree of irregularity and complexity of physical, physiological, social, and econometric systems. Based on Shannon entropy and conditional entropy (CE), various techniques have been proposed; among them, approximate entropy, sample entropy, fuzzy entropy, distribution entropy, permutation entropy, and dispersion entropy are probably the most well-known. After a presentation of the basic information-theoretic functionals, these measures are detailed, together with recent proposals inspired by nearest neighbors and parametric approaches. Moreover, the role of dimension, data length, and parameters in using these measures

Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non-invasive, widely used approach to evaluating neural control of the heart. This study evaluates the relationship between HRV indices and the presence and severity of OSA. We hypothesize that HRV, especially the nonlinear methods, can serve as an easy-to-collect marker for OSA early risk stratification. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA-free (N = 26), OSA-mild (N = 39), OSA-moderate (N = 37), and OSA-severe (N = 55). The electrocardiogram was extracted from the PSG recordings, and a 15-min beat-by-beat series of RR intervals were generated every hour during the first 6 h of sleep. Linear and nonlinear HRV approaches were employed to calculate 32 indices of HRV. Specifically, time- and frequency-domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis. Results with indices of sympathovagal balance provided support to reinforce previous knowledge that patients with OSA have sympathetic overactivity. Nonlinear indices showed that HRV dynamics of patients with OSA display a loss of physiologic complexity that could contribute to their higher risk of development of cardiovascular disease. Moreover, many HRV indices were found to be linked with clinical scores of PSG. Therefore, a complete set of HRV indices, especially the ones obtained by the nonlinear approaches, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful for in a quick diagnosis of OSA, and supporting early interventions that could potentially reduce the development of comorbidities.

The classical small Rho GTPase (Rho, Rac, and Cdc42) protein family is mainly responsible for regulating cell motility and polarity, membrane trafficking, cell cycle control, and gene transcription. Cumulative recent evidence supports important roles for these proteins in the maintenance of genomic stability. Indeed, DNA damage response (DDR) and repair mechanisms are some of the prime biological processes that underlie several disease phenotypes, including genetic disorders, cancer, senescence, and premature aging. Many reports guided by different experimental approaches and molecular hypotheses have demonstrated that, to some extent, direct modulation of Rho GTPase activity, their downstream effectors, or actin cytoskeleton regulation contribute to these cellular events. Although much attention has been paid to this family in the context of canonical actin cytoskeleton remodeling, here we provide a contextualized review of the interplay between Rho GTPase signaling pathways and the DDR and DNA repair signaling components. Interesting questions yet to be addressed relate to the spatiotemporal dynamics of this collective response and whether it correlates with different subcellular pools of Rho GTPases. We highlight the direct and indirect targets, some of which still lack experimental validation data, likely associated with Rho GTPase activation that provides compelling evidence for further investigation in DNA damage-associated events and with potential therapeutic applications in translational medicine.