The alpha7 integrin subunit in astrocytes promotes endothelial blood-brain barrier integrity.

The blood-brain barrier (BBB) is a vascular endothelial cell boundary that partitions the circulation from the central nervous system to promote normal brain health. We have a limited understanding of how the BBB is formed during development and maintained in adulthood. We used quantitative transcriptional profiling to investigate whether specific adhesion molecules are involved in BBB functions, with an emphasis on understanding how astrocytes interact with endothelial cells. Our results reveal a striking enrichment of multiple genes encoding laminin subunits as well as the laminin receptor gene Itga7, which encodes the alpha7 integrin subunit, in astrocytes. Genetic ablation of Itga7 in mice led to aberrant BBB permeability and progressive neurological pathologies. Itga7-/- mice also showed a reduction in laminin protein expression in parenchymal basement membranes. Blood vessels in the Itga7-/- brain showed separation from surrounding astrocytes and had reduced expression of the tight junction proteins claudin 5 and ZO-1. We propose that the alpha7 integrin subunit in astrocytes via adhesion to laminins promotes endothelial cell junction integrity, all of which is required to properly form and maintain a functional BBB.

The ß8 integrin cytoplasmic domain activates extracellular matrix adhesion to promote brain neurovascular development.

In the developing mammalian brain, neuroepithelial cells interact with blood vessels to regulate angiogenesis, blood-brain barrier maturation and other key neurovascular functions. Genetic studies in mice have shown that neurovascular development is controlled, in part, by Itgb8, which encodes the neuroepithelial cell-expressed integrin ß8 subunit. However, these studies have involved complete loss-of-function Itgb8 mutations, and have not discerned the relative roles for the ß8 integrin extracellular matrix (ECM) binding region versus the intracellular signaling tail. Here, Cre/lox strategies have been employed to selectively delete the cytoplasmic tail of murine Itgb8 without perturbing its transmembrane and extracellular domains. We report that the ß8 integrin cytoplasmic domain is essential for inside-out modulation of adhesion, including activation of latent-TGFßs in the ECM. Quantitative sequencing of the brain endothelial cell transcriptome identifies TGFß-regulated genes with putative links to blood vessel morphogenesis, including several genes linked to Wnt/ß-catenin signaling. These results reveal that the ß8 integrin cytoplasmic domain is essential for the regulation of TGFß-dependent gene expression in endothelial cells and suggest that cross-talk between TGFßs and Wnt pathways is crucial for neurovascular development.

Glial Cell Adhesion Molecule (GlialCAM) Determines Proliferative versus Invasive Cell States in Glioblastoma.

The malignant brain cancer glioblastoma (GBM) contains groups of highly invasive cells that drive tumor progression as well as recurrence after surgery and chemotherapy. The molecular mechanisms that enable these GBM cells to exit the primary mass and disperse throughout the brain remain largely unknown. Here we report using human tumor specimens and primary spheroids from male and female patients that glial cell adhesion molecule (GlialCAM), which has normal roles in brain astrocytes and is mutated in the developmental brain disorder megalencephalic leukoencephalopathy with subcortical cysts (MLC), is differentially expressed in subpopulations of GBM cells. High levels of GlialCAM promote cell-cell adhesion and a proliferative GBM cell state in the tumor core. In contrast, GBM cells with low levels of GlialCAM display diminished proliferation and enhanced invasion into the surrounding brain parenchyma. RNAi-mediated inhibition of GlialCAM expression leads to activation of proinvasive extracellular matrix adhesion and signaling pathways. Profiling GlialCAM-regulated genes combined with cross-referencing to single-cell transcriptomic datasets validates functional links among GlialCAM, Mlc1, and aquaporin-4 in the invasive cell state. Collectively, these results reveal an important adhesion and signaling axis comprised of GlialCAM and associated proteins including Mlc1 and aquaporin-4 that is critical for control of GBM cell proliferation and invasion status in the brain cancer microenvironment.SIGNIFICANCE STATEMENT Glioblastoma (GBM) contains heterogeneous populations of cells that coordinately drive proliferation and invasion. We have discovered that glial cell adhesion molecule (GlialCAM)/hepatocyte cell adhesion molecule (HepaCAM) is highly expressed in proliferative GBM cells within the tumor core. In contrast, GBM cells with low levels of GlialCAM robustly invade into surrounding brain tissue along blood vessels and white matter. Quantitative RNA sequencing identifies various GlialCAM-regulated genes with functions in cell-cell adhesion and signaling. These data reveal that GlialCAM and associated signaling partners, including Mlc1 and aquaporin-4, are key factors that determine proliferative and invasive cell states in GBM.

Mlc1-Expressing Perivascular Astrocytes Promote Blood-Brain Barrier Integrity.

In the mammalian brain, perivascular astrocytes (PAs) closely juxtapose blood vessels and are postulated to have important roles in the control of vascular physiology, including regulation of the blood-brain barrier (BBB). Deciphering specific functions for PAs in BBB biology, however, has been limited by the ability to distinguish these cells from other astrocyte populations. In order to characterize selective roles for PAs in vivo, a new mouse model has been generated in which the endogenous megalencephalic leukoencephalopathy with subcortical cysts 1 (Mlc1) gene drives expression of Cre fused to a mutated estrogen ligand-binding domain (Mlc1-T2A-CreERT2). This knock-in mouse model, which we term MLCT, allows for selective identification and tracking of PAs in the postnatal brain. We also demonstrate that MLCT-mediated ablation of PAs causes severe defects in BBB integrity, resulting in premature death. PA loss results in aberrant localization of Claudin 5 and -VE-Cadherin in endothelial cell junctions as well as robust microgliosis. Collectively, these data reveal essential functions for Mlc1-expressing PAs in regulating endothelial barrier integrity in mice and indicate that primary defects in astrocytes that cause BBB breakdown may contribute to human neurologic disorders.SIGNIFICANCE STATEMENT Interlaced among the billions of neurons and glia in the mammalian brain is an elaborate network of blood vessels. Signals from the brain parenchyma control the unique permeability properties of cerebral blood vessels known as the blood-brain barrier (BBB). However, we understand very little about the relative contributions of different neural cell types in the regulation of BBB functions. Here, we show that a specific subpopulation of astrocyte is essential for control of BBB integrity, with ablation of these cells leading to defects in endothelial cell junctions, BBB breakdown, and resulting neurologic deficits.

The vascular endothelial cell-expressed prion protein doppel promotes angiogenesis and blood-brain barrier development.

The central nervous system (CNS) contains a complex network of blood vessels that promote normal tissue development and physiology. Abnormal control of blood vessel morphogenesis and maturation is linked to the pathogenesis of various neurodevelopmental diseases. The CNS-specific genes that regulate blood vessel morphogenesis in development and disease remain largely unknown. Here, we have characterized functions for the gene encoding prion protein 2 (Prnd) in CNS blood vessel development and physiology. Prnd encodes the glycosylphosphatidylinositol (GPI)-linked protein doppel, which is expressed on the surface of angiogenic vascular endothelial cells, but is absent in quiescent endothelial cells of the adult CNS. During CNS vascular development, doppel interacts with receptor tyrosine kinases and activates cytoplasmic signaling pathways involved in endothelial cell survival, metabolism and migration. Analysis of mice genetically null for Prnd revealed impaired CNS blood vessel morphogenesis and associated endothelial cell sprouting defects. Prnd-/- mice also displayed defects in endothelial barrier integrity. Collectively, these data reveal novel mechanisms underlying doppel control of angiogenesis in the developing CNS, and may provide new insights about dysfunctional pathways that cause vascular-related CNS disorders.

Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients.

Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios.

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Neuropilin 1 balances ß8 integrin-activated TGFß signaling to control sprouting angiogenesis in the brain.

Angiogenesis in the developing central nervous system (CNS) is regulated by neuroepithelial cells, although the genes and pathways that couple these cells to blood vessels remain largely uncharacterized. Here, we have used biochemical, cell biological and molecular genetic approaches to demonstrate that ß8 integrin (Itgb8) and neuropilin 1 (Nrp1) cooperatively promote CNS angiogenesis by mediating adhesion and signaling events between neuroepithelial cells and vascular endothelial cells. ß8 integrin in the neuroepithelium promotes the activation of extracellular matrix (ECM)-bound latent transforming growth factor ß (TGFß) ligands and stimulates TGFß receptor signaling in endothelial cells. Nrp1 in endothelial cells suppresses TGFß activation and signaling by forming intercellular protein complexes with ß8 integrin. Cell type-specific ablation of ß8 integrin, Nrp1, or canonical TGFß receptors results in pathological angiogenesis caused by defective neuroepithelial cell-endothelial cell adhesion and imbalances in canonical TGFß signaling. Collectively, these data identify a paracrine signaling pathway that links the neuroepithelium to blood vessels and precisely balances TGFß signaling during cerebral angiogenesis.

Drosophila Lipophorin Receptors Recruit the Lipoprotein LTP to the Plasma Membrane to Mediate Lipid Uptake.

Lipophorin, the main Drosophila lipoprotein, circulates in the hemolymph transporting lipids between organs following routes that must adapt to changing physiological requirements. Lipophorin receptors expressed in developmentally dynamic patterns in tissues such as imaginal discs, oenocytes and ovaries control the timing and tissular distribution of lipid uptake. Using an affinity purification strategy, we identified a novel ligand for the lipophorin receptors, the circulating lipoprotein Lipid Transfer Particle (LTP). We show that specific isoforms of the lipophorin receptors mediate the extracellular accumulation of LTP in imaginal discs and ovaries. The interaction requires the LA-1 module in the lipophorin receptors and is strengthened by a contiguous region of 16 conserved amino acids. Lipophorin receptor variants that do not interact with LTP cannot mediate lipid uptake, revealing an essential role of LTP in the process. In addition, we show that lipophorin associates with the lipophorin receptors and with the extracellular matrix through weak interactions. However, during lipophorin receptor-mediated lipid uptake, LTP is required for a transient stabilization of lipophorin in the basolateral plasma membrane of imaginal disc cells. Together, our data suggests a molecular mechanism by which the lipophorin receptors tether LTP to the plasma membrane in lipid acceptor tissues. LTP would interact with lipophorin particles adsorbed to the extracellular matrix and with the plasma membrane, catalyzing the exchange of lipids between them.

The receptor for the complement C3a anaphylatoxin (C3aR) provides host protection against Listeria monocytogenes-induced apoptosis.

Listeria monocytogenes is a Gram-positive intracellular bacterium that is acquired through tainted food and may lead to systemic infection and possible death. Despite the importance of the innate immune system in fighting L. monocytogenes infection, little is known about the role of complement and its activation products, including the potent C3a anaphylatoxin. In a model of systemic L. monocytogenes infection, we show that mice lacking the receptor for C3a (C3aR(-/-)) are significantly more sensitive to infection compared with wild-type mice, as demonstrated by decreased survival, increased bacterial burden, and increased damage to their livers and spleens. The inability of the C3aR(-/-) mice to clear the bacterial infection was not caused by defective macrophages or by a reduction in cytokines/chemokines known to be critical in the host response to L. monocytogenes, including IFN-γ and TNF-α. Instead, TUNEL staining, together with Fas, active caspase-3, and Bcl-2 expression data, indicates that the increased susceptibility of C3aR(-/-) mice to L. monocytogenes infection was largely caused by increased L. monocytogenes-induced apoptosis of myeloid and lymphoid cells in the spleen that are required for ultimate clearance of L. monocytogenes, including neutrophils, macrophages, dendritic cells, and T cells. These findings reveal an unexpected function of C3a/C3aR signaling during the host immune response that suppresses Fas expression and caspase-3 activity while increasing Bcl-2 expression, thereby providing protection to both myeloid and lymphoid cells against L. monocytogenes-induced apoptosis.

The C5a anaphylatoxin receptor (C5aR1) protects against Listeria monocytogenes infection by inhibiting type 1 IFN expression.

Listeria monocytogenes is a major cause of mortality resulting from food poisoning in the United States. In mice, C5 has been genetically linked to host resistance to listeriosis. Despite this genetic association, it remains poorly understood how C5 and its activation products, C5a and C5b, confer host protection to this Gram-positive intracellular bacterium. In this article, we show in a systemic infection model that the major receptor for C5a, C5aR1, is required for a normal robust host immune response against L. monocytogenes. In comparison with wild-type mice, C5aR1(-/-) mice had reduced survival and increased bacterial burden in their livers and spleens. Infected C5aR1(-/-) mice exhibited a dramatic reduction in all major subsets of splenocytes, which was associated with elevated caspase-3 activity and increased TUNEL staining. Because type 1 IFN has been reported to impede the host response to L. monocytogenes through the promotion of splenocyte death, we examined the effect of C5aR1 on type 1 IFN expression in vivo. Indeed, serum levels of IFN-α and IFN-ß were significantly elevated in L. monocytogenes-infected C5aR1(-/-) mice. Similarly, the expression of TRAIL, a type 1 IFN target gene and a proapoptotic factor, was elevated in NK cells isolated from infected C5aR1(-/-) mice. Treatment of C5aR1(-/-) mice with a type 1 IFNR blocking Ab resulted in near-complete rescue of L. monocytogenes-induced mortality. Thus, these findings reveal a critical role for C5aR1 in host defense against L. monocytogenes through the suppression of type 1 IFN expression.

The geometric semantics of algebraic quantum mechanics.

In this paper, we will present an ongoing project that aims to use model theory as a suitable mathematical setting for studying the formalism of quantum mechanics. We argue that this approach provides a geometric semantics for such a formalism by means of establishing a (non-commutative) duality between certain algebraic and geometric objects.

Hypertriglyceridemia-induced acute necrotizing pancreatitis: Poor clinical outcomes requiring revisiting management modalities.

Hypertriglyceridemia-induced acute pancreatitis (HTG-AP) is the third most common cause of AP after gallstones and alcohol. Supportive measures, intravenous insulin, and plasmapheresis are possible treatment modalities for HTG-AP; however, definitive guidelines evaluating the best therapeutic approach are not clearly established. We present a rare case of a 42-year-old male without known comorbidities who was found to have HTG-AP. Despite early initiation of intravenous insulin and plasmapheresis and the initial decline in his triglycerides level, his condition was complicated by necrotizing pancreatitis and subsequent multi-organ failure. Future studies are warranted to evaluate the role of plasmapheresis in HTG-AP and its efficacy.

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring.

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

Targeted disruption of the gene encoding the murine small subunit of carboxypeptidase N (CPN1) causes susceptibility to C5a anaphylatoxin-mediated shock.

Carboxypeptidase N (CPN) is a plasma zinc metalloprotease, which consists of two enzymatically active small subunits (CPN1) and two large subunits (CPN2) that protect the protein from degradation. Historically, CPN has been implicated as a major regulator of inflammation by its enzymatic cleavage of functionally important arginine and lysine amino acids from potent phlogistic molecules, such as the complement anaphylatoxins C3a and C5a. Because of no known complete CPN deficiencies, the biological impact of CPN in vivo has been difficult to evaluate. Here, we report the generation of a mouse with complete CPN deficiency by targeted disruption of the CPN1 gene. CPN1(-/-) mice were hypersensitive to lethal anaphylactic shock due to acute complement activation by cobra venom factor. This hypersensitivity was completely resolved in CPN1(-/-)/C5aR(-/-) but not in CPN1(-/-)/C3aR(-/-) mice. Moreover, CPN1(-/-) mice given C5a i.v., but not C3a, experienced 100% mortality. This C5a-induced mortality was reduced to 20% when CPN1(-/-) mice were treated with an antihistamine before C5a challenge. These studies describe for the first time a complete deficiency of CPN and demonstrate 1) that CPN plays a requisite role in regulating the lethal effects of anaphylatoxin-mediated shock, 2) that these lethal effects are mediated predominantly by C5a-induced histamine release, and 3) that C3a does not contribute significantly to shock following acute complement activation.

Transplantation of human embryonic stem cell-derived alveolar epithelial type II cells abrogates acute lung injury in mice.

Respiratory diseases are a major cause of mortality and morbidity worldwide. Current treatments offer no prospect of cure or disease reversal. Transplantation of pulmonary progenitor cells derived from human embryonic stem cells (hESCs) may provide a novel approach to regenerate endogenous lung cells destroyed by injury and disease. Here, we examine the therapeutic potential of alveolar type II epithelial cells derived from hESCs (hES-ATIICs) in a mouse model of acute lung injury. When transplanted into lungs of mice subjected to bleomycin (BLM)-induced acute lung injury, hES-ATIICs behaved as normal primary ATIICs, differentiating into cells expressing phenotypic markers of alveolar type I epithelial cells. Without experiencing tumorigenic side effects, lung injury was abrogated in mice transplanted with hES-ATIICs, demonstrated by recovery of body weight and arterial blood oxygen saturation, decreased collagen deposition, and increased survival. Therefore, transplantation of hES-ATIICs shows promise as an effective therapeutic to treat acute lung injury.

Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy.

Background.Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals.Objective.To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case.Approach.Simulations were used to evaluate the model order selection when calculating the RSA estimates introduced before, as well as three different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals.Main results.It was found that using a single model order for all the calculations suffices to characterize RSA correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 min might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration.Significance.Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.