Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality

Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability. © 2019 Elsevier B.V

Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality

Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability. © 2019 Elsevier B.V

Cepstrum Feature Selection for the Classification of Sleep Apnea-Hypopnea Syndrome based on Heart Rate Variability

Abstract Cepstrum Coefficients are analyzed in order to study its performance in Sleep Apnea Introduction The Sleep Apnea Hypopnea Syndrome (SAHS) is a respiratory disorder characterized by frequent breathing pauses and a collapse of pharynx during sleep. If breathing ceases completely, then the event is called apnea. In case breathing does not cease but there is a reduction in the volume of air entering the lungs, then the event is called hipopnea. Previous studies have tried to diagnostic SAHS with the RR series obtained from the electrocardiogram (ECG) [1] with good performance, anyway the underlying regulatory mechanisms during apnea are still poorly understood. This fact makes necessary to explore appropriate feature estimation techniques in order to extract as much information as possible. In previous contribution [2] we have used cepstrum features without taking into consideration any selection criteria. In this paper we apply forward feature selection in order to improve apnea screening performance and find coefficients which describe with more detail the RR pattern in presence of SAHS. We have selected features from a specific cepstrum coefficients set composed by the first 60 elements containing information about periodic structures of the RR series but also about the system modelled by the filter-type elements. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) have been proposed in order to quantify apnea minutes. The system will provide also a global score of the presence of clinically significant apnea based on the minute by minute apnea detection. A subject will be classified globally as SAHS is the percentage of minutes with apnea is at least 16%. Database The database was provided by Prof. Dr. Thomas Penzel for Computers in Cardiology 2000 challenge [3]. The data have been divided divided into a learning set (L set) and a test set (T set) of equal size. Each set consists of 35 recordings, containing a single ECG signal digitized at 100 Hz with 12-bit resolution, continuously for approximately 8 hours. Each recording includes a set of reference annotations, one for each minute, which indicates the presence or absence of apnea during that minute. These reference annotations were made by human experts on the basis of simultaneously recorded respiration signals. Group A (apnea) contains recordings with at least 10