Monitoring differences in the function of the autonomic nervous system in patients with chronic insomnia using a wearable device.

STUDY OBJECTIVES: to characterize possible differences in the function of the ANS in patients with chronic insomnia compared to a control group, using a wearable device, in order to determine whether those findings allow diagnosing this medical entity. METHODS: Thirty-two patients with chronic insomnia and nineteen patients without any sleep disorder, as a control group, were recruited prospectively. Both groups of patients underwent an in-patient night with simultaneous polysomnography and wearable device recording Empatica E4 a diverse array of physiological signals, including electrodermal activity, temperature, accelerometry, and photoplethysmography, providing a comprehensive resource for in-depth sleep analysis. RESULTS: In polysomnography, patients suffering from insomnia showed a significant decrease in sleep efficiency and total sleep time, prolonged sleep latency, and increased wakefulness after sleep onset. Accelerometry results were statistically significant differences in the three axis (x, y, z) just in stage N3, no differences were observed between both groups in REM sleep. The lowest temperature was reached in REM sleep in both groups. Peripheral temperature did not decrease during the different sleep phases compared to wakefulness in insomnia, unlike in the control group. Heart rate was higher in patients with insomnia than in controls during wakefulness and sleep stage. Heart rate variability was lower in stage N3 and higher in REM sleep compared to wakefulness in both groups. Sweating was significantly higher in patients with insomnia compared to the control group, as indicated by Skin Conductance Variability values and Sudomotor Nerve. CONCLUSIONS: Our study suggests that patients with insomnia have increased sympathetic activity during sleep, showing a higher heart rate. Temperature and sweating significantly influence the different sleep phases.

KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge.

KnowSeq R/Bioc package is designed as a powerful, scalable and modular software focused on automatizing and assembling renowned bioinformatic tools with new features and functionalities. It comprises a unified environment to perform complex gene expression analyses, covering all the needed processing steps to identify a gene signature for a specific disease to gather understandable knowledge. This process may be initiated from raw files either available at well-known platforms or provided by the users themselves, and in either case coming from different information sources and different Transcriptomic technologies. The pipeline makes use of a set of advanced algorithms, including the adaptation of a novel procedure for the selection of the most representative genes in a given multiclass problem. Similarly, an intelligent system able to classify new patients, providing the user the opportunity to choose one among a number of well-known and widespread classification and feature selection methods in Bioinformatics, is embedded. Furthermore, KnowSeq is engineered to automatically develop a complete and detailed HTML report of the whole process which is also modular and scalable. Biclass breast cancer and multiclass lung cancer study cases were addressed to rigorously assess the usability and efficiency of KnowSeq. The models built by using the Differential Expressed Genes achieved from both experiments reach high classification rates. Furthermore, biological knowledge was extracted in terms of Gene Ontologies, Pathways and related diseases with the aim of helping the expert in the decision-making process. KnowSeq is available at Bioconductor (https://bioconductor.org/packages/KnowSeq), GitHub (https://github.com/CasedUgr/KnowSeq) and Docker (https://hub.docker.com/r/casedugr/knowseq).