Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O-2 concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O-2 flow automatically by real-time classifying the intensity of a patient's physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O-2 flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA

Portable Oxygen Therapy: Is the 6-Minute Walking Test Overestimating the Actual Oxygen Needs?

The appropriate titration for the personalized oxygen needs of patients with chronic obstructive pulmonary disease (COPD) and severe hypoxemia is a determining factor in the success of long-term oxygen therapy. There are no standardized procedures to assist in determining the patient's needs during the physical activities of daily life. Despite that effort tests are a wide broad approach, further research concerning the development of protocols to titrate O-2 therapy is needed. The main objective of this study was to assess whether the level of oxygen titrated through the 6-minute walking test (6MWT) for patients with COPD and exertional hypoxemia is adequate to meet the patients' demand during their activities of daily living. Physiological and subjective variables were estimated for a study population during two walking tests: a 6MWT and a 20-minute walking circuit (20MWC), designed ad-hoc to reproduce daily physical activities more truthfully. The results indicate that in a significant proportion of patients, the 6MWT might not accurately predict their oxygen needs at a domiciliary environment. Therefore, the titration of the portable O-2 therapy could not be optimal in these cases, with the detrimental impact on the patient's health (hyperoxia episodes), the autonomy of the oxygen device, and the decrease of time out of the home

Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach

Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland–Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil’s size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals

Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach

Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland-Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil's size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals.This study was supported by grants co-financed by the “Fondo Europeo de Desarrollo Re gional” (FEDER)-UE “A way to build Europe” from the “Ministerio de Economía y Competitividad” (MINECO: RTI2018-099778-B-I00); the “Programa Operativo de Andalucía FEDER, Iniciativa Territo rial Integrada ITI 2014-2020 Consejería Salud, Junta de Andalucía” (PI-0080-2017); the “Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020), Consejería de Economía, Conocimiento, Em presas y Universidad, Junta de Andalucía (P20_00958)”; the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement Nº 955684; the “In stituto de Investigación e Innovación en Ciencias Biomédicas de Cádiz (LI19/06IN-CO22; IN-CO09; IN-CO07)”; the “Consejería de Economía, Innovación, Ciencia y Empleo de la Junta de Andalucía” (CTS-510 and TIC-212); the “Centro de Investigación Biomédica en Red de Salud Mental-CIBERSAM” (CB/07/09/0033); and the “Universidad de Cádiz, contrato predoctoral fpuUCA 2019”.Ye