Cardiovascular-related proteomic changes in ECFCs exposed to the serum of COVID-19 patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection significantly affects the cardiovascular system, causing vascular damage and thromboembolic events in critical patients. Endothelial dysfunction represents one of the first steps in response to COVID-19 that might lead to cardiovascular complications and long-term sequelae. However, despite the enormous efforts in the last two years, the molecular mechanisms involved in such processes remain poorly understood. Herein, we analyzed the protein changes taking place in endothelial colony forming cells (ECFCs) after the incubation with the serum from individuals infected with COVID-19, whether asymptomatic or critical patients, by application of a label free-quantitative proteomics approach. Specifically, ECFCs from healthy individuals were incubated ex-vivo with the serum of either COVID-19 negative donors (PCR-/IgG-, n:8), COVID-19 asymptomatic donors at different infective stages (PCR+/ IgG-, n:8and PCR-/IgG+, n:8), or hospitalized critical COVID-19 patients (n:8), followed by proteomics analysis. In total, 590 proteins were differentially expressed in ECFCs in response to all infected serums. Predictive analysis highlighted several proteins like CAPN5, SURF4, LAMP2 or MT-ND1, as highly discriminating features between the groups compared. Protein changes correlated with viral infection, RNA metabolism or autophagy, among others. Remarkably, the angiogenic potential of ECFCs in response to the infected serums was impaired, and many of the protein alterations in response to the serum of critical patients were associated with cardiovascular-related pathologies.

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.

Serum microRNAs targeting ACE2 and RAB14 genes distinguish asymptomatic from critical COVID-19 patients.

Despite the extraordinary advances achieved to beat COVID-19 disease, many questions remain unsolved, including the mechanisms of action of SARS-CoV-2 and which factors determine why individuals respond so differently to the viral infection. Herein, we performed an in silico analysis to identify host microRNA targeting ACE2, TMPRSS2, and/or RAB14, all genes known to participate in viral entry and replication. Next, the levels of six microRNA candidates previously linked to viral and respiratory-related pathologies were measured in the serum of COVID-19-negative controls (n = 16), IgG-positive COVID-19 asymptomatic individuals (n = 16), and critical COVID-19 patients (n = 17). Four of the peripheral microRNAs analyzed (hsa-miR-32-5p, hsa-miR-98-3p, hsa-miR-423-3p, and hsa-miR-1246) were upregulated in COVID-19 critical patients compared with COVID-19-negative controls. Moreover, hsa-miR-32-5p and hsa-miR-1246 levels were also altered in critical versus asymptomatic individuals. Furthermore, these microRNA target genes were related to viral infection, inflammatory response, and coagulation-related processes. In conclusion, SARS-CoV-2 promotes the alteration of microRNAs targeting the expression of key proteins for viral entry and replication, and these changes are associated with disease severity. The microRNAs identified could be taken as potential biomarkers of COVID-19 progression as well as candidates for future therapeutic approaches against this disease.

The serum of COVID-19 asymptomatic patients up-regulates proteins related to endothelial dysfunction and viral response in circulating angiogenic cells ex-vivo.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already caused 6 million deaths worldwide. While asymptomatic individuals are responsible of many potential transmissions, the difficulty to identify and isolate them at the high peak of infection constitutes still a real challenge. Moreover, SARS-CoV-2 provokes severe vascular damage and thromboembolic events in critical COVID-19 patients, deriving in many related deaths and long-hauler symptoms. Understanding how these processes are triggered as well as the potential long-term sequelae, even in asymptomatic individuals, becomes essential. METHODS: We have evaluated, by application of a proteomics-based quantitative approach, the effect of serum from COVID-19 asymptomatic individuals over circulating angiogenic cells (CACs). Healthy CACs were incubated ex-vivo with the serum of either COVID-19 negative (PCR -/IgG -, n:8) or COVID-19 positive asymptomatic donors, at different infective stages: PCR +/IgG - (n:8) and PCR -/IgG + (n:8). Also, a label free quantitative approach was applied to identify and quantify protein differences between these serums. Finally, machine learning algorithms were applied to validate the differential protein patterns in CACs. RESULTS: Our results confirmed that SARS-CoV-2 promotes changes at the protein level in the serum of infected asymptomatic individuals, mainly correlated with altered coagulation and inflammatory processes (Fibrinogen, Von Willebrand Factor, Thrombospondin-1). At the cellular level, proteins like ICAM-1, TLR2 or Ezrin/Radixin were only up-regulated in CACs treated with the serum of asymptomatic patients at the highest peak of infection (PCR + /IgG -), but not with the serum of PCR -/IgG + individuals. Several proteins stood out as significantly discriminating markers in CACs in response to PCR or IgG + serums. Many of these proteins particiArticle title: Kindly check and confirm the edit made in the article title.pate in the initial endothelial response against the virus. CONCLUSIONS: The ex vivo incubation of CACs with the serum of asymptomatic COVID-19 donors at different stages of infection promoted protein changes representative of the endothelial dysfunction and inflammatory response after viral infection, together with activation of the coagulation process. The current approach constitutes an optimal model to study the response of vascular cells to SARS-CoV-2 infection, and an alternative platform to test potential inhibitors targeting either the virus entry pathway or the immune responses following SARS-CoV-2 infection.

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.

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 O2 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 O2 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 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 O2 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 O2 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 O2 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.

Physiological closed-loop control in intelligent oxygen therapy: A review.

BACKGROUND AND OBJECTIVE: Oxygen therapy has become a standard care for the treatment of patients with chronic obstructive pulmonary disease and other hypoxemic chronic lung diseases. In current systems, manually continuous adjustment of O2 flow rate is a time-consuming task, often unsuccessful, that requires experienced staff. The primary aim of this systematic review is to collate and report on the principles, algorithms and accuracy of autonomous physiological close-loop controlled oxygen devices as well to present recommendations for future research and studies in this area. METHODS: A literature search was performed on medical database MEDLINE, engineering database IEEE-Xplore and wide-raging scientific databases Scopus and Web of Science. A narrative synthesis of the results was carried out. RESULTS: A summary of the findings of this review suggests that when compared to the conventional manual practice, the closed-loop controllers maintain higher saturation levels, spend less time below the target saturation, and save oxygen resources. Nonetheless, despite of their potential, autonomous oxygen therapy devices are scarce in real clinical applications. CONCLUSIONS: Robustness of control algorithms, fail-safe mechanisms, limited reliability of sensors, usability issues and the need for standardized evaluating methods of assessing risks can be among the reasons for this lack of matureness and need to be addressed before the wide spreading of a new generation of automatic oxygen devices.

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review.

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD.

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients' quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study.

COPD places an enormous burden on the healthcare systems and causes diminished health-related quality of life. The highest proportion of human and economic cost is associated with admissions for acute exacerbation of respiratory symptoms (AECOPD). Since prompt detection and treatment of exacerbations may improve outcomes, early detection of AECOPD is a critical issue. This pilot study was aimed to determine whether a mobile health system could enable early detection of AECOPD on a day-to-day basis. A novel electronic questionnaire for the early detection of COPD exacerbations was evaluated during a 6-months field trial in a group of 16 patients. Pattern recognition techniques were applied. A k-means clustering algorithm was trained and validated, and its accuracy in detecting AECOPD was assessed. Sensitivity and specificity were 74.6 and 89.7 %, respectively, and area under the receiver operating characteristic curve was 0.84. 31 out of 33 AECOPD were early identified with an average of 4.5 ± 2.1 days prior to the onset of the exacerbation that was considered the day of medical attendance. Based on the findings of this preliminary pilot study, the proposed electronic questionnaire and the applied methodology could help to early detect COPD exacerbations on a day-to-day basis and therefore could provide support to patients and physicians.

A novel multimodal tool for telemonitoring patients with COPD.

Introduction: Among factors that underlie high rates of non-participation reported in telehealth interventions are the low older users' acceptance of information technologies and the low levels of non-compliance with therapy of chronic patients. Therefore, inclusion of potential users into design stages of assistive technologies is challenging. In this paper, the design, implementation and evaluation of a multimodal mobile application for telemonitoring chronic obstructive pulmonary disease (COPD) is presented. The goal of the study was to assess the usability and feasibility of the designed tool. Methods: An iterative user-centered design methodology was applied to implement a prototype that satisfied users' requirements. Feasibility (compliance, COPD knowledge and satisfaction) of the application was assessed in a 6-month field trial with COPD patients. Results: A usable, effective and efficient prototype was released after the development process. A high compliance (86.1%) and an increasing in COPD knowledge were achieved in the field trial. Conclusions: The findings reveal the importance of integrating usability in the design development processes to improve adherence to routine tasks and to reduce the high rates of non-participation reported in recent evaluation studies of telehealth interventions. The presented tool can help to recognize early symptoms of deterioration and to support patients in COPD self-management.

Development and Evaluation of an Automated, Home-Based, Electronic Questionnaire for Detecting COPD Exacerbations.

Collaboration between patients and their medical and technical experts enabled the development of an automated questionnaire for the early detection of COPD exacerbations (AQCE). The questionnaire consisted of fourteen questions and was implemented on a computer system for use by patients at home in an un-supervised environment. Psychometric evaluation was conducted after a 6-month field trial. Fifty-two patients were involved in the development of the questionnaire. Reproducibility was studied using 19 patients (ICC = 0.94). Sixteen out of the 19 subjects started the 6 month-field trial with the computer application. Cronbach's alpha of 0.81 was achieved. In the concurrent validity analysis, a correlation of 0.80 (p = 0.002) with the CCQ was reported. The results suggest that AQCE is a valid and reliable questionnaire, showing that an automated home-based electronic questionnaire may enable early detection of exacerbations of COPD.

Healthcare Engineering Defined: A White Paper.

Engineering has been playing an important role in serving and advancing healthcare. The term "Healthcare Engineering" has been used by professional societies, universities, scientific authors, and the healthcare industry for decades. However, the definition of "Healthcare Engineering" remains ambiguous. The purpose of this position paper is to present a definition of Healthcare Engineering as an academic discipline, an area of research, a field of specialty, and a profession. Healthcare Engineering is defined in terms of what it is, who performs it, where it is performed, and how it is performed, including its purpose, scope, topics, synergy, education/training, contributions, and prospects.

Computerized analysis of respiratory sounds during COPD exacerbations.

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a major event in the natural course of the disease, and is associated with significant mortality and socioeconomic impact. Abnormal respiratory sounds are commonly present in patients with AECOPD. Computerized analysis of these sounds can assist in diagnosis and in evaluation during follow-up. Exploratory data analysis methods were applied to respiratory sounds in these patients when they were hospitalized because of exacerbation. Two different patterns of presentation and evolution of respiratory sounds in AECOPD were found and described from the method of computerized respiratory sound analysis and unsupervised clustering that was devised. Based on the findings of the study, remote monitoring of respiratory sounds may be useful for the detection and/or follow-up of COPD exacerbation.