Systemic inflammation, systemic effects and comorbidities in chronic obstructive pulmonary disease

© 2018, Serbian Medical Society. All right reserved. Chronic obstructive pulmonary disease (COPD) is known to be characterized by inflammation both in the stable phase of the disease and during exacerbation. It has been shown that certain inflammatory mediators have a high level in systemic circulation, indicating systemic inflammation in COPD. The first recognized systemic effect of COPD is a disorder of the state of nourishment. Certain diseases, including COPD, can lead to cachexia where patients lose muscle mass despite adequate caloric intake. Inflammation in COPD also has an effect on increased protein catabolism, which leads to a decrease in body weight. Increased activity of enzymes matrix metalloproteinases family (MMP) in patients with COPD can lead to lung tissue destruction and the development of osteoporosis. It is considered that the most important role in the association between COPD and CVD disease is systemic inflammation. Low level of inflammation in small airways in COPD and Atherosclerotic plaques, may be a potential factor in the development of both pathological processes. Systemic manifestations of COPD include numerous endocrine disorders of the pituitary gland, thyroid gland, gonads, adrenal glands and pancreas. The mechanisms by which HOBP affects the endocrine function are not entirely clear, but are likely to include hypoxemia, hypercapnia, systemic inflammation, and the use of systemic glucocorticoids. Explanation for significant depressive disorder in more advanced stages in COPD can be expressive dyspnoea, decreased physical activity, worse exercise tolerance, frequent exacerbations and systemic inflammation which can lead to further physical activity decrease, social isolation, fear, and depression

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance

Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis

This study investigates the feasibility of classifying physiological stress states usingMachine Learning (ML) algorithms on short-term (ST,∼5min) and ultra-short-term (UST, < 5 min, down to 10 heartbeats) heart rate (HRV) or pulse rate variability (PRV) features computed from inter-beat interval time series. Three widely employed ML algorithms were used, i.e. Naive Bayes Classifier, Support Vector Machines, and Neural Networks, on various time-, frequency and information domain HRV/PRV indices on a single-feature basis. Data were collected from healthy individuals during different physiological states including rest, postural and mental stress. Results highlighted comparable values using either HRV or PRV indices, and higher accuracy (>65% for most features and all classifiers) when classifying postural than mental stress. While decreasing the time series length, time-domain indices resulted still reliable down to ∼10 s, contrary to UST frequency-domain features which reported lower accuracy below 60 heartbeats

Risk factors for brain metastases in surgically staged IIIA non-small cell lung cancer patients treated with surgery, radiotherapy and chemotherapy

mortality among patients with carcinomas. The aim of this study was to point out risk factors for brain metastases (BM) appearance in patients with IIIA (N2) stage of nonsmall cell lung cancer (NSCLC) treated with three-modal therapy. Methods. We analyzed data obtained from 107 patients with IIIA (N2) stage of NSCLC treated surgically with neoadjuvant therapy. The frequency of brain metastases was examined regarding age, sex, histological type and the size of tumor, nodal status, the sequence of radiotherapy and chemotherapy application and the type of chemotherapy. Results. Two and 3-year incidence rates of BM were 35% and 46%, respectively. Forty-six percent of the patients recurred in the brain as their first failure in the period of three years. Histologically, the patients with nonsquamous cell lung carcinoma had significantly higher frequency of metastases in the brain compared with the group of squamous cell lung carcinoma (46%: 30%; p = 0.021). Examining treatment-related parameters, treatment with taxane-platinum containing regimens was associated with a lower risk of brain metastases, than platinum-etoposide chemotherapy regimens (31%: 52%; p = 0.011). Preoperative radiotherapy, with or without postoperative treatment, showed lower rate of metastases in the brain compared with postoperative radiotherapy treatment only (33%: 48%; p = 0.035). Conclusion. Brain metastases are often site of recurrence in patients with NSCLC (IIIA-N2). Autonomous risk factors for brain metastases in this group of patients are non-squamous NSCLC, N1-N2 nodal status, postoperative radiotherapy without preoperative radiotherapy

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics

The holistic perspective of the INCISIVE Project: artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.This research received funding mainly from the European Union’s Horizon 2020 research and innovation program under grant agreement no 952179. It was also partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under contracts PID2019-107255GB and 2017-SGR-1414.Peer ReviewedArticle signat per 30 autors/es: Ivan Lazic (1), Ferran Agullo (2), Susanna Ausso (3), Bruno Alves (4), Caroline Barelle (4), Josep Ll. Berral (2), Paschalis Bizopoulos (5), Oana Bunduc (6), Ioanna Chouvarda (7), Didier Dominguez (3), Dimitrios Filos (7), Alberto Gutierrez-Torre (2), Iman Hesso (8), Nikša Jakovljević (1), Reem Kayyali (8), Magdalena Kogut-Czarkowska (9), Alexandra Kosvyra (7), Antonios Lalas (5) , Maria Lavdaniti (10,11), Tatjana Loncar-Turukalo (1),Sara Martinez-Alabart (3), Nassos Michas (4,12), Shereen Nabhani-Gebara (8), Andreas Raptopoulos (6), Yiannis Roussakis (13), Evangelia Stalika (7,11), Chrysostomos Symvoulidis (6,14), Olga Tsave (7), Konstantinos Votis (5) Andreas Charalambous (15) / (1) Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; (2) Barcelona Supercomputing Center, 08034 Barcelona, Spain; (3) Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain; (4) European Dynamics, 1466 Luxembourg, Luxembourg; (5) Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece; (6) Telesto IoT Solutions, London N7 7PX, UK: (7) School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (8) Department of Pharmacy, Kingston University London, London KT1 2EE, UK; (9) Timelex BV/SRL, 1000 Brussels, Belgium; (10) Nursing Department, International Hellenic University, 57400 Thessaloniki, Greece; (11) Hellenic Cancer Society, 11521 Athens, Greece; (12) European Dynamics, 15124 Athens, Greece; (13) German Oncology Center, Department of Medical Physics, Limassol 4108, Cyprus; (14) Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece; (15) Department of Nursing, Cyprus University of Technology, Limassol 3036, CyprusPostprint (published version

Evaluation of pig welfare in lairage and process hygiene in a single abattoir

Food safety is indirectly affected by the welfare of food animals, due to close links between animal welfare, animal health and food borne diseases. Stress factors and poor welfare can lead to increased susceptibility to disease among animals and may intensify the fecal shedding of food borne pathogens, e.g. Salmonella, Campylobacter, Yersinia, and human pathogenic STEC in the pre-slaughter phase: on-farm, in transport and in lairage. This study evaluated two aspects: a) assessment of pig welfare in abattoir lairage founded on animal-based categories, and b) the relationship between pig welfare and microbial process hygiene at slaughter. The results revealed that the animal-based category ‘manure on the body’ assessed in abattoir lairage corresponded with microbial process hygiene at slaughter

Illness perception in tuberculosis by implementation of the Brief Illness Perception Questionnaire : a TBNET study

How patients relate to the experience of their illness has a direct impact over their behavior. We aimed to assess illness perception in patients with pulmonary tuberculosis (TB) by means of the Brief Illness Perception Questionnaire (BIPQ) in correlation with patients’ demographic features and clinical TB score. Our observational questionnaire based study included series of consecutive TB patients enrolled in several countries from October 2008 to January 2011 with 167 valid questionnaires analyzed. Each BIPQ item assessed one dimension of illness perceptions like the consequences, timeline, personal control, treatment control, identity, coherence, emotional representation and concern. An open question referred to the main causes of TB in each patient’s opinion. The over-all BIPQ score (36.25 ± 11.054) was in concordance with the clinical TB score (p ≤ 0.001). TB patients believed in the treatment (the highest item-related score for treatment control) but were unsure about the illness identity. Illness understanding and the clinical TB score were negatively correlated (p < 0.01). Only 25% of the participants stated bacteria or TB contact as the first ranked cause of the illness. For routine clinical practice implementation of the BIPQ is convenient for obtaining fast and easy assessment of illness perception with potential utility in intervention design. This time saving effective personalized approach may improve communication with TB patients and contribute to better behavioral strategies in disease control

Immunomonitoring of Monocyte and Neutrophil Function in Critically Ill Patients: From Sepsis and/or Trauma to COVID-19.

Immune cells and mediators play a crucial role in the critical care setting but are understudied. This review explores the concept of sepsis and/or injury-induced immunosuppression and immuno-inflammatory response in COVID-19 and reiterates the need for more accurate functional immunomonitoring of monocyte and neutrophil function in these critically ill patients. in addition, the feasibility of circulating and cell-surface immune biomarkers as predictors of infection and/or outcome in critically ill patients is explored. It is clear that, for critically ill, one size does not fit all and that immune phenotyping of critically ill patients may allow the development of a more personalized approach with tailored immunotherapy for the specific patient. In addition, at this point in time, caution is advised regarding the quality of evidence of some COVID-19 studies in the literature