A mathematical model of interleukin-6 dynamics during exercise

Physical exercise is known to reduce the chronic inflammatory status that leads to Type 2 Diabetes. Its beneficial effects seem to be exerted trough a primary production of the cytokine Interleukin-6 (IL-6) which triggers a cascade of anti-inflammatory cytokines. Consequently, IL-6 has a central role in the description of the metabolic effects of exercise. The aim of this study was to develop a model of IL-6 dynamics during exercise. A model constituted by two non-linear differential equations is proposed. Since IL-6 production seems to be dependent not only on exercise duration but also on exercise intensity, input to the model is represented by heart rate, which is known to correlate well with exercise intensity. Model implementation in a Matlab-based parametric identification procedure allowed optimization of adjustable characteristic coefficients of IL-6 dynamics during exercise. From the reported results, it can be concluded that this model is a suitable tool to reproduce IL-6 time course during the execution of a physical exercise. This model was the first step of a project aimed at describing the complete immune system response to exercise and at giving a comprehensive sight of the effects that exercise has on the metabolic system

A system model of the effects of exercise on plasma Interleukin-6 dynamics in healthy individuals: Role of skeletal muscle and adipose tissue

Interleukin-6 (IL-6) has been recently shown to play a central role in glucose homeostasis, since it stimulates the production and secretion of Glucagon-like Peptide-1 (GLP-1) from intestinal L-cells and pancreas, leading to an enhanced insulin response. In resting conditions, IL-6 is mainly produced by the adipose tissue whereas, during exercise, skeletal muscle contractions stimulate a marked IL-6 secretion as well. Available mathematical models describing the effects of exercise on glucose homeostasis, however, do not account for this IL-6 contribution. This study aimed at developing and validating a system model of exercise’s effects on plasma IL-6 dynamics in healthy humans, combining the contributions of both adipose tissue and skeletal muscle. A two-compartment description was adopted to model plasma IL-6 changes in response to oxygen uptake’s variation during an exercise bout. The free parameters of the model were estimated by means of a cross-validation procedure performed on four different datasets. A low coefficient of variation (<10%) was found for each parameter and the physiologically meaningful parameters were all consistent with literature data. Moreover, plasma IL-6 dynamics during exercise and post-exercise were consistent with literature data from exercise protocols differing in intensity, duration and modality. The model successfully emulated the physiological effects of exercise on plasma IL-6 levels and provided a reliable description of the role of skeletal muscle and adipose tissue on the dynamics of plasma IL-6. The system model here proposed is suitable to simulate IL-6 response to different exercise modalities. Its future integration with existing models of GLP-1-induced insulin secretion might provide a more reliable description of exercise’s effects on glucose homeostasis and hence support the definition of more tailored interventions for the treatment of type 2 diabetes

correction formula approach to evaluate fatigue damage induced by non gaussian stress state

Abstract In the present paper the authors define an original analytical expression of a corrective coefficient to evaluate fatigue damage induced by a non-Gaussian stress state affected by high Kurtosis (values higher than 5) and by zero Skewness. This approach starts from a previous activity in which the authors solved an analogous problem but for light non-Gaussian stress states (Kurtosis value less than 5). The proposed procedure assumes to know the fatigue damage induced by Gaussian equivalent stress state time domain process. This characteristic allows the proposed procedure to be easily adopted inside the so-called Frequency Domain Fatigue Methods but in parallel with the statistical analysis of the system time domain response (Kurtosis and Skewness evaluation). Interesting considerations about its applicability will be proposed as concerns the non-Gaussianity and non-Stationarity of the inputs when the system is a flexible component excited in its frequency range

evaluation of fatigue damage with an energy criterion of simple implementation

Abstract Many theoretical methods for multiaxial fatigue life prediction are present in literature, most of them based on their effectiveness on knowledge of the entire stress time history. This represents the great applicative limit. The incapacity to study real situations, not only deterministic one, let the authors to develop a simple and rigorous criterion, which helps the designer who works in this area. The criterion is presented focusing the attention on the basic premise, highlighting its applicability, its practicality and its computational power. To do that, the Authors take into account the deterministic or random character of the individual constraint components and their degree of correlation. In order to verify the method, simulations of multiaxial loads conditions, developed in the time domain, will be carried out with various correlation levels between the stress components on which the method will be applied

Cardiorespiratory DB: Collection of cardiorespiratory data acquired during normal breathing, deep breathing and breath holding

The database is constituted by 50 datasets containing cardiorespiratory signals acquired from 50 healthy volunteer subjects (one dataset for each subject; 23 males and 27 females; age: 23±5 years) while performing normal breathing, deep breathing, and breath holding, and two spreadsheet files, namely the “SubjectsInfo.xlsx” and “DBInfo.xlsx” containing the metadata of subjects (including demographic data) and of acquired signals, respectively. Cardiorespiratory signals consisted in simultaneously recorded 12-lead electrocardiograms acquired by the clinical M12 Global InstrumentationR digital Holter ECG recorder, and single-lead electrocardiograms and respiration signals acquired by the wearable chest strap BioHarness 3.0 by Zephyr. The database may be useful to: (1) validate the use of wearable sensors in the acquisition of cardiorespiratory data during different respiration kinds, including apnea; (2) investigate the physiological association between cardiovascular and respiratory systems; (3) validate algorithms able to indirectly extract the respiration signal from the electrocardiogram; (4) study the fatigue level induced by a series of controlled respiration patterns; and (5) investigate the effect of COVID-19 infection on the cardiorespiratory system

Estimation of Tidal Volume during Exercise Stress Test from Wearable-Device Measures of Heart Rate and Breathing Rate

Tidal volume (TV), defined as the amount of air that moves in or out of the lungs with each respiratory cycle, is important in evaluating the respiratory function. Although TV can be reliably measured in laboratory settings, this information is hardly obtainable under everyday living conditions. Under such conditions, wearable devices could provide valuable support to monitor vital signs, such as heart rate (HR) and breathing rate (BR). The aim of this study was to develop a model to estimate TV from wearable-device measures of HR and BR during exercise. HR and BR were acquired through the Zephyr Bioharness 3.0 wearable device in nine subjects performing incremental cycling tests. For each subject, TV during exercise was obtained with a metabolic cart (Cosmed). A stepwise regression algorithm was used to create the model using as possible predictors HR, BR, age, and body mass index; the model was then validated using a leave-one-subject-out cross-validation procedure. The performance of the model was evaluated using the explained variance (R-2), obtaining values ranging from 0.65 to 0.72. The proposed model is a valid method for TV estimation with wearable devices and can be considered not subject-specific and not instrumentation-specific

Initial investigation of athletes’ electrocardiograms acquired by wearable sensors during the pre-exercise phase

Aim: The aim of this study is to support large-scale prevention programs fighting sport-related sudden cardiac death by providing a set of electrocardiographic features representing a starting point in the development of normal reference values for the pre-exercise phase. Background: In people with underlying, often unknown, cardiovascular abnormalities, increased cardiovascular load during exercise can trigger sport-related sudden cardiac death. Prevention remains the only weapon to contrast sport-related sudden cardiac death. So far, no reference values have been proposed for electrocardiograms of athletes acquired with wearable sensors in the pre-exercise phase, consisting of the few minutes immediately before the beginning of the training session. Objective: To perform an initial investigation of athletes’ electrocardiograms acquired by wearable sensors during the pre-exercise phase. Methods: The analyzed electrocardiograms, acquired through BioHarness 3.0 by Zephyr, belong to 51 athletes (Sport Database and Cycling Database of the Cardiovascular Bioengineering Lab of the Università Politecnica delle Marche, Italy). Preliminary values consist of interquartile ranges of six electrocardiographic features which are heart rate, heart-rate variability, QRS duration, ST level, QT interval, and corrected QT interval. Results: For athletes 35 years old or younger, preliminary values were [72;91]bpm, [26;47]ms, [85;104]ms, [-0.08;0.08]mm, [326;364]ms and [378;422]ms, respectively. For athletes older than 35 years old, preliminary values were [71;94]bpm, [16;65]ms, [85;100]ms, [-0.11;0.07]mm, [330;368]ms and [394;414]ms, respectively. Conclusion: Availability of preliminary reference values could help identify those athletes who, due to electrocardiographic features out of normal ranges, are more likely to develop cardiac complications that may lead to sport-related sudden cardiac death

Ruolo della chirurgia endovascolare nelle rotture aortiche del politraumatizzato con lesioni polidistrettuali di pertinenza chirurgica

Nel politraumatizzato con gravi lesioni polidistrettuali di interesse chirurgico il trattamento endovascolare (TEV) della rottura posttraumatica dell’ aorta toracica (RPAT) rappresenta oggi una valida alternativa terapeutica al trattamento chirurgico convenzionale. Nella nostra esperienza (ottobre 2001-novembre 2004) abbiamo osservato 5 casi di RPAT (3 rotture istmiche, 2 rotture aorta toracica discendente) in gravi politraumatizzati, tutti di sesso maschile, di età compresa fra i 23 ed i 42 anni (media 32,4), trattate con successo con TEV. Il Glasgow Coma Score (GCS) era compreso fra 5 e 13. Tutti i pazienti sono stati sottoposti, dopo adeguata stabilizzazione del quadro clinico-emodinamico, ad angio-TC total body al fine di valutare la lesione aortica ed identificare le altre lesioni associate. In 4 casi erano coinvolti più distretti corporei di pertinenza chirurgica (3 casi: trauma osseo, addominale e neurochirurgico; 1 caso: trauma osseo, addominale, neurochirurgico e toracico). Il TEV è stato eseguito sempre in sala operatoria previa arteriografia digitale. La durata media della procedura angio-radiologica è stata di 105 minuti (range 80 - 125). Non si è verificata nessuna complicanza né immediata né a distanza (follow-up = medio 24 mesi; range 12-36). In conclusione il TEV delle RPAT offre in pazienti ‘critici’ una valida opzione terapeutica alla chirurgia tradizionale in grado di stabilizzare il quadro clinico e trattare successivamente ‘in sicurezza’ le altre gravi lesioni chirurgiche associate

Long-range correlation and multifractality in Bach's Inventions pitches

We show that it can be considered some of Bach pitches series as a stochastic process with scaling behavior. Using multifractal deterend fluctuation analysis (MF-DFA) method, frequency series of Bach pitches have been analyzed. In this view we find same second moment exponents (after double profiling) in ranges (1.7-1.8) in his works. Comparing MF-DFA results of original series to those for shuffled and surrogate series we can distinguish multifractality due to long-range correlations and a broad probability density function. Finally we determine the scaling exponents and singularity spectrum. We conclude fat tail has more effect in its multifractality nature than long-range correlations.Comment: 18 page, 6 figures, to appear in JSTA

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information