Psychological well-being of teachers: influence of burnout, personality, and psychosocial climate.

Introduction: Those who are professionally dedicated to teaching can be exposed with their work to situations that influence their perception of psychological well-being. This study aims to evaluate how the factors of personality, emotional intelligence, burnout and the psychosocial climate derived from the work environment of teachers influence their levels of psychological well-being, to verify whether these variables allow us to establish a predictive model of psychological well-being by means of multiple regression analysis. Methods: Participants were a group of 386 teachers in early childhood, Primary and Secondary education, both in training and in active service (71.5% women; 28.5% men). A correlation and multiple regression analysis were performed to establish a predictive model of psychological well-being. We used 5 instruments: Psychosocial Climate at Work Scales (ECPT); verall Personality Assessment Scale (OPERAS); Questionnaire for Evaluation of Burnout Syndrome at Work (CESQT); Spanish adaptation of the Riff Psychological Well-being Scales (EBP) and Spanish validation of the Trait Meta-Mood Scale (TMMS-24). Results: Most of the relationships were significant, and the multiple regression analysis explains 58.5% of the global variance of psychological well-being in teachers, being emotional stability the most relevant and main predictor of psychological well-being, explaining its 38.1%. Discussion: Personality shows a great influence in psychological well-being of teachers, particularly emotional stability. The ability to establish predictive models to explain psychological well-being in educational environments is confirmed.

Lesser mealworm (A. diaperinus) protein as a replacement for dairy proteins in the production of O/W emulsions: Droplet coalescence studies using microfluidics under controlled conditions.

Dairy proteins are commonly used to stabilize oil-in-water (O/W) emulsions, which can be replaced by other sustainable sources of proteins, such as insects. This study investigated the potential of lesser mealworm protein concentrate (LMPC) as a sustainable alternative to whey protein isolate (WPI) in stabilizing oil-in-water (O/W) emulsions using microfluidics. The frequency of coalescence (Fcoal) was calculated using images of emulsion droplets obtained near the inlet and outlet of the coalescence channel. The stability of O/W emulsions, produced using sunflower oil (SFO) or hexadecane and stabilized with varying concentrations of LMPC and WPI (0.02% to 0.0005% w/v), was compared under controlled conditions. The dispersed phase fraction (5.3%-14.3% v/v), protein adsorption time onto oil droplets (0.0398-0.158 s), and pH (pH = 3 and pH = 7) were also studied. Fcoal was greatest (0.42 s-1) when the protein concentration was lowest (0.0005%), the oil percentage was highest (14.3%), the adsorption period was shortest (0.0398 s), and the pH was 3. Droplet diameters did not vary significantly, with values between 55 and 118 µm, across protein concentrations or adsorption periods, but a rise in oil fraction resulted in a substantial increase in droplet diameters. Increases in protein content, adsorption duration, and oil percentage all resulted in increased stability (reduction of Fcoal). While LMPC and WPI showed similar results in microfluidic experiments and other test conditions, further research is needed to verify LMPC's efficacy as a replacement for WPI in food emulsification. Nonetheless, the findings suggest that LMPC has potential as a substitute for WPI in this application.

Model of psychosocial determinants of health in processes of social exclusion.

The substantial increase in the number of families facing social exclusion in Europe and its direct relationship with health inequities is a challenge for studies approaching the social determinants of health and policies dealing with welfare and social inclusion. We start from the premise that reducing inequality (SDG10), has a value and contributes on other goals such as improving health and well-being (SDG3), ensuring quality education (SDG4), promoting gender equality (SDG5) and decent work (SDG8). In this study, we identify disruptive risk factors and psychological and social well-being factors that influence self-perceived health in trajectories of social exclusion. The research materials used a checklist of exclusion patterns, life cycles and disruptive risk factors, Goldberg's General Health Questionnaire (GHQ-12), Ryff's Psychological Well-being (PWB) Scale and Keyes' Social Well-being Scale. The sample consists of 210 people (aged between 16 and 64 years): 107 people in a situation of social inclusion and 103 people in a situation of social exclusion. The data treatment involved statistical analysis, including correlation study and multiple regression analysis, aimed at developing a model of psychosocial factors that may act as health modulators, considering social factors as predictors in the regression model. The results showed that individuals in the sample, in a situation of social exclusion, have a greater accumulation of disruptive risk factors, and these are related to having fewer psychosocial and cognitive resources to cope with stressful situations: less self-acceptance, less mastery of the environment, less purpose in life, less level of social integration and social acceptance. Finally, analysis showed that in the absence of social integration and purpose in life, self-perceived health statuses decline. This work allows us to use the model obtained as a basis for confirming that there are dimensions of psychological and social well-being that should be considered stress-buffering factors in trajectories of social exclusion. These findings can help design psychoeducational programs for prevention and intervention with the aim of improving psychological adjustment and health states, as well as to promote proactive and reactive policies to reduce health inequalities.

Effects of upper respiratory tract anatomy and head movement on the buoyant flow and particle dispersion generated in a violent expiratory event.

In the wake of the COVID-19 pandemic, interest in understanding the turbulent dispersion of airborne pathogen-laden particles has significantly increased. The ability of infectious particles to stay afloat and disperse in indoor environments depends on their size, the environmental conditions and the hydrodynamics of the flow generated by the exhalation. In this work we analyze the impact of three different aspects, namely, the buoyancy force, the upper airways geometry and the head rotation during the exhalation on the short-term dispersion. Large-Eddy Simulations have been used to assess the impact of each separate effect on the thermal puff and particle cloud evolution over the first 2 s after the onset of the exhalation. Results obtained during this short-term period suggest that due to the rapid mixing of the turbulent puff, buoyancy forces play a moderate role on the ability of the particles to disperse. Because of the enhanced mixing, buoyancy reduces the range and increases the vertical size of the small particle clouds. In comparison to the fixed frame case, head rotation has been found to notably affect the size and shape of the cloud by enhancing the vertical transport as the exhalation axial direction sweeps vertically during the exhalation. The impact of the upper airway geometry, in comparison to an idealized mouth consisting in a pipe of circular section, has been found to be the largest when it is considered along with the head rotation.

Comparison between fully resolved and time-averaged simulations of particle cloud dispersion produced by a violent expiratory event.

In this work we compare the DNS results (Fabregat et al. 2021, Fabregat et al. 2021) for a mild cough already reported in the literarure with those obtained with a compressible URANS equations with a k-ϵ turbulence model. In both cases, the dispersed phase has been modelled as spherical Lagrangian particles using the one-way coupling assumption. Overall, the URANS model is capable of reproducing the observed tendency of light particles under 64 µm in diameter to rise due to the action of the drag exerted by the buoyant puff generated by the cough. Both DNS and URANS found that particles above 64 µm will tend to describe parabolic trajectories under the action of gravitational forces. Grid independence analysis allows to qualify the impact of increasing mesh resolution on the particle cloud statistics as flow evolves. Results suggest that the k-ϵ model overpredicts the horizontal displacement of the particles smaller than 64 µm while the opposite occurs for the particles larger than 64 µm.

Emotional Intelligence, Psychological Well-Being and Burnout of Active and In-Training Teachers.

The main activating variables of psychological well-being and Emotional Intelligence that influence teachers include the process of evaluating well-being, their motivation, and their ability to perceive and regulate sources of stress and burnout. The relationship and influence of psychological well-being and emotional intelligence was analyzed with the adequate regulation of burnout. Those who participated included 386 active teachers (55%), and teachers in training (45%), studying for degrees in Pre-School and Primary Education, and Master's degrees in Secondary Education Teacher Training of which 71.5% were women. The following were used: Psychological Well-Being Scales, Trait Meta-Mood Scale and the Spanish Burnout Inventory. Pearson's correlation analysis and multiple regression analysis were performed. The results showed that enthusiasm for the teaching job is related to psychological well-being, especially domain of the environment and personal growth. Multiple regression analysis made it possible to establish a predictive model of well-being, showing that psychological well-being is the main adjustment predictor and/or the mismatch in the work of the teaching staff in both samples, through an adequate regulation of positive relationships, mastery of their environment and having a purpose in life.

CSI-SF: Propiedades psicométricas de la versión española del inventario breve de estrategias de afrontamiento / CSI-SF: Psychometric properties of Spanish version of the coping strategies inventory - short form

Antecedentes/Objetivo: El estudio establece las propiedades psicométricas de la adaptación española de la versión abreviada del Inventario de Estrategias de Afrontamiento (CSI-SF) publicado por Addison et al. (2007). La prueba utiliza un modelo de dos ejes para clasificar las estrategias de afrontamiento (de compromiso y de evitación) y las categorías objetivas del afrontamiento (centrada en el problema y centrada en la emoción). Método: Participaron 940 personas (62.87% mujeres; 37.12% hombres) divididas en dos submuestras. Se realizó un análisis factorial exploratorio (AFE) y un análisis factorial confirmatorio (AFC), así mismo la correlación de Pearson y el Alfa de Cronbach para examinar la fiabilidad y validez de la adaptación al español del CSI-SF. Resultados: El análisis de consistencia interna reveló una alta fiabilidad para todas las escalas (EFE = .890, PFE = .836. PFD = .767, EFD = .934), y todos los índices de ajuste utilizados para examinar el CSI-SF versión española proporcionaron soporte para su uso como una medida adecuada de las estrategias de afrontamiento del estrés. Discusión/Conclusión: La escala CSI-SF versión española es una prueba que proporciona un diagnóstico rápido y eficiente cuyos factores explican el 62.79% de la varianza común total de las estrategias de afrontamiento que se emplean frente a la situación de estrés en distintos ámbitos.(AU)

Background / Objective: The study establishes the psychometric properties of the Spanish adaptation of the abbreviated version of the Coping Strategies Inventory (CSI-SF) published by Addison et al. (2007). The test uses a two-axis model to classify coping strategies (commitment and avoidance) and objective categories of coping (problem-focused and emotion-focused). Method: 940 people participated (62.87% women; 37.12% men) aged between 18 and 66 years (x̄= 33.2; dt = 12.01). An exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were carried out, as well as Pearson's correlation and Cronbach's Alpha to examine the reliability and validity of the Spanish adaptation of the CSI-SF. Results: Internal consistency analysis revealed high reliability for all scales, and all adjustment indexes used to examine the CSI-SF Spanish version provided support for its use as an adequate measure of stress coping strategies. Discussion/Conclusions: The CSI-SF scale Spanish version is a test that provides a quick and efficient diagnosis of the coping strategies used in the face of stress in different settings.

Experimental Study of the Deposition of Magnetic Particles on the Walls of Microchannels.

This study analyzes experimentally the deposition of magnetic beads on the walls of a square microchannel by the action of a nearby cubical magnet. The deposition has been studied for different magnetic bead sizes, flow rates, magnetic conditions and with solutions of magnetic and non-magnetic particles. Images of the time evolution of the deposition under the different conditions have been analyzed to determine the spatial distribution of the accumulation and the growth rate of the depositions. It has been found that the way in which the magnetic beads are deposited on the walls of the microchannel depends strongly on their size and the magnetic configuration. The accumulation of the major part of particles is on the wall closest to the magnet and, depending on the size of the particles, near the magnet leading and trailing edges or near the center of the magnet. The experiments with magnetic and non-magnetic particles revealed the screening effect of the non-magnetic particles on the deposition. In this case, the non-magnetic particles displace the deposition toward the region near the center of the magnet and near the trailing edge.

Direct numerical simulation of turbulent dispersion of evaporative aerosol clouds produced by an intense expiratory event.

Airborne particles are a major route for transmission of COVID-19 and many other infectious diseases. When a person talks, sings, coughs, or sneezes, nasal and throat secretions are spewed into the air. After a short initial fragmentation stage, the expelled material is mostly composed of spherical particles of different sizes. While the dynamics of the largest droplets are dominated by gravitational effects, the smaller aerosol particles, mostly transported by means of hydrodynamic drag, form clouds that can remain afloat for long times. In subsaturated air environments, the dependence of pathogen-laden particle dispersion on their size is complicated due to evaporation of the aqueous fraction. Particle dynamics can significantly change when ambient conditions favor rapid evaporation rates that result in a transition from buoyancy-to-drag dominated dispersion regimes. To investigate the effect of particle size and evaporation on pathogen-laden cloud evolution, a direct numerical simulation of a mild cough was coupled with an evaporative Lagrangian particle advection model. The results suggest that while the dispersion of cough particles in the tails of the size distribution are unlikely to be disrupted by evaporative effects, preferential aerosol diameters (30-40 µm) may exhibit significant increases in the residence time and horizontal range under typical ambient conditions. Using estimations of the viral concentration in the spewed fluid and the number of ejected particles in a typical respiratory event, we obtained a map of viral load per volume of air at the end of the cough and the number of virus copies per inhalation in the emitter vicinity.

Direct numerical simulation of the turbulent flow generated during a violent expiratory event.

A main route for SARS-CoV-2 (severe acute respiratory syndrome coronavirus) transmission involves airborne droplets and aerosols generated when a person talks, coughs, or sneezes. The residence time and spatial extent of these virus-laden aerosols are mainly controlled by their size and the ability of the background flow to disperse them. Therefore, a better understanding of the role played by the flow driven by respiratory events is key in estimating the ability of pathogen-laden particles to spread the infection. Here, we numerically investigate the hydrodynamics produced by a violent expiratory event resembling a mild cough. Coughs can be split into an initial jet stage during which air is expelled through mouth and a dissipative phase over which turbulence intensity decays as the puff penetrates the environment. Time-varying exhaled velocity and buoyancy due to temperature differences between the cough and the ambient air affect the overall flow dynamics. The direct numerical simulation (DNS) of an idealized isolated cough is used to characterize the jet/puff dynamics using the trajectory of the leading turbulent vortex ring and extract its topology by fitting an ellipsoid to the exhaled fluid contour. The three-dimensional structure of the simulated cough shows that the assumption of a spheroidal puff front fails to capture the observed ellipsoidal shape. Numerical results suggest that, although analytical models provide reasonable estimates of the distance traveled by the puff, trajectory predictions exhibit larger deviations from the DNS. The fully resolved hydrodynamics presented here can be used to inform new analytical models, leading to improved prediction of cough-induced pathogen-laden aerosol dispersion.

The Italian version of the Work Psychosocial Climate Scale (Escala Clima Psicosocial en el Trabajo).

BACKGROUND: The organizational climate is a quality of the internal environment of an organization that is shared by its members, can be described in terms of the values of the characteristics of an organization, influencing the workers' behaviour. A number of empirical studies have examined the relationship between climate perceptions and a variety of variables such as job satisfaction, performance, psychological well-being, absenteeism and turnover. OBJECTIVE: The aim of the present study was to verify the psychometric properties of the Work Psychosocial Climate Scale in the Italian context. METHODS: The participants were 1063 Italian workers. The survey comprised: Work Psychosocial Climate Scale, Majer D'Amato Organizational Questionnaire 10, Work and Organizational Motivation Inventory, Mindfulness Organising Scale, Job Satisfaction Scale, and Organizational outcomes (Turnover intentions, Exit and neglect, Performance). RESULTS: The results provide evidence for the reliability and validity of the Italian version of the Work Psychosocial Climate Scale, after some modifications of the original version. CONCLUSIONS: The evaluation of the psychosocial climate in an organization is one of most important steps to recognize the indicators of work-related stress. The Work Psychosocial Climate Scale allows to conduct the evaluation with a reduced number of items.

Bayesian Machine Scientist to Compare Data Collapses for the Nikuradse Dataset.

Ever since Nikuradse's experiments on turbulent friction in 1933, there have been theoretical attempts to describe his measurements by collapsing the data into single-variable functions. However, this approach, which is common in other areas of physics and in other fields, is limited by the lack of rigorous quantitative methods to compare alternative data collapses. Here, we address this limitation by using an unsupervised method to find analytic functions that optimally describe each of the data collapses for the Nikuradse dataset. By descaling these analytic functions, we show that a low dispersion of the scaled data does not guarantee that a data collapse is a good description of the original data. In fact, we find that, out of all the proposed data collapses, the original one proposed by Prandtl and Nikuradse over 80 years ago provides the best description of the data so far, and that it also agrees well with recent experimental data, provided that some model parameters are allowed to vary across experiments.

A Bayesian machine scientist to aid in the solution of challenging scientific problems.

Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.

Dynamics of a capsule flowing in a tube under pulsatile flow.

We analyze numerically the behavior of a deformable micro-capsule confined in a pipe under a pulsatile flow. The capsule moves and is deformed by the action of a pulsatile flow inside the tube with a non-null mean velocity. This configuration can be found in the nature and in many bioengineering systems where artificial capsules are driven by micro-pumps through micro-channels. The capsule is considered as a thin hyperelastic membrane, which encloses an internal fluid. As it has been demonstrated in the literature, this model represents a wide range of artificial capsules, for example, the alginate-based capsules, typically used in bioengineering applications. A hybrid isogeometric finite element method and boundary element method based on a T-spline discretization and formulated in the time domain is used to solve the mechanical and hydrodynamical equations. The influence of the relative rigidity of the membrane, frequency and amplitude of the pulsatile flow is studied. Results show that the behavior of the capsule differs from steady flows and it depends strongly on the frequency of the flow and mechanical characteristic of the capsule.

Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.

PURPOSE: Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. METHODS: 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. RESULTS: A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. CONCLUSIONS: Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation.

PURPOSE: Advanced morphology analysis and image-based hemodynamic simulations are increasingly used to assess the rupture risk of intracranial aneurysms (IAs). However, the accuracy of those results strongly depends on the quality of the vessel wall segmentation. METHODS: To evaluate state-of-the-art segmentation approaches, the Multiple Aneurysms AnaTomy CHallenge (MATCH) was announced. Participants carried out segmentation in three anonymized 3D DSA datasets (left and right anterior, posterior circulation) of a patient harboring five IAs. Qualitative and quantitative inter-group comparisons were carried out with respect to aneurysm volumes and ostia. Further, over- and undersegmentation were evaluated based on highly resolved 2D images. Finally, clinically relevant morphological parameters were calculated. RESULTS: Based on the contributions of 26 participating groups, the findings reveal that no consensus regarding segmentation software or underlying algorithms exists. Qualitative similarity of the aneurysm representations was obtained. However, inter-group differences occurred regarding the luminal surface quality, number of vessel branches considered, aneurysm volumes (up to 20%) and ostium surface areas (up to 30%). Further, a systematic oversegmentation of the 3D surfaces was observed with a difference of approximately 10% to the highly resolved 2D reference image. Particularly, the neck of the ruptured aneurysm was overrepresented by all groups except for one. Finally, morphology parameters (e.g., undulation and non-sphericity) varied up to 25%. CONCLUSIONS: MATCH provides an overview of segmentation methodologies for IAs and highlights the variability of surface reconstruction. Further, the study emphasizes the need for careful processing of initial segmentation results for a realistic assessment of clinically relevant morphological parameters.

A Criterion for the Complete Deposition of Magnetic Beads on the Walls of Microchannels.

This paper analyzes numerical simulations of the trajectories of magnetic beads in a microchannel, with a nearby permanent cubical magnet, under different flow and magnetic conditions. Analytically derived local fluid velocities and local magnetic forces have been used to track the particles. A centered position and a lateral position of the magnet above the microchannel are considered. The computed fractions of deposited particles on the walls are compared successfully with a new theoretically derived criterion that imposes a relation between the sizes of the magnet and the microchannel and the particle Stokes and Alfvén numbers to obtain the complete deposition of the flowing particles on the wall. In the cases in which all the particles, initially distributed uniformly across the section of the microchannel, are deposited on the walls, the simulations predict the accumulation of the major part of particles on the wall closest to the magnet and near the first half of the streamwise length of the magnet.

The Computational Fluid Dynamics Rupture Challenge 2013--Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms.

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent "outliers" (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber.

Thrombus formation is a multiscale phenomenon triggered by platelet deposition over a protrombotic surface (eg. a ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in the early diagnosis of thrombotic events, the integration of computational models of thrombus formation at different scales requires a comprehensive understanding of the role and limitation of each modelling approach. We propose three different modelling approaches to predict platelet deposition. Specifically, we consider measurements of platelet deposition under blood flow conditions in a perfusion chamber for different time periods (3, 5, 10, 20 and 30 minutes) at shear rates of 212 s(-1), 1390 s(-1) and 1690 s(-1). Our modelling approaches are: i) a model based on the mass-transfer boundary layer theory; ii) a machine-learning approach; and iii) a phenomenological model. The results indicate that the three approaches on average have median errors of 21%, 20.7% and 14.2%, respectively. Our study demonstrates the feasibility of using an empirical data set as a proxy for a real-patient scenario in which practitioners have accumulated data on a given number of patients and want to obtain a diagnosis for a new patient about whom they only have the current observation of a certain number of variables.