Development of Clinical Decision Support Systems based on Mathematical Models of Physiological Systems

In the last years of research, I focused my studies on different physiological problems. Together with my supervisors, I developed/improved different mathematical models in order to create valid tools useful for a better understanding of important clinical issues. The aim of all this work is to develop tools for learning and understanding cardiac and cerebrovascular physiology as well as pathology, generating research questions and developing clinical decision support systems useful for intensive care unit patients. I. ICP-model Designed for Medical Education We developed a comprehensive cerebral blood flow and intracranial pressure model to simulate and study the complex interactions in cerebrovascular dynamics caused by multiple simultaneous alterations, including normal and abnormal functional states of auto-regulation of the brain. Individual published equations (derived from prior animal and human studies) were implemented into a comprehensive simulation program. Included in the normal physiological modelling was: intracranial pressure, cerebral blood flow, blood pressure, and carbon dioxide (CO2) partial pressure. We also added external and pathological perturbations, such as head up position and intracranial haemorrhage. The model performed clinically realistically given inputs of published traumatized patients, and cases encountered by clinicians. The pulsatile nature of the output graphics was easy for clinicians to interpret. The manoeuvres simulated include changes of basic physiological inputs (e.g. blood pressure, central venous pressure, CO2 tension, head up position, and respiratory effects on vascular pressures) as well as pathological inputs (e.g. acute intracranial bleeding, and obstruction of cerebrospinal outflow). Based on the results, we believe the model would be useful to teach complex relationships of brain haemodynamics and study clinical research questions such as the optimal head-up position, the effects of intracranial haemorrhage on cerebral haemodynamics, as well as the best CO2 concentration to reach the optimal compromise between intracranial pressure and perfusion. We believe this model would be useful for both beginners and advanced learners. It could be used by practicing clinicians to model individual patients (entering the effects of needed clinical manipulations, and then running the model to test for optimal combinations of therapeutic manoeuvres). II. A Heterogeneous Cerebrovascular Mathematical Model Cerebrovascular pathologies are extremely complex, due to the multitude of factors acting simultaneously on cerebral haemodynamics. In this work, the mathematical model of cerebral haemodynamics and intracranial pressure dynamics, described in the point I, is extended to account for heterogeneity in cerebral blood flow. The model includes the Circle of Willis, six regional districts independently regulated by autoregulation and CO2 reactivity, distal cortical anastomoses, venous circulation, the cerebrospinal fluid circulation, and the intracranial pressure-volume relationship. Results agree with data in the literature and highlight the existence of a monotonic relationship between transient hyperemic response and the autoregulation gain. During unilateral internal carotid artery stenosis, local blood flow regulation is progressively lost in the ipsilateral territory with the presence of a steal phenomenon, while the anterior communicating artery plays the major role to redistribute the available blood flow. Conversely, distal collateral circulation plays a major role during unilateral occlusion of the middle cerebral artery. In conclusion, the model is able to reproduce several different pathological conditions characterized by heterogeneity in cerebrovascular haemodynamics and can not only explain generalized results in terms of physiological mechanisms involved, but also, by individualizing parameters, may represent a valuable tool to help with difficult clinical decisions. III. Effect of Cushing Response on Systemic Arterial Pressure. During cerebral hypoxic conditions, the sympathetic system causes an increase in arterial pressure (Cushing response), creating a link between the cerebral and the systemic circulation. This work investigates the complex relationships among cerebrovascular dynamics, intracranial pressure, Cushing response, and short-term systemic regulation, during plateau waves, by means of an original mathematical model. The model incorporates the pulsating heart, the pulmonary circulation and the systemic circulation, with an accurate description of the cerebral circulation and the intracranial pressure dynamics (same model as in the first paragraph). Various regulatory mechanisms are included: cerebral autoregulation, local blood flow control by oxygen (O2) and/or CO2 changes, sympathetic and vagal regulation of cardiovascular parameters by several reflex mechanisms (chemoreceptors, lung-stretch receptors, baroreceptors). The Cushing response has been described assuming a dramatic increase in sympathetic activity to vessels during a fall in brain O2 delivery. With this assumption, the model is able to simulate the cardiovascular effects experimentally observed when intracranial pressure is artificially elevated and maintained at constant level (arterial pressure increase and bradicardia). According to the model, these effects arise from the interaction between the Cushing response and the baroreflex response (secondary to arterial pressure increase). Then, patients with severe head injury have been simulated by reducing intracranial compliance and cerebrospinal fluid reabsorption. With these changes, oscillations with plateau waves developed. In these conditions, model results indicate that the Cushing response may have both positive effects, reducing the duration of the plateau phase via an increase in cerebral perfusion pressure, and negative effects, increasing the intracranial pressure plateau level, with a risk of greater compression of the cerebral vessels. This model may be of value to assist clinicians in finding the balance between clinical benefits of the Cushing response and its shortcomings. IV. Comprehensive Cardiopulmonary Simulation Model for the Analysis of Hypercapnic Respiratory Failure We developed a new comprehensive cardiopulmonary model that takes into account the mutual interactions between the cardiovascular and the respiratory systems along with their short-term regulatory mechanisms. The model includes the heart, systemic and pulmonary circulations, lung mechanics, gas exchange and transport equations, and cardio-ventilatory control. Results show good agreement with published patient data in case of normoxic and hyperoxic hypercapnia simulations. In particular, simulations predict a moderate increase in mean systemic arterial pressure and heart rate, with almost no change in cardiac output, paralleled by a relevant increase in minute ventilation, tidal volume and respiratory rate. The model can represent a valid tool for clinical practice and medical research, providing an alternative way to experience-based clinical decisions. In conclusion, models are not only capable of summarizing current knowledge, but also identifying missing knowledge. In the former case they can serve as training aids for teaching the operation of complex systems, especially if the model can be used to demonstrate the outcome of experiments. In the latter case they generate experiments to be performed to gather the missing data.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Biomolecular studies in Alzheimer’s Disease models: investigations in vitro and in vivo

The Alzheimer’s disease (AD), the most prevalent form of age-related dementia, is a multifactorial and heterogeneous neurodegenerative disease. The molecular mechanisms underlying the pathogenesis of AD are yet largely unknown. However, the etiopathogenesis of AD likely resides in the interaction between genetic and environmental risk factors. Among the different factors that contribute to the pathogenesis of AD, amyloid-beta peptides and the genetic risk factor apoE4 are prominent on the basis of genetic evidence and experimental data. ApoE4 transgenic mice have deficits in spatial learning and memory associated with inflammation and brain atrophy. Evidences suggest that apoE4 is implicated in amyloid-beta accumulation, imbalance of cellular antioxidant system and in apoptotic phenomena. The mechanisms by which apoE4 interacts with other AD risk factors leading to an increased susceptibility to the dementia are still unknown. The aim of this research was to provide new insights into molecular mechanisms of AD neurodegeneration, investigating the effect of amyloid-beta peptides and apoE4 genotype on the modulation of genes and proteins differently involved in cellular processes related to aging and oxidative balance such as PIN1, SIRT1, PSEN1, BDNF, TRX1 and GRX1. In particular, we used human neuroblastoma cells exposed to amyloid-beta or apoE3 and apoE4 proteins at different time-points, and selected brain regions of human apoE3 and apoE4 targeted replacement mice, as in vitro and in vivo models, respectively. All genes and proteins studied in the present investigation are modulated by amyloid-beta and apoE4 in different ways, suggesting their involvement in the neurodegenerative mechanisms underlying the AD. Finally, these proteins might represent novel potential diagnostic and therapeutic targets in AD.

Nature and relevance of inherited blueprints on business models choices and innovation: an assessment

One important metaphor, referred to biological theories, used to investigate on organizational and business strategy issues is the metaphor about heredity; an area requiring further investigation is the extent to which the characteristics of blueprints inherited from the parent, helps in explaining subsequent development of the spawned ventures. In order to shed a light on the tension between inherited patterns and the new trajectory that may characterize spawned ventures’ development we propose a model aimed at investigating which blueprints elements might exert an effect on business model design choices and to which extent their persistence (or abandonment) determines subsequent business model innovation. Under the assumption that academic and corporate institutions transmit different genes to their spin-offs, we hence expect to have heterogeneity in elements that affect business model design choices and its subsequent evolution. This is the reason why we carry on a twofold analysis in the biotech (meta)industry: under a multiple-case research design, business model and especially its fundamental design elements and themes scholars individuated to decompose the construct, have been thoroughly analysed. Our purpose is to isolate the dimensions of business model that may have been the object of legacy and the ones along which an experimentation and learning process is more likely to happen, bearing in mind that differences between academic and corporate might not be that evident as expected, especially considering that business model innovation may occur.

Estimation of Quasi-Rational DSGE Models

Small-scale dynamic stochastic general equilibrium have been treated as the benchmark of much of the monetary policy literature, given their ability to explain the impact of monetary policy on output, inflation and financial markets. One cause of the empirical failure of New Keynesian models is partially due to the Rational Expectations (RE) paradigm, which entails a tight structure on the dynamics of the system. Under this hypothesis, the agents are assumed to know the data genereting process. In this paper, we propose the econometric analysis of New Keynesian DSGE models under an alternative expectations generating paradigm, which can be regarded as an intermediate position between rational expectations and learning, nameley an adapted version of the "Quasi-Rational" Expectatations (QRE) hypothesis. Given the agents' statistical model, we build a pseudo-structural form from the baseline system of Euler equations, imposing that the length of the reduced form is the same as in the `best' statistical model.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Learning from failure--rationale and design for a study about discontinuation of randomized trials (DISCO study)

Background Randomized controlled trials (RCTs) may be discontinued because of apparent harm, benefit, or futility. Other RCTs are discontinued early because of insufficient recruitment. Trial discontinuation has ethical implications, because participants consent on the premise of contributing to new medical knowledge, Research Ethics Committees (RECs) spend considerable effort reviewing study protocols, and limited resources for conducting research are wasted. Currently, little is known regarding the frequency and characteristics of discontinued RCTs. Methods/Design Our aims are, first, to determine the prevalence of RCT discontinuation for specific reasons; second, to determine whether the risk of RCT discontinuation for specific reasons differs between investigator- and industry-initiated RCTs; third, to identify risk factors for RCT discontinuation due to insufficient recruitment; fourth, to determine at what stage RCTs are discontinued; and fifth, to examine the publication history of discontinued RCTs. We are currently assembling a multicenter cohort of RCTs based on protocols approved between 2000 and 2002/3 by 6 RECs in Switzerland, Germany, and Canada. We are extracting data on RCT characteristics and planned recruitment for all included protocols. Completion and publication status is determined using information from correspondence between investigators and RECs, publications identified through literature searches, or by contacting the investigators. We will use multivariable regression models to identify risk factors for trial discontinuation due to insufficient recruitment. We aim to include over 1000 RCTs of which an anticipated 150 will have been discontinued due to insufficient recruitment. Discussion Our study will provide insights into the prevalence and characteristics of RCTs that were discontinued. Effective recruitment strategies and the anticipation of problems are key issues in the planning and evaluation of trials by investigators, Clinical Trial Units, RECs and funding agencies. Identification and modification of barriers to successful study completion at an early stage could help to reduce the risk of trial discontinuation, save limited resources, and enable RCTs to better meet their ethical requirements.

Effect of Classroom Gender Composition on Students' Development of Self-Regulated Learning Competencies

Success in any field depends on a complex interplay among environmental and personal factors. A key set of personal factors for success in academic settings are those associated with self-regulated learners (SRL). Self-regulated learners choose their own goals, select and organize their learning strategies, and self-monitor their effectiveness. Behaviors and attitudes consistent with self-regulated learning also contribute to self-confidence, which may be important for members of underrepresented groups such as women in engineering. This exploratory study, drawing on the concept of "critical mass", examines the relationship between the personal factors that identify a self-regulated learner and the environmental factors related to gender composition of engineering classrooms. Results indicate that a relatively student gender-balanced classroom and gender match between students and their instructors provide for the development of many adaptive SRL behaviors and attitudes.

Rationale, scope, and 20-year experience of vascular surgical training with lifelike pulsatile flow models

Vascular surgical training currently has to cope with various challenges, including restrictions on work hours, significant reduction of open surgical training cases in many countries, an increasing diversity of open and endovascular procedures, and distinct expectations by trainees. Even more important, patients and the public no longer accept a "learning by doing" training philosophy that leaves the learning curve on the patient's side. The Vascular International (VI) Foundation and School aims to overcome these obstacles by training conventional vascular and endovascular techniques before they are applied on patients. To achieve largely realistic training conditions, lifelike pulsatile models with exchangeable synthetic arterial inlays were created to practice carotid endarterectomy and patch plasty, open abdominal aortic aneurysm surgery, and peripheral bypass surgery, as well as for endovascular procedures, including endovascular aneurysm repair, thoracic endovascular aortic repair, peripheral balloon dilatation, and stenting. All models are equipped with a small pressure pump inside to create pulsatile flow conditions with variable peak pressures of ~90 mm Hg. The VI course schedule consists of a series of 2-hour modules teaching different open or endovascular procedures step-by-step in a standardized fashion. Trainees practice in pairs with continuous supervision and intensive advice provided by highly experienced vascular surgical trainers (trainer-to-trainee ratio is 1:4). Several evaluations of these courses show that tutor-assisted training on lifelike models in an educational-centered and motivated environment is associated with a significant increase of general and specific vascular surgical technical competence within a short period of time. Future studies should evaluate whether these benefits positively influence the future learning curve of vascular surgical trainees and clarify to what extent sophisticated models are useful to assess the level of technical skills of vascular surgical residents at national or international board examinations. This article gives an overview of our experiences of >20 years of practical training of beginners and advanced vascular surgeons using lifelike pulsatile vascular surgical training models.

LEARNING FROM NEAR MISSES IN MEDICATION ERRORS: A BAYESIAN APPROACH

Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of events prevented injury. We use data from a large voluntary reporting system of 836,174 medication errors from 1999 to 2005 to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models for estimating the log odds of selecting a given cause (or contributing factor) of error given harm has occurred and the log odds of selecting the same cause given that harm did not occur. The posterior distribution of the correlation between these two vectors of log-odds is used as a measure of the evidence supporting the use of data from near misses and their causes and contributing factors to prevent medical errors. In addition, we identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm versus no harm. These causes and contributing factors should also be a focus in the design of prevention strategies. This paper provides important evidence on the utility of data from near misses, which constitute the vast majority of errors in our data.

Effects of instructional changes on student learning of electrochemistry in an IB chemistry course

This study investigated the effectiveness of incorporating several new instructional strategies into an International Baccalaureate (IB) chemistry course in terms of how they supported high school seniors’ understanding of electrochemistry. The three new methods used were (a) providing opportunities for visualization of particle movement by student manipulation of physical models and interactive computer simulations, (b) explicitly addressing common misconceptions identified in the literature, and (c) teaching an algorithmic, step-wise approach for determining the products of an aqueous solution electrolysis. Changes in student understanding were assessed through test scores on both internally and externally administered exams over a two-year period. It was found that visualization practice and explicit misconception instruction improved student understanding, but the effect was more apparent in the short-term. The data suggested that instruction time spent on algorithm practice was insufficient to cause significant test score improvement. There was, however, a substantial increase in the percentage of the experimental group students who chose to answer an optional electrochemistry-related external exam question, indicating an increase in student confidence. Implications for future instruction are discussed.