Understanding and Treating Mycobacterium tuberculosis Infection: A Multi-Scale Modeling Approach.

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis (Mtb), remains a significant burden on global health. Central to both host immune responses and antibiotic treatment are structures known as granulomas. In this dissertation we used computational and experimental approaches at a single granuloma level to understand how immune responses to Mtb contribute to both bacterial control and persistence. In addition, we predicted the dynamics of antibiotics in granulomas and designed improved treatment strategies. We built a hybrid multi-scale model of Mtb infection that integrates the cytokines tumor necrosis factor-α (TNF) and interleukin-10 (IL-10). We predicted that a balance of TNF and IL-10 is essential to infection control with minimal host-induced tissue damage. We extended our description of TNF and IL-10 to include simplified models of intracellular signaling driving macrophage polarization, which suggests that the temporal dynamics of macrophage polarization in granulomas are predictive of granuloma outcome. Next, we focused on determining the role of IL-10 in controlling antimicrobial activity. We predicted a transient role for IL-10 in controlling a trade-off between early host immunity antimicrobial responses and tissue damage. This trade-off determines sterilization of granulomas. Lastly, using an experimental model of granuloma formation, we measured significant gradients of TNF in granulomas. xxii We developed a pharmacokinetic and pharmacodynamic model of oral dosing of rifampin and isoniazid used to treat Mtb and incorporated it into our computational model. We predicted that oral antibiotic strategies fail due to sub-optimal exposure in granulomas, which leads to bacterial regrowth between doses. We extended our platform to include a description of inhaled formulations dosed to the lungs with reduced frequencies. We predicted that dosing every two-weeks with an inhaled formulation of isoniazid is feasible with increased sterilization capabilities and reduced toxicity, while an inhaled formulation of rifampin has equivalent sterilization capabilities, but early associated toxicity and infeasible carrier loadings. The keys to understanding immune responses and successful antibiotic treatment of TB lie in the dynamics at the site of infection. Our results help identify the roles of cytokines during Mtb infection, provide new possibilities for immune related therapies, and guide design of better antibiotic strategies.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108883/1/ncilfone_1.pd

Integration of Wi-Fi mobile nodes in a Web of Things Testbed

Abstract The Internet of Things (IoT) is supposed to connect billions of devices to the Internet through IP-based communications. The main goal is to foster a rapid deployment of Web-enabled everyday objects, allowing end users to manage and control smart things in a simple way, by using Web browsers. This paper focuses on the integration of Wi-Fi nodes, hosting HTTP resources, into a Web of Things Testbed (WoTT). The main novelty of the proposed approach is that the WoTT integrates new nodes by using only standard mechanisms, allowing end-users to interact with all Smart Objects without worrying about protocol-specific details

Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences

Looking for an objective parameter to identify early vocal dysfunctions in healthy prceived singers

The finding of minimal laryngeal dysfunctions in professional voice users is essential to prevent the onset of organic vocal pathologies. The purpose of this study is to identify an objective parameter that supports the phoniatric evaluation in detecting minimal laryngeal dysfunctions in singers. 54 professional and non-professional singers have been evaluated with laryngostroboscopy, Multi-Dimensional Voice Program (MDVP), Dysphonia Severity Index (DSI), maximum phonation time (TMF), minimum intensity of sound emission (I-min), maximum frequency (F-max), voice handicap index (VHI), singing voice handicap index (SVHI), manual phonogram and audiometric examination. The SVHI of all the “healthy” singers was on average 23.7 ± 22.5, while that of the “dysfunctional” 20.9 ± 18. No statistically significant difference was found between the SVHI scores of the total of healthy singers compared to the scores of the dysfunctional ones on the VSL (p = 0.6). The between-group comparison of the means of individual parameter values of DSI, TMF, F-max, Jitter, Shimmer, NHR, and SPI was not statistically significant (respectively p = 0.315, 0.2, 0.18, 0.09, 0.2, 0.08, 0.3). The only parameter analyzed that was statistically significant was the I-min (p < 0.05). SVHI is a valid instrument for the evaluation after a therapy but in our experience, it is not useful in distinguishing healthy from dysfunctional patients. The minimum intensity of sound emission measured with the sound level meter (I-low2) resulted a reliable parameter to identify minimal laryngeal dysfunctions and a useful tool in supporting the phoniatric diagnostic-therapeutic process in singers

Unilateral vocal fold paralysis post-thyroidectomy: does early intervention allow for better voice recovery?

Objective: Thyroidectomy is the primary cause of unilateral vocal fold paralysis (UVFP). A delay in rehabilitation may cause dysfunctional phenomena and worsen dysphonia. The main aim is to investigate the impact of early Speech Therapy (ST) on voice recovery in UVFP post-thyroidectomy and propose an appropriate treatment schedule. Patients and methods: 93 patients with UVFP were analysed. 72 presented transient paralysis and 21 permanent ones. Individuals with permanent paralysis were retrospectively divided in two groups. Group A was composed of 11 patients (8 F, 3 M; mean age: 50.5 ± 8.6) who received ST within 8 weeks; Group B comprised 10 patients (7 F, 3 M; mean age: 57 ± 11.5) treated after more than 8 weeks. Videolaryngostroboscopy (VLS) was assessed and both objective and subjective voice parameters were collected. The non-parametric Wilcoxon test was applied to the sample. Results: The resolution of supraglottic compensations was observed in 91% of cases in Group A, whereas in only 40% of cases in Group B. A functional glottal closure occurred in 73% of patients in group A, while it was completely absent in group B. Group A showed a statistically significant difference between the values of Jitter, NHR, TMF and VHI collected pre-ST compared to that collected after 1 year. Conversely, a statistically significant difference was found only for VHI values in group B. Conclusions: Early ST brings benefits to patients with permanent UVFP, both on voice recovery and on quality of life. A ST protocol should be applied both before and after thyroidectomy. The ST treatment should start early after surgery

Quantitative Systems Pharmacology Approaches Applied to Microphysiological Systems (MPS): Data Interpretation and Multi-MPS Integration

Our goal in developing Microphysiological Systems (MPS) technology is to provide an improved approach for more predictive preclinical drug discovery via a highly integrated experimental/computational paradigm. Success will require quantitative characterization of MPSs and mechanistic analysis of experimental findings sufficient to translate resulting insights from in vitro to in vivo. We describe herein a systems pharmacology approach to MPS development and utilization that incorporates more mechanistic detail than traditional pharmacokinetic/pharmacodynamic (PK/PD) models. A series of studies illustrates diverse facets of our approach. First, we demonstrate two case studies: a PK data analysis and an inflammation response––focused on a single MPS, the liver/immune MPS. Building on the single MPS modeling, a theoretical investigation of a four-MPS interactome then provides a quantitative way to consider several pharmacological concepts such as absorption, distribution, metabolism, and excretion in the design of multi-MPS interactome operation and experiments.United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (W911NF-12-2-0039)National Institutes of Health (U.S.) Microphysiological Systems Program (4-UH3-TR000496-03)Massachusetts Institute of Technology. Center for Environmental Health Sciences (NIEHS Grant P30-ES002109

Dynamic balance of pro‐ and anti‐inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long‐term control of infection while also preventing an over‐zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro‐ and anti‐inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host‐directed therapies.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146448/1/imr12671.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146448/2/imr12671_am.pd

Building Confidence in Quantitative Systems Pharmacology Models : An Engineer's Guide to Exploring the Rationale in Model Design and Development

This tutorial promotes good practice for exploring the rationale of systems pharmacology models. A safety systems engineering inspired notation approach provides much needed rigor and transparency in development and application of models for therapeutic discovery and design of intervention strategies. Structured arguments over a model's development, underpinning biological knowledge, and analyses of model behaviors are constructed to determine the confidence that a model is fit for the purpose for which it will be applied

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle

Funding: This work was supported by the Medical Research Council [grant number MR/P014704/1] and the PreDiCT-TB consortium (IMI Joint undertaking grant agreement number 115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EF-PIA companies’ in kind contribution.Progress in shortening the duration of tuberculosis (TB) treatment is hampered by the lack of a predictive model that accurately reflects the diverse environment within the lung. This is important as TB has been shown to produce distinct localisations to different areas of the lung during different disease stages, with the environmental heterogeneity within the lung of factors such as air ventilation, blood perfusion and oxygen tension believed to contribute to the apical localisation witnessed during the post-primary form of the disease. Building upon our previous model of environmental lung heterogeneity, we present a networked metapopulation model that simulates TB across the whole lung, incorporating these notions of environmental heterogeneity across the whole TB life-cycle to show how different stages of the disease are influenced by different environmental and immunological factors. The alveolar tissue in the lung is divided into distinct patches, with each patch representing a portion of the total tissue and containing environmental attributes that reflect the internal conditions at that location. We include populations of bacteria and immune cells in various states, and events are included which determine how the members of the model interact with each other and the environment. By allowing some of these events to be dependent on environmental attributes, we create a set of heterogeneous dynamics, whereby the location of the tissue within the lung determines the disease pathological events that occur there. Our results show that the environmental heterogeneity within the lung is a plausible driving force behind the apical localisation during post-primary disease. After initial infection, bacterial levels will grow in the initial infection location at the base of the lung until an adaptive immune response is initiated. During this period, bacteria are able to disseminate and create new lesions throughout the lung. During the latent stage, the lesions that are situated towards the apex are the largest in size, and once a post-primary immune-suppressing event occurs, it is the uppermost lesions that reach the highest levels of bacterial proliferation. Our sensitivity analysis also shows that it is the differential in blood perfusion, causing reduced immune activity towards the apex, which has the biggest influence of disease outputs.Publisher PDFPeer reviewe