Predicting Tumor Response to Radiotherapy Based on Estimation of Non-Treatment Parameters

Though clinicians can now collect detailed information about a variety of tumor characteristics as a tumor evolves, it remains difficult to predict the efficacy of a given treatment prior to administration. Additionally, the process of data collection may be invasive and expensive. Thus, the creation of a framework for predicting patient response to treatment using only information collected prior to the start of treatment could be invaluable. In this study, we employ ordinary differential equation models for tumor growth and utilize synthetic data from a cellular automaton model for calibration. We investigate which parameters have the most influence upon treatment efficacy by comparing parameter distributions associated with treatment outcomes. Additionally, we develop a framework for estimating the probability of observing complete tumor remission following a simulated radiotherapy regimen based only on a patient’s non-treatment parameters, so that treatment efficacy could be predicted prior to administration

Bayesian information-theoretic calibration of patient-specific radiotherapy sensitivity parameters for informing effective scanning protocols in cancer

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients' parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of $n$ high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget

Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types

The Lotka-Volterra model is widely used to model interactions between two species. Here, we generate synthetic data mimicking competitive, mutualistic and antagonistic interactions between two tumor cell lines, and then use the Lotka-Volterra model to infer the interaction type. Structural identifiability of the Lotka-Volterra model is confirmed, and practical identifiability is assessed for three experimental designs: (a) use of a single data set, with a mixture of both cell lines observed over time, (b) a sequential design where growth rates and carrying capacities are estimated using data from experiments in which each cell line is grown in isolation, and then interaction parameters are estimated from an experiment involving a mixture of both cell lines, and (c) a parallel experimental design where all model parameters are fitted to data from two mixtures simultaneously. In addition to assessing each design for practical identifiability, we investigate how the predictive power of the model-i.e., its ability to fit data for initial ratios other than those to which it was calibrated-is affected by the choice of experimental design. The parallel calibration procedure is found to be optimal and is further tested on in silico data generated from a spatially-resolved cellular automaton model, which accounts for oxygen consumption and allows for variation in the intensity level of the interaction between the two cell lines. We use this study to highlight the care that must be taken when interpreting parameter estimates for the spatially-averaged Lotka-Volterra model when it is calibrated against data produced by the spatially-resolved cellular automaton model, since baseline competition for space and resources in the CA model may contribute to a discrepancy between the type of interaction used to generate the CA data and the type of interaction inferred by the LV model.Comment: 25 pages, 18 figure

Suicide Screening in Primary Care: Use of an Electronic Screener to Assess Suicidality and Improve Provider Follow-Up for Adolescents

Purpose The purpose of this study was to assess the feasibility of using an existing computer decision support system to screen adolescent patients for suicidality and provide follow-up guidance to clinicians in a primary care setting. Predictors of patient endorsement of suicidality and provider documentation of follow-up were examined. Methods A prospective cohort study was conducted to examine the implementation of a CDSS that screened adolescent patients for suicidality and provided follow-up recommendations to providers. The intervention was implemented for patients aged 12–20 years in two primary care clinics in Indianapolis, Indiana. Results The sample included 2,134 adolescent patients (51% female; 60% black; mean age = 14.6 years [standard deviation = 2.1]). Just over 6% of patients screened positive for suicidality. A positive endorsement of suicidality was more common among patients who were female, depressed, and seen by an adolescent−medicine board-certified provider as opposed to general pediatric provider. Providers documented follow-up action for 83% of patients who screened positive for suicidality. Documentation of follow-up action was correlated with clinic site and Hispanic race. The majority of patients who endorsed suicidality (71%) were deemed not actively suicidal after assessment by their provider. Conclusions Incorporating adolescent suicide screening and provider follow-up guidance into an existing computer decision support system in primary care is feasible and well utilized by providers. Female gender and depressive symptoms are consistently associated with suicidality among adolescents, although not all suicidal adolescents are depressed. Universal use of a multi-item suicide screener that assesses recency might more effectively identify suicidal adolescents

Combating Tuberculosis: Using Time-Dependent Sensitivity Analysis to Develop Strategies for Treatment and Prevention

Although many organizations throughout the world have worked tirelessly to control tuberculosis (TB) epidemics, no country has yet been able to eradicate the disease completely. We present two compartmental models representing the spread of a TB epidemic through a population. The first is a general TB model; the second is an adaptation for regions in which HIV is prevalent, accounting for the effects of TB/HIV co-infection. Using active subspaces, we conduct time-dependent sensitivity analysis on both models to explore the significance of certain parameters with respect to the spread of TB. We use the results of this sensitivity analysis to determine the most effective strategies for treatment and prevention throughout the epidemic

Radiative association and inverse predissociation of oxygen atoms

The formation of \mbox{O}_2 by radiative association and by inverse predissociation of ground state oxygen atoms is studied using quantum-mechanical methods. Cross sections, emission spectra, and rate coefficients are presented and compared with prior experimental and theoretical results. At temperatures below 1000~K radiative association occurs by approach along the $1\,{}^3\Pi_u$ state of \mbox{O}_2 and above 1000~K inverse predissociation through the \mbox{B}\,{}^3\Sigma_u^- state is the dominant mechanism. This conclusion is supported by a quantitative comparison between the calculations and data obtained from hot oxygen plasma spectroscopy.Comment: submitted to Phys. Rev. A (Sept. 7., 1994), 19 pages, 4 figures, latex (revtex3.0 and epsf.sty

An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models

The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated

Effect of Behavioural Practice Targeted at the Motor Action Selection Network After Stroke

Motor action selection engages a network of frontal and parietal brain regions. After stroke, individuals activate a similar network, however, activation is higher, especially in the contralesional hemisphere. The current study examined the effect of practice on action selection performance and brain activation after stroke. Sixteen individuals with chronic stroke (Upper Extremity Fugl–Meyer motor score range: 18–61) moved a joystick with the more-impaired hand in two conditions: Select (externally cued choice; move right or left based on an abstract rule) and Execute (simple response; move same direction every trial). On Day 1, reaction time (RT) was longer in Select compared to Execute, which corresponded to increased activation primarily in regions in the contralesional action selection network including dorsal premotor, supplementary motor, anterior cingulate and parietal cortices. After 4 days of practice, behavioural performance improved (decreased RT), and only contralesional parietal cortex significantly increased during Select. Higher brain activation on Day 1 in the bilateral action selection network, dorsolateral prefrontal cortex and contralesional sensory cortex predicted better performance on Day 4. Overall, practice led to improved action selection performance and reduced brain activation. Systematic changes in practice conditions may allow the targeting of specific components of the motor network during rehabilitation after stroke

The role of taxonomic expertise in interpretation of metabarcoding studies

Abstract The performance of DNA metabarcoding approaches for characterizing biodiversity can be influenced by multiple factors. Here, we used morphological assessment of taxa in zooplankton samples to develop a large barcode database and to assess the congruence of taxonomic identification with metabarcoding under different conditions. We analysed taxonomic assignment of metabarcoded samples using two genetic markers (COI, 18S V1–2), two types of clustering into molecular operational taxonomic units (OTUs, ZOTUs), and three methods for taxonomic assignment (RDP Classifier, BLASTn to GenBank, BLASTn to a local barcode database). The local database includes 1042 COI and 1108 18S (SSU) barcode sequences, and we added new high-quality sequences to GenBank for both markers, including 109 contributions at the species level. The number of phyla detected and the number of taxa identified to phylum varied between a genetic marker and among the three methods used for taxonomic assignments. Blasting the metabarcodes to the local database generated multiple unique contributions to identify OTUs and ZOTUs. We argue that a multi-marker approach combined with taxonomic expertise to develop a curated, vouchered, local barcode database increases taxon detection with metabarcoding, and its potential as a tool for zooplankton biodiversity surveys

Biodiversity of protists and nematodes in the wild nonhuman primate gut

Documenting the natural diversity of eukaryotic organisms in the nonhuman primate (NHP) gut is important for understanding the evolution of the mammalian gut microbiome, its role in digestion, health and disease, and the consequences of anthropogenic change on primate biology and conservation. Despite the ecological significance of gut-associated eukaryotes, little is known about the factors that influence their assembly and diversity in mammals. In this study, we used an 18S rRNA gene fragment metabarcoding approach to assess the eukaryotic assemblage of 62 individuals representing 16 NHP species. We find that cercopithecoids, and especially the cercopithecines, have substantially higher alpha diversity than other NHP groups. Gut-associated protists and nematodes are widespread among NHPs, consistent with their ancient association with NHP hosts. However, we do not find a consistent signal of phylosymbiosis or host-species specificity. Rather, gut eukaryotes are only weakly structured by primate phylogeny with minimal signal from diet, in contrast to previous reports of NHP gut bacteria. The results of this study indicate that gut-associated eukaryotes offer different information than gut-associated bacteria and add to our understanding of the structure of the gut microbiome.Fil: Mann, Allison E.. University of British Columbia; CanadáFil: Mazel, Florent. University of British Columbia; CanadáFil: Lemay, Matthew A.. University of British Columbia; CanadáFil: Morien, Evan. University of British Columbia; CanadáFil: Billy, Vincent. University of British Columbia; CanadáFil: Kowalewski, Miguel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia". Estación Biológica de Usos Múltiples (Sede Corrientes); ArgentinaFil: Di Fiore, Anthony. University of Texas at Austin; Estados UnidosFil: Link, Andrés. Universidad de los Andes; ColombiaFil: Goldberg, Tony L.. University of Wisconsin; Estados UnidosFil: Tecot, Stacey. University of Arizona; Estados UnidosFil: Baden, Andrea L.. City University Of New York. Hunter College; Estados UnidosFil: Gomez, Andres. University of Minnesota; Estados UnidosFil: Sauther, Michelle L.. State University of Colorado at Boulder; Estados UnidosFil: Cuozzo, Frank P.. Lajuma Research Centre; SudáfricaFil: Rice, Gillian A. O.. Dartmouth College; Estados UnidosFil: Dominy, Nathaniel J.. Dartmouth College; Estados UnidosFil: Stumpf, Rebecca. University of Illinois at Urbana; Estados UnidosFil: Lewis, Rebecca J.. University of Texas at Austin; Estados UnidosFil: Swedell, Larissa. University of Cape Town; Sudáfrica. City University of New York; Estados UnidosFil: Amato, Katherine. Northwestern University; Estados UnidosFil: Wegener Parfrey, Laura. University of British Columbia; Canad