King Ghidorah technique for a trifurcation lesion in a very high-risk NSTEMI.

A 66-year-old male Jehovah's Witness arrived at the emergency room having experienced typical persistent angina.

Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling.

Fibroblasts in a confluent monolayer are known to adopt elongated morphologies in which cells are oriented parallel to their neighbors. We collected and analyzed new microscopy movies to show that confluent fibroblasts are motile and that neighboring cells often move in anti-parallel directions in a collective motion phenomenon we refer to as "fluidization" of the cell population. We used machine learning to perform cell tracking for each movie and then leveraged topological data analysis (TDA) to show that time-varying point-clouds generated by the tracks contain significant topological information content that is driven by fluidization, i.e., the anti-parallel movement of individual neighboring cells and neighboring groups of cells over long distances. We then utilized the TDA summaries extracted from each movie to perform Bayesian parameter estimation for the D'Orsgona model, an agent-based model (ABM) known to produce a wide array of different patterns, including patterns that are qualitatively similar to fluidization. Although the D'Orsgona ABM is a phenomenological model that only describes inter-cellular attraction and repulsion, the estimated region of D'Orsogna model parameter space was consistent across all movies, suggesting that a specific level of inter-cellular repulsion force at close range may be a mechanism that helps drive fluidization patterns in confluent mesenchymal cell populations.

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding.

Surface plasmon resonance (SPR) is an extensively used technique to characterize antigen-antibody interactions. Affinity measurements by SPR typically involve testing the binding of antigen in solution to monoclonal antibodies (mAbs) immobilized on a chip and fitting the kinetics data using 1:1 Langmuir binding model to derive rate constants. However, when it is necessary to immobilize antigens instead of the mAbs, a bivalent analyte (1:2) binding model is required for kinetics analysis. This model is lacking in data analysis packages associated with high throughput SPR instruments and the packages containing this model do not explore multiple local minima and parameter identifiability issues that are common in non-linear optimization. Therefore, we developed a method to use a system of ordinary differential equations for analyzing 1:2 binding kinetics data. Salient features of this method include a grid search on parameter initialization and a profile likelihood approach to determine parameter identifiability. Using this method we found a non-identifiable parameter in data set collected under the standard experimental design. A simulation-guided improved experimental design led to reliable estimation of all rate constants. The method and approach developed here for analyzing 1:2 binding kinetics data will be valuable for expeditious therapeutic antibody discovery research.

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa.

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

Estimation of Parameter Distributions for Reaction-Diffusion Equations with Competition using Aggregate Spatiotemporal Data.

Reaction-diffusion equations have been used to model a wide range of biological phenomenon related to population spread and proliferation from ecology to cancer. It is commonly assumed that individuals in a population have homogeneous diffusion and growth rates; however, this assumption can be inaccurate when the population is intrinsically divided into many distinct subpopulations that compete with each other. In previous work, the task of inferring the degree of phenotypic heterogeneity between subpopulations from total population density has been performed within a framework that combines parameter distribution estimation with reaction-diffusion models. Here, we extend this approach so that it is compatible with reaction-diffusion models that include competition between subpopulations. We use a reaction-diffusion model of glioblastoma multiforme, an aggressive type of brain cancer, to test our approach on simulated data that are similar to measurements that could be collected in practice. We use Prokhorov metric framework and convert the reaction-diffusion model to a random differential equation model to estimate joint distributions of diffusion and growth rates among heterogeneous subpopulations. We then compare the new random differential equation model performance against other partial differential equation models' performance. We find that the random differential equation is more capable at predicting the cell density compared to other models while being more time efficient. Finally, we use k-means clustering to predict the number of subpopulations based on the recovered distributions.

Simultaneous Measurement of Striatal Dopamine and Hydrogen Peroxide Transients Associated with L-DOPA Induced Rotation in Hemiparkinsonian Rats.

Parkinson's disease (PD) is a neurodegenerative disorder commonly treated with levodopa (L-DOPA), which eventually induces abnormal involuntary movements (AIMs). The neurochemical contributors to these dyskinesias are unknown; however, several lines of evidence indicate an interplay of dopamine (DA) and oxidative stress. Here, DA and hydrogen peroxide (H2O2) were simultaneously monitored at discrete recording sites in the dorsal striata of hemiparkinsonian rats using fast-scan cyclic voltammetry. Mass spectrometry imaging validated the lesions. Hemiparkinsonian rats exhibited classic L-DOPA-induced AIMs and rotations as well as increased DA and H2O2 tone over saline controls after 1 week of treatment. By week 3, DA tone remained elevated beyond that of controls, but H2O2 tone was largely normalized. At this time point, rapid chemical transients were time-locked with spontaneous bouts of rotation. Striatal H2O2 rapidly increased with the initiation of contraversive rotational behaviors in lesioned L-DOPA animals, in both hemispheres. DA signals simultaneously decreased with rotation onset. The results support a role for these striatal neuromodulators in the adaptive changes that occur with L-DOPA treatment in PD and reveal a precise interplay between DA and H2O2 in the initiation of involuntary locomotion.

Evaluación de la resistencia a los antibióticos de cepas de Escherichia coli aisladas en carne de cerdo comercializada en los mercados municipales de la ciudad de Guatemala / Evaluation of antibiotics resistance of Escherichia coli strains isolated in pork sold in municipal markets of Guatemala City

La resistencia a los antimicrobianos es un problema de salud pública a nivel mundial que va en aumento y se ve reflejada en la falta de eficacia de los tratamientos de infecciones bacterianas con antibióticos en humanos y en animales. El presente estudio tuvo como objetivo evaluar la resistencia a los antibióticos de cepas de Escherichia coli aisladas en carne de cerdo expendida en los mercados municipales de la ciudad de Guatemala. Se identificaron los antibióticos que presentaron mayor resistencia y mayor sensibilidad in vitro frente a las cepas de E. coli aisladas a partir de 76 muestras de carne de cerdo. Se realizó un muestreo aleatorio simple con afijación proporcional por mercado. Para la identificación de las cepas de E. coli se utilizó la prueba de IMViC y para evaluar la resistencia a los antimicrobianos se utilizó la prueba de Kirby Bauer empleando 9 antibióticos. Se aisló E. coli en el 55% (42/76) de las muestras. La resistencia en las 42 cepas aisladas fue: tetraciclina (83%) neomicina (50%) y sulfametoxasole + trimetoprim (50%). 83% de las cepas (35/42) fueron resistentes a 2 antibióticos y 50% (21/42) a 3 antibióticos o más. Se obtuvo mayor sensibilidad con ceftriaxona (91%), amikacina (83%), gentamicina (65%) y ácido nalidíxico (65%). Se concluye que existe resistencia a los antibióticos evaluados, lo que constituye un riesgo para la salud pública ya que se encuentra en cepas aisladas en un alimento para consumo humano.

Antimicrobial resistance is a global public health threat that is increasing and is reflected in the lack of efficacy of bacterial infection treatments with antibiotics in humans and animals. The objective of this study was to evaluate the resistance to antibiotics of Escherichia coli strains isolated from pork in the municipal markets of Guatemala City. Antibiotics with the highest resistance and those with the highest sensitivity in vitro against the strains of E. coli were evaluated. A simple random sampling was carried out with proportional allocation by market, and 76 samples were collected. IMViC test was used to identify the E. coli strains, and antibiotics resistance was evaluated using the Kirby Bauer with nine different antibiotics. E. coli was isolated in 55% (42/76) of the samples. Resistance was evaluated in the 42 isolates. Antibiotic resistance was detected to tetracycline (83%), neomycin (50%), and sulfamethoxazole + trimethoprim (50%). All isolates presented resistance to at least one antibiotic; it was determined that 83% (35/42) showed resistance to two antibiotics and 50% (21/42) showed resistance to three antibiotics or more. The sensitivity obtained was higher for ceftriaxone (91%), amikacin (83%), gentamicin (65%), and nalidixic acid (65%). In conclusion, antibiotic resistance was detected, which constitutes a risk to public health since it is found in isolated strains in food for human consumption.

Predicting Inter-individual Variability During Lipid Resuscitation of Bupivacaine Cardiotoxicity in Rats: A Virtual Population Modeling Study.

INTRODUCTION: Intravenous lipid emulsions (ILE) have been credited for successful resuscitation in drug intoxication cases where other cardiac life-support methods have failed. However, inter-individual variability can function as a confounder that challenges our ability to define the scope of efficacy for lipid interventions, particularly as relevant data are scarce. To address this challenge, we developed a quantitative systems pharmacology model to predict outcome variability and shed light on causal mechanisms in a virtual population of rats subjected to bupivacaine toxicity and ILE intervention. MATERIALS AND METHODS: We combined a physiologically based pharmacokinetic-pharmacodynamic model with data from a small study in Sprague-Dawley rats to characterize individual-specific cardiac responses to lipid infusion. We used the resulting individual parameter estimates to posit a population distribution of responses to lipid infusion. On that basis, we constructed a large virtual population of rats (N = 10,000) undergoing lipid therapy following bupivacaine cardiotoxicity. RESULTS: Using unsupervised clustering to assign resuscitation endpoints, our simulations predicted that treatment with a 30% lipid emulsion increases bupivacaine median lethal dose (LD50) by 46% when compared with a simulated control fluid. Prior experimental findings indicated an LD50 increase of 48%. Causal analysis of the population data suggested that muscle accumulation rather than liver accumulation of bupivacaine drives survival outcomes. CONCLUSION: Our results represent a successful prediction of complex, dynamic physiological outcomes over a virtual population. Despite being informed by very limited data, our mechanistic model predicted a plausible range of treatment outcomes that accurately predicts changes in LD50 when extrapolated to putatively toxic doses of bupivacaine. Furthermore, causal analysis of the predicted survival outcomes indicated a critical synergy between scavenging and direct cardiotonic mechanisms of ILE action.

Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis.

Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid arthritis. The structure of the resulting vessel networks determines their ability to deliver nutrients and remove waste products from biological tissues. Here we simulate the Anderson-Chaplain model of angiogenesis at different parameter values and quantify the vessel architectures of the resulting synthetic data. Specifically, we propose a topological data analysis (TDA) pipeline for systematic analysis of the model. TDA is a vibrant and relatively new field of computational mathematics for studying the shape of data. We compute topological and standard descriptors of model simulations generated by different parameter values. We show that TDA of model simulation data stratifies parameter space into regions with similar vessel morphology. The methodologies proposed here are widely applicable to other synthetic and experimental data including wound healing, development, and plant biology.

Learning differential equation models from stochastic agent-based model simulations.

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data.

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2].

Learning Equations from Biological Data with Limited Time Samples.

Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets; however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data are sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions.

A tutorial review of mathematical techniques for quantifying tumor heterogeneity.

Intra-tumor and inter-patient heterogeneity are two challenges in developing mathematical models for precision medicine diagnostics. Here we review several techniques that can be used to aid the mathematical modeller in inferring and quantifying both sources of heterogeneity from patient data. These techniques include virtual populations, nonlinear mixed effects modeling, non-parametric estimation, Bayesian techniques, and machine learning. We create simulated virtual populations in this study and then apply the four remaining methods to these datasets to highlight the strengths and weak-nesses of each technique. We provide all code used in this review at https://github.com/jtnardin/Tumor-Heterogeneity/ so that this study may serve as a tutorial for the mathematical modelling community. This review article was a product of a Tumor Heterogeneity Working Group as part of the 2018-2019 Program on Statistical, Mathematical, and Computational Methods for Precision Medicine which took place at the Statistical and Applied Mathematical Sciences Institute.

Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.

BACKGROUND: Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative assessment of phenotypes, particularly for complex subcellular morphologies. The PVD neuron in Caenorhabditis elegans, which is responsible for harsh touch and thermosensation, undergoes structural degeneration as nematodes age characterized by the appearance of dendritic protrusions. Analysis of these neurodegenerative patterns is labor-intensive and limited to qualitative assessment. RESULTS: In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans. We apply a convolutional neural network algorithm (Mask R-CNN) to identify neurodegenerative subcellular protrusions that appear after cold-shock or as a result of aging. A multiparametric phenotypic profile captures the unique morphological changes induced by each perturbation. We identify that acute cold-shock-induced neurodegeneration is reversible and depends on rearing temperature and, importantly, that aging and cold-shock induce distinct neuronal beading patterns. CONCLUSION: The results of this work indicate that implementing deep learning for challenging image segmentation of PVD neurodegeneration enables quantitatively tracking subtle morphological changes in an unbiased manner. This analysis revealed that distinct patterns of morphological alteration are induced by aging and cold-shock, suggesting different mechanisms at play. This approach can be used to identify the molecular components involved in orchestrating neurodegeneration and to characterize the effect of other stressors on PVD degeneration.

Learning partial differential equations for biological transport models from noisy spatio-temporal data.

We investigate methods for learning partial differential equation (PDE) models from spatio-temporal data under biologically realistic levels and forms of noise. Recent progress in learning PDEs from data have used sparse regression to select candidate terms from a denoised set of data, including approximated partial derivatives. We analyse the performance in using previous methods to denoise data for the task of discovering the governing system of PDEs. We also develop a novel methodology that uses artificial neural networks (ANNs) to denoise data and approximate partial derivatives. We test the methodology on three PDE models for biological transport, i.e. the advection-diffusion, classical Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) and nonlinear Fisher-KPP equations. We show that the ANN methodology outperforms previous denoising methods, including finite differences and both local and global polynomial regression splines, in the ability to accurately approximate partial derivatives and learn the correct PDE model.

A Novel Stuttering Disfluency Classification System Based on Respiratory Biosignals.

Stuttering is the principal fluency disorder that affects 1% of the world population. Growing with this disorder can impact the quality of life of the adults who stutter (AWS). To manage this condition, it is necessary to measure and assess the stuttering severity before, during and after any therapeutic process. The respiratory biosignal activity could be an option for automatic stuttering assessment, however, there is not enough evidence of its use for this purposes. Thus, the aim of this research is to develop a stuttering disfluency classification system based on respiratory biosignals. Sixty-eight participants (training: AWS=27, AWNS=33; test: AWS=9) were asked to perform a reading task while their respiratory patterns and pulse were recorded through a standardized system. Segmentation, feature extraction and Multilayer Perceptron Neural Network (MLP) was implemented to differentiate block and non-block states based on the respiratory biosignal activity. 82.6% of classification accuracy was obtained after training and testing the neural network. This work presents an accurate system to classify block and non-block states of speech from AWS during reading tasks. It is a promising system for future applications such as screening of stuttering, monitoring and biofeedback interventions.

Detection of Bladder Contractions From the Activity of the External Urethral Sphincter in Rats Using Sparse Regression.

Bladder overactivity and incontinence and dysfunction can be mitigated by electrical stimulation of the pudendal nerve applied at the onset of a bladder contraction. Thus, it is important to predict accurately both bladder pressure and the onset of bladder contractions. We propose a novel method for prediction of bladder pressure using a time-dependent spectrogram representation of external urethral sphincter electromyographic (EUS EMG) activity and a least absolute shrinkage and selection operator regression model. There was a statistically significant improvement in prediction of bladder pressure compared with methods based on the firing rate of EUS EMG activity. This approach enabled prediction of the onset of bladder contractions with 91% specificity and 96% sensitivity and may be suitable for closed-loop control of bladder continence.

Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model.

In this paper, we present a new method for the prediction and uncertainty quantification of data-driven multivariate systems. Traditionally, either mechanistic or non-mechanistic modeling methodologies have been used for prediction; however, it is uncommon for the two to be incorporated together. We compare the forecast accuracy of mechanistic modeling, using Bayesian inference, a non-mechanistic modeling approach based on state space reconstruction, and a novel hybrid methodology composed of the two for an age-structured population data set. The data come from cannibalistic flour beetles, in which it is observed that the adults preying on the eggs and pupae result in non-equilibrium population dynamics. Uncertainty quantification methods for the hybrid models are outlined and illustrated for these data. We perform an analysis of the results from Bayesian inference for the mechanistic model and hybrid models to suggest reasons why hybrid modeling methodology may enable more accurate forecasts of multivariate systems than traditional approaches.

A Comparison of Oral Hygiene Products and Professional Care: A six-week randomized clinical trial.

Purpose: To investigate the anti-gingivitis efficacy of a novel oral hygiene routine consisting of a two-step stannous fluoride dentifrice and hydrogen peroxide whitening gel system, an interactive oscillating-rotating electric toothbrush, and expanded polytetrafluoroethylene floss.Methods: A total of 52 participants (n=52;mean age 35.8±11.23 years) were enrolled in the study and randomized 1:1 to the experimental hygiene group or control (dental prophylaxis followed by use of standard sodium fluoride dentifrice and a manual toothbrush). Participants were instructed to brush twice daily; those in the experimental group were instructed to floss once daily. Oral examinations were conducted at Baseline, Week 2, Week 4, and Week 6.Results: Both groups experienced significant declines in the mean number of bleeding sites from Baseline at all time points, evident as early as Week 2. Bleeding sites continued to decline throughout the trial in the experimental group, whereas they showed an increasing trend between Weeks 2 and 6 in the control group. The experimental group had 55% fewer bleeding sites at Week 2, 85% fewer bleeding sites at Week 4, and 98% fewer bleeding sites at Week 6 (p<0.0001 for all) as compared to the control group. At Week 6, 84% of participants in the experimental group had no bleeding, while all participants in the control group had bleeding.Conclusion: The experimental oral hygiene group showed significantly greater reductions in gingival bleeding than the control oral hygiene group, with benefits seen as early as Week 2 and increasing over the six-week study.

Evolución clínica de pacientes con Diabetes Mellitus tipo 1 del consultorio de especialidades, Instituto Salvadoreño del Seguro Social. Enero a diciembre 2017 / Clinical evolution of patients with type 1 Diabetes Mellitus from the specialty clinic, "Instituto Salvadoreño del Seguro Social". January to December 2017

La diabetes mellitus tipo 1 es una enfermedad que corresponde al cinco por ciento de todos los casos de diabetes en el mundo. En el consultorio de especialidades del Instituto Salvadoreño del Seguro Social existen alrededor de 70 pacientes con Diabetes Mellitus tipo I vistos por la subespecialidad de endocrinología. No existe un registro actualizado de estos casos en el Instituto Salvadoreño del Seguro Social, solo se cuenta con expediente clínico con sus respectivos controles médicos cada 3 meses, por lo que es imperante determinar la evolución clínica de estos pacientes. Se realizó una investigación a partir de expedientes clínicos de pacientes de la consulta externa de endocrinología, donde se obtendrán datos como hemoglobina glicosilada, glicemia en ayunas, glicemia postprandial, presión arterial, filtrado glomerular, triglicéridos, lipoproteínas de alta densidad, actividad física y tabaquismo. La importancia del estudio radica en caracterizar a la población con Diabetes Mellitus Tipo 1 del consultorio de especialidades del ISSS, verificar si se cumplen los objetivos metabólicos ya establecidos por guías internacionales como el Diabetes Care y Asociación Americana de Diabetes que son: hemoglobina glicosilada <7%, glicemia en ayunas< 130 mg/dl, glicemia postprandial <180 mg/dl, presión arterial <140/90, triglicéridos <150 mg/dl. En base a esas metas se pudo sugerir medidas en las que se pueda mejorar el tratamiento y calidad de vida de estos pacientes; el ISSS a su vez seguirá promoviendo una atención de calidad y a la vez puede ahorrarse costos en complicaciones a largo plazo producidos por esta patología