scholarly journals HIV Model Parameter Estimates from Interruption Trial Data including Drug Efficacy and Reservoir Dynamics

PLoS ONE ◽  
2012 ◽  
Vol 7 (7) ◽  
pp. e40198 ◽  
Author(s):  
Rutao Luo ◽  
Michael J. Piovoso ◽  
Javier Martinez-Picado ◽  
Ryan Zurakowski
Keyword(s):  
Drug Efficacy ◽  
Trial Data ◽  
Parameter Estimates ◽  
Hiv Model ◽  
Model Parameter

Optimized blood sampling protocols and sequential design of kinetic experiments

1981 ◽  
Vol 240 (5) ◽  
pp. R259-R265 ◽  
Author(s):  
J. J. DiStefano
Keyword(s):  
Endocrine System ◽  
Primary Objective ◽  
Blood Sampling ◽  
Optimal Designs ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Reverse T3 ◽  
Model Parameter ◽  
Maximum Accuracy ◽  
Sampling Protocols

Design of optimal blood sampling protocols for kinetic experiments is discussed and evaluated, with the aid of several examples--including an endocrine system case study. The criterion of optimality is maximum accuracy of kinetic model parameter estimates. A simple example illustrates why a sequential experiment approach is required; optimal designs depend on the true model parameter values, knowledge of which is usually a primary objective of the experiment, as well as the structure of the model and the measurement error (e.g., assay) variance. The methodology is evaluated from the results of a series of experiments designed to quantify the dynamics of distribution and metabolism of three iodothyronines, T3, T4, and reverse-T3. This analysis indicates that 1) the sequential optimal experiment approach can be effective and efficient in the laboratory, 2) it works in the presence of reasonably controlled biological variation, producing sufficiently robust sampling protocols, and 3) optimal designs can be highly efficient designs in practice, requiring for maximum accuracy a number of blood samples equal to the number of independently adjustable model parameters, no more or less.

scholarly journals Genetic determinants of COVID-19 drug efficacy revealed by genome-wide CRISPR screens

2020 ◽  
Author(s):  
Wei Jiang ◽  
Ailing Yang ◽  
Jingchuan Ma ◽  
Dawei Lv ◽  
Mingxian Liu ◽  
...  
Keyword(s):  
Clinical Trial Data ◽  
Drug Efficacy ◽  
Metabolic Activation ◽  
Trial Data ◽  
Genetic Determinants ◽  
Immunomodulatory Agents ◽  
Genetic Biomarkers ◽  
Preliminary Information ◽  
Genome Wide ◽  
Screen Approach

AbstractImmunomodulatory agents dexamethasone and colchicine, antiviral drugs remdesivir, favipiravir and ribavirin, as well as antimalarial drugs chloroquine phosphate and hydroxychloroquine are currently used in the combat against COVID-191–16. However, whether some of these drugs have clinical efficacy for COVID-19 is under debate. Moreover, these drugs are applied in COVID-19 patients with little knowledge of genetic biomarkers, which will hurt patient outcome. To answer these questions, we designed a screen approach that could employ genome-wide sgRNA libraries to systematically uncover genes crucial for these drugs’ action. Here we present our findings, including genes crucial for the import, export, metabolic activation and inactivation of remdesivir, as well as genes that regulate colchicine and dexamethasone’s immunosuppressive effects. Our findings provide preliminary information for developing urgently needed genetic biomarkers for these drugs. Such biomarkers will help better interpret COVID-19 clinical trial data and point to how to stratify COVID-19 patients for proper treatment with these drugs.

scholarly journals A Regionalised Neyman-Scott Model of Rainfall with Convective and Stratiform Cells

1997 ◽  
Vol 1 (1) ◽  
pp. 71-80 ◽  
Author(s):  
P. S. P. Cowpertwait ◽  
P. E. O'Connell
Keyword(s):  
Cluster Model ◽  
Regression Models ◽  
Parameter Estimates ◽  
Model Parameter ◽  
Lack Of Fit ◽  
Dependent Variables ◽  
The Uk ◽  
Poisson Cluster ◽  
Residual Errors ◽  
Urban Sites

Abstract. A single-site Neyman-Scott Poisson cluster model of rainfall, with convective and stratiform cells, is fitted to data for 112 sites scattered throughout the UK using harmonic variables to account for seasonality. The model is regionalised by regressing the estimates of the harmonic variables on site dependent variables (e.g. altitude) to enable rainfall to be simulated at any ungauged site in the UK. An assessment of the residual errors indicates that the regression models can be used with reasonable confidence for urban sites. Furthermore, the regional variations of the model parameter estimates are found to be in agreement with meteorological knowledge and observation. Simulated I h extreme rainfalls are found to compare favourably with observed historical values, although some lack-of-fit is evident for higher aggregation levels.

Marginal DCS events: their relation to decompression and use in DCS models

2009 ◽  
Vol 107 (5) ◽  
pp. 1539-1547 ◽  
Author(s):  
Laurens E. Howle ◽  
Paul W. Weber ◽  
Richard D. Vann ◽  
Mark C. Campbell
Keyword(s):  
Parameter Optimization ◽  
Probabilistic Model ◽  
Decompression Sickness ◽  
Trial Data ◽  
Model Optimization ◽  
Model Parameter ◽  
Likelihood Functions ◽  
Combining Data ◽  
Model Predictions ◽  
Model Parameter Optimization

We consider the nature and utility of marginal decompression sickness (DCS) events in fitting probabilistic decompression models to experimental dive trial data. Previous works have assigned various fractional weights to marginal DCS events, so that they contributed to probabilistic model parameter optimization, but less so than did full DCS events. Inclusion of fractional weight for marginal DCS events resulted in more conservative model predictions. We explore whether marginal DCS events are correlated with exposure to decompression or are randomly occurring events. Three null models are developed and compared with a known decompression model that is tuned on dive trial data containing only marginal DCS and non-DCS events. We further investigate the technique by which marginal DCS events were previously included in parameter optimization, explore the effects of fractional weighting of marginal DCS events on model optimization, and explore the rigor of combining data containing full and marginal DCS events for probabilistic DCS model optimization. We find that although marginal DCS events are related to exposure to decompression, empirical dive data containing marginal and full DCS events cannot be combined under a single DCS model. Furthermore, we find analytically that the optimal weight for a marginal DCS event is 0. Thus marginal DCS should be counted as no-DCS events when probabilistic DCS models are optimized with binomial likelihood functions. Specifically, our study finds that inclusion of marginal DCS events in model optimization to make the dive profiles more conservative is counterproductive and worsens the model's fit to the full DCS data.

Event Location with Sparse Data: When Probabilistic Global Search is Important

2020 ◽  
Author(s):  
Stephen Arrowsmith ◽  
Junghyun Park ◽  
Il-Young Che ◽  
Brian Stump ◽  
Gil Averbuch
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Arrival Time ◽  
Real Data ◽  
Parameter Estimates ◽  
Model Parameter ◽  
Event Location ◽  
Seismic Location ◽  
Probability Density Function Pdf

Abstract Locating events with sparse observations is a challenge for which conventional seismic location techniques are not well suited. In particular, Geiger’s method and its variants do not properly capture the full uncertainty in model parameter estimates, which is characterized by the probability density function (PDF). For sparse observations, we show that this PDF can deviate significantly from the ellipsoidal form assumed in conventional methods. Furthermore, we show how combining arrival time and direction-of-arrival constraints—as can be measured by three-component polarization or array methods—can significantly improve the precision, and in some cases reduce bias, in location solutions. This article explores these issues using various types of synthetic and real data (including single-component seismic, three-component seismic, and infrasound).

scholarly journals PATTERN RECOGNITION OF LONGITUDINAL TRIAL DATA WITH NONIGNORABLE MISSINGNESS: AN EMPIRICAL CASE STUDY

2009 ◽  
Vol 08 (03) ◽  
pp. 491-513 ◽  
Author(s):  
HUA FANG ◽  
KIMBERLY ANDREWS ESPY ◽  
MARIA L. RIZZO ◽  
CHRISTIAN STOPP ◽  
SANDRA A. WIEBE ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Missing Data ◽  
Growth Pattern ◽  
Growth Patterns ◽  
Trial Data ◽  
Parameter Estimates ◽  
Nonignorable Missing Data ◽  
Fuzzy Clustering Method ◽  
Nonignorable Missing

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical generality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data.

scholarly journals Aspects of Theophylline Clearance in Children

1997 ◽  
Vol 25 (5) ◽  
pp. 497-501 ◽  
Author(s):  
B. J. Anderson ◽  
N. H. G. Holford ◽  
G. A. Woollard
Keyword(s):  
Plasma Concentrations ◽  
Volume Of Distribution ◽  
Pharmacokinetic Parameters ◽  
Parameter Estimates ◽  
Power Model ◽  
Theophylline Clearance ◽  
Concentration Data ◽  
Model Parameter ◽  
Age Related ◽  
Menten Constant

Michaelis-Menten pharmacokinetic parameters for theophylline were estimated in a three-month infant following an accidental overdose of intravenous aminophylline. Fitting of time-concentration data was performed using nonlinear regression with MKMODEL. A mixed order elimination model was superior to a first order model. Parameter estimates were standardized to a 70 kg human using an allometric power model. Parameter estimates (SE) were: maximum rate of metabolism (Vmax) 71(42) mg.h–1, Michaelis-Menten constant (Km) 32.3 (33.5) mg.l–1, volume of distribution (Vd) 46.9 (2.6) l. This Michaelis-Menten constant is lower than that reported for adults and consequently non-linear elimination will occur at lower plasma concentrations in infants than in adults. Theophylline clearance has traditionally been reported as directly proportional to body weight. This per kilogram model gives an erroneous impression that clearance is greatest in early childhood and then decreases with age until adult rates are reached in late adolescence. Age-related clearance values reported in the literature were reviewed using an allometric 3/4 power model. This size model demonstrates that clearance increases in infancy and reaches adult rates in the first one to two years of life.

Respirometry-based on-line model parameter estimation at a full-scale WWTP

2002 ◽  
Vol 45 (4-5) ◽  
pp. 335-343 ◽  
Author(s):  
H. Spanjers ◽  
G.G. Patry ◽  
K.J. Keesman
Keyword(s):  
Wastewater Treatment ◽  
Parameter Estimation ◽  
Activated Sludge ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Parameter Estimates ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
On Line ◽  
Set Up

This paper describes part of a project to develop a systematic approach to knowledge extraction from on-line respirometric measurements in support of wastewater treatment plant control and operation. The paper deals with the following issues: (1) test of the implementation of an automatic set-up consisting of a continuous laboratory respirometer integrated in a mobile trailer with sampling and dosing equipment, and data-acquisition and communication system; (2) assessment of activated sludge/sewage characteristics from sludge respirograms by model parameter estimation; (3) comparison of the parameter estimates with regular plant data and information obtained from supplementary wastewater respirograms. The paper describes the equipment and some of its measuring results from a period of one week at a large-scale wastewater treatment plant. The measurements were evaluated in terms of the common activated sludge modelling practice. The automatic set-up allowed reliable measurements during at least one week. The data were used to calibrate two different version of the model, and independent parameter estimates were obtained.

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