ライフサイエンスコーパス: compartment model

1 calculated using the standard single-tissue-compartment model.

2 atography-mass spectrometry, were fit to a 7-compartment model.

3 distribution volume were calculated with a 4-compartment model.

4 sic too and well fitted to a first order two compartment model.

5 Rate constants were calculated by use of a 3-compartment model.

6 O-H2O data, using the standard single-tissue-compartment model.

7 tification programs, using the same 1-tissue-compartment model.

8 uld not be adequately fitted with a 1-tissue-compartment model.

9 n) activity was assessed by a 1- or 2-tissue-compartment model.

10 lgus pancreas was quantified with a 1-tissue-compartment model.

11 ic analysis software that applied a 1-tissue-compartment model.

12 CD1 and C57BL/6, using the standard 2-tissue-compartment model.

13 fter injection using the single-injection, 2-compartment model.

14 ) and fitted the data better than a 1-tissue-compartment model.

15 curves could be described using the 2-tissue-compartment model.

16 abolites were modeled with a first-order one-compartment model.

17 fter injection using the single-injection, 2-compartment model.

18 compartment model was superior to a 1-tissue-compartment model.

19 d model performed marginally better than a 2-compartment model.

20 thod (t* = 20 min) but not with the 1-tissue-compartment model.

21 n the corneal data fit compared with the two-compartment model.

22 IDIFs and myocardium curves to a dual-output compartment model.

23 n/mL of myocardium) was calculated using a 3-compartment model.

24 e to those of 60-min dynamic imaging and a 3-compartment model.

25 n ordinary differential equation-based multi-compartment model.

26 g the arterial input function and a 2-tissue-compartment model.

27 r for the 3-compartment model than for the 2-compartment model.

28 after bolus (15)O-water injection using a 1-compartment model.

29 clearance estimates than the conventional 2-compartment model.

30 in nonuniform regions was described with a 2-compartment model.

31 within 2% of the estimate provided by the 4-compartment model.

32 Data were analyzed using a 1-tissue compartment model.

33 are best described by a reversible 2-tissue-compartment model.

34 the kinetics of its plasma appearance in a 2-compartment model.

35 s best described by an irreversible 2-tissue-compartment model.

36 bution (VT) was estimated using the 1-tissue-compartment model.

37 curves were better described by the 2-tissue-compartment model.

38 lated by using the Tofts pharmacokinetic two-compartment model.

39 in binding potential (k3/k4) in the 2-tissue-compartment model.

40 eriocular administration can be described by compartment models.

41 can be used as input functions for 2- and 3-compartment models.

42 described using standard plasma input tissue-compartment models.

43 rs provide evidence in support of the stable compartments model.

44 g/kg ranged from 49% to 97%, as estimated by compartment modeling.

45 rected plasma input function for traditional compartment modeling.

46 t a sufficient surrogate of VT from 2-tissue-compartment modeling.

47 versible uptake rate constant) comparable to compartment modeling.

48 volume fraction (VB) were computed by using compartment modeling.

49 me ratio (DVR) were estimated using 2-tissue-compartment modeling.

50 lalanine metabolism was determined using two-compartment modelling.

51 ponential analysis (1/k(mono)) or a simple 2-compartment model (1/k(4)).

52 compartmental modeling with 1- and 2-tissue compartment models (1TC and 2TC), data-driven estimation

53 tics were characterized with both a 1-tissue-compartment model (1TCM) and a 2-tissue-compartment mode

54 For kinetic analysis, a 1-tissue compartment model (1TCM) provided a good fit to the data

55 bution (VT) was estimated by 1- and 2-tissue-compartment modeling (1TCM and 2TCM, respectively) and L

56 a 1-tissue-compartment model and a 2-tissue-compartment model (2TCM) with metabolite-corrected plasm

57 tion volumes (V(T), in mL/g) than a 2-tissue compartment model (2TCM).

58 s across regions was the reversible 2-tissue-compartment model (2TCM4k), and 90 min resulted as the o

59 lated with 2- and 4-parameter arterial-input compartment models, a 3-parameter reference tissue compa

60 models: irreversible and reversible 2-tissue-compartment models, a reversible 1-tissue model, and 2 m

61 eters were measured with a dual-input single-compartment model: absolute arterial blood flow (F(a)),

62 11)C-MP-10 were well described by a 2-tissue-compartment model, allowing robust estimates of the regi

63 t correlation with the irreversible 2-tissue-compartment model analysis and can be used for accurate

64 curve fittings are >0.90 using a (18)F-FDG 3-compartment model and >0.99 for Patlak analysis.

65 urves for 90 min were analyzed by a 1-tissue-compartment model and a 2-tissue-compartment model (2TCM

66 e entire age range was well described by a 2-compartment model and a previously reported problem, res

67 ysis of the pancreas was performed using a 1-compartment model and an image-derived input function.

68 f each brain region was calculated using a 3-compartment model and an operational equation that inclu

69 f each brain region was calculated using a 3-compartment model and an operational equation that inclu

70 imilation method, we developed an integrated compartment model and assimilation filtering forecast mo

71 flux rate was obtained using a single-tissue-compartment model and compared with transmural MBF (MBFT

72 parameters were calculated using a 1-tissue-compartment model and converted to MBF and MFR.

73 e to propofol using a two-dimensional linear compartment model and estimated the model parameters spe

74 f cellular glutamine metabolism, by both a 1-compartment model and Logan analysis.

75 ing data were modeled well with the 1-tissue-compartment model and MA1 methods.

76 The 2-tissue-compartment model and multilinear analysis-1 were applie

77 ated as distribution volume using a 2-tissue-compartment model and serial concentrations of parent ra

78 ivity curves were analyzed with the 1-tissue-compartment model and the multilinear analysis method (M

79 vity curves were well fitted by the 2-tissue-compartment model and the multilinear analysis-1 (MA1) m

80 implementation of the 2-tissue-irreversible-compartment model and the Patlak method using a descendi

81 tration-time data were fitted into an open 2-compartment model and total clearance, central compartme

82 Dox PK was described with a two-compartment model and tumor drug disposition kinetics we

83 e estimated by the use of IF(liver) in the 3-compartment model and with those estimated by the Patlak

84 One- and 2-tissue-compartment modeling and linear graphic analysis provide

85 vent of novel techniques such as multitissue compartment models and connectomics can help characteris

86 t (1T2K) and 2-tissue 4-rate-constant (2T4K) compartment models and either metabolite-corrected or un

87 well with volume of distribution from single-compartment models and Logan analyses.

88 For this purpose, 1- and 2-tissue compartment models and Logan graphic analysis (LGA) were

89 ut models; single-tissue and 2-tissue (2TCM) compartment models and plasma-input Logan and reference

90 bution volume ratio estimates obtained using compartment models and simplified methods were highly co

91 g both arterial sampling in combination with compartment models and simplified reference methods.

92 nput functions included 1-, 2-, and 3-tissue-compartment models and the Logan plot.

93 Body weight and composition (4-compartment model) and RMR (indirect calorimetry) were m

94 the uptake model, 0.85 and 0.80 for the one-compartment model, and 0.87 and 0.87 for model-independe

95 mates were obtained with a standard 2-tissue-compartment model, and brain-wide V(ND) was estimated wi

96 The kinetic model appears to represent a two-compartment model, and the average retention times for b

97 titative data measurements were based on a 2-compartment model, and the following variables were calc

98 tment models, a 3-parameter reference tissue compartment model, and the Logan graphic approach.

99 TBF was estimated using a standard, single-compartment model, and the replicate data were used to a

100 tope labeling experiments and the well-known compartment modeling, and we demonstrate that an appropr

101 From a physiologically based compartment model, aortic contrast enhancement curves we

102 ions and the metabolic rate constants in a 3-compartment model are simultaneously estimated, was used

103 -compartment models compared with DXA and 4 -compartment models are partly attributable to deviations

104 e-sample method is presented, based on the 3-compartment model as reference standard.

105 A 4-compartment model assessment of body composition was mad

106 Body composition was assessed with a 3-compartment model based on body weight, total body water

107 We find that a four-compartment model, based on the known biology of haemato

108 We describe a compartment model-based correction algorithm to deconvol

109 A 3-compartment model best described 18F-FHBG kinetics in mi

110 ice expressing HSV1-sr39tk in the liver; a 2-compartment model best described the kinetics in control

111 The 1-tissue-compartment model best explained the observed brain and

112 ix 30-min windows and compared with 1-tissue-compartment model BP (ND) Simulations were performed to

113 triatum, and substantia nigra between the 2T-compartment model BPND and the SRTM BPND (r = 0.57, 0.82

114 direct calorimetry and body composition as 3-compartment model by air displacement plethysmography an

115 n this model, fish were schematized as a six-compartment model by assuming that blood was the medium

116 Convex Analysis of Mixtures - Compartment Modeling (CAM-CM) signal deconvolution tool

117 The model parameter k3 from the 3-compartment model can be used as a noninvasive estimate

118 We found that a three compartment model can describe doxorubicin pharmacokinet

119 ating and exploring a parameter space of two-compartment models can be applied to other neurons.

120 nal pharmacokinetic model consisted of a one-compartment model characterised by clearance (CL) and vo

121 Individual variations between 2-compartment models compared with DXA and 4 -compartment

122 compartment model was superior to a 1-tissue-compartment model, consistent with measurable amounts of

123 The compartment model corresponds to the myofibrillar space

124 Different implementations of 1- and 2-compartment models demonstrate an excellent correlation

125 A 2-tissue-compartment model described the data well, and a signifi

126 A 2-tissue-compartment model described the time-activity curves wel

127 We constructed three-compartment models describing VEGF isoforms and receptor

128 We constructed a 5-compartment model designed to predict the plasma time-ac

129 Beyond a 2-compartment model, detailed changes in organ and tissue

130 two compartment organism model over a single compartment model due to the differences in ephippial eg

131 The two-compartment model explained the experimental time-series

132 For BR subjects, the two-compartment model fitted significantly better on 1 out o

133 An unconstrained 2-tissue-compartment model fitted the data well, and distribution

134 partment kinetic model for (82)Rb and to a 3-compartment model for (13)N-ammonia.

135 PET data were analyzed with the 2-tissue-compartment model for (18)F-FDG, and the results were ev

136 rate constants (KRCs) as calculated with a 2-compartment model for both SD1 and SD2 were compared wit

137 nt reports have questioned the traditional 2-compartment model for calculating tracer clearance after

138 models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA).

139 ination rates using a simple first-order one-compartment model for selected dioxin congeners based on

140 se a simple scaling argument, derive a multi-compartment model for tumour growth, and consider in viv

141 acted and quantified by SUVs and by 2-tissue-compartment modeling for calculation of distribution vol

142 We added one-compartment models for glutathione metabolites and a two

143 ical relationships to parameterize classical compartment models for infectious micro- and macroparasi

144 As an alternative, we introduce the use of compartment models for interpreting data collected from

145 We introduce a pair of compartment models for the honey bee nest-site selection

146 Body composition was calculated with a 3-compartment model from body mass, body volume (hydrodens

147 perfusion was estimated using 82Rb and a two-compartment model from dynamic PET scans on 11 healthy v

148 The unconstrained 2-tissue-compartment model gave excellent V(T) identifiability (

149 Four-compartment models had the smallest variability across t

150 pt that the Fermi model outperformed the one-compartment model if MPR was used as the outcome measure

151 e the bias and agreement between DXA and a 4-compartment model in predicting the percentage of fat ma

152 d Education (ORISE) were based on a simple 3-compartment model in which all activity not measured in

153 A 4-compartment model in which body fat in kg is divided by

154 cokinetic studies were fitted to a linear, 2-compartment model in which dose reduction led to incompl

155 harmacokinetics were best described by a two-compartment model in which weight, severe liver disease,

156 ods that estimate FM, including 2-, 3- and 4-compartment models in pregnant women at term, and to det

157 rovide new evidence in support of the stable compartments model in mammalian cells.The different comp

158 A compartment model, in which the blood input function wit

159 Consistent with this observation, a 2-compartment model indicated a relatively low estimate of

160 Two-compartment modeling indicated that the second phase of

161 The 2-tissue-compartment model is appropriate to quantify the perfusi

162 nt of tracer trapping, suggesting that the 1-compartment model is preferable.

163 A 3-compartment model is used to determine vascular permeabi

164 tmentalization observed in our compact multi-compartment models is qualitatively consistent with expe

165 The median two-compartment model K(21) exchange rate in the tumors, 0.0

166 An integrated software package, Compartment Model Kinetic Analysis Tool (COMKAT), is pre

167 A 3-compartment model led to lower and probably more accurat

168 Maternal dolutegravir was described by a two-compartment model linked to a fetal and breastmilk compa

169 ssue-compartment model (1TCM) and a 2-tissue-compartment model, Logan graphical methods (both with an

170 tted array and established that a simple two-compartment model may be used to accurately extract intr

171 The F(V) values obtained by using the single-compartment model (mean F(V), 0.47 min(-1)) showed excel

172 two populations of conductance-based, single-compartment model neurons.

173 The 1-tissue-compartment model of (82)Rb tracer kinetics is a reprodu

174 Here a compartment model of a layer 5 pyramidal cell was used t

175 his study was to develop a voltage dependent compartment model of Ca(2+) dynamics in frog skeletal mu

176 A compartment model of Ca(2+) indicated the effect of EGTA

177 In this study, we developed a multi-compartment model of cardiac metabolism with detailed pr

178 FMN survival and, second, demonstrate a two-compartment model of CD4+ T cell activation.

179 ed a cognitive model of RT and a biophysical compartment model of diffusion-weighted MRI (DWI) to cha

180 deuterium bromide dilution tests, and a four-compartment model of FM, total body water (TBW), bone mi

181 usion rates were used as inputs to a new two-compartment model of insulin kinetics and hepatic and ex

182 We have formulated a three-compartment model of muscle activation that includes bot

183 Using a multi-compartment model of the Aplysia axon, we demonstrate th

184 ted approximately 600,000 versions of a four-compartment model of the LP neuron and distributed 11 di

185 We generated a concise single compartment model of the secretion mechanism, fitted to

186 Here we present the development of a new compartment model of the thalamic relay cell guided by t

187 We developed a compartment model of the United States to simulate diffe

188 Both two- and one-compartment models of AHCVR were fitted to the data.

189 The 3-compartment model parameter, k3, correlated well with th

190 ompartment model (QPET and syngo MBF) or a 1-compartment model (PMOD).

191 The stable compartments model postulates that permanent cisternae c

192 The stable compartments model predicts that each cisterna is a long

193 A plasma input 2-tissue-compartment model provided good fits to the PET data, an

194 sion analysis (NLR) to a reversible 2-tissue-compartment model, providing volumes of distribution (V(

195 rform PET MBF quantification with either a 2-compartment model (QPET and syngo MBF) or a 1-compartmen

196 Pancreatic F(V) values from the single-compartment model ranged from 0.961 to 6.405 min(-1) (me

197 cMR(glc) value based on IF(blood) and the 3-compartment model served as a standard for comparisons w

198 rrelated well with results from the 2-tissue-compartment model, showing that parametric methods can b

199 ma input function, using the 1- and 2-tissue-compartment models (TCMs) as well as the Logan analysis

200 e found to be systematically lower for the 3-compartment model than for the 2-compartment model.

201 Finally, we show that a three-compartment model that includes a subspace compartment b

202 Here we develop a two-compartment model that quantifies the interplay between

203 ngton 4-compartment model, the Wells et al 4-compartment model, the isotope dilution model, dual-ener

204 Six methods (the Pennington 4-compartment model, the Wells et al 4-compartment model,

205 )O-water was performed using a single tissue compartment model to calculate blood flow; a 2-tissue co

206 cted using an irreversible 1-plasma 2-tissue-compartment model to calculate surrogate biomarkers of t

207 erial input function were analyzed using a 3-compartment model to estimate k(3), which represents the

208 MBF-Ace was estimated using a simple 1-compartment model to estimate net tracer uptake, K1 (K1

209 whole brain were quantified using a 1-tissue-compartment model to estimate the rate of entry (K(1)) o

210 Adding a two-compartment model to handle the temporal distribution of

211 h kinetic analysis software using a 1-tissue-compartment model to obtain the uptake rate constant K(1

212 tion, v(p)) were determined by fitting a two-compartment model to plaque and blood gadolinium concent

213 We present a new multi-compartment model to simulate heterogeneously vasculariz

214 The fit of the cellular 2-compartment model to the (18)F-FDG CIMR measurements was

215 rmined in conscious dogs by applying a three-compartment model to the plasma clearance data of intrav

216 nstrate that an appropriate application of a compartment model to turnover of proteins from mammalian

217 ative time-activity curves, testing 1- and 2-compartment models to describe kinetics.

218 We assessed the abilities of 1-, 2-, and 3-compartment models to kinetically describe cerebral time

219 dium can be quantified using a single-tissue-compartment model together with a metabolite-corrected a

220 Two one-compartment models, together with the physiologically ba

221 min across confluent EC monolayers using a 2-compartment model under basal culture conditions.

222 e of receptor density) was calculated with a compartment model using brain and arterial plasma data.

223 del of delivery and retention and a 1-tissue-compartment model using the first 10 min of data (1C(10)

224 (18)F-AV45 VT was determined from 2-tissue-compartment modeling using a metabolite-corrected plasma

225 curves (tissue curves) were fit to 2- and 3-compartment models using Levenberg-Marquardt nonlinear r

226 alyzed by Logan plots and by 1- and 2-tissue-compartment models using unbound, unmetabolized arterial

227 VT values were obtained from different compartment models, using different input functions with

228 lity values were 10.7% +/- 2.2% for 2-tissue compartment model V(T) and 11.9% +/- 2.2% for LGA V(T) P

229 Regional 2-tissue compartment model V(T) values were about 3 and were rath

230 A 2-compartment model was able to describe (18)F-FDOPA kinet

231 A 2-compartment model was able to describe (18)F-FDOPA kinet

232 A one-compartment model was applied by using the aortic and pa

233 A one-compartment model was applied to each set of time-enhanc

234 A cellular 2-compartment model was applied to estimate the cellular p

235 In most lesions, the reversible 2-tissue-compartment model was chosen as the most appropriate acc

236 ce after a single intravenous injection, a 3-compartment model was evaluated in this study.

237 A 2-compartment model was fitted by Bayesian regression to y

238 A 2-tissue-compartment model was fitted to the PET data, using meta

239 A single-sample procedure based on the 3-compartment model was found to eliminate most of the kno

240 A reversible 2-tissue-compartment model was preferred for (18)F-DCFPyL kinetic

241 The 2-parameter arterial-input compartment model was statistically superior to the 4-pa

242 brain and plasma data showed that a 2-tissue-compartment model was superior to a 1-tissue-compartment

243 brain and plasma data showed that a 2-tissue-compartment model was superior to a 1-tissue-compartment

244 The 2-tissue compartment model was the most appropriate kinetic model

245 A dual-input single-compartment model was used to compute parameters includi

246 In addition, a 2-tissue-compartment model was used to compute the volume of dist

247 A 2-tissue-compartment model was used to determine BPND for the str

248 nt model to calculate blood flow; a 2-tissue compartment model was used to estimate (18)F-FDG rate pa

249 A standard 2-compartment model was used to measure (18)F-FDG kinetic

250 reversible and irreversible 1- and 2-tissue-compartment models was performed to calculate the kineti

251 roscopies, positron emission tomography, and compartment modeling, we demonstrate that siRNA nanopart

252 and K(FDG) estimated by IF(blood) and the 3-compartment model were 0.22 +/- 0.05 mL/min/g, 0.48 +/-

253 After compartment models were evaluated, (11)C-(+)-PHNO volume

254 Tissue compartment models were not able to describe the kinetic

255 Results: Tissue compartment models were not able to describe the kinetic

256 ues obtained with the 1-tissue- and 2-tissue-compartment models were similar to values obtained with

257 One- and 2-tissue-compartment models were used to estimate pancreas and sp

258 plot analysis) and brain kinetics (2-tissue-compartment model) were characterized with either a meas

259 Here, we propose a multi-compartment model which mimics the dynamics of tumoural

260 The irreversible 3-tissue-compartment model, which included both the parent and th

261 kinetics were well described by the 1-tissue-compartment model, which was used to provide estimates f

262 A 2-compartment model with 4 rate constants adequately descr

263 ed using the validated irreversible 2-tissue compartment model with a metabolite-corrected arterial i

264 PET data were analyzed using the two-tissue compartment model with an arterial plasma input function

265 he blood kinetics of AlexaFFA followed a two-compartment model with an initial fast compartment half-

266 mong the 6 models investigated, the 2-tissue-compartment model with arterial input described the time

267 ial, BP(ND)) were analyzed with a two-tissue compartment model with arterial input function.

268 were analyzed using the validated two-tissue compartment model with arterial plasma input function wi

269 e-activity curve using a reversible 2-tissue-compartment model with blood volume fraction.

270 tion criterion, the reversible single-tissue-compartment model with blood volume parameter was the pr

271 est fits were obtained using an irreversible compartment model with blood volume parameter.

272 e best described by an irreversible 2-tissue-compartment model with blood volume parameter.

273 A reversible single-tissue-compartment model with blood volume seems to be a good c

274 P(ND)) was estimated using a one-tissue (1T) compartment model with centrum semiovale as the referenc

275 A 3-compartment model with corrections for metabolites and p

276 A 3-compartment model with corrections for tissue blood volu

277 oefficients for the embryonic body and a one-compartment model with diffusive exchange were calculate

278 kinetics, which could be described by a two-compartment model with fast and slow washout rates.

279 A one-compartment model with first-order absorption and dispos

280 A 1-compartment model with first-order absorption and elimin

281 FOR RANIBIZUMAB WERE BEST DESCRIBED BY A ONE-COMPARTMENT MODEL WITH FIRST-ORDER ABSORPTION INTO AND F

282 of BSH was found to be consistent with a two-compartment model with first-order elimination from the

283 terial plasma input single-tissue reversible compartment model with fitted blood volume fraction seem

284 Thus, a two-compartment model with five crucial ionic currents in th

285 the beginning of hypercapnia and a 1-tissue-compartment model with flow-dependent extraction correct

286 o simulated data using the dual-input single-compartment model with known perfusion property values a

287 A two-compartment model with linear elimination and two indivi

288 arable to those of the irreversible 2-tissue-compartment model with only a parent input function, ind

289 We develop a quantitative three-compartment model with predictive power regarding the dy

290 A one-compartment model with zero-order absorption and allomet

291 A 1-compartment model with zero-order absorption and allomet

292 day and analyzed using single- and 2-tissue-compartment models with and without a blood volume param

293 The data were fit to different compartment models with first-order input and dispositio

294 hermore, the PBTK model outperformed the one-compartment models with respect to simulating chemical c

295 oint-neuron models, we created compact multi-compartment models with up to four compartments, which w

296 ansfer compartment, retina, and distribution compartment) model with elimination from the periocular

297 te biliary excretion, were best fit by a two compartment model, with both linear and non-linear DTX c

298 ity curves were fitted using 1- and 2-tissue-compartment models, with goodness-of-fit tests showing a

299 FMAU kinetics were measured using a 3-compartment model yielding the flux (K1 x k3/(k2 + k3))

300 Data were analyzed with a standard 2-tissue-compartment model yielding the unidirectional uptake rat