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PubMed1990

Bulbospinal and intraspinal thyrotropin releasing hormone systems: modulation of spinal cord pain transmission.

Recent research indicates that pain processing by spinal nociceptive neurons is modulated by: 1) descending bulbospinal pathways originating in the rostral ventral medulla (RMV) and 2) short intraspinal peptide systems located in the dorsal horn. Immunohistochemical studies have identified both bulbospinal and intraspinal thyrotropin releasing hormone (TRH) systems, however, the role of these two TRH systems in pain modulation has not been defined. The purpose of the present study was to explore the role of the bulbospinal and intraspinal TRH systems in pain modulation. Three TRH mediated neural network were examined in electrophysiologic experiments; each model predicting specific testable outcomes. Model A represented a direct TRH projection from RVM to spinal cord. This model predicted that the effect of RVM stimulation and TRH micropressure administration on dorsal horn unit activity would be in the same direction, either both inhibitory effects or both excitatory. Of 44 dorsal horn units inhibited by RVM stimulation, 82% were excited by TRH, therefore, Model A was rejected. Model B interposed an intraspinal neuron between the bulbospinal TRH projection and the dorsal horn nociceptive unit. This model predicted that desensitization of spinal TRH receptor systems should block the effect of RVM stimulation on dorsal horn neuron. In TRH desensitization experiments, no attenuation or blockade of RVM stimulation was observed, therefore, the effect of RVM stimulation did not appear mediated by TRH and Model B was rejected.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗

PubMed1977

A model to predict the incidence of fracture in femoral components.

The paper presents a probability model for the fracture of femoral components based on the assumption that the components are defect-free, and that they fracture by a fatigue process. The model predicts the probability of fracture for patients of different body weight, and the incidence of fractures versus body weight, and versus time. The results are compared with recently published survey data. In addition the model is used to demonstrate the effect of improvements in the mechanical properties of components. Finally the paper discusses the modifications needed to demonstrate the effect of variations in component design, prosthesis orientation, and level of patient activity, and the effect of time-dependent processes such as bone resorption, acrylic disintegration and shock loads.

Body Weight↗

PubMed1998

Analytic model to predict the strength of tendon repairs.

We developed an analytic model to predict suture load-sharing immediately after flexor tendon repair in the hand. Tendon repair was mathematically modeled as two nonlinear springs in parallel, representing separate core and peripheral sutures that were in series with a third nonlinear spring representing the tendon. To serve as a basis for, and validation of, our analytic model, fresh human flexor digitorum profundus tendons were harvested and mechanically tested either intact or after surgical repair in a variety of ways: core suture alone, superficial peripheral suture alone, deep peripheral suture alone, core suture plus superficial peripheral suture, and core suture plus deep peripheral suture. The stiffness and strength of the composite repairs predicted with use of the analytic model were comparable with those determined experimentally. Furthermore, the model predicted inequities in suture load-sharing, with 64% of the applied load carried by the peripheral suture when it was placed superficially, as compared with 77% when the peripheral suture was placed deep. Our results demonstrate a disparity in load-sharing within composite suture systems, the rectification of which may lead to significant improvement in the repair strength. To this end, we expect that our analytic model will serve as a basis for the design of more efficient, and consequently stronger, suture techniques.

Aged↗

PubMedDate not supplied

An evaluation of 24-hour urinary creatinine excretion for use in identification of incomplete urine collections and adjustment of absorbed dose of pesticides.

The accurate quantification of the absorbed dose of pesticides following occupational exposures generally requires complete 24-hour urine collections, often over extended periods of time. Difficulty in obtaining volunteer cooperation may result in incomplete urine collections. Traditionally, 24-hour urinary creatinine has been used to identify incomplete urine samples and has been used to standardize pesticide and other chemical dose estimates. More recently, the use of creatinine to standardize dose estimates has been questioned, as has its utility in the identification of incomplete urine collections. This research evaluates the use of personal observation, statistical methods, and published models to predict creatinine excretion to identify and adjust for incomplete urine collections. Based on the use of published creatinine prediction models, an evaluation of the day-to-day creatinine excretion within subjects, and personal observation, a small number of suspected urine samples were identified. Although it is likely that these samples were incomplete, correction of these urine volumes based on the published models did little to improve pesticide dose prediction. Further, results indicate that subjects who report missed urine samples may be able to estimate the missing volumes with some accuracy. In future pesticide exposure studies, the use of self-reported missed volumes may help to increase the accuracy of dose prediction when there is strong cooperation with collection procedures. A statistical model to predict creatinine excretion in professional turf applicators was developed to provide a preliminary screening for urinary completeness for future studies in which compliance with urinary collection is thought to be insufficient.

Adult↗

PubMed2005

Bioavailability models for predicting acute and chronic toxicity of zinc to algae, daphnids, and fish in natural surface waters.

Bioavailability models predicting acute and/or chronic zinc toxicity to a green alga (Pseudokirchneriella subcapitata), a crustacean (Daphnia magna), and a fish (Oncorhynchus mykiss) were evaluated in a series of experiments with spiked natural surface waters. The eight selected freshwater samples had varying levels of bioavailability modifying parameters: pH (5.7-8.4), dissolved organic carbon (DOC, 2.48-22.9 mg/L), Ca (1.5-80 mg/L), Mg (0.79-18 mg/L), and Na (3.8-120 mg/L). In those waters, chronic zinc toxicity (expressed as 10% effective concentrations [EC10]) varied up to 20-fold for the alga (72-h EC10 from 27.3 to 563 microg Zn/L), and approximately sixfold for the crustacean (21-d EC10 from 59.2 to 387 microg Zn/L), and fivefold for the fish (30-d LC10, lethal concentration for 10% of the organisms, from 185 to 902 microg Zn/L). For P. subcapitata a refined bioavailability model was developed by linking an empirical equation, which predicts toxicity expressed as free Zn2+ activity as a function of pH, to the geochemical speciation model WHAM/Model V. This model and previously developed acute and/or chronic biotic ligand models for D. magna and 0. mykiss generally predicted most effect concentrations by an error of less than a factor of two. In waters with pH > 8, however, chronic toxicity to D. magna was underestimated by a factor 3 to 4. Based on the results of this validation exercise and earlier research, we determined applicability ranges for pH (6-8) and Ca (5-160 mg/L) in which all three developed models are valid. Within these ranges, all three models may be considered useful tools for taking into account bioavailability in regulatory assessments of zinc.

Animals↗

PubMed1979

A pharmacokinetic model for predicting the concentration of methotrexate in plasma subsequent to intrathecal injection.

A pharmacokinetic model has been established in which plasma methotrexate (MTX) concentrations following intrathecal administration are predicted. The model is based upon cerebrospinal fluid MTX concentrations following intrathecal injection of MTX, and plasma MTX levels subsequent to i.v. injection of the drug in dogs. Anticipated plasma MTX levels were compared with actual measured concentrations. The model predicts changes in the plasma MTX concentration curve as a result of varying of the injected dose, reduction in plasma MTX clearance, and reduced transfer of MTX from cerebrospinal fluid to the blood. It is suggested that plasma MTX levels be measured at 3, 5, 7 and 10 h following intrathecal injection to facilitate an early assessment of unusually slow plasma MTX decay. Thus, administration of leucovorin, to prevent systemic toxicity, may be more rapidly commenced. Also, if such measurements indicate that the transfer of MTX from cerebrospinal fluid to the blood is reduced. MTX dose of the subsequent intrathecal injections should be reduced to minimize risks of systemic toxicity and neurotoxicity.

Animals↗

PubMed2003

Models for predicting dry matter intake of Holsteins during the prefresh transition period.

The objectives of this study were to develop and validate a model for predicting dry matter intake (DMI) of Holsteins during the prefresh transition period. The original database (ODB) for model development was established by compiling parity, body condition score (BCS), and DMI data during the final 3 wk of gestation from 366 Holsteins fed 24 different diets that were used in eight experiments conducted at three universities. For model validation, a validation database (VDB) was established by compiling data from 333 prefresh transition Holsteins fed 25 different diets that were used in eight experiments conducted at five universities. Dry matter intake during the prefresh transition period was fitted to an exponential function: DMI(t) = a + pe(kt), where DMI(t) = DMI as a percentage of body weight (BW) at time t, a = asymptotic intercept at time--infinity, p = change in intake (kg) from the asymptotic intercept until parturition, k = rate constant influencing the shape of the curve, and t = day relative to parturition expressed as days pregnant--280. The model developed from the ODB predicted DMI of heifers in the VDB with satisfactory accuracy and precision. However, this was not true for cows, probably due to differences in BCS of cows and diets fed to cows from the two data sets. When a subset of cows was selected from each data set that had similar BCS (> 4.0) and were fed similar diets, accuracy and precision of the model predicting DMI was improved. Finally, both databases were combined to develop final models for predicting DMI of heifers and cows. Proposed models for predicting mean daily DMI of heifers and cows during the prefresh transition period were DMI(t) = 1.713-0.688e(0.344t) (R2 = 0.96) and DMI(t) = 1.979-0.756e(0.154t) (R2 = 0.97), respectively. Adjustment factors for animal and dietary factors were generated to demonstrate the plausibility of adaptive fitting of the prediction. The regression coefficients of prediction models (a, p, and k) were affected by BCS and dietary organic macronutrient concentrations.

Animal Nutritional Physiological Phenomena↗

PubMed1999

Development, characterization and application of predictive-toxicology models.

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover, the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.

Algorithms↗

PubMedDate not supplied

Three-dimensional quantitative structure-property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): enthalpy of vaporization.

Three-dimensional Quantitative Structure-Property Relationship (QSPR) models have been derived using Comparative Molecular Field Analysis (CoMFA) to correlate the vaporization enthalpies of a representative set of polychlorinated biphenyls (PCBs) at 298.15 K with their CoMFA-calculated physicochemical properties. Various alignment schemes, such as inertial, as is, and atom fit, were employed in this study. The CoMFA models were also developed using different partial charge formalisms, namely, electrostatic potential (ESP) charges and Gasteiger-Marsili (GM) charges. The most predictive model for vaporization enthalpy (Delta(vap)H(m)(298.15 K)), with atom fit alignment and Gasteiger-Marsili charges, yielded r2 values 0.852 (cross-validated) and 0.996 (conventional). The vaporization enthalpies of PCBs increased with the number of chlorine atoms and were found to be larger for the meta- and para-substituted isomers. This model was used to predict Delta(vap)H(m)(298.15 K) of the entire set of 209 PCB congeners.

Journal Article↗

PubMed1995

Analysis of time courses of metabolic precursors and products in heterogeneous rat brain tissue: limitations of kinetic modeling for predictions of intracompartmental concentrations from total tissue activity.

The efficacy of various kinetic models to predict time courses of total radioactivity and levels of precursor and metabolic products was evaluated in heterogeneous samples of freeze-blown brain of rats administered [14C]deoxyglucose ([14C]DG). Two kinetic models designed for homogeneous tissues, i.e., a no-product-loss, three-rate-constant (3K) model and a first-order-product-loss, four-rate-constant (4K) model, and a third kinetic model designed for heterogeneous tissues without product loss [Tissue Heterogeneity (TH) Model] were examined. In the 45-min interval following a pulse of [14C]DG, the fit of the TH Model to total tissue radioactivity was not statistically significantly better than that of the 3K Model, yet the TH Model described the time courses of [14C]DG and its metabolites more accurately. The TH- and 4K-Model-predicted time courses of [14C]DG and its metabolites were similar. Whole-brain glucose utilization (CMRglc) calculated with the TH or 3K Model, approximately 75 mumol 100 g-1 min-1, was similar to values previously determined by model-independent techniques, whereas CMRglc calculated with the 4K Model was 44% higher. In a separate group of rats administered a programmed infusion to attain a constant arterial concentration of [14C]DG that minimizes effects of tissue heterogeneity as well as any product loss, CMRglc calculated with all three models was 79 mumol 100 g-1 min-1 at 45 min after initiation of the infusion. Statistical comparisons of goodness of fit of total tissue radioactivity were, therefore, not indicative of which models best describe the tissue precursor and product pools or which models provide the most accurate rates of glucose utilization.

Animals↗

PubMed2003

Models to predict emissions of health-damaging pollutants and global warming contributions of residential fuel/stove combinations in China.

Residential energy use in developing countries has traditionally been associated with combustion devices of poor energy efficiency, which have been shown to produce substantial health-damaging pollution, contributing significantly to the global burden of disease, and greenhouse gas (GHG) emissions. Precision of these estimates in China has been hampered by limited data on stove use and fuel consumption in residences. In addition limited information is available on variability of emissions of pollutants from different stove/fuel combinations in typical use, as measurement of emission factors requires measurement of multiple chemical species in complex burn cycle tests. Such measurements are too costly and time consuming for application in conjunction with national surveys. Emissions of most of the major health-damaging pollutants (HDP) and many of the gases that contribute to GHG emissions from cooking stoves are the result of the significant portion of fuel carbon that is diverted to products of incomplete combustion (PIC) as a result of poor combustion efficiencies. The approximately linear increase in emissions of PIC with decreasing combustion efficiencies allows development of linear models to predict emissions of GHG and HDP intrinsically linked to CO2 and PIC production, and ultimately allows the prediction of global warming contributions from residential stove emissions. A comprehensive emissions database of three burn cycles of 23 typical fuel/stove combinations tested in a simulated village house in China has been used to develop models to predict emissions of HDP and global warming commitment (GWC) from cooking stoves in China, that rely on simple survey information on stove and fuel use that may be incorporated into national surveys. Stepwise regression models predicted 66% of the variance in global warming commitment (CO2, CO, CH4, NOx, TNMHC) per 1 MJ delivered energy due to emissions from these stoves if survey information on fuel type was available. Subsequently if stove type is known, stepwise regression models predicted 73% of the variance. Integrated assessment of policies to change stove or fuel type requires that implications for environmental impacts, energy efficiency, global warming and human exposures to HDP emissions can be evaluated. Frequently, this involves measurement of TSP or CO as the major HDPs. Incorporation of this information into models to predict GWC predicted 79% and 78% of the variance respectively. Clearly, however, the complexity of making multiple measurements in conjunction with a national survey would be both expensive and time consuming. Thus, models to predict HDP using simple survey information, and with measurement of either CO/CO2 or TSP/CO2 to predict emission factors for the other HDP have been derived. Stepwise regression models predicted 65% of the variance in emissions of total suspended particulate as grams of carbon (TSPC) per 1 MJ delivered if survey information on fuel and stove type was available and 74% if the CO/CO2 ratio was measured. Similarly stepwise regression models predicted 76% of the variance in COC emissions per MJ delivered with survey information on stove and fuel type and 85% if the TSPC/CO2 ratio was measured. Ultimately, with international agreements on emissions trading frameworks, similar models based on extensive databases of the fate of fuel carbon during combustion from representative household stoves would provide a mechanism for computing greenhouse credits in the residential sector as part of clean development mechanism frameworks and monitoring compliance to control regimes.

Air Pollutants↗

PubMedDate not supplied

A model for predicting contaminant removal by adsorption within the International Space Station water processor: 1. Multicomponent equilibrium modeling.

A thermodynamic model is developed to predict adsorption equilibrium in the International Space Station water processor's multifiltration beds. The model predicts multicomponent adsorption equilibrium behavior using single-component isotherm parameters and fictitious components representing the background matrix. The fictitious components are determined by fitting total organic carbon and tracer isotherms with the ideal adsorbed solution theory. Multicomponent isotherms using a wastewater with high surfactant and organic compound concentrations are used to validate the equilibrium description on a coconut-shell-based granular activated carbon (GAC), coal-based GAC, and a polymeric adsorbent.

Adsorption↗

PubMed2005

A model for predicting outcomes in patients with differentiated thyroid cancer and model performance in comparison with other classification systems.

BACKGROUND: This study was performed to determine the prognostic factors for differentiated thyroid cancer, and to establish a mathematical prognostic model. STUDY DESIGN: A retrospective study was conducted in 347 differentiated thyroid cancer patients. Univariate and multivariate prognostic factor analyses were carried out using the Kaplan-Meier and Cox regression methods. RESULTS: Without adjustment for treatment in the multivariate analysis, age, tumor size, angioinvasion, and distant metastasis were significant predictors of outcomes. The very low-risk, low-risk, high-risk, and very high-risk groups were identified from the logistic regression equation. Overall and event-free survival estimations at 10 years were 100% and 100% for very low-risk patients, 88% and 75% for low-risk patients, 30% and 16% for high-risk patients, and 5% and 0% for very high-risk patients. Inclusion of treatment in the multivariate analysis showed, in addition to other variables, that both total or near total thyroidectomy (versus thyroidectomy less than total and near total thyroidectomy, p = 0.0002; hazard ratio, 0.4; 95% CI, 0.3-0.7) and adjuvant radioactive iodine treatment (versus no treatment with radioactive iodine, p = 0.0001; hazard ratio, 0.5; 95% CI, 0.2-0.8) were associated with a reduced hazard of death in the followup period. By subgroup analysis, total and near total thyroidectomy, along with radioactive iodine, appeared to provide a survival benefit for all patients except those in the very low-risk group. CONCLUSIONS: The proposed mathematical model is satisfactory for predicting outcomes. Total and near total thyroidectomy along with radioactive iodine treatment might provide a survival advantage for differentiated thyroid cancer, except for those with very low risk.

Adenocarcinoma, Follicular↗

PubMed2000

The relationship between clinical and kinematic responses from human subject testing in rear-end automobile collisions.

Recent experiments have produced a linked data set of clinical and kinematic responses for human subjects exposed to controlled low-speed rear-end collisions. The purpose of this paper was to examine this paired data set and determine whether the presence or absence of clinical symptoms could be predicted from the peak linear and angular kinematic response of the head and neck. The data were generated using 42 male and female human subjects seated normally in the front passenger seat of a stationary vehicle struck from behind to produce vehicle speed changes of 4 and 8 km/h. Pre- and post-test clinical examinations documented the presence, severity and duration of whiplash-associated disorders (WAD). Logistic regression and backward elimination of independent variables were used to develop the prediction model. The analysis yielded a 16 parameter model that was significantly related (odds ratio = 21.2; P = 0.0069) to the presence or absence of transient whiplash symptoms. The model correctly predicted symptom presence in 13 of 23 tests (sensitivity 57%) and symptom absence in 49 of 52 tests (specificity 94%) in a population of 75 with a symptom prevalence of 31%. The model's positive predictive value was 81% and its negative predictive value was 83%. Despite statistical significance, the model did not discriminate between the presence and absence of symptoms in all tests, and indicated that factors other than the selected peak kinematic responses influenced symptom production.

Accidents, Traffic↗

PubMedDate not supplied

Children's and adults' models for predicting teleological action: the development of a biology-based model.

Understanding that only living things must act to gain self-beneficial goals is important for developing a theory-like understanding of the living world. This research studied the models that preschoolers, fifth graders, and adults use to guide their predictions of self-beneficial, goal-directed (i.e., teleological) action. Four possible models have been suggested: finalist, complexity based, biology based, and animal based. In Study 1, participants (N = 104) were assigned to one of two conditions that differed in whether a beneficial or neutral object was pictured; they were asked to predict whether animals, plants, machines, and simple artifacts would modify their movement in the direction of that object. Preschoolers' predictions were consistent with an animal-based model, fifth graders' predictions were consistent with biology-based and complexity-based models, and adults' predictions were consistent with a biology-based model. Analysis of both individual response patterns and explanations supported these findings, but also showed that a significant number of preschoolers and fifth graders were finalist, and that very few individual fifth graders followed a complexity-based teleology. In Study 2, participants (N = 84) reported whether the animals, plants, machines, and simple artifacts in Study 1 had psychological capacities. All age groups attributed psychological capacities to animals at levels higher than other domains and at above-chance levels. The evidence from these two studies suggests that preschoolers, unlike fifth graders and adults, predict teleological action for plants and animals on the basis of these entities' inferred psychological capacities.

Adult↗

PubMed1990

Prospective evaluation of a model for predicting etoposide plasma protein binding in cancer patients.

Etoposide protein binding in cancer patients is variable and has been related mathematically to a linear model consisting of serum albumin and total bilirubin [percentage unbound = (1.4 x total bilirubin) - (6.8 x albumin) + 34.4]. In this prospective evaluation of the model, plasma samples were obtained following the administration of etoposide in 31 patients, and the unbound percentage (% unbound) of etoposide in plasma was determined by equilibrium dialysis. The mean measured % unbound was 15.3 +/- 11.6 (SD), and the mean model predicted % unbound was 16.7 +/- 10.1. The relation between predicted and measured etoposide % unbound was highly correlated (r2 = 0.92, P = 0.001). The model was precise but with a slight bias toward overpredicting % unbound (mean prediction error, 1.36%; 95% confidence interval, 0.09 to 2.6%). In patients with abnormal total bilirubin (i.e., greater than 1.5 mg/dl) or with hypoalbuminemia (i.e., less than 3.3 g/dl), the model was both precise and unbiased. These results demonstrate that etoposide % unbound can be predicted using serum albumin and total bilirubin. This model should be useful in prospectively identifying patients at increased risk of experiencing altered pharmacological effects due to altered protein binding of etoposide.

Adult↗

PubMed2003

Generation of predictive pharmacophore models for CCR5 antagonists: study with piperidine- and piperazine-based compounds as a new class of HIV-1 entry inhibitors.

Predictive pharmacophore models were developed for a large series of piperidine- and piperazine-based CCR5 antagonists as anti-HIV-1 agents reported by Schering-Plough Research Institute in recent years. The pharmacophore models were generated using a training set consisting of 25 carefully selected antagonists based on well documented criteria. The activity spread, expressed in K(i), of training set molecules was from 0.1 to 1300 nM. The most predictive pharmacophore model (hypothesis 1), consisting of five features, namely, two hydrogen bond acceptors and three hydrophobic, had a correlation (r) of 0.920 and a root mean square of 0.879, and the cost difference between null cost and fixed cost was 44.46 bits. The model was cross-validated by randomizing the data using the CatScramble technique. The results confirmed that the pharmacophore models generated from the test set were not due to chance correlation. The best model (hypothesis 1) was validated using test set molecules (total of 78) and performed well in classifying active and inactive molecules correctly. The model was further validated by mapping onto it a diverse set of six CCR5 antagonists identified by five different pharmaceutical companies. The best model correctly predicted these compounds as being highly active. These multiple validation approaches provide confidence in the utility of the predictive pharmacophore model developed in this study as a 3D query tool in virtual screening to retrieve new chemical entities as potent CCR5 antagonists. The model can also be used in predicting biological activities of compounds prior to undertaking their costly synthesis.

Algorithms↗

PubMedDate not supplied

Optimizing heat shock protein expression induced by prostate cancer laser therapy through predictive computational models.

Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.

Animals↗