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Results for “Predictive modeling”

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PubMed1996

Metabolism of butadiene by mice, rats, and humans: a comparison of physiologically based toxicokinetic model predictions and experimental data.

1,3-Butadiene is a carcinogen in rats and mice, with mice being substantially more sensitive than rats. Our recent research is directed toward obtaining a better understanding of the cancer risk of butadiene in humans by evaluating species-dependent differences in the formation of the toxic metabolites epoxybutene and diepoxybutane. The recent data include in vitro studies on butadiene metabolism using tissues from humans, rats, and mice as well as experimental data and physiological model predictions for butadiene in blood and butadiene epoxides in blood, lung, and liver after exposure of rats and mice to inhaled butadiene. The findings suggest that humans would be more like rats and less like mice regarding the formation of butadiene epoxides. These research findings permit a reassessment of some default options that are used in carcinogen risk assessments. The research approach employed can be a useful strategy for developing mechanistic and toxicokinetic data to supplant default assumptions used in carcinogen risk assessments.

Animals↗

PubMed2003

Whole-body skeletal muscle mass: development and validation of total-body potassium prediction models.

BACKGROUND: A substantial proportion of total body potassium (TBK) in humans is found in skeletal muscle (SM), thus affording a means of predicting total-body SM from whole-body counter-measured (40)K. There are now > 30 whole-body counters worldwide that have large cross-sectional and longitudinal TBK databases. OBJECTIVE: We explored 2 SM prediction approaches, one based on the assumption that the ratio of TBK to SM is stable in healthy adults and the other on a multiple regression TBK-SM prediction equation. DESIGN: Healthy subjects aged >or= 20 y were recruited for body-composition evaluation. TBK and SM were measured by whole-body (40)K counting and multislice magnetic resonance imaging, respectively. A conceptual model with empirically derived data was developed to link TBK and adipose tissue-free SM as the ratio of TBK to SM. RESULTS: A total of 300 subjects (139 men and 161 women) of various ethnicities with a mean (+/- SD) body mass index (in kg/m(2)) of 25.1 +/- 5.4 met the study entry criteria. The mean conceptual model-derived TBK-SM ratio was 122 mmol/kg, which was comparable to the measurement-derived TBK-SM ratios in men and women (119.9 +/- 6.7 and 118.7 +/- 8.4 mmol/kg, respectively), although the ratio tended to be lower in subjects aged >or= 70 y. A strong linear correlation was observed between TBK and SM (r = 0.98, P < 0.001), with sex, race, and age as small but significant prediction model covariates. CONCLUSIONS: Two different types of prediction models were developed that provide validated approaches for estimating SM mass from (40)K measurements by whole-body counting. These methods afford an opportunity to predict SM mass from TBK data collected in healthy adults.

Absorptiometry, Photon↗

PubMed1997

Image discrimination models predict detection in fixed but not random noise.

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

Discrimination, Psychological↗

PubMed1998

The pH dependence of predictive models relating electrophoretic mobility to peptide chemico-physical properties in capillary zone electrophoresis.

We applied best fitting procedures to capillary electrophoresis (CE) mobility values, measured at varying acidic pH, of a set of 21 peptides with a molecular mass ranging from about 350 to 1850 Da. This method allowed the contemporary measurements of C-terminus and carboxylic group of the side-chain of aspartic and glutamic acid dissociation constants and of peptide Stokes radius at different protonation stages. Stokes radius was related to peptide molecular mass M at the power of a fractional coefficient, and best correlation was found at pH 2.25, the fractional coefficient being equal to 0.68. This value is close to that proposed by R. E. Offord (Nature 1966, 211, 591-593), who suggested a proportionality between the polymer Stokes radius and M(2/3). The coefficient value decreases at higher pH, reaching a value of 0.58 at pH 4.25, corresponding to a mean peptide conformational transition towards more compact structures as a consequence of C-terminus dissociation. The measurement of the dissociation constants of each peptide allowed us to determine the percentage error on peptide charge predictions performed utilizing mean dissociation constants. Even for the charge, the best predictive performance is obtained at the most acidic edge of the range of the pH studied, mainly at pH 2.25. Conclusively, this study shows that the best performance of predictive models for peptide CE mobility is obtainable in the very acidic pH range (2.25-2.50) and in the absence of electroosmotic flow, and that a satisfactory predictive equation of peptide electrophoretic mobility (m2V(-1)s(-1) is given by mu = 85.4(Z/M(0.68))10(-8).

Amino Acid Sequence↗

PubMed2004

Evaluation and a predictive model of airborne fungal concentrations in school classrooms.

Exposure to airborne fungal products may be associated with health effects ranging from non-specific irritation of the respiratory tract or mucus membranes to inflammation provoked by specific fungal antigens. While concentrations of airborne fungi are frequently measured in indoor air quality investigations, the significance of these measurements in the absence of visual mold colonization is unclear. This study was undertaken to evaluate concentrations of airborne fungal concentrations in school classrooms within a defined geographic location in British Columbia, Canada, and to build a model to clarify determinants of airborne fungal concentration. All elementary schools within one school district participated in the study. Classrooms examined varied by age, construction and presence or absence of mechanical ventilation. Airborne fungal propagules were collected inside classrooms and outdoors. Variables describing characteristics of the environment, buildings and occupants were measured and used to construct a predictive model of fungal concentration. The classrooms studied were not visibly contaminated by fungal growth. The data were evaluated using available guidelines. However, the published guidelines did not take into account significant aspects of the local environment. For example, there was a statistically significant effect of season on the fungal concentrations and on the proportional representation of fungal genera. Rooms ventilated by mechanical means had significantly lower geometric mean concentrations than naturally ventilated rooms. Environmental (temperature, outdoor fungal concentration), building (age) and ventilation variables accounted for 58% of the variation in the measured fungal concentrations. A methodology is proposed for the evaluation of airborne fungal concentration data which takes into account local environmental conditions as an aid in the evaluation of fungal bioaerosols in public buildings.

Air Microbiology↗

PubMed2000

A self-learning predictive model of articulator movements during speech production.

A model is presented which predicts the movements of flesh points on the tongue, lips, and jaw during speech production, from time-aligned phonetic strings. Starting from a database of x-ray articulator trajectories, means and variances of articulator positions and curvatures at the midpoints of phonemes are extracted from the data set. During prediction, the amount of articulatory effort required in a particular phonetic context is estimated from the relative local curvature of the articulator trajectory concerned. Correlations between position and curvature are used to directly predict variations from mean articulator positions due to coarticulatory effects. Use of the explicit coarticulation model yields a significant increase in articulatory modeling accuracy with respect to x-ray traces, as compared with the use of mean articulator positions alone.

Adolescent↗

PubMed2000

[Prognostic factors and predictive model of non-Hodgkin's lymphoma: pathological prognostic groupings and international prognostic index].

The non-Hodgkin's lymphomas are a diverse group of neoplasms, pathological prognostic groupings, based on survival, are essential to facilitate clinical comparisons of therapeutic trials. In addition, a model, based on important prognostic factors, is also necessary for predicting therapeutic outcome in patients with certain pathological prognostic group. In this paper, four models of pathological prognostic groupings, such as Working Formulation classification, National Cancer Institute classification, lymphoma clinico-pathologic(LCP) classification, and LCP schema, are reviewed for clinical usage. Comments on international prognostic index as a predictive model for aggressive lymphoma are described with its perspective for predictive capacity of therapeutic outcome.

Humans↗

PubMed1999

Prediction models for the histology of residual masses after chemotherapy for metastatic testicular cancer. ReHiT Study Group.

Patients with metastatic non-seminomatous testicular cancer can be cured by cisplatin-based chemotherapy. After chemotherapy, surgical resection is a generally accepted treatment to remove remnants of the initial metastases since residual tumor may still be present (mature teratoma or viable cancer cells). We review here several policies for the selection of patients for retroperitoneal lymph node dissection. We consider one simple policy as a reference, which bases the selection solely on the diameter of the residual mass (> or = 10 mm). Further, we distinguish 4 rule-based policies, which combine several clinical characteristics (e.g., primary tumor teratoma-positive or insufficient reduction in size), and 2 probability-based policies, where a regression or tree model is used that statistically combines well-known, important clinical predictors for the absence of residual tumor. The policies were evaluated in an international data set containing 716 patients. The reference policy would leave 204 masses < 10 mm unresected, where mature teratoma was present in 50 (25%) and cancer in 11 (5%). Compared with this policy, most of the rule-based policies left fewer patients with residual tumor unresected, at the expense of more resections. The probability-based policies could refine the selection without such an increase in the number of resections. Prediction models for the residual histology therefore merit wider application in clinical practice.

Germinoma↗

PubMed1998

Validation of a model predicting enrollment status in a chemoprevention trial for breast cancer.

We evaluated the performance of a regression model in predicting enrollment status in a chemoprevention trial for breast cancer using a population independent of that from which the model was derived. In years 1 and 2 of recruitment, questionnaires were completed by eligible participants following attendance at informational meetings about the Breast Cancer Prevention Trial. The variables in the original model, based on women recruited in year 1, included not being able to take estrogen replacement therapy (ERT), concern about the side effects of tamoxifen, the possibility of getting a placebo, the out-of-pocket expenses associated with the trial, and disagreement with the statement "significant others would be reassured if the respondent was taking tamoxifen." These variables were used to predict enrollment status of women newly recruited to the trial in year 2. Among the 89 women in the study population who responded to the questionnaire, 66% did not enroll in the trial. By applying the original logistic regression model, enrollment status in the trial was correctly predicted for 72% of year 2 questionnaire respondents. Age and risk scores, as binary variables, were used in a derived logistic model to determine whether they provided additional predictive information on enrollment status. The resulting four-factor model, which predicted nonenrollment, included: age of > or = 50 years, not being able to take ERT, expressed concern that significant others would not be reassured if the respondent was taking tamoxifen, and concern about out-of-pocket expenses associated with the trial. This model correctly classified 76% of the respondents. The logistic regression models performed reasonably well in predicting enrollment status. Not being able to take ERT remained the strongest factor predicting nonenrollment. More research is needed to evaluate factors that motivate persons to seek participation in primary chemoprevention trials in culturally diverse populations.

Anticarcinogenic Agents↗

PubMed1998

Monitoring in situ liver metabolism in rats using microdialysis. Comparison of microdialysis mass-transport model predictions to experimental metabolite generation data.

The generation of metabolites from two model compounds, phenacetin and acetaminophen, included in the perfusion fluid of a microdialysis probe implanted into rat liver was studied. When 60 microM phenacetin was included in the perfusion fluid using a flow rate of 1.0 microL/min, acetaminophen and acetaminophen sulfate were recovered at concentrations that ranged between 0.4 and 1.6 microM. Acetaminophen sulfate ([AS]gain) diffused back into the microdialysis probe on a micromolar percentage basis of 8.9+/-2.4% (n = 3) when acetaminophen was passed through the probe at a concentration between 11 and 12 microM. When 220-240 microM acetaminophen was passed through the probe, the percentage of acetaminophen sulfate recovered was 4.8+/-1.4% (n = 3) (P < 0.1 compared to the 11 microM group). No acetaminophen glucuronide was detected in the dialysate samples. A mathematical model that describes mass transport in microdialysis sampling was used to predict the concentration of metabolite that could be recovered into the dialysate after the loss of a substrate compound that undergoes metabolism. The model predicts a metabolite recovery of 23.6% using estimates for phenacetin metabolism and 21.5% using estimates for acetaminophen metabolism. The results presented here indicate that microdialysis has potential to be used to study local in situ metabolism and with further refinements of the microdialysis mass-transport model may be used to estimate in vivo metabolic formation rates.

Acetaminophen↗

PubMed2005

Risk-adjusted predictive models of mortality after index arterial operations using a minimal data set.

BACKGROUND: Reducing the data required for a national vascular database (NVD) without compromising the statistical basis of comparative audit is an important goal. This work attempted to model outcomes (mortality and morbidity) from a small and simple subset of the NVD data items, specifically urea, sodium, potassium, haemoglobin, white cell count, age and mode of admission. METHODS: Logistic regression models of risk of adverse outcome were built from the 2001 submission to the NVD using all records that contained the complete data required by the models. These models were applied prospectively against the equivalent data from the 2002 submission to the NVD. RESULTS: As had previously been found using the P-POSSUM (Portsmouth POSSUM) approach, although elective abdominal aortic aneurysm (AAA) repair and infrainguinal bypass (IIB) operations could be described by the same model, separate models were required for carotid endarterectomy (CEA) and emergency AAA repair. For CEA there were insufficient adverse events recorded to allow prospective testing of the models. The overall mean predicted risk of death in 530 patients undergoing elective AAA repair or IIB operations was 5.6 per cent, predicting 30 deaths. There were 28 reported deaths (chi(2) = 2.75, 4 d.f., P = 0.600; no evidence of lack of fit). Similarly, accurate predictions were obtained across a range of predicted risks as well as for patients undergoing repair of ruptured AAA and for morbidity. CONCLUSION: A 'data economic' model for risk stratification of national data is feasible. The ability to use a minimal data set may facilitate the process of comparative audit within the NVD.

Aortic Aneurysm, Abdominal↗

PubMed2004

Concepts and tools for predictive modeling of microbial dynamics.

Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.

Bacteria↗

PubMed2006

[Noninvasive assessment of liver fibrosis in chronic hepatitis B using a predictive model].

OBJECTIVE: To develop a diagnostic model comprising clinical and serum markers for assessing HBV-related liver fibrosis. METHODS: 270 chronic hepatitis B patients were randomly allocated to either an estimation group (195 cases) or a validation group (75 cases). Liver biopsies were done and staging of fibrosis was assessed. Twenty-six common clinical and serum markers were analyzed initially in the estimation group to derive a predictive model to discriminate the stages of fibrosis. The model created was then assessed with ROC analysis. It was also applied to the validation group to test its accuracy. RESULTS: Among 13 variables associated with liver fibrosis selected by univariate analysis, age, gamma glutamyltranspeptidase (GGT), hyaluronic acid (HA), and platelet count (PLT) were identified by multivariate logistic regression analysis as independent factors of fibrosis. A fibrosis index constructed from the above four markers was established. In ROC analysis, the AUC was 0.889 for the estimation group and 0.850 for the validation group for discriminating > or =S3 from < or=S2. Using the optimal cutoff score 3.0, the sensitivity of the index was 90.2%, the specificity 76.1%, and the accuracy was 82%. There was a positive linear relationship between the index scores and the fibrosis stages (r = 0.731, P<0.001). The AUC for identifying > or=S2 was 0.873 with sensitivity/specificity of 79%/82%, cutoff score 2.2; The AUC for identifying S4 was 0.872 with sensitivity/specificity of 83%/75%, cutoff score 5.4. There were no significant differences in diagnostic efficacy in the model between the estimation and the validation group (P>0.05). CONCLUSION: A model for assessment of liver fibrosis was established with easily accessible markers. It appears to be sensitive, accurate and reproducible, suggesting it could be used to assist or replace liver biopsy to detect dynamic changes of HBV-related liver fibrosis.

Adolescent↗

PubMed2002

Model predictive control helps to regulate slow processes--robust barrel temperature control.

Slow temperature control is a challenging control problem. The problem becomes even more challenging when multiple zones are involved, such as in barrel temperature control for extruders. Often, strict closed-loop performance requirements (such as fast startup with no overshoot and maintaining tight temperature control during production) are given for such applications. When characteristics of the system are examined, it becomes clear that a commonly used proportional plus integral plus derivative (PID) controller cannot meet such performance specifications for this kind of system. The system either will overshoot or not maintain the temperature within the specified range during the production run. In order to achieve the required performance, a control strategy that utilizes techniques such as model predictive control, autotuning, and multiple parameter PID is formulated. This control strategy proves to be very effective in achieving the desired specifications, and is very robust.

Computer Simulation↗

PubMed2002

GIS prediction model of malaria transmission in Jiangsu province.

OBJECTIVES: To perform GIS spatial analysis on malaria transmission patterns in Jiangsu after setting up a malaria database and developing GIS model of malaria transmission in Jiangsu province. METHODS: The epidemiological GIS database of malaria in Jiangsu province was established using ArcView 3.0a software. The climate data covering Jiangsu province and its peripheral area were extracted from the FAOCLIM database, the total growing degree days (TGDD) for Plasmodium vivax were calculated, and spatial distribution for TGDD was analyzed by ArcVeiw 3.0a. RESULTS: The predicted malaria distribution map based on TGDD was created, which showed that the transmission of malaria decreased gradually from west to east, which can be divided into three belts according to the degree of transmission. The 14-year mean morbidity distribution map of malaria in Jiangsu showed that the middle and west parts of Jiangsu is the most serious endemic area. The morbidity in the areas along the Taihu valley, such as Suzhou, Wuxi and Changzhou, as well as Nantong and a few of northern counties are the lowest. The morbidity of other places is at the middle level. The 14-year mean morbidity distribution map of malaria is correlated with predicted malaria distribution map for TGDD. CONCLUSION: It is possible to monitor the malaria transmission by GIS predicted model based on TGDD.

China↗

PubMed2005

Predictability model of the need for extracorporeal membrane oxygenation in neonates with meconium aspiration syndrome treated with inhaled nitric oxide.

BACKGROUND: As the use of inhaled nitric oxide (iNO) resulted in a decline in the need for extracorporeal membrane oxygenation (ECMO) in neonates with hypoxic respiratory failure, iNO has become an accepted treatment modality even in non-ECMO centers. However, because not all neonates respond to iNO, the timely identification and transfer of nonresponders to an ECMO center are important. OBJECTIVES: The objective of this study was to identify the risk factors predictive of the need of ECMO in neonates with hypoxic respiratory failure after the first 6 hours of iNO treatment in an ECMO center. METHODS AND PATIENT POPULATION: Forty-nine patients with hypoxic respiratory failure transferred for iNO therapy and potential ECMO during a 2-year period were identified in this retrospective study. None of the patients had received iNO before admission. Strict clinical guidelines were used to standardize lung inflation, cardiovascular support, and iNO administration and weaning and to define treatment failure. The relationship between treatment failure (ie, the need for ECMO) and a set of suspected risk factors after 6 hours of iNO administration was examined by logistic regression analysis. RESULTS: Twenty-two neonates responded to iNO (non-ECMO group) whereas 27 neonates failed and met ECMO criteria (ECMO group). There was no difference between the 2 groups in demographic data, ventilatory support, air leak syndrome at 6 hours of iNO treatment, and survival to discharge. However, the dose and duration of iNO therapy were predictive of the need for ECMO with an adjusted odds ratio of 1.12 (95% CI, 1.01-1.25; P = .04) and 0.45 (95% CI, 0.27-0.65; P = .0002), respectively. CONCLUSIONS: By the end of the first 6 hours of iNO treatment and under the specific conditions established by the use of the clinical guidelines, the dose and the duration of iNO administration were predictive of the probability for the need of ECMO in this patient population. Thus, one can establish a center-specific predictability model for the need of ECMO in neonates with hypoxic respiratory failure treated with iNO if strict clinical guidelines for iNO administration and weaning and respiratory and cardiovascular support are used in the given center.

Administration, Inhalation↗

PubMed2003

Escitalopram, the S-(+)-enantiomer of citalopram, is a selective serotonin reuptake inhibitor with potent effects in animal models predictive of antidepressant and anxiolytic activities.

OBJECTIVE: The pharmacological profile of escitalopram, the S-(+)-enantiomer of citalopram, was studied and compared with citalopram and the R-(-)-enantiomer, R-citalopram. METHODS: Inhibition of the serotonin transporter (5-HTT) was studied in COS-1 cells expressing the human 5-HTT (h-5-HTT) and in rat brain synaptosomes. In vitro selectivity was studied relative to noradrenaline transporter (NAT) and dopamine transporter (DAT) function in rat brain synaptosomes, and affinities for other binding sites were determined. In vivo 5-HT activity was measured as inhibition of neuronal firing rate in rat dorsal raphe nucleus (DRN) and enhancement of 5-hydroxytryptophan (5-HTP)-induced behaviour (mouse and rat). Furthermore, studies were conducted in models of antidepressant (mouse forced-swim test), anxiolytic [foot-shock-induced ultrasonic vocalization (USV) in adult rats and mouse black and white box] and anti-aggressive activity (socially isolated mice). RESULTS: Escitalopram inhibited 5-HTT functions approximately 2 times more potently than citalopram and at least 40 times more potently than R-citalopram. Escitalopram showed insignificant activity at other monoamine transporters and 144 other binding sites. Escitalopram inhibited 5-HT neuronal firing in DRN and potentiated 5-HTP-induced behaviours more potently than citalopram; R-citalopram was inactive. Escitalopram and citalopram, but not R-citalopram, reduced forced-swimming-induced immobility and facilitated exploratory behaviour in the black and white box. Escitalopram and citalopram inhibited USV potently; R-citalopram was several times less potent. Escitalopram, citalopram and R-citalopram inhibited aggressive behaviour weakly. Escitalopram and citalopram had very potent anti-aggressive effects when co-administered with l-5-HTP. CONCLUSION: Escitalopram is a very selective 5-HT reuptake inhibitor. It is more potent than its racemate citalopram and is effective in animal models predictive of antidepressant and anxiolytic activities.

Action Potentials↗

PubMed2002

A development environment for predictive modelling in foods.

Waikato Environment for Knowledge Analysis (WEKA) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning/data mining algorithms. Non-programmers interact with the software via a user interface component called the Knowledge Explorer. Applications constructed from the WEKA class libraries can be run on any computer with a web-browsing capability, allowing users to apply machine learning techniques to their own data regardless of computer platform. This paper describes the user interface component of the WEKA system in reference to previous applications in the predictive modelling of foods.

Algorithms↗