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At least 163 records · Page 9Linked to original sources
PubMed1989

Variability of safe dose estimates when using complicated models of the carcinogenic process. A case study: methylene chloride.

Advances in understanding carcinogenesis have led to the development of mathematical models that have biologically interpretable parameters. These models utilize more of the available scientific data than the empirical models routinely employed for quantifying carcinogenic risk. They also require consideration of sources of uncertainty in risk estimates that were previously ignored, such as animal-to-animal variability of physiological and pharmacological constants. A numerical technique is proposed for studying the consequences of incorporating the intrapopulation variability of biologically interpretable parameters into the risk assessment process. To demonstrate the technique, the variability of safe dose estimates for exposure to methylene chloride is considered. The results suggest that intrapopulation variability of the model parameters can increase the variability of safe dose estimates an appreciable amount.

Animals↗
PubMed2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy.

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Databases, Protein↗
PubMed1991

Analysis of failure time data with ordinal categories of response.

When failure times are observed, additional information concerning the type of failure is often recorded. A method which simultaneously models the failure times and additional information in the form of ordinal categories is discussed. An application to clinical trial data, in which the failure times are times of onset of headache, and the headaches are classified into the ordinal categories mild, moderate and severe, illustrates how this method may be used and how the final model can be interpreted. The continuation ratio model, which is used in this method, is described in detail.

Clinical Trials as Topic↗
PubMed1990

[Strategies in the use of isolated cardiomyocytes in relation to other models in experimental cardiology].

Selection of the optimal model for a specific experiment considerably determines the results and their correct interpretation. The model of the isolated cardiomyocyte is increasingly being used in experimental cardiology as it provides several advantages in comparison to models in which the heart tissue remains relatively complete. Similarly as in other models, the factors limiting its use have to be known also in the case of the isolated cardiomyocyte. To minimalize misinterpretation of results the given problem is to be handled at all available levels.

Heart↗
PubMed1987

Mass transport in dissolution kinetics. I: Convective diffusion to assess the role of fluid viscosity under forced flow conditions.

The quantitative influence of viscosity on dissolution kinetics is assessed under laminar flow conditions by utilizing a convective diffusion model for drug dissolution. Functional dependency of three types of viscosity inducing agents is established with respect to the parameters of fluid flow rate, diffusivity, and solubility. Studies of aqueous solutions of sucrose and of glycerol demonstrate that the decrease in dissolution rate of ethyl p-aminobenzoate is related to the decrease in solute diffusivity in these solutions, whereas the solubility change in the glycerol solutions has an additional independent simultaneous effect. Dissolution in hydroxypropyl cellulose solutions remains constant under fixed fluid flow conditions because of the negligible effect of the polymer upon the drug diffusivity. A change in fluid flow rate, however, alters the dissolution rate and correlates quantitatively with the rate of shear in the convective diffusion model. The interpretation of the effect of viscosity on dissolution kinetics with the convective diffusion model explains these phenomena quantitatively in terms of the fundamental mass transport processes.

Cellulose↗
PubMed1986

Interpretation of oxygen disappearance curves measured in blood perfused tissues.

We have developed a two compartment (tissue and blood) lumped parameter model to interpret oxygen disappearance curves (O2 DCs) measured in vivo with PO2 microelectrodes in tissues which are perfused with blood. To include the properties of the oxyhemoglobin equilibrium curve (HEC), we used an algorithm we have recently developed for both standard and nonstandard conditions. The new blood and tissue model is more useful than a previous analysis using the Hill equation for blood and constant oxygen consumption (VO2). The model can be adapted for constant (zero-order) consumption, Michelis-Menten kinetics, or for double cytochrome systems. Examples for the former include brain, and for the latter, carotid body. The models are discussed in relationship to experimental microelectrode measurements in gerbil brain and in cat carotid body after blood flow occlusion.

Animals↗
PubMed1991

Utility of phenomenological models for describing temperature dependence of bacterial growth.

We compared three unstructured mathematical models, the master reaction, the square root, and the damage/repair models, for describing the relationship between temperature and the specific growth rates of bacteria. The models were evaluated on the basis of several criteria: applicability, ease of use, simple interpretation of model parameters, problem-free determination of model parameters, statistical evaluation of goodness of fit (chi 2 test), and biological relevance. Best-fit parameters for the master reaction model could be obtained by using two consecutive nonlinear least-square fits. The damage/repair model proved to be unsuited for the data sets considered and was judged markedly overparameterized. The square root model allowed nonproblematical parameter estimation by a nonlinear least-square procedure and, together with the master reaction model, was able to describe the temperature dependence of the specific growth rates of Klebsiella pneumoniae NCIB 418, Escherichia coli NC3, Bacillus sp. strain NCIB 12522, and the thermotolerant coccobacillus strain NA17. The square root and master reaction models were judged to be equally valid and superior to the damage/repair model, even though the square root model is devoid of a conceptual basis.

Bacteria↗
PubMed1989

A strategy for analysing multiple risk factors with application to cervical pain syndrome.

When studying the possible effects of several factors in a given disease, two major problems arise: (1) confounding, and (2) multiplicity of tests. Frequently, in order to cope with the problem of confounding factors, models with multiple explanatory variables are used. However, the correlation structure of the variables may be such that the corresponding tests have low power: in its extreme form this situation is coined by the term "multicollinearity". As the problem of multiplicity is still relevant in these models, the interpretation of results is, in most cases, very hazardous. We propose a strategy--based on a tree structure of the variables--which provides a guide to the interpretation and controls the risk of erroneously rejecting null hypotheses. The strategy was applied to a study of cervical pain syndrome involving 990 subjects and 17 variables. Age, sex, head trauma, posture at work and psychological status were all found to be important risk factors.

Adult↗
PubMed2026

Predicting food taste with bound-driven optimization.

The prediction of sensory attributes from ingredient-level formulations is an emerging challenge at the intersection of food science and artificial intelligence. We address the fundamental question of whether the taste of a food can be predicted from its ingredients by treating recipes as composite materials. We apply Hashin-Shtrikman (HS) and Reuss-Voigt (RV) bounds, techniques originally developed for elastic moduli, as a null-hypothesis additive baseline for five taste dimensions (sweetness, sourness, bitterness, umami, saltiness) on a curated dataset of 70 recipes decomposed into 115 distinct ingredients scored against a library of 209 ingredient-level taste references with trained-panel ground truth. This baseline systematically under-predicts perceived taste: 77% of actual taste values exceeded the HS upper bound, with the exceedance rate ranging from 26% (bitterness) to 97% (saltiness). We traced this gap to specific processing chemistry (Maillard reactions, caramelization, evaporative concentration, protein hydrolysis, and nucleotide synergy) and introduced a hybrid model that augments the HS baseline with eight chemistry-proxy features encoding these mechanisms. Our results show that our interpretable hybrid model eliminates the systematic bias and reduces mean absolute error by 27%-62% for sweetness, sourness, umami, and saltiness while using only 10 interpretable features, achieving performance comparable to a black-box Lasso regression on 115 per-ingredient features. We further demonstrate constrained inverse design via Differential Evolution, recovering ingredient formulations that match target taste profiles subject to compositional bounds. Our work demonstrates how key chemical processes during food preparation can inform and augment physics-based and machine learning models, providing a quantitative fingerprint of processing chemistry's contribution to taste perception and paving the way for model-driven food formulation with targeted sensory characteristics.

Composite material bounds↗
PubMed1990

Comments on a time-dependent version of the linear-quadratic model.

The accuracy and interpretation of the "LQ + time" model (E = D(alpha + beta d) - gamma T) are discussed. Evidence is presented, based on data in the literature, that this model does not accurately describe the changes in isoeffect dose occurring with protraction of the overall treatment time during fractionated irradiation of the lung. This lack of fit of the model explains, in part, the surprisingly large values of gamma/alpha that have been derived from experimental lung data. The large apparent time factors for lung suggested by the model are also partly explained by the fact that gamma T/alpha, despite having units of dose, actually measures the influence of treatment time on the effect scale, not the dose scale, and is shown to consistently overestimate the change in total dose. The unusually high values of alpha/beta that have been derived for lung using the model (approximately 5 Gy) are shown to be influenced by the method by which the model was fitted to data. Reanalyses of the data using a more statistically valid regression procedure produce estimates of alpha/beta more typical of those usually cited for lung (approximately 3 Gy). Most importantly, published isoeffect data from lung indicate that the true deviation from the linear-quadratic (LQ) model is nonlinear in time, instead of linear, and also depends on other factors such as the effect level and the size of dose per fraction. Thus, we do not advocate the use of the "LQ + time" expression as a general isoeffect model.

Animals↗
PubMedDate not supplied

Multiplicative and additive models with external controls in a cohort study of cancer mortality.

The use of additive and multiplicative hazard models is examined for a cohort study of 2696 women followed up for 12 years. The multiplicative model implied that women with a haemoglobin level less than 12 g/dl were at higher risk from cancer, and the additive model showed that this risk was confined to women after the menopause. Despite difficulties in fitting and in interpretation, additive model can be useful in the analysis of cohort studies.

Adult↗
PubMed1978

General theory of critical periods and development of obesity.

The general systems theory (GST), the general theory of organization (GTO), and the general theory of critical periods (GTCP) have been applied to some nutritional problems. This theoretical approach seems to be in good agreement with most of the data of the literature and with the personal experience, pointing at the possibility to use a simple general model to interpret the complex problem of obesity.

Critical Period, Psychological↗
PubMed1991

A framework for the interpretation of first-order interaction in logit modeling.

Several suggestions have been tendered for interpreting first-order interaction in log-linear analysis. Occasionally these methods result either in a loss of information or in results that are difficult to grasp on an intuitive level. It is argued that interpreting effect parameters in terms of odds ratios provides an elegant and intuitively appealing conceptual framework that has great generality across models. The interaction term then resembles a cross-product term in linear regression. In both forms of analysis, the partial effect of a given predictor on the response is composed of a constant and a correction that is a function of the other predictor involved in the interaction. This framework is especially appealing for models in which the logit is based on a bifurcation of the dependent variable. Although odds ratios are still useful for summarizing effects on polytomous dependent variables, greater caution must be exercised to avoid misleading interpretations.

Humans↗
PubMed1991

Combining physiologic models and symbolic methods to interpret time-varying patient data.

This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.

Adult↗
PubMed1975

Intrauterine pressure wave form characteristics in hypocontractile labor before and after oxytocin administration.

The data demonstrate that the contractions of hypocontractile active labor and normal spontaneous labor are different in several measures in addition to maximal amplitude. Furthermore, when the pathophysiology is corrected by the use of oxytocin, the contractions resemble those of normal spontaneous labor except in the maximal rate of tension development. Our data tend to support the subcellular model of uterine contractility, although the incompleteness of these models limits interpretation.

Adrenocorticotropic Hormone↗
PubMed2026

Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.

Although machine learning models can predict antimicrobial susceptibility from bacterial whole genome sequencing (WGS), state-of-the-art approaches are computationally demanding or dependent on knowledge of genetic resistance determinants. Here, we describe an efficient data-driven approach to predicting minimum inhibitory concentration (MIC) by progressively extending and refining predictive genome segments, independent of prior knowledge of resistance determinants. Resultant models had high interpretability - known and potentially novel resistance determinants were captured. Using 762 clinical E. coli strains, 71.6% of predictions were within one dilution of the measured MIC. Models trained with this algorithm generalised better onto external data (F1 score = 0.85) compared with alternative models trained on annotated resistance determinants (F1 = 0.82) or k-mer counts (F1 = 0.74). Computational demands were low (RAM usage 23.6GB vs 38.8GB for k-mer model). These advantages represent an important advance in predicting antimicrobial susceptibility from WGS, with potential applications for clinical diagnostics, drug development, and surveillance.

Journal Article↗
PubMed1991

Domain of validity of classical models of leucine metabolism assessed by compartmental modeling.

Whole-body modeling of in vivo leucine (an essential amino acid) metabolism is fundamentally difficult due to the complexity of the system. This has favored the use of two simple kinetic models, the so-called primary and reciprocal pool models, to interpret tracer data, but their domain of validity is uncertain. We define here the error of these two approaches by using comprehensive compartmental models of leucine metabolism as true representations of the leucine system. Of particular interest is the comparison of the two simple models with an 11-compartment model characterized by a rich intracellular compartmentation that has recently been proposed as a sound physiological description of the system. Formulas are derived that define in structural terms the error of the primary and reciprocal pool models.

Humans↗