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PubMed2003

Formulation of a mathematical model to predict solar water disinfection.

A mathematical model was formulated that will facilitate the prediction of solar disinfection by analyzing the effect of sunlight exposure (x(1)) and the load of bacterial contamination (x(2)), as predictor variables, on the efficiency of solar disinfection (y). Aliquots of 0.1 ml containing average numbers of E. coli, ranging between 1 and 5 x 10(3)cells/ml raw water, were introduced into each of the 96 wells of polystyrene microtitre plates. Plates, with the lid on, were exposed to sunlight for varying exposures ranging between 1.04 x 10(3) and 8.40 x 10(3)kJ m(-2). Double strength nutrient broth was then added. After 48 h incubation wells containing visible contamination were considered as containing one cell or more that survived the exposure. Data showed that disinfection is dependent both on the load of bacterial contamination and sunlight exposure. This relationship is characterized by curves having shoulders followed by a steep decline and then tailing off in an asymptotic fashion. The shoulder size increased with the increase of the contamination load, however, the slope remains the same. Statistical analysis indicates a positive correlation among the variables (R(2) = 0.893); the mathematical model, y=1-(1-e(-kx(1)))(x(2)), represents the relationship, with k being the solar inactivation constant. The exposure required to produce a given decontamination level can be predicted using the equation: x(1)=-1/kln[1-(1-y)(-1/x(2))]e(-micro/rho.m/A), where micro is the linear attenuation coefficient (m(-1)), rho is the density, m is the mass and A is the area of the exposed part of the sample. The predictor variables (x(1), x(2)) strongly influence the efficiency of solar disinfection, which can be predicted using the suggested mathematical model. The present data provides a means to predict the efficiency of solar disinfection as an approach to improve the quality of drinking water mainly in developing countries with adequate sunshine all year-round.

Disinfection↗

PubMed1994

Lactational transfer of tetrachloroethylene in rats.

Tetrachloroethylene (PCE) is a commonly used organic solvent and a suspected human carcinogen, reportedly transferred to human breast milk following inhalation exposure. Transfer of PCE to milk may represent a threat to the nursing infant. A physiologically based pharmacokinetic (PBPK) model was developed to quantitatively assess the transfer of inhaled PCE into breast milk and the consequent exposure of the nursing infant. The model was validated in lactating rats. Lactating Sprague-Dawley female rats were exposed via inhalation to PCE at concentrations ranging from 20-1000 ppm, and then returned to their nursing, 10- to 11-day-old pups. Tetrachloroethylene concentrations in the air, blood, milk, and tissue were determined by gas chromatography and compared to model predictions. The model described the distribution of inhaled PCE in maternal blood and milk, as well as the nursed pup's gastrointestinal tract, blood, and tissue. Several computer simulations of PCE distribution kinetics in exhaled air, blood, and milk of exposed human subjects were run and compared with limited human data available from the literature. It is concluded that the PBPK model successfully described the concentration of PCE in both lactating rats and humans. Although predictions vs. observations were good, the model slightly underpredicted the peak whole pup PCE concentration and underpredicted systemic clearance of PCE from the pup.

Adipose Tissue↗

PubMed2005

Mathematical model that predicts lower leg motion in response to electrical stimulation.

Electrical stimulation of skeletal muscles of patients with upper motor neuron lesions can be used to restore functional movements such as standing or walking. Mathematical muscle models can assist in designing stimulation patterns that will enable patients to perform particular tasks more efficiently. In this study we extend our previous model to allow us to predict changes in knee joint angle in response to electrical stimulation of the human quadriceps femoris muscle. The model was tested both with and without inertial loads placed around the ankle joints of healthy subjects. Results showed that the model predicted the knee extensions with a RMS angle error that was generally <or=8 degrees. The coefficients of determination between the measured and predicted data showed the model accounted for approximately 71%, approximately 94%, approximately 73%, and approximately 89% of the variances in the experimental maximum excursion, time to maximum excursion, maximum shortening velocity, and time to maximum shortening velocity, respectively. This study showed that our general non-isometric model predicted the lower limb motion in response to a range of stimulation frequencies and patterns, and external loads. This model can be implemented in an algorithm for controlling the position of the lower leg during the swing phase of gait during functional electrical stimulation.

Electric Stimulation↗

PubMed2005

Prediction of small for gestational age by logistic regression in twins.

BACKGROUND: Small for gestational age (SGA) is one of the major determinants of perinatal mortality and morbidity, and may relate in adult diseases. Early prediction of SGA could be helpful for health care providers and public health workers in guiding antenatal management and prevention. The reported methods of SGA prediction are not satisfactory because the diagnostic performance is poor and the interval between prediction and delivery is too short. AIMS: To establish a SGA prediction model for twin pregnancies based on variables obtainable in early gestation. METHODS: We used a large twin registry United States data (1995-1997). The study subjects were randomly divided into two groups: group 1 to establish the prediction model by logistic regression and group 2 to validate the prediction model. SGA was defined as birth weight for gestational age z scores less than 10th percentiles. Pair of twin was the unit of analysis. Two sets of multiple logistic regression analyses with different outcome measures - one or both twins SGAs and both twins SGAs - were used to establish the prediction model. RESULTS: The sensitivity, specificity, and positive predictive value were 52.3, 62.5, and 21.5%, respectively, at the cutoff value 0.16 in a SGA prediction model based on maternal race, education, marital status, parity, prenatal care visit initiation, cigarette smoking, and paternal race. CONCLUSIONS: A prediction model based on determinants that can be obtained at early gestation might be useful in the management of pregnancies with high risk of SGA in twins.

Adult↗

PubMed2006

Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples.

BACKGROUND: Prediction models for spontaneous pregnancy may be useful tools to select subfertile couples that have good fertility prospects and should therefore be counselled for expectant management. We assessed the accuracy of a recently published prediction model for spontaneous pregnancy in a large prospective validation study. METHODS: In 38 centres, we studied a consecutive cohort of subfertile couples, referred for an infertility work-up. Patients had a regular menstrual cycle, patent tubes and a total motile sperm count (TMC) >3 x 10(6). After the infertility work-up had been completed, we used a prediction model to calculate the chance of a spontaneous ongoing pregnancy (www.freya.nl/probability.php). The primary end-point was time until the occurrence of a spontaneous ongoing pregnancy within 1 year. The performance of the pregnancy prediction model was assessed with calibration, which is the comparison of predicted and observed ongoing pregnancy rates for groups of patients and discrimination. RESULTS: We included 3021 couples of whom 543 (18%) had a spontaneous ongoing pregnancy, 57 (2%) a non-successful pregnancy, 1316 (44%) started treatment, 825 (27%) neither started treatment nor became pregnant and 280 (9%) were lost to follow-up. Calibration of the prediction model was almost perfect. In the 977 couples (32%) with a calculated probability between 30 and 40%, the observed cumulative pregnancy rate at 12 months was 30%, and in 611 couples (20%) with a probability of >or=40%, this was 46%. The discriminative capacity was similar to the one in which the model was developed (c-statistic 0.59). CONCLUSIONS: As the chance of a spontaneous ongoing pregnancy among subfertile couples can be accurately calculated, this prediction model can be used as an essential tool for clinical decision-making and in counselling patients. The use of the prediction model may help to prevent unnecessary treatment.

Adult↗

PubMed2026

Unveiling m7G modification patterns and causal drivers governing intracranial aneurysm rupture risk through multi-omics validation and m7G-MeRIP-seq profiling.

Intracranial aneurysm (IA) rupture causes severe brain hemorrhage with high mortality, yet its molecular drivers remain unclear and better risk prediction is urgently needed. Using transcriptomics, single-cell analysis, and genetic data, we investigated the role of N7-methylguanosine (m7G) RNA modification in IA. We identified distinct m7G modification patterns, validated their methylation features in patient samples, and incorporated these patterns into a machine learning-based rupture prediction model. The presence and characteristics of m7G patterns significantly improved model performance, achieving high predictive accuracy across three independent cohorts (AUC 0.91-0.95). Genetic analyses further identified three causal m7G-related genes (NSUN2, IFIT5, SNUPN), and laboratory experiments confirmed their altered expression and methylation in ruptured aneurysms. Overall, our findings demonstrate that m7G modifications play a key role in IA rupture. The validated prediction model offers strong clinical potential for rupture risk assessment, and the identified genes represent promising therapeutic targets.

Humans↗

PubMed1995

A mathematical model for predicting the temperature distribution in laser-induced hyperthermia. Experimental evaluation and applications.

A time-dependent mathematical model for the heat transfer in laser-induced hyperthermia has been developed. The model calculates the temperature distribution in surface-irradiated tissues. Good agreement was found between the predictions of the model and in vitro experimental results obtained for bovine liver irradiated with an expanded beam from a Nd:YAG laser. Surface evaporation of water was included in the model and experimentally verified. The discrepancy between the measured and the calculated rise in temperature at three different depths on the axis of symmetry of the irradiating beam was found to be less than 5% after 15 min of irradiation. When irradiating in air and not accounting for the surface evaporation in the model, the accuracy of the model predictions was only 75-80%. The model was then used to investigate the influence of surface evaporation of water on the total temperature distribution theoretically in a clinically relevant case. From the numerical simulations, it was shown that, simply by providing a moistened liver surface, the maximum steady-state temperature could be forced into the tissue to a depth of 4 mm. It was also shown that, by employing the numerical model during the initial phase of hyperthermia treatment, overshooting of the temperature during the transient thermal build-up time could be prevented.

Animals↗

PubMed1977

A nonlinear model for predicting radiographic contrast.

A computerized model has been developed which permits ideal maximum radiographic contrast, associated with clinically meaningful changes in x-ray attenuation for any combination of tissues, to be predicted from the spectral energy of the primary beam and the characteristics of the film. Results suggest that these factors influence available contrast in ways which depend on the specific diagnostic task to be accomplished. Hence, it appears that no single technique is optimal for all tissue configurations. Although contrast computed in this way does not take into account other factors of diagnostic interest, such as image resolution and context-dependent cues to lesion identity, it is an independent factor which can profoundly influence the detectability of specific changes in tissue attenuation and, as such, it may be considered a reasonable basis for predicting diagnostic performance.

Computers↗

PubMed2000

Mathematical models for predicting G-level tolerances.

The mathematical models developed in this article predict the following human G-level tolerances: 1) rapid onset relaxed (ROR); 2) gradual onset relaxed (GOR); and, 3) straining-rapid onset. Included in the model are specific functions of: 1) anti-G suit; 2) positive pressure breathing (PBG); 3) baroreceptor reflex; 4) handgrip reflex; 5) anti-G straining maneuver (AGSM) increasing intrathoracic pressures (Pi); 6) leg elevation; and, 7) reclining seatback angles < or = 55 degrees. These functions are based on sound physiologic principles. Also discussed in the development of this model, but not included in the models, were: 1) isometric muscle contraction reflex; 2) Qigong (Q-G) maneuver; and, 3) straining GOR tolerances. The straining GOR tolerance profile was calculated to be a measure of G-duration tolerance and not G-level tolerance. A maximum P of 125 mm Hg from the AGSM was used in these models that could be augmented with PBG to 185 mm Hg. G-level tolerance predictions using this model were validated with published data.

Adaptation, Physiological↗

PubMedDate not supplied

Testing a surface tension-based model to predict the salting out of polycyclic aromatic hydrocarbons in model environmental solutions.

Molar-based Setschenow constants (Ks) for six alkali and alkaline earth metal-based inorganic salts were determined at 20 degrees C to evaluate their influence on the solubilities, and thus the aqueous activity coefficients, of three polycyclic aromatic hydrocarbons (PAHs). The six salts tested exhibited a wide range of Ks values, varying from 0.105 +/- 0.009 M(-1) (for NaClO4 and pyrene) to 1.29 +/- 0.17 M(-1) (for K2SO4 and perylene). In general, salting out effects with these electrolytes were observed in the order Ca2+ > Na+ > K+ and SO4(2-) > Cl- > ClO4-, consistent with previous reports. However, the expected salting out trend of perylene > pyrene > naphthalene was only observed with K2SO4. In CaCl2 solutions, the Ks value of pyrene was significantly lower than that of naphthalene. For NaCl, KCl and NaClO4, pyrene Ks values were found to be lower than, but not significantly different from, those of naphthalene. Setschenow constants for all six salts were predicted using a semi-empirical, thermodynamically-based equation that relates the standard free energy change associated with transferring solutes from water to a salt solution to the difference in surface tensions between the two solutions. With this equation, predicted Ks values were in reasonable agreement with observed Ks values (generally within +/- 50%). Lack of better agreement between predicted and observed values likely reflects the inability of the simple surface tension model to account for all interactions among the cations, anions, PAH molecules and water molecules in the respective systems.

Electrolytes↗

PubMed2026

MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.

BACKGROUND: Gliomas are the most common primary tumors of the central nervous system. Their treatment remains highly challenging, with high rates of associated disability and mortality. Conventional prognostic indicators no longer adequately satisfy the clinical demands of precision medicine. Therefore, it is essential to further explore novel prognostic biomarkers to enable accurate risk stratification and to provide new reference indicators for personalized precision therapy. PURPOSES: This study aimed to investigate the prognostic significance of vascular endothelial growth factor A (VEGFA) in patients diag nosed with lower-grade gliomas (LGGs) using an MRI based radiomics model. METHODS: Data regarding VEGFA expression and clinical records of LGG patients were retrieved from The Cancer Genome Atlas (TCGA). Corresponding preoperative MRI data were obtained from The Cancer Imaging Archive (TCIA) for radiomic feature extraction. Patients were stratified into high- and low- VEGFA expression groups based on survival information from the current cohort using the survminer package. The overall survival (OS) was assessed using Kaplan-Meier analysis and Cox proportional hazards regression. Predictive models were developed using logistic regression (LR), and model performance was evaluated via receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values reported. An optimized model incorporating the Akaike information criterion (AIC) was also constructed (AIC-LR). RESULTS: VEGFA expression was significantly associated with OS (P = 0.002). Multivariate Cox regression confirmed VEGFA as an independent prognostic factor (hazard ratio [HR] = 2.545, 95% confidence interval: 1.422-4.555). Furthermore, VEGFA expression correlated with immune infiltration levels, particularly of M1 and M2 macrophages and T follicular helper cells, and was associated with enrichment in Wnt signaling and B cell receptor signaling pathways. The LR and AIC-LR models demonstrated acceptable predictive performance, with AUCs of 0.728 (95% CI: 0.612-0.843) and 0.725(95% CI: 0.612-0.839) in the training cohort, and 0.704 (95% CI: 0.562-0.847) and 0.718(95% CI: 0.576-0.861) in the validation cohort, respectively. CONCLUSIONS: The MRI based radiomics model showed potential for noninvasive assessment of VEGFA expression and may provide auxiliary information for prognostic evaluation in LGG. Further validation in larger samples and independent external cohorts is required before clinical application.

Radiomics↗

PubMed2000

Correlation model to predict residual immiscible organic contaminants in sandy soils.

Researchers in both environmental and petroleum engineering have conducted studies in one-dimensional columns to quantify the amount of residual nonaqueous phase liquids (NAPL) trapped in the porous media as a function of capillary, viscous and buoyancy forces. From these previous studies, it is proven that significant amounts of the original NAPL spill remain as a trapped residual. The objective of this research was to extend this body of work and to develop a correlation model that could predict residual NAPL saturation as a function of common soil characteristics and fluid properties. These properties include parameters derived from sieve analysis, namely, the uniformity coefficient (C(u)), the coefficient of gradation (C(c)), as well as fluid properties (interfacial tension, viscosity and density). Over 100 column experiments were conducted across a range of nine different soil gradations. The data produced by these tests, along with measured soil and fluid properties, were used to generate correlation models to predict residual NAPL saturation (S(rn)). The first correlation model predicts S(rn) for the region where residual NAPL saturation is independent of the capillary number, and dependent on C(u), C(c) and the Bond number. The second correlation model predicts S(rn) for the region where residual NAPL saturation is dependent on capillary number, as well as C(u), C(c) and the Bond number. The third correlation model predicts S(rn) over the entire region as a function of C(u), C(c) and the total trapping number. The correlation models have a R(2) value of 0.972, 0.934 and 0.825, respectively. Hence, the models may potentially be integrated into site characterization approaches.

Chemical Phenomena↗

PubMed1985

Toxicity modeling and prediction with pattern recognition.

Empirical models can be constructed relating the change in toxicity to the change in chemical structure for series of similar compounds or mixtures. The first step is to translate the variation in structure to quantitative numbers. This gives a data table, a data matrix denoted by X, which then is analyzed. The same type of the models can be used to relate the variation of in vivo data to the variation of a battery of in vitro tests. A single data analytical model cannot be applied to a set of compounds of diverse chemical structure. For such data sets, separate models must be developed for each subgroup of compounds. The data analytical problem then partly is one of classification, pattern recognition (PARC). The assumption of structural and biological similarity within each subset of modeled compounds is then essential for empirical models to apply. PARC is often used to classify compounds as active (toxic) or inactive. The data structure is then often asymmetric which puts special demands on the data analysis, making the traditional PARC methods inapplicable. Depending on the desired information from the data analysis and on the type of available data, four levels of PARC can be distinguished: (I) the data X are used to develop rules for classifying future compounds into one of the classes represented in X; (II) same as I, but the possibility of future compounds belonging to "unknown" classes not represented in X is taken into account; (III) same as II, plus the quantitative prediction of one activity variable (here toxicity) in some classes; (IV) same as III, but several quantitative activity (toxicity) variables are predicted.

Mathematics↗

PubMed1997

Validity of four models for predicting outcome in critically ill acute renal failure patients.

OBJECTIVE: Several mathematical models are used to predict the survival of patients in renal failure and have become standards. Few of these models have been sufficiently validated, however, and their applicability at other institutions has been established. DESIGN: We attempted to validate four models used to predict mortality in the setting of acute renal failure (Lohr, Bullock, Cioffi, APACHE II) with a registry of 512 ICU patients who received acute dialysis at our institution between 1988 and 1992. Using mortality as an endpoint, we applied each model as described by the original authors and compared the predicted results against the actual outcomes recorded in the registry. RESULTS: The Lohr Model is based on five risk factors thought to predict mortality or survival. Although we found these five factors were strongly associated with mortality (p < 0.001), more than 20% of the highest-risk group survived. The Bullock Model predicted only 20% of the mortality of our data. For the Cioffi Model, the original study found a clear discrimination score (survivors: 1.76, non-survivors: -0.423); however, we found no significant difference in the score discriminating survivors and non-survivors (survivors: 0.70, non-survivors: 0.71, p = 0.96). APACHE II is a risk model consisting of a premorbid assessment of chronic health and 12 physiologic variables taken during ICU admission. We applied APACHE II to both our cardiac and non-cardiac patients, and found that this score did not discriminate between survivors and non-survivors (p values ranged from 0.37 to 0.62) in acute renal failure in either case: the original APACHE II system was not applied to cardiac surgery patients. CONCLUSIONS: We conclude that models with good performance in their institutions of origin may not be valid in other institutions. Before using a predictive model, validity testing at the specific institution should be undertaken.

APACHE↗

PubMed2004

A phenomenological model for predicting fatigue life in bovine trabecular bone.

Cyclic loading of bone during daily activities can lead to fatigue degradation and increased risk of fracture in both the young and elderly population. Damage processes under cyclic loading in trabecular bone result in the reduction of the elastic modulus and accumulation of residual strain. These effects increase with increasing stress levels, leading to a progressive reduction in fatigue life. The present work analyzes the effect of stress and strain variation on the above damage processes in bovine trabecular bone, and develops a phenomenological model relating fatigue life to the imposed stress level. The elastic modulus reduction of the bone specimens was observed to depend on the maximum compressive strain, while the rate of residual strain accumulation was a function of the stress level. A model was developed for the upper and lower bounds of bone elastic modulus reduction with increasing number of cycles, at each stress range. The experimental observations were described well by the model. The model predicted the bounds of the fatigue life with change in fatigue stress. The decrease in the fatigue life with increasing stress was related to corresponding increases in the residual strain accumulation rates at the elevated stress levels. The model shows the validity of fatigue predictions from relatively few cyclic experiments, by combining trends observed in the monotonic and the cyclic tests. The model also presents a relatively simple procedure for predicting the endurance limit for bovine trabecular bone specimens.

Adaptation, Physiological↗

PubMed2005

Development of a model to predict growth of Clostridium perfringens in cooked beef during cooling.

The objective of this work was to develop a new model to predict the growth of Clostridium perfringens in cooked meat during cooling. All data were collected under changing temperature conditions. Individual growth curves were fit using DMFit. Germination outgrowth and lag (GOL) time was modeled versus temperature at the end of GOL using conservative assumptions. Each growth curve was used to estimate a series of exponential growth rates at a series of temperatures. The squareroot model was used to describe the relationship between the square root of the average exponential growth rate and effective temperature. Predictions from the new model were in close agreement with the data used to create the model. When predictions from the model were compared with new observations, fail-dangerous predictions were made a majority of the time. When GOL time was predicted exactly, many fail-dangerous predictions shifted toward the fail-safe direction. Two important facts regarding C. perfringens should impact future modeling research with this organism and may have broader food safety policy implications: (i) the normal variability in the response of the organism from replicate to replicate may be quite large (1 log CFU) and may exceed the current U.S. Food Safety Inspection Service performance standard, and (ii) the accuracy of the GOL time model has a profound influence upon the overall prediction, with small differences in GOL time prediction (approximately 1 h) having a very large effect on the predicted final concentration of C. perfringens.

Animals↗

PubMed1978

An assessment of a property analyzer model in predicting adaptation patterns of simple and complex figures.

Selective adaptation patterns of simple contours and geometrical illusions under monocular stimulation were studied with five subjects. A strict property analyzer model predicted that adaptation patterns would mirror figural contours of both types of forms. The results supported this hypothesis: the geometrical illusions generated adaptation patterns reflecting their physical stimulus properties rather than the illusory percept resulting from contextual juxtaposition. The results are discussed with respect to a model of feature extraction which assumes that property analyzers possess a sensitivity bandwidth.

Form Perception↗

PubMed2002

A mathematical model that predicts the force-frequency relationship of human skeletal muscle.

In previous work we developed and validated a mathematical model that predicted force output from skeletal muscles subjected to six-pulse stimulation trains under isometric condition. The current study investigated the model's ability to predict force responses to longer stimulation trains under both nonfatigued and fatigued conditions. Using the six-pulse train model to predict the force produced by longer stimulation trains showed that the model was successful, but a modified parameter identification scheme was required. For most of the trains tested the model accounted for 95% of the variance in the experimental forces produced by stimulation trains, with mean frequencies from 12.5 to 100 HZ, train durations from 485 to 1000 ms, and number of pulses from 14 to 50 for both nonfatigued and fatigued muscles. The success of our mathematical model in predicting forces produced by stimulations with a wide range of frequencies, durations, and number of pulses implies great potential of the model for the identification of optimal activation patterns that should be used during functional electrical stimulation.

Actins↗