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PubMedDate not supplied

Use of 13C NMR spectrometric data to produce a predictive model of estrogen receptor binding activity.

We have developed a spectroscopic data-activity relationship (SDAR) model based on 13C NMR spectral data for 30 estrogenic chemicals whose relative binding affinities (RBA) are available for the alpha (ERalpha) and beta (ERbeta) estrogen receptors. The SDAR models segregated the 30 compounds into strong and medium binding affinities. The SDAR model gave a leave-one-out (LOO) cross-validation of 90%. Two compounds that were classified incorrectly in the SDAR model were in the transition zone between classifications. Real and predicted 13C NMR chemical shifts were used with test compounds to evaluate the predictive behavior of the SDAR model. The 13C NMR SDAR model using predicted 13C NMR data for the test compounds provides a rapid, reliable, and simple way to screen whether a compound binds to the estrogen receptors.

Carbon Isotopes↗

PubMedDate not supplied

Tracking the drift of a human body in the coastal ocean using numerical prediction models of the oceanic, atmospheric and wave conditions.

This paper describes the use of numerical models to infer the path of a floating human body in the Ligurian Sea (north-west Mediterranean) during the month of January 2001. The prevailing oceanic currents were obtained from a state-of-the-art real-time nowcast/forecast ocean circulation model, while the sea state was inferred from a numerical model of the surface gravity waves, both driven by regional atmospheric models. The surface currents (from the ocean model) and the drift ones at the ocean surface, as inferred from the wave model, were used to drive a Lagrangian model of the drifting body to deduce its plausible trajectory along the Ligurian coast. The inferred path is reasonably consistent with location and time of the discovery on the French coast. This note illustrates the utility of numerical prediction models at the disposal of modern forensic science in the fields of ocean sciences.

Journal Article↗

PubMed1990

Crystal structure of a protein-toxin alpha 1-purothionin at 2.5A and a comparison with predicted models.

Alpha 1-Purothionin (alpha 1-P), a wheatgerm protein and lytic toxin, has a secondary and tertiary structure similar to that of crambin as revealed by CD and NMR studies. alpha 1-P crystallizes in the tetragonal space group 1422 with unit cell dimensions: a = b = 53.59 and c = 69.79 A. X-ray diffraction data have been measured to 2.5 A Bragg spacing. The crystal structure has been determined by molecular replacement methods, using an energy-minimized alpha 1-P model structure derived from crambin (Whitlow and Teeter: Journal of Biomolecular Structure and Dynamics 2:831-848, 1985, Journal of the American Chemical Society 108:7163-7172, 1986). The energy-minimized model gives a slightly cleaner rotation solution and better refinement against the x-ray data than do the crambin or unminimized alpha 1-P structures. The final crystallographic residual with the data in the 10-2.5 A resolution range is 0.216. The refined alpha 1-P structure has a backbone rms difference of 0.74 A from crambin and 0.55 A from the energy-minimized alpha 1-P model. A low resolution NMR model of alpha 1-P calculated from metric matrix distance geometry and restrained molecular dynamics differs from crambin's backbone by 2.3 A rms deviation (Clore et al.: EMBO Journal 5:2729-2735, 1986). Backbone dihedral angles for our predicted model differ from the refined alpha 1-P structure in only one region (at a turn where there is a deletion relative to crambin). The NMR model had differences in four regions.

Antimicrobial Cationic Peptides↗

PubMed1992

A predictive model for the clinical response to low dose ara-C: a study of 102 patients with myelodysplastic syndromes or acute leukaemia.

The response to treatment with low-dose ara-C was studied in 102 consecutive patients; 79 with myelodysplastic syndrome (MDS) and 23 with acute myelogenous leukaemia (AML) following MDS. The aim was to find variables that could predict the response to treatment. All patients had clinical symptoms related to cytopenia. Peripheral blood values, bone marrow morphology histology and chromosomes were analysed before the start of treatment. The median survival of the patients was 9 months and a poor survival was predicted by advanced age, low platelet counts, the presence of pseudo-Pelger morphology and > or = 2 chromosomal aberrations. Thirty patients (29%) responded with either a complete remission or a significant increase in haemoglobin level. For the remaining 71%, the treatment was ineffective and in some cases hazardous. The factors associated with a poor response to treatment could be divided into two groups: one included low platelet counts and the presence of chromosomal aberrations, both signs of progressive MDS with a short survival, and the other comprised morphological findings, indicating ineffective haemopoiesis. Patients with platelet counts > 150 x 10(9)/l had a response rate of 55% compared to 23.5% in patients with subnormal platelet counts. Logistic regression identified low bone marrow cellularity, absence of ring sideroblasts and < 2 chromosomal aberrations as predictors of a favourable response in patients with platelet counts < 150 x 10(9)/l. These factors and the platelet count were combined in a predictive model which can divide patients into three groups with different probabilities of response: a favourable group, 38.6% of the patients, with a response rate of > 50%, an intermediate group, 32.7% of the patients, with a response rate of 24%, and an unfavourable group, 28.7% of the patients, with only 3% responses. While low-dose ara-C is an effective treatment for some patients, it is ineffective and hazardous for others. We present a model that can facilitate therapeutic decision making in two-thirds of patients with MDS and MDS-AML by identifying patients who should not be treated with low-dose ara-C as well as patients with a relatively high probability of response.

Aged↗

PubMed2004

Stability of DNA duplexes with Watson-Crick base pairs: a predicted model.

The conformational stability (difference between the free energies of the folded and unfolded states, DeltaG degrees ) of a DNA duplex is considered as a function of component energy terms, hydrophobic, base stacking, hydrogen bonding, van der Waals, and electrostatic, and a trinucleotide-level helix stiffness parameter measured in terms of its Young's modulus. Hydrophobic and base stacking energy components were determined with the use of the crystal structure data of 30 DNA duplexes judicially selected within a resolution of 1.5 A, and hydrogen bonding, van der Waals and electrostatic terms were determined through an extensive review of experimental and theoretical studies. The stiffness indices for the trinucleotides were the ones realized by M. M. Gromiha [(2000) J. Biol. Phys. 26, 43-50] using the crystal structure data of 70 DNA duplexes. The unfolded state was treated in the classical way to determine its stability. Thermodynamically determined DeltaG degrees values for 111 DNA duplexes, with the number of base pairs ranging from 4 to 16, were selected in two sets, and the regression equation formed with one set was used to predict the stabilities of the other set, taking the energy components and the stiffness parameter to be independent variables. The computed energy terms indicate that the base stacking and hydrogen bonding forces are the dominant and the hydrophobic and electrostatic forces the weak partners in imparting stability to the duplexes. This model predicts DeltaG degrees values for DNA duplexes examined with a level of accuracy similar to that used for predictions made by the widely used nearest-neighbor models. The uniqueness of this model is that it combines the crystal and thermodynamic data for interpretation of conformational stability.

Base Pairing↗

PubMed2003

Predictive model for motorcycle accidents at three-legged priority junctions.

In conjunction with a nationwide motorcycle safety program, the provision of exclusive motorcycle lanes has been implemented to overcome link-motorcycle accidents along trunk roads in Malaysia. However, not much work has been done to address accidents at junctions involving motorcycles. This article presents the development of predictive model for motorcycle accidents at three-legged major-minor priority junctions of urban roads in Malaysia. The generalized linear modeling technique was used to develop the model. The final model reveals that motorcycle accidents are proportional to the power of traffic flow. An increase in nonmotorcycle and motorcycle flows entering the junctions is associated with an increase in motorcycle accidents. Nonmotorcycle flow on major roads had the highest effect on the probability of motorcycle accidents. Approach speed, lane width, number of lanes, shoulder width, and land use were found to be significant in explaining motorcycle accidents at the three-legged major-minor priority junctions. These findings should enable traffic engineers to specifically design appropriate junction treatment criteria for nonexclusive motorcycle lane facilities.

Accidents, Traffic↗

PubMed2001

Novel Y283C mutation of the A subunit for coagulation factor XIII: molecular modelling predicts its impaired protein folding and dimer formation.

In an Italian patient with severe factor XIII deficiency, a novel mutation, Y283C (TAT to TGT), was identified heterozygously by nucleotide sequencing analysis in exon VII of the gene for the A subunit. The presence of this mutation was confirmed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis in the proband and his brother. Molecular modelling predicts that the mutant molecule would be misfolded. It is probable that the impaired folding of the mutant Y283C A subunit led to its instability, which is at least in part responsible for the factor XIII deficiency of this patient.

Child↗

PubMed1998

A multiplicative statistical model predicts the size distribution of unruptured intracranial aneurysms.

A statistical model for characterizing the erratic nature of aneurysm evolution is developed and tested. This model is based upon a multiplicative hypothesis, whereby it is theorized that the progressive changes in the size of a given aneurysm are determined by random multipliers. Such a model would predict that within a large population of aneurysms, a lognormal histogram for aneurysm sizes would occur (i.e. the logarithms of aneurysm size would have a normal distribution). When applied to previously published clinical data of unruptured aneurysms by Crompton (1966) and McCormick et al. (1970), the model is found to adequately describe both sets of data. The methods introduced in this paper illustrate the utility of incorporating statistical and clinical insights with fundamental biometry for studying the complex phenomena of aneurysm growth and rupture.

Aneurysm, Ruptured↗

PubMed2005

A model predicting the effect of speech of varying intelligibility on work performance.

UNLABELLED: Speech is the most distracting sound in (open-plan) offices. Several laboratory studies have shown that speech impairs the performance of, for example, reading and short-term memory. It is not the sound level of speech that determines its distracting power but its intelligibility, which can be physically determined by measuring the Speech Transmission Index (STI). The aim of this study was to develop a mathematical model that predicts how much the performance is reduced due to speech of varying intelligibility. The model was based on the literature according to which performance decrements have been 4-45% depending on the task. The best performance occurs when speech is absent (STI=0.0), and the strongest performance decrement occurs when speech is perfectly heard (STI=1.0). The shape of the performance vs. STI between 0.0 and 1.0 was adopted from the general speech intelligibility theory. The performance starts to decrease when STI exceeds 0.2. Highest performance decrease is reached already when STI exceeds 0.60. PRACTICAL IMPLICATIONS: The prediction model can be exploited in the evaluation of work performance in different acoustical conditions in open-plan offices when STI is known. It can be utilized to promote actions aiming at better acoustical conditions.

Forecasting↗

PubMed1999

A predictive model for relapse in high-risk primary breast cancer patients treated with high-dose chemotherapy and autologous stem-cell transplant.

High-dose chemotherapy (HDCT) is currently under evaluation for high-risk primary breast cancer (HRPBC), defined by extensive axillary nodal involvement or inflammatory breast carcinoma. Phase II studies of HDCT for HRPBC show that 30-40% of patients eventually relapse. We retrospectively reviewed 176 patients enrolled in clinical trials of HDCT for HRPBC at the University of Colorado and analyzed 23 potential predictive variables for relapse. All of the patients received the same regimen, with cyclophosphamide, cisplatin, and BCNU. Nine patients who experienced a toxic death were excluded from this analysis. The resulting predictive model was subsequently tested in an independent patient set treated at Duke University with the same HDCT regimen. Nodal ratio (number of involved nodes:number of sampled nodes), tumor size, grade, stage, estrogen receptor, progesterone receptor, and clinical inflammatory breast carcinoma correlated with risk of relapse. Nodal ratio, tumor size, and the combined estrogen receptor/progesterone receptor status were independent predictors. A scoring system using those three variables determines the risk of relapse, with a sensitivity and specificity of 60 and 90%, respectively, and a positive and negative predictive value of 65 and 88%, respectively. The differences in relapse-free survival and overall survival between high- and low-score patients were highly significant (P<0.000001). This model was subsequently validated in the Duke patient set. This model can identify two subgroups of HRPBC patients with low (12%) and high (65%) risk for recurrence after HDCT. Future research that tests new therapies will focus on those patients with a high score.

Adult↗

PubMed2001

Prediction models for eye irritation potential based on endpoints of the HETCAM and neutral red uptake tests.

The aim of this study was to explore the possibility of distinguishing between eye irritants (I; EU risk phrases R36 and R41) and nonirritants (NI), by using in vitro endpoints of the hen's egg test on the chorioallantoic membrane (the HETCAM test) and the neutral red uptake (NRU) test. Prediction models were derived by applying binary logistic regression to the in vitro data for these endpoints, which were taken from the report of a German validation study on the use of the HETCAM and 3T3 NRU tests as alternatives to the Draize eye irritation test. Whereas the validation study led to the conclusion that the combined use of the two tests enables a satisfactory discrimination between severe (R41) and nonsevere (NI, R36) eye irritants, the results of the present study indicate that the two in vitro tests can also be used to discriminate between nonirritants (NI) and irritants (R36 and R41).

3T3 Cells↗

PubMed2026

Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression.

Major depressive disorder (MDD) is associated with widespread alterations in functional brain networks across the lifespan. However, heterogeneity in atypical brain development among patients with MDD remains largely uncharacterized. Using a multisite resting-state functional MRI dataset consisting of 1,105 MDD patients and 1,065 healthy controls, we constructed a harmonized multicenter brain age prediction model based on individualized functional topography and identified two patient subgroups with positive or negative brain age gaps (BAGs). In patients with a positive BAG (BAG+), expansion of the salience network (SAL) into the dorsolateral prefrontal and ventrolateral prefrontal cortices, in addition to contraction of the sensorimotor and dorsal attention networks (DAN), contributes to accelerated brain aging. Conversely, in the negative BAG (BAG-) group, SAL expansion into the orbitofrontal cortex (OFC) and contraction of the visual and sensorimotor networks (SMN) were linked to delayed brain development. These subgroups also exhibited distinct neurodevelopmental trajectories. Clinically, BAG+ patients showed stronger associations between higher-order network topography and mood symptoms, whereas BAG- patients exhibited links between visual/default mode network topography and insomnia. At the molecular level, both groups showed enrichment of genes related to synaptic signaling but displayed distinct expression patterns and divergent expression trajectories in key neurodevelopmental gene sets. Notably, antidepressant treatment modulated the brain in ways that were specific to each subgroup. These findings reveal heterogeneous neurodevelopmental profiles in MDD with distinct biological and clinical signatures, offering insights into personalized precision medicine for this disorder.

Humans↗

PubMed2006

A predictive model for the detection of tumor lysis syndrome during AML induction therapy.

Tumor lysis syndrome (TLS) is defined by metabolic derangements occurring in the setting of rapid tumor destruction. In acute myelogenous leukemia (AML), TLS frequency, risk stratification, monitoring, and management strategies are based largely on case series and data from other malignancies. A single-center, retrospective cohort study was conducted to estimate TLS incidence and identify TLS predictive factors in a patient population undergoing myeloid leukemia induction chemotherapy. This study included 194 patients, aged 18-86 years, with AML or advanced myelodysplastic syndrome undergoing primary myeloid leukemia induction chemotherapy. Nineteen patients (9.8%) developed TLS. In univariate analysis, elevated pre-chemotherapy values for uric acid (P < 0.0001), creatinine (P = 0.0025), lactate dehydrogenase (LDH) (P = 0.0001), white blood cell (P = 0.0058), gender (P = 0.0064) and chronic myelomonocytic leukemia history (P = 0.0292) were significant predictors. In multivariate analysis, LDH (P = 0.0042), uric acid (P < 0.0001) and gender (P = 0.0073) remained significant TLS predictors. A predictive model was then designed using a scoring system based on these factors. This analysis may lay the groundwork for the development of the first evidence-based guidelines for TLS monitoring and management in this patient population.

Adolescent↗

PubMed1994

The role of renal function in outcome-prediction models.

General clinical scoring systems are relatively recently developed statistical tools available to clinicians for purposes including comparison of outcome data, evaluation of new therapies, quality assurance, and evaluation of resource utilization. As statistical devices, they are valid when applied to patient groups, not individual prognostication. The most well known general systems are the APACHE (Acute Physiology and Chronic Health Evaluation), SAPS (Simplified Acute Physiology System), and MPM (Mortality Prediction Model). Each of these systems has considered renal dysfunction as a contributor to mortality and, as the systems have matured, have given increasing importance to the presence of renal failure as a predictor of mortality. Cardiac surgery patients make up a large part of many critical-care physicians' practice, but are not presently considered in any of the general scoring systems. In addition, the outcome for these patients is well known to be significantly affected by the presence of renal failure. Specific scoring systems have been developed that evaluate cardiac surgery patients in much the same fashion as do the general scoring systems.

APACHE↗

PubMed2006

Dasatinib (BMS-354825) pharmacokinetics and pharmacodynamic biomarkers in animal models predict optimal clinical exposure.

PURPOSE: Chronic myeloid leukemia (CML) is caused by reciprocal translocation between chromosomes 9 and 22, forming BCR-ABL, a constitutively activated tyrosine kinase. Imatinib mesylate, a selective inhibitor of BCR-ABL, represents current frontline therapy for CML; however, emerging evidence suggests that drug resistance to imatinib may limit its long-term success. To improve treatment options, dasatinib (BMS-354825) was developed as a novel, oral, multi-targeted kinase inhibitor of BCR-ABL and SRC family kinases. To date, dasatinib has shown promising anti-leukemic activity in preclinical models of CML and in phase I/II clinical studies in patients with imatinib-resistant or imatinib-intolerant disease. EXPERIMENTAL DESIGN: The pharmacokinetic and pharmacodynamic biomarkers of dasatinib were investigated in K562 human CML xenografts grown s.c. in severe combined immunodeficient mice. Tumoral levels of phospho-BCR-ABL/phospho-CrkL were determined by Western blot. RESULTS: Following a single oral administration of dasatinib at a preclinical efficacious dose of 1.25 or 2.5 mg/kg, tumoral phospho-BCR-ABL/phospho-CrkL were maximally inhibited at approximately 3 hours and recovered to basal levels by 24 hours. The time course and extent of the inhibition correlated with the plasma levels of dasatinib in mice. Pharmacokinetic/biomarker modeling predicted that the plasma concentration of dasatinib required to inhibit 90% of phospho-BCR-ABL in vivo was 10.9 ng/mL in mice and 14.6 ng/mL in humans, which is within the range of concentrations achieved in CML patients who responded to dasatinib treatment in the clinic. CONCLUSIONS: Phospho-BCR-ABL/phospho-CrkL are likely to be useful clinical biomarkers for the assessment of BCR-ABL kinase inhibition by dasatinib.

Adaptor Proteins, Signal Transducing↗

PubMed2006

Predictive models attribute effects on fish assemblages to toxicity and habitat alteration.

Biological assessments should both estimate the condition of a biological resource (magnitude of alteration) and provide environmental managers with a diagnosis of the potential causes of impairment. Although methods of quantifying condition are well developed, identifying and proportionately attributing impairment to probable causes remain problematic. Furthermore, analyses of both condition and cause have often been difficult to communicate. We developed an approach that (1) links fish, habitat, and chemistry data collected from hundreds of sites in Ohio (USA) streams, (2) assesses the biological condition at each site, (3) attributes impairment to multiple probable causes, and (4) provides the results of the analyses in simple-to-interpret pie charts. The data set was managed using a geographic information system. Biological condition was assessed using a RIVPACS (river invertebrate prediction and classification system)-like predictive model. The model provided probabilities of capture for 117 fish species based on the geographic location of sites and local habitat descriptors. Impaired biological condition was defined as the proportion of those native species predicted to occur at a site that were observed. The potential toxic effects of exposure to mixtures of contaminants were estimated using species sensitivity distributions and mixture toxicity principles. Generalized linear regression models described species abundance as a function of habitat characteristics. Statistically linking biological condition, habitat characteristics including mixture risks, and species abundance allowed us to evaluate the losses of species with environmental conditions. Results were mapped as simple effect and probable-cause pie charts (EPC pie diagrams), with pie sizes corresponding to magnitude of local impairment, and slice sizes to the relative probable contributions of different stressors. The types of models we used have been successfully applied in ecology and ecotoxicology, but they have not previously been used in concert to quantify impairment and its likely causes. Although data limitations constrained our ability to examine complex interactions between stressors and species, the direct relationships we detected likely represent conservative estimates of stressor contributions to local impairment. Future refinements of the general approach and specific methods described here should yield even more promising results.

Animals↗

PubMed2000

Clinical prediction model for differentiation of disseminated Histoplasma capsulatum and Mycobacterium avium complex infections in febrile patients with AIDS.

BACKGROUND: Disseminated infection with Histoplasma capsulatum and Mycobacterium avium complex (MAC) in patients with AIDS are frequently difficult to distinguish clinically. METHODS: We retrospectively compared demographic information, other opportunistic infections, medications, symptoms, physical examination findings and laboratory parameters at the time of hospital presentation for 32 patients with culture documented disseminated histoplasmosis and 58 patients with disseminated MAC infection. RESULTS: Positive predictors of histoplasma infection by univariate analysis included lactate dehydrogenase level, white blood cell (WBC) count, platelet count, alkaline phosphatase level, and CD4 cell count. By multivariate logistic regression analysis, those characteristics that remained significant included a lactate dehydrogenase value > or =500 U/L (risk ratio [RR], 42; 95% confidence interval [CI], 18.53-97.5; p < .001), alkaline phosphatase < or =300 U/L (RR, 9.35; 95% CI, 2.61-33.48; p = .008), WBC < or =4.5 x 10(6)/L (RR, 21.29; 95% CI, 6.79-66.75; p = .008), and CD4 cell count (RR, 0.958; 95% CI, 0.946-0.971; p = .001). CONCLUSIONS: A predictive model for distinguishing disseminated histoplasmosis from MAC infection was developed using lactate dehydrogenase and alkaline phosphatase levels as well as WBC count. This model had a sensitivity of 83%, a specificity of 91%, and a misclassification rate of 13%.

AIDS-Related Opportunistic Infections↗

PubMed2004

Evaluation of a prediction model for long-term fracture risk.

UNLABELLED: The NOF cost-effectiveness model, based on clinical risk factors and femoral neck aBMD, predicted overall fracture risk in a cohort of postmenopausal women followed for up to 22 years. INTRODUCTION: To assess the ability of a statistical model to predict long-term fracture risk for a population of postmenopausal women, we compared observed fractures to those predicted by the National Osteoporosis Foundation's (NOF) cost-effectiveness model. MATERIALS AND METHODS: In this population-based study, 393 postmenopausal Rochester, MN, women had baseline measurements of femoral neck areal BMD (aBMD) and assessment of the clinical risk factors (personal fracture history, family history of osteoporotic fracture, low body weight, and smoking status) that were included in the NOF model. They were then followed prospectively for up to 22 years. Fractures were ascertained by periodic interview and review of community medical records. Standardized incidence ratios (SIRs) compared observed fractures to predicted numbers. RESULTS: During 4782 person-years of follow-up, 212 women experienced 503 fractures, two-thirds of which were caused by moderate trauma. When undiagnosed (incidentally noted) vertebral and rib fractures were excluded, there was general concordance between observed and predicted fractures of the hip (SIR, 0.78; 95% CI, 0.56-1.01), distal forearm (SIR, 1.22; 95% CI, 0.86-1.68), spine (SIR, 0.76; 95% CI, 0.50-1.11), and all other sites combined (SIR, 1.18; 95% CI, 0.97-1.42). Fracture prediction by the NOF model was about as good after 10 years as it was earlier during follow-up. CONCLUSIONS: This study validates the ability of a statistical model based on femoral neck aBMD and common clinical risk factors to predict the actual occurrence of fractures in a cohort of postmenopausal white women.

Absorptiometry, Photon↗