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At least 1,225 records · Page 68Linked to original sources
PubMed2005

Sequential molecular and cellular events during neoplastic progression: a mouse syngeneic ovarian cancer model.

Studies performed to identify early events of ovarian cancer and to establish molecular markers to support of early detection and the development of chemopreventive regimens have been hindered by a lack of adequate cell models. Taking advantage of the spontaneous transformation of mouse ovarian surface epithelial (MOSE) cells in culture, we isolated and characterized distinct transitional stages of ovarian cancer as the cells progressed from a premalignant nontumorigenic phenotype to a highly aggressive malignant phenotype. Transitional stages were concurrent with progressive increases in proliferation, anchorage-independent growth capacity, in vivo tumor formation, and aneuploidy. During neoplastic progression, our ovarian cancer model underwent distinct remodeling of the actin cytoskeleton and focal adhesion complexes, concomitant with downregulation and/or aberrant subcellular localization of two tumor-suppressor proteins E-cadherin and connexin-43. In addition, we demonstrate that epigenetic silencing of E-cadherin through promoter methylation is associated with neoplastic progression of our ovarian cancer model. These results establish critical interactions between cellular cytoskeletal remodeling and epigenetic silencing events in the progression of ovarian cancer. Thus, our MOSE model provides an excellent tool to identify both cellular and molecular changes in the early and late stages of ovarian cancer, to evaluate their regulation, and to determine their significance in an immunocompetent in vivo environment.

Actins↗
PubMed2002

Assessment of a potential tumor-seeking manganese metalloporphyrin contrast agent in a mouse model.

The performance of a newly developed potential tumor-seeking magnetic resonance (MR) contrast agent alpha-Aqua-13,17-bis(1-carboxypropionyl) carbamoylethyl-3,8-bis(1-phenethyloxyethyl)-beta-hydroxy-2,7,12,18-tetramethyl-porphyrinato manganese (III) (HOP-8P) was tested using a mouse model. Tumor-bearing (SCC-VII) mice were imaged using a 1.5T MR imager before and after intravenous administration of 0.1 mmol/kg of HOP-8P. A biodistribution analysis was performed using an optical emission spectrometer. Significant enhancement of the transplanted tumor was observed in MR images 24 h after intravenous injection of HOP-8P. The biodistribution assessment of manganese also correlated with the results of the imaging study. During the 24-h period following contrast administration, HOP-8P was consistently cleared from the circulation, liver, kidneys, and muscle; however, it was progressively accumulated within the tumor. HOP-8P is a promising tumor-seeking metalloporphyrin MR contrast agent with a wide imaging window.

Analysis of Variance↗
PubMedDate not supplied

Can prosody aid the automatic classification of dialog acts in conversational speech?

Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, which use mainly word information, could be improved by adding prosodic information. The study is based on more than 1000 conversations from the Switchboard corpus. DAs were hand-annotated, and prosodic features (duration, pause, F0, energy, and speaking rate) were automatically extracted for each DA. In training, decision trees based on these features were inferred; trees were then applied to unseen test data to evaluate performance. Performance was evaluated for prosody models alone, and after combining the prosody models with word information--either from true words or from the output of an automatic speech recognizer. For an overall classification task, as well as three subtasks, prosody made significant contributions to classification. Feature-specific analyses further revealed that although canonical features (such as F0 for questions) were important, less obvious features could compensate if canonical features were removed. Finally, in each task, integrating the prosodic model with a DA-specific statistical language model improved performance over that of the language model alone, especially for the case of recognized words. Results suggest that DAs are redundantly marked in natural conversation, and that a variety of automatically extractable prosodic features could aid dialog processing in speech applications.

Decision Trees↗
PubMed2003

League tables and school effectiveness: a mathematical model.

'School performance tables', an alphabetical list of secondary schools along with aggregates of their pupils' performances in national tests, have been published in the UK since 1992. Inevitably, the media have responded by publishing ranked 'league tables'. Despite concern over the potentially divisive effect of such tables, the current government has continued to publish this information in the same form. The effect of this information on standards and on the social make-up of the community has been keenly debated. Since there is no control group available that would allow us to investigate this issue directly, we present here a simple mathematical model. Our results indicate that, while random fluctuations from year to year can cause large distortions in the league-table positions, some schools still establish themselves as 'desirable'. To our surprise, we found that 'value-added' tables were no more accurate than tables based on raw exam scores, while a different method of drawing up the tables, in which exam results are averaged over a period of time, appears to give a much more reliable measure of school performance.

Adolescent↗
PubMed2005

Comparison of statistical analysis and Bayesian Networks in the evaluation of dissolution performance of BCS Class II model drugs.

This project compared the effect of formulation variables on the dissolution performance of model Biopharmaceutics Classification System (BCS) Class II drugs from hard gelatin capsules using statistical analysis and Bayesian networks. The drugs chosen for this study were carbamazepine (CAR), chlorpropamide (CHL), diazepam (DIA), ketoprofen (KET), and naproxen (NAP). Formulations contained anhydrous lactose, microcrystalline cellulose, sodium stearyl fumerate, sodium lauryl sulfate, and croscarmellose sodium. A Box-Behnken experimental design was used in the statistical analysis. The weakly acidic drugs were tested using USP apparatus II with capsule sinkers in 0.1M pH 6.8 Potassium Phosphate buffer. The weakly basic drugs were tested using USP apparatus I in 0.1N HCl buffer. Mean dissolution profiles were compared via calculation of the similarity factor. The Box-Behnken experimental design was found to be useful in assessing primary and secondary excipient effects on dissolution. The Bayesian Network developed for the dataset mirrored the key excipient effects on dissolution performance.

Bayes Theorem↗
PubMed2006

Haemodynamic response to acute hypovolaemia, rapid blood volume expansion and adrenaline administration in an infant animal model.

We performed a prospective experimental animal study in seven sedated and mechanically ventilated piglets weighing 9+/-0.8 kg, to assess the haemodynamic response to acute hypovolaemia, rapid blood volume expansion and adrenaline (epinephrine) administration in an infant animal model. Withdrawal of 20 mL/kg of blood (hypovolaemia), rapid infusion of 20 mL/kg of blood (expansion) and the administration of 0.01 mg/kg of adrenaline were made in each animal. Heart rate, mean blood pressure (MBP), central venous pressure (CVP), pulmonary capillary pressure (PCP), cardiac index (CI), systemic vascular resistance index (SVRI), left ventricular contractility (Dp/dtmax), blood volume variables, including intrathoracic blood volume index (ITBI), global end-diastolic volume (GEDVI) and extravascular lung water index (ELWI). Hypovolaemia produced a significant decrease in the pressure, volume and CI variables, with an increase in SVRI and a decrease in Dp/dtmax. After expansion, all variables returned towards normal, with persistence of the SVRI increase and Dp/dtmax decrease. Changes in the blood volume variables (ITBI and GEDVI) were larger than in the pressure variables (CVP, PCP) in the case of both hypovolaemia and expansion. Adrenaline caused a slight increase in heart rate, MBP, CVP, PCP and Dp/dtmax with a greater increase in SVRI. None of the interventions led to changes in ELWI. We conclude that acute hypovolaemia produces an increase in SVRI and a decrease in Dp/dtmax that does not return fully to normal with restoration of blood volume. ITBI and GEDVI are more sensitive to changes in blood volume than CVP and PCP. Rapid blood volume expansion and adrenaline administration do not affect extravascular lung water.

Acute Disease↗
PubMed2006

A pilot study examining the performance of polynomial-modeled ventricular shock electrograms for rhythm discrimination in implantable devices.

BACKGROUND: Inappropriate shocks continue to be a problem for patients with implantable defibrillators (ICD). We evaluated the performance of polynomial-modeled ventricular electrograms (EGM) to discriminate between supraventricular tachycardia (SVT) and ventricular tachycardia (VT). METHODS: Seven sets of EGM from patients having both SVT and VT documented during a single ICD interrogation were included. The cardiac cycle was analyzed off-line in two parts, QR and RQ segments, which were modeled separately using third-order and sixth-order polynomial equations, respectively. These segments were then analyzed to determine which polynomial coefficients were most significant for rhythm discrimination. RESULTS: When analyzing the QR segment during arrhythmia, there were statistically significant (P<0.05) correlations in 4 of 4 (100%) of the QR coefficients when comparing normal sinus rhythm (NSR) to SVT and 2 of 4 (50%) when comparing NSR to VT or SVT to VT. When analyzing the RQ segment during arrhythmia, there were statistically significant (P<0.05) correlations in 4 of 7 (57%) of the RQ coefficients when comparing NSR to SVT, 5 of 7 (71%) when comparing NSR to VT, and 3 of 7 (43%) when comparing SVT to VT. Using a cutoff value of 50% change from NSR, the ratio of first-order to zero-order QR coefficient was able to completely separate VT from SVT (P=0.03) in this series of patients. CONCLUSION: Our data demonstrate the feasibility of simple polynomial equations that reproduce the depolarization and repolarization phases of human ventricular shock EGM. The ratio of first-order to zero-order QR coefficient was able to reliably discriminate between SVT and VT while reducing the polynomial model to a first-order system. The results of this pilot trial may serve as the basis for a larger prospective trial implementing a discrimination algorithm for use in low computational power implantable devices.

Algorithms↗
PubMed2000

Modeling spoken word recognition performance by pediatric cochlear implant users using feature identification.

OBJECTIVE: Computational simulations were carried out to evaluate the appropriateness of several psycholinguistic theories of spoken word recognition for children who use cochlear implants. These models also investigate the interrelations of commonly used measures of closed-set and open-set tests of speech perception. DESIGN: A software simulation of phoneme recognition performance was developed that uses feature identification scores as input. Two simulations of lexical access were developed. In one, early phoneme decisions are used in a lexical search to find the best matching candidate. In the second, phoneme decisions are made only when lexical access occurs. Simulated phoneme and word identification performance was then applied to behavioral data from the Phonetically Balanced Kindergarten test and Lexical Neighborhood Test of open-set word recognition. Simulations of performance were evaluated for children with prelingual sensorineural hearing loss who use cochlear implants with the MPEAK or SPEAK coding strategies. RESULTS: Open-set word recognition performance can be successfully predicted using feature identification scores. In addition, we observed no qualitative differences in performance between children using MPEAK and SPEAK, suggesting that both groups of children process spoken words similarly despite differences in input. Word recognition ability was best predicted in the model in which phoneme decisions were delayed until lexical access. CONCLUSIONS: Closed-set feature identification and open-set word recognition focus on different, but related, levels of language processing. Additional insight for clinical intervention may be achieved by collecting both types of data. The most successful model of performance is consistent with current psycholinguistic theories of spoken word recognition. Thus it appears that the cognitive process of spoken word recognition is fundamentally the same for pediatric cochlear implant users and children and adults with normal hearing.

Case-Control Studies↗
PubMed2000

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility.

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility is presented. Four levels are considered: operational, diagnostic, mechanistic, and probabilistic evaluations. First, a comprehensive model evaluation should be conducted in at least two distinct geographical locations and for several meteorological episodes. Next, streamlined evaluations can be conducted for other similar applications if the comprehensive evaluation is deemed satisfactory. In all cases, the operational evaluation alone is insufficient, and some diagnostic evaluation must always be carried out. Recommendations are provided for designing field measurement programs that can provide the data needed for such model performance evaluations.

Air Pollution↗
PubMed2004

Quantitative justification for target concentration intervention--parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides.

AIMS: [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. METHODS: Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (> or =20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. RESULTS: A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V1), 19.5 l 70 kg(-1); volume of peripheral compartment (V2) 11.2 l 70 kg(-1). CONCLUSIONS: Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Adult↗
PubMed2001

Combined effects of cocaine and nicotine on cardiovascular performance in a canine model.

BACKGROUND: With the proliferation of cocaine abuse, increased incidence of catastrophic cardiovascular events such as angina pectoris, myocardial infarction, ventricular arrhythmias, or sudden death are reported. Many of these patients also smoke cigarettes before and after cocaine use, leading to a high frequency of simultaneous exposure to both drugs. Cocaine's and nicotine's independent effects on cardiodynamics are well documented, but combined effects of both on complete cardiovascular hemodynamics remain unknown. HYPOTHESIS: The study aimed to determine whether these effects are additive, synergistic, or antagonistic and was therefore designed to investigate the cardiovascular changes produced as a result of combined administration of cocaine and nicotine in a canine model. METHODS: Initially, in phase 1, 30 experiments were performed to study the dose-response curve of both drugs. In phase II and III, 12 dogs were subjected to 30 experiments. In phase II, cocaine was given intravenously (IV) followed by nicotine. In phase III, sequence of drug administration was reversed to study the effects on hemodynamics and coronary artery blood flow reserve. RESULTS: Hemodynamic parameters observed were Phase I: Dose-response curve established the IV bolus dose of cocaine 2 mg/kg and nicotine 50 microg/kg. Phase II: Cocaine increased heart rate, blood pressure, and dP/dt, but nicotine administration after cocaine produced marked significant synergistic excitatory effects: dP/dt increased from 1,810 +/- 210 to 6,300 +/- 460 (p < 0.003). Phase III: Nicotine significantly increased heart rate, mean arterial pressures, left ventricular end-diastolic pressure, pulmonary artery, pulmonary capillary wedge, and right atrial pressures. Nicotine increased dP/dt (1,810 +/- 192 to 5,000 +/- 160 mmHg/s; p < 0.004). These excitatory effects of nicotine were attenuated by cocaine when administered as a second drug (dP/dt decreased to 1,925 +/- 144 from 5,000 +/- 160 mmHg/s;p < 0.004). CONCLUSIONS: Cocaine, when administered alone, caused increase in heart rate, blood pressure, and dP/dt, but nicotine showed a significant increase in all the hemodynamic parameters. Both drugs reduced coronary blood flow reserve. In combination, cocaine plus nicotine administration had synergistic excitatory effects in dogs. A reversed drug combination, that is, nicotine plus cocaine, attenuated the excitatory effects of nicotine.

Animals↗
PubMed1989

[Study on BLUP with reduced animal model in evaluation of performance tested males].

This study is computed in best linear unbiased prediction (BLUP) with the reduced animal model. BLUP is a powerful method to evaluate genetic value of animals, but the computation is sometimes costly because of the large set of equations to be solved. Equivalent linear models with a reduced number of elements of breeding values can reduce computations substantially. Using a general animal model and a reduced animal model predictions of additive genetic merit are described for the case of sire and maternal grandsire. A simple numerical example is used to illustrate the identity of the two solutions. The reduced animal model can be useful for evaluation of males that have been performance-tested and have, in some case, progeny that have been tested as is the case with beef cattle.

Animals↗
PubMed1981

Faculty performance evaluation: the CIPP-SAPS model.

The issues of faculty performance evaluation for allied health professionals are addressed. Daniel Stufflebeam's CIPP (content-input-process-product) model is introduced and its development in a CIPP-SAPS (self-administrative-peer-student) model is pursued. Data sources for the SAPS portion of the model are discussed. A suggestion for the use of the CIPP-SAPS model within a teaching contract plan is explored.

Allied Health Personnel↗
PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29&#x2009;709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85)&#x2009;and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗
PubMed1992

Modeling effects on children's form kinematics, performance outcome, and cognitive recognition of a sport skill: an integrated perspective.

This study examined the effectiveness of visual and verbal models during the initial phase of complex sport skill acquisition in children. Thirty-six girls between the ages of 7-0 and 8-11 years were randomly assigned to one of three experimental conditions in a 3 x 4 (Model Type x Trial Block) factorial design. A visual model performing an underhand modified softball pitch was viewed prior to each of four blocks of five practice trials. Verbal performance cues were added to the model at the second, third, or fourth trial block, according to the experimental condition. A multivariate repeated measures analysis of variance revealed a significant trial block main effect, with subjects showing improved matching of form kinematics to the model with increasing trials but no differences in performance outcome. More dramatic changes coincident with the addition of verbal cues were observed for some kinematic variables. Children exhibited better cognitive recognition of correct form as a function of increased exposure to the visual model.

Analysis of Variance↗
PubMed2001

Connectionist inference models.

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modelling.

Animals↗
PubMed2006

Matching 2.5D face scans to 3D models.

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.

Algorithms↗