Biomedical subjects

F A Hamprecht

Publications and source records attributed to F A Hamprecht.

5 recordsLinked to original sources

PubMed2006

Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors.

We describe the optimal high-level postprocessing of single-voxel (1)H magnetic resonance spectra and assess the benefits and limitations of automated methods as diagnostic aids in the detection of recurrent brain tumor. In a previous clinical study, 90 long-echo-time single-voxel spectra were obtained from 52 patients and classified during follow-up (30/28/32 normal/non-progressive tumor/tumor). Based on these data, a large number of evaluation strategies, including both standard resonance line quantification and algorithms from pattern recognition and machine learning, were compared in a quantitative evaluation. Results from linear and non-linear feature extraction, including ICA, PCA and wavelet transformations, and also the data from resonance line quantification were combined systematically with different classifiers such as LDA, chemometric methods (PLS, PCR), support vector machines and ensemble methods. Classification accuracy was assessed using a leave-one-out cross-validation scheme and the area under the curve (AUC) of the receiver operator characteristic (ROC). A regularized linear regression on spectra with binned channels reached 91% classification accuracy compared with 83% from quantification. Interpreting the loadings of these regressions, we find that lipid and lactate signals are too unreliable to be used in a simple machine rule. Choline and NAA are the main source of relevant information. Overall, we find that fully automated pattern recognition algorithms perform as well as, or slightly better than, a manually controlled and optimized resonance line quantification.

Algorithms↗

PubMed2001

Fibrillation power, an alternative method of ECG spectral analysis for prediction of countershock success in a porcine model of ventricular fibrillation.

BACKGROUND: Noninvasive prediction of defibrillation success after cardiac arrest and cardiopulmonary resuscitation (CPR) may help in determining the optimal time for a countershock, and thus increase the chance for survival. METHODS: In a porcine model (n=25) of prolonged cardiac arrest, advanced cardiac life support was provided by administration of two or three doses of either vasopressin or epinephrine after 3 or 8 min of basic life support. After 4 min of ventricular fibrillation and 18 min of life support, defibrillation was attempted. The denoised power spectral density of 10 s intervals of the ventricular fibrillation electrocardiogram (ECG) was estimated from averaged and smoothed Fourier transforms. We have eliminated the spectral contribution of artifacts from manual chest compressions and provide a definition for the contribution of ventricular fibrillation to the power spectral density. This contribution is quantified and termed "fibrillation power". RESULTS: We tested fibrillation power and two established methods in their discrimination of survivors (n=16) vs. non-survivors (n=9) in the last minute before the countershock. A fibrillation power > or =79 dB predicted successful defibrillation with sensitivity, specificity, positive predictive value and negative predictive value of 98%, 98%, 99% and 97% while a mean fibrillation frequency > or =7.7 Hz was predictive with 85%, 83%, 90% and 77% and a mean amplitude > or =0.49 mV was predictive with 95%, 90%, 94% and 91%. CONCLUSIONS: We suggest that fibrillation power is an alternative source of information on the status of a fibrillating heart and that it may match the established mean frequency and amplitude analysis of ECG in predicting successful countershock during CPR.

Animals↗

PubMed2001

Preliminary results on the prediction of countershock success with fibrillation power.

In a study of artifact-free ventricular fibrillation episodes in 54 patients, 28 of whom experienced return of spontaneous circulation (ROSC), the power of different indicators to predict the ROSC was compared. Taking the average of sensitivity, specificity and positive and negative predictive value, the dominant frequency reaches 76%, the mean amplitude 72% and fibrillation power 71%. There is little correlation between the three indicators.

Electric Countershock↗

PubMed1998

Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.

The program SEAL is suited to describe the electrostatic, steric, hydrophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calculated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similarity can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity matrix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set, the test set predictivities (r2pred = 0.53-0.84) of different PLS models, based on a weighted sum of molecular properties, are superior to published results of CoMFA and CoMSIA studies (r2pred = 0.31-0.40), regardless of whether a common alignment or individual, pairwise alignments of all molecules are used in the calculation of the similarity matrices. Training and test set selections have a significant influence on the external predictivities of the models. Although the SEAL similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantitative similarity-activity analyses (3D QSiAR) is proposed for approaches which correlate 3D structure-derived similarity matrices with biological activities.

Drug Design↗

PubMed1997

Generation of pseudonative protein structures for threading.

The threading approach to protein structure prediction suffers from the limited number of substantially different folds available as templates. A method is presented for the generation of artificial protein structures, amenable to threading, by modification of native ones. The artificial structures so generated are compared to the native ones and it is shown that, within the accuracy of the pseudoenergy function or force field used, these two types of structures appear equally useful for threading. Since a multitude of pseudonative artificial structures can be generated per native structure, the pool of pseudonative template structures for threading can be enormously enlarged by the inclusion of the pseudonative artificial structures.

Computer Simulation↗