Ferenc Czegledy

Direct quantitation of right and left ventricular volumes with nuclear magnetic resonance imaging in patients with primary pulmonary hypertension

To test the utility of electrocardiographically gated spin echo nuclear magnetic resonance (NMR) imaging in quantitating right and left ventricular volumes and function in patients with primary pulmonary hypertension, right and left ventricular end-diastolic and end-systolic volumes, stroke volumes and ejection fractions were determined in 11 patients with primary pulmonary hypertension and in 10 subjects with normal echocardiographic findings. Ventricular chamber volumes were computed by summing the ventricular chamber volumes of each NMR slice at end-diastole and end-systole. This technique was verified by comparison of results obtained by this method and with the water displacement volumes of eight water-filled latex balloons and ventricular casts of eight excised bovine hearts. In the patients with primary pulmonary hypertension, right ventricular volume indexes were 121 +/- 45 ml/m2 at end-diastole and 70.1 +/- 41.6 ml/m2 at end-systole; both values were significantly greater than values in the normal subjects (67.9 +/- 13.4 and 27.9 +/- 7.5 ml/m2, respectively). Left ventricular end-diastolic volume index was significantly less in the patients (44.9 +/- 9.7 ml/m2) than in the normal subjects (68.9 +/- 13.1 ml/m2). There was no significant difference in left ventricular end-systolic volume between the two groups (24.4 +/- 8.6 and 27.1 +/- 7.8 ml/m2, respectively). Right and left ventricular ejection fractions in the patients with primary pulmonary hypertension (0.43 +/- 0.21 and 0.46 +/- 0.15, respectively) were significantly less than values in normal subjects (0.59 +/- 0.09 and 0.6 +/- 0.11, respectively).(ABSTRACT TRUNCATED AT 250 WORDS)

Are most transporters and channels beta barrels

Given the sequence of transporters or channels of unknown secondary structure, it is usual to predict their putative transmembrane regions as α-helical. However, recent evidence for a facilitative glucose transporter (GLUT1_ appears inconsistent with such predictions, which has led us to propose an alternative folding model for GLUTs based on the 16-stranded antiparallel β-barrel of porins. Here we apply the same predictive algorithms we used for GLUTs to several other membrane proteins. For some of them, a high-resolution structure has been derived (β-barrels: Rhodobacter capsulatus andEscherichia coli porins; multihelical: colicin A, bacteriorhodopsin, and reaction center L chain); we use them to test the prediction procedures. The other proteins we analyze (GLUT1, CHIP28, acetylcholine receptor alpha subunit, lac permease, Na+-glucose cotransporter, shaker K+ channel, sarcoplasmic reticulum Ca2+-ATPase) are representative of classes of similar membrane proteins. As with GLUTs, we find that the predicted transmembrane segments of these proteins are consistently shorter than expected for transmembrane spanning α-helices, but are of the correct length and number for the proteins to fold instead as porin-like β-barrels.

A model of ion channel kinetics based on deterministic, chaotic motion in a potential with two local minima

Models of the gating of ion channels have usually assumed that the switching between the open and closed states is a random process without a mechanistic basis. We explored the properties of a deterministic model of channel gating based on a chaotic dynamic system. The channel is modeled as a nonlinear oscillator, that has a potential function with two minima, which correspond to the stable open and closed states, and is driven by a periodic driving force. The properties of the model are like some properties of single channel data and unlike other properties. The model is like the data in that: the current switches between two well-defined states, this switching is nonperiodic, and there are subconductance states. These subconductance states are subharmonic resonances, due to the nonlinearities in the equation of the model, rather than stable conformational states due to local minima in the potential energy. The model is not like the data in that the current fluctuates too much within in each state and there are sometimes periodic fluctuations within a state. At the present time, the selection of the most appropriate channel model (Markov, chaotic, or other) is not possible, and in addition to chaotic models, other nonlinear models may be suitable.

Predictive Evidence for a Porin-Type .BETA.-Barrel Fold in CHIP28 and other Members of the MIP Family. A Restricted-Pore Model Common to Water Channels and Facilitators.

Water channels are the subject of much current attention, as they may be central for cell functions in a host of tissues. We have analyzed the possible fold of facilitators and water channels of the MIP family based on structural predictions, on findings about the topology of CHIP28, and on the biophysical characteristics of water channels. We developed predictions for the following proteins: MIP26, NOD26, GLP, BIB, γ-TIP, FA-CHIP, CHIP28k, WCH-CD1, and CHIP28. We utilized Kyte Doolittle hydrophobicity, Eisenbergs amphiphilicity, Chou-Fasman-Prevelige propensities, and our own Union algorithm. We found that hydrophobic amphiphilic segments likely to be transmembrane were consistently shorter than required for α-helical segments, but of the correct length for β-strands. Turn propensity was high at frequent intervals, consistent with transmembrane β-strands. We propose that these proteins fold as porin-like 16-stranded antiparallel β-barrels. In water channels, from the size of molecules excluded, an extramembrane loop(s) would enter the pore and restrict it to a bottleneck with a width 4 Å ⩽w ⩽5 Å. A similar but more mobile loop(s) would act as gate and binding site for the facilitators of the MIP family.

A mathematical model of the right ventricular muscle geometry and mass

An understanding of the geometry of the right ventricular (RV) free wall is imperative for both modelling its mechanics and assessing its mass by imaging techniques such as echocardiography. In this paper, a new model of the RV free wall geometry is discussed in which the wall is assumed to have a parabolic long-axis and a circular short-axis curvature respectively. By use of analytic geometry, mathematical expressions for RV surface area, volume and mass were derived. In vitro model validation was carried out in the following manner: (1) echocardiographic images of 16 isolated calf hearts were obtained; (2) measurements were made from the images to determine the parameters required by the model; (3) wall mass was determined by use of these parameters; and (4) the calculated wall mass was then compared with actual RV wall mass (determined by weighting). The model was found to be very accurate for determination of RV free wall mass (R = 0.92); it should prove useful in the study of the stress-strain relationships for the RV and for precise quantitative assessment of RV free wall mass.

Fractal, Chaotic, and Self-Organizing Critical System: Descriptions of the Kinetics of Cell Membrane Ion Channels

Channels are proteins in the cell membrane that spontaneously fluctuate between conformational shapes that are closed or open to the passage of ions. The kinetics of these changes in conformational state can be described in different ways, that suggest different physical properties for the ion channel protein. We describe kinetic models based on: 1) random switching between a few independent states, 2) random switching between many states that are cooperatively linked together, 3) deterministic, chaotic, nonlinear oscillations, amplifying themselves until the channel switches states, and 4) deterministic local interactions that self-organize the fluctuations in channel structure near a phase transition, switching it between different states.

A Mathematical Description Of The Right Ventricular Free Muscle Wall Geometry And Mass

An understanding of the geometry of the right ventricular (RV) free wall is imperative for both modeling its mechanics and assessing its mass by imaging techniques such as echocardiography. In this paper, we discuss a new model of the RV free wall geometry in which the RV free wall is assumed to be shell-like with a parabolic long-axis and a circular short-axis curvature respectively. By use of analytic geometry a formula for RV free wall surface area, volume and mass is derived. The model is validated by comparison of the calculated RV wall mass to actual mass resulting in a correlation coefficient of .93.

Biological systems: Stochastic, deterministic or both

Many systems in nature, including biological systems, have very complex dynamics which generate random-looking time series. To better understand a particular dynamical system, it is often of interest to determine whether the system is caused by deterministic subsystems (e.g. chaotic systems), stochastic subsystems, or both. Although there are now several different approaches to determine this from time series data (e.g. correlation dimension and Lyapunov exponent calculations), these methods often require large amounts of stationary data (biological data is frequently nonstationary for long time scales), can often mis-identify certain systems, and can be subject to other technical problems. Alternatively, one can use methods that measure the complexity in a particular system which seldom make assumptions about a particular system, such as assuming the presence of stationarity. Additionally, mathematical and computational modeling techniques can be used to test different hypothesis about the dynamics of biological systems.

Analysis and modeling of biological systems using fractal geometry

Fractal objects which, by definition, are objects that have scale-invariant shapes and fractional scaling dimensions (fractal dimension) with magnitudes related to the complexity of the objects, are ubiquitous in nature. In particular, many biological structures and systems have fractal properties and therefore may be well studied and modelled using fractal geometry. In the box counting algorithm, one of several different approaches to calculate the fractal dimension, one determines, for several values ofτ, the number of boxesN(τ) with side lengthτ needed to completely cover the studied object. IfN(τ) andr are found to be related by the power law relationshipN(r) ∼r−D, whereD is the scaling dimension, and ifD is a non-integer, then the object is fractal andD is the fractal dimension.In certain circumstances in which one may need to calculate the fractal dimension of a three-dimensional fractal from a two-dimensional projection (e.g. an X-ray), a new mathematical relationship may be utilized to obtain the actual dimensionD; it readsD=−log[1−(1−R2−Dp)R]/logR + 3, whereDp is the fractal dimension of the two-dimensional planar projection of the object andR is the box size used in calculating the box-counting dimension.Fractal dimension calculations have been found to be particularly useful as quantitative indices of the degree of coronary vascularity and the degree of heart interbeat interval variability. Fractal growth models such as diffusion limited aggregation (DLA) can be used to model artery growth.To sum up, fractal geometry is very useful in studying and modeling certain scale-invariant biological structures or systems which may not be easily described with Euclidean shapes.

1/f2 and 1/f power spectra of short-term interbeat interval time series

Power spectral scaling and correlation properties of physical and biological dynamical systems are useful in system characterization and in giving insight into their mechanisms. Since, heart rate has been found to vary with respect to time in a very complicated manner, analysis of this variation using power spectral scaling and correlation techniques can give insight into the various physiologic systems which are involved in heart rate control. 1/f power spectrum, one of the most ubiquitous types of power spectra found in nature, has previously been found to be characteristic of normal cardiac interbeat interval time series for frequencies less than 2 X 10-2 Hz. This frequency domain corresponds to relatively long-term interbeat interval variation. The scaling properties of short-term heart rate variability (related to short-term heart rate control by the baroreceptor reflex), on the other hand, have not as yet been examined analytically. To accomplish this now, we analyzed the scaling properties of the power spectra of cardiac interbeat interval time series of five minute durations in 10 normal individuals and in 10 patients with heart failure. By studying the scaling and correlation properties of the power spectra of short-term interbeat interval time series we may gain more insight into the non-linear characteristics of baroreceptor reflex heart rate regulation.