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Biomedical subjects

N Indurkhya

Publications and source records attributed to N Indurkhya.

2 recordsLinked to original sources

The relationships between casual and ambulatory blood pressure measurements and central hemodynamics in essential human hypertension.

OBJECTIVE: To determine the association between ambulatory blood pressure (ABP) and central hemodynamics in hypertensive patients and between the area under the 24-h blood pressure curve and the hemodynamic indexes. PATIENT POPULATION: Forty untreated essential hypertensive patients (28 previously untreated, 12 withdrawn from therapy for > 12 weeks). METHODS: Patients underwent casual and 24-h ABP monitoring and invasive measurements of central hemodynamics. Central measures of ABP included 24-h mean, awake, and sleep values guided by activity journals. The ABP data were modeled by Fourier series and the ability of the smoothed and unsmoothed data to predict hemodynamics was compared. Individual blood pressure curves were analyzed by calculating the area under the curve using different threshold awake and sleep values to test the correlations between this form of blood pressure load and hemodynamics. RESULTS: Hemodynamic measures were not predicted by casual blood pressure but were related to ABP. Total peripheral resistance was strongly predicted by the area under the diastolic blood pressure (DBP) curve using an awake threshold of 90 mmHg and a sleep threshold of 80 mmHg (r = 0.56, P < 0.001). Data smoothing using Fourier transformation did not alter any correlations between ABP and hemodynamics. Exercise stroke index, an indicator of cardiac function impaired in early hypertensive heart disease, was also best predicted by area under the DBP curve using the same thresholds as above (r = -0.56, P < 0.001). CONCLUSIONS: These data imply that integrated areas under the ABP curve are related to hemodynamic hypertensive indexes and could be used to assess the extent of hypertensive burden in clinical trials.

Adult

Transmembrane segment prediction from protein sequence data.

We consider the automated identification of transmembrane domains in membrane protein sequences. 324 proteins (containing 1585 segments) were examined, representing every protein in the PIR database having the transmembrane domain feature annotation. Machine learning techniques were used to evaluate the efficacy of alternative hydrophobicity measures and windowing techniques. We describe a simpler measure of hydrophobicity and a new variable window size concept. We demonstrate that these techniques are superior to some previous techniques in minimizing the segment error rate. Using these new techniques, we describe an algorithm that has a 7.9% segment error rate on the sampled proteins, while classifying 16.7% of the amino acid residues as transmembrane.

Algorithms