G Vijaya | PubMed Health

PubMed1984

Endocrine testicular functions in leprosy.

Pituitary testicular functions were evaluated in leprosy. Forty-three men with leprosy were studied by basal estimations of plasma LH, FSH, Prolactin, Testosterone, 17-beta estradiol, metoclopramide stimulated prolactin responses and hCG stimulated testosterone responses. Fifteen young healthy men with proven fertility were studied as control subjects. The hormone estimations were related to the histologic changes observed in the testicular biopsies of leprous hypogonadism. In lepromatous leprosy (n:18) the basal plasma gonadotropin levels were significantly increased (FSH 29.5 +/- 2.3 mlU/ml; LH 21.5 +/- 1.9 mlU/ml, mean +/- SE). The plasma gonadotropins were in the normal range in tuberculoid leprosy. In borderline leprosy, the basal mean plasma FSH was normal, whereas LH levels were significantly increased (22.5 +/- 1.2 mlU/ml). The basal plasma testosterone levels were significantly decreased in lepromatous leprosy (1.6 +/- 0.12 ng/ml), tuberculoid leprosy (4.2 +/- 1.7 ng/ml) and borderline leprosy (1.8 +/- 0.18 ng/ml). The basal plasma 17-beta estradiol levels were significantly elevated in all the three types of leprosy. The basal plasma prolactin levels in plasma were significantly increased in lepromatous and tuberculoid leprosy. During hCG stimulation tests, the peak plasma testosterone responses were significantly reduced in both lepromatous leprosy and tuberculoid leprosy subjects. The blunted testosterone responses during hCG stimulation tests in leprosy correlated with the high basal 17-beta estradiol levels (r = 0.58; P less than 0.05). These results strongly suggest that hypogonadism in leprosy results from primary testicular failure. The significant elevation of plasma 17-beta estradiol levels in lepromatous, tuberculoid and borderline leprosy could play a role in hypogonadism of leprosy.

Adult↗

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ANN-based QRS-complex analysis of ECG.

Reliable detection the QRS complex in either a normal or an abnormal ECG and its analysis is the first and foremost task in almost every ECG signal analysis system aimed at the diagnostic interpretation of ECG. Conventionally, detection of the QRS complex is accomplished using a rule-based/algorithmic approach. This work, uses the learn and generalize approach of an artificial neural network (ANN) for the detection of QRS complexes in either a normal or an abnormal ECG. This is followed by the analysis of the QRS complex to designate and measure the morphological components within the QRS complex in all 12 standard leads. An ANN has been developed to detect the QRS complex in ECG and trained, with the help of back propagation algorithm, on more than a hundred ECGs selected from the CSE Data Set-3. The trained ANN was tested on all the recordings of the CSE Data Set-3 and the sensitivity has been found to be 99.11%. Subsequent to the identification of the QRS complex, an analysis of this complex and measurement of peak amplitudes of the component waves is done. The results are validated using the CSE multilead measurement results. Both the QRS detection and the QRS analysis software developed in C-language have been successfully implemented on a PC-AT. The results are found to be in agreement with visual measurements carried out by medical experts.

Electrocardiography↗