To measure the amount and affinity of insulin antibodies, we performed a trial to establish a new method for quantitative and qualitative analysis of these antibodies by using surface plasmon resonance (BIAcore system).
Real-time detection of insulin antibody interaction and kinetic analysis were performed using the BIAcore system.
Eight diabetic patients with insulin antibodies and whose fasting total immunoreactive insulin levels were more than 100 microU/ml were selected. The patients with and without recurrent hypoglycemia were classified into hypoglycemic episode-positive or hypoglycemic episode-negative groups, respectively. Seven diabetic patients without insulin antibodies were selected as controls.
In the 8 patients, the concentration of insulin antibodies ranged from 2.91 to 16.3 microg/ml and insulin antibodies were not detected in the control group. The apparent KD (dissociation constant) and kd (the dissociation rate constant) values of the patients were much larger than those seen for the anti-human insulin monoclonal antibody. The KD values were significantly higher in the hypoglycemic episode-positive group than in the hypoglycemic episode-negative group (p<0.05). No significant differences in the concentration, the ka (the association rate constant) and the kd values were noted between the groups.
The data suggests that insulin antibodies of the patients have an apparently lower affinity status in sera as compared with that for the anti-human insulin monoclonal antibody, and dissociate easily from the immune-complex in the sera, especially in cases where there is recurrent hypoglycemia in the patients. Therefore insulin antibody characteristics are one of the causative factors in hypoglycemic episodes.
[Show abstract][Hide abstract] ABSTRACT: We identified 1113 articles (103 reviews, 1010 primary research articles) published in 2005 that describe experiments performed using commercially available optical biosensors. While this number of publications is impressive, we find that the quality of the biosensor work in these articles is often pretty poor. It is a little disappointing that there appears to be only a small set of researchers who know how to properly perform, analyze, and present biosensor data. To help focus the field, we spotlight work published by 10 research groups that exemplify the quality of data one should expect to see from a biosensor experiment. Also, in an effort to raise awareness of the common problems in the biosensor field, we provide side-by-side examples of good and bad data sets from the 2005 literature.
[Show abstract][Hide abstract] ABSTRACT: The advent of ultra-low-noise amplification has shifted the emphasis of low-noise design towards passive circuitry. Expressions for the noise parameters for a passive two-port are derived in terms of the scattering matrix and applied to the problem of design of power combining circuits. Simple guidelines for low-noise combiner design are presented
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