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Biomedical subjects

V Pradel

Publications and source records attributed to V Pradel.

2 recordsLinked to original sources

Correlation between apoptosis microarray gene expression profiling and histopathological lymph node lesions.

AIMS: Microarray technology has recently led to the identification of molecular prognostic subgroups in non-Hodgkin's lymphomas. To determine the usefulness of ready made macroarrays as routine diagnostic tools in haematopathology, lymph node biopsies were analysed using a cDNA macroarray containing genes involved in apoptosis, including caspases. METHODS: Nine biopsy specimens were analysed using total frozen tissues: four samples of B cell follicular lymphoma, two of B cell diffuse large cell lymphoma, and three of non-neoplastic lymph nodes from benign lymphadenitis. Nine cell populations were sorted from fresh tissues: malignant B cells from two patients with follicular lymphoma and two with diffuse large cell lymphoma, reactive B cells from two benign lymph nodes, reactive T cells from one benign lymph node, and virgin (mantle zone) B cells and germinal centre B cells from benign tonsils. Immunohistochemistry (IHC) on paraffin wax sections was performed for the localisation of caspases 2, 3, 4, 7, 8, and 9. RESULTS: In the clustered array data, sorted cells from samples sharing common histological lesions were grouped together, whereas the array/histology correlation was less satisfactory for tissues. The expression profiles of both the array and IHC methods correlated for most caspases and samples. CONCLUSIONS: Variations in array profiles of sorted cell populations can be associated with specific histological features, suggesting a possible diagnostic application of ready made apoptosis macroarrays in haematopathology.

Apoptosis↗

Can fructosamine be a surrogate for HbA(1c) in evaluating the achievement of therapeutic goals in diabetes?

OBJECTIVE: To determine if fructosamine can be used as a surrogate for HbA(1c) to monitor whether therapeutic goals in diabetes mellitus are achieved when HbA(1c) cannot be used for this purpose (hemoglobinopathies, anemia...). MATERIAL AND METHODS: Blood samples of 76 diabetic patients and 30 healthy subjects characterized by the absence of any risk of interference in the interpretations of HbA(1c) and fructosamine were studied in order to, first, deduce from the correlation a prediction of HbA(1c) from the fructosamine values, second, to evaluate the predictive value of such predicted HbA(1c) in the determination of poor metabolic control as defined by UKPDS and DCCT studies. RESULTS: The correlation between predicted HbA(1c) and actual fructosamine was fair (r=0.88) in diabetic patients but not in control subjects (r=0.01). It was therefore only possible to estimate HbA(1c) from fructosamine in diabetic patients. Nevertheless, the range of positive and negative predictive values of estimated HbA(1c) to detect a poor metabolic control defined by two thresholds of HbA(1c) (7%, 7.5%) was 91-93% and 86-87%, respectively. Then, even in this highly selected population, the risk of misclassification was around 10% when fructosamine was used to estimate HbA(1c). These results were unchanged when fructosamine was corrected by plasma protein level. CONCLUSIONS: This study shows the limitations to use fructosamine in place of HbA(1c) to evaluate the efficacy of antidiabetic treatments, even in a selected population.

Adult↗