Functionalized metallic carbon nanotube devices for pH sensing.
Explore the source record for details and available documents.
Journal Article↗
Biomedical subjects
Alicia Maroto
Publications and source records attributed to Alicia Maroto.
Uncertainty in aflatoxin B1 analysis using information from proficiency tests.
We describe how to calculate uncertainty in the determination of aflatoxin B(1) in nuts and maize using an HPLC method with prederivatisation with trifluoroacetic acid and fluorescence detection. The uncertainty is easily calculated using the information obtained from the participation in several proficiency tests. All the sources of uncertainty are grouped in two terms: (1) matrix variability, intermediate precision and sample heterogeneity, and (2) method concordance. This methodology has the advantage that no extra work needs to be done because all the information to calculate uncertainty comes from proficiency tests. The uncertainty values were calculated using samples whose concentration ranged between 2 and 20 microg L(-1). The relative standard uncertainty computed for aflatoxin B(1) was 16.3%.
Aflatoxin B1↗
A single primer pair immunoglobulin polymerase chain reaction assay as a useful tool in fine-needle aspiration biopsy differential diagnosis of lymphoid malignancies.
BACKGROUND: Different polymerase chain reaction (PCR) assays have proved to be useful tools for the detection of clonal immunoglobulin heavy chain (IgH) gene rearrangements in frozen or paraffin-embedded tissues. However, the usefulness of a single primer pair PCR assay on fine-needle biopsy aspirates has not been proven yet. METHODS: The authors conducted a wide prospective study of 148 lymph nodes and extranodal lymphoid infiltrates obtained by fine-needle aspiration biopsy (FNAB). The power of a single primer pair PCR amplification of the hypervariable CDRIII region IgH genes was evaluated using a pair of consensus primers. The PCR cytologic results were compared with the final clinicopathologic diagnosis of each case assessed by combining cytologic and/or immunophenotypic data and histologic features or clinical follow-up. RESULTS: Among the 139 cases with an evaluable PCR result, 35 of 40 (87%) B-cell non-Hodgkin lymphomas were detected as a monoclonal band. Monoclonal IgH bands also were detected in two of the five (40%) T-cell lymphomas, two of the seven (29%) Hodgkin lymphomas, and 5 of the 87 (6%) reactive lymphoid disorders. These results are similar to those obtained by other authors using seminested PCR or combining different PCR tests in each sample obtained by FNAB or excisional biopsy. CONCLUSIONS: The results of the current study demonstrate the convenience of a single primer pair PCR amplification over seminested methods in terms of lower cost and workload. The existence of PCR false-negative and false-positive results for lymphoma makes it necessary to combine the information obtained by PCR with cytologic and/or immunophenotypic data to optimize the number of lymphoid malignancies correctly diagnosed by FNAB.
Biopsy, Needle↗
Effect of non-significant proportional bias in the final measurement uncertainty.
The trueness of an analytical method can be assessed by calculating the proportional bias of the method in terms of apparent recovery. If the apparent recovery does not differ significantly from one, the analytical method has not a significant bias. If this is the case, the bias is neglected and the uncertainty associated with this bias is included in the uncertainty budget of results. However, when assessing trueness there is always a probability of incorrectly concluding that the proportional bias is not significant. Therefore, the uncertainty of results may be underestimated. In this paper, we study how non-significant bias affects the uncertainty of analytical results. Moreover, we study how to avoid the underestimation of uncertainty by including the non-significant bias calculated in the uncertainty budget. To answer these questions, we have used the Monte-Carlo method to simulate the process of estimating the apparent recovery of a biased analytical method and, subsequently, the future results this method provides. The results of the simulation show that non-significant bias may underestimate the uncertainty of analytical results when bias contributes in more than 20% to the overall uncertainty. Uncertainty is specially underestimated when bias contributes in more than 50% to the overall uncertainty.
Journal Article↗