Two new 3' PML breakpoints in t(15;17)(q22;q21)-positive acute promyelocytic leukemia.

In the present article, two new types of PML/RARA junctions are described. Both were identified in diagnostic samples from two t(15;17)(q22;q21)-positive acute promyelocytic leukemia (APL) patients who failed to achieve complete remission. By using different sets of primers, reverse transcriptase polymerase chain reaction (RT-PCR) of PML/RARA junctions showed atypical larger bands compared with those generated from the three classical PML breakpoints already described. Sequence analysis of the fusion region of the amplified cDNAs allowed us to determine the specificity of these fragments in both patients. This analysis showed two new hybrid transcripts that were 53 and 306 base pairs (bp) longer than that expressed by the NB4 cell line (PML breakpoint within intron 6), and are the result of the direct joining of RARA exon 3 with PML exon 7a (patient 2) or the 5' portion of PML exon 7b (patient 1), respectively. In patient 1, RT-PCR analysis of the reciprocal RARA/PML junction showed a smaller transcript than that expected in bcr1 cases, while in patient 2 no amplified fragment was obtained. Cytogenetic analysis and/or fluorescence in situ hybridization (FISH) showed that both patients had the t(15;17) translocation. The clinical and hematological profiles expressed by the two patients carrying these unexpected types of PML/RARA rearrangement did not differ significantly from that commonly seen in other APLs with the exception of the poor outcome. Genes Chromosomes Cancer 27:35-43, 2000.

Heteroduplex analysis of VDJ amplified segments from rearranged IgH genes for clonality assessments in B-cell non-Hodgkin's lymphoma. A comparison between different strategies.

BACKGROUND AND OBJECTIVE: The main difficulty of PCR-based clonality studies for B-cell lymphoproliferative disorders (B-LPD) is discrimination between monoclonal and polyclonal PCR products, especially when there is a high background of polyclonal B cells in the tumor sample. Actually, PCR-based methods for clonality assessment require additional analysis of the PCR products in order to discern between monoclonal and polyclonal samples. Heteroduplex analysis represents an attractive approach since it is easy to perform and avoids the use of radioactive substrates or expensive equipment. DESIGN AND METHODS: We studied the sensitivity and specificity of heteroduplex PCR analysis for monoclonal detection in samples from 90 B-cell non Hodgkin's lymphoma (B-NHL) patients and in 28 individuals without neoplastic B-cell disorders (negative controls). Furthermore, in 42 B-NHL and in the same 28 negative controls, we compared heteroduplex analysis vs the classical PCR technique. We also compared ethidium bromide (EtBr) vs. silver nitrate (AgNO(3)) staining as well as agarose vs. polyacrylamide gel electrophoresis (PAGE). RESULTS: Using two pair consensus primers sited at VH (FR3 and FR2) and at JH, 91% of B-NHL samples displayed monoclonal products after heteroduplex PCR analysis using PAGE and AgNO(3) staining. Moreover, no polyclonal sample showed a monoclonal PCR product. By contrast, false positive results were obtained when using agarose (5/28) and PAGE without heteroduplex analysis: 2/28 and 8/28 with EtBr and AgNO(3) staining, respectively. In addition, false negative results only appeared with EtBr staining: 13/42 in agarose, 4/42 in PAGE without heteroduplex analysis and 7/42 in PAGE after heteroduplex analysis. INTERPRETATION AND CONCLUSIONS: We conclude that AgNO(3) stained PAGE after heteroduplex analysis is the most suitable strategy for detecting monoclonal rearrangements in B-NHL samples because it does not produce false-positive results and the risk of false-negative results is very low.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.