Estimation of growth fraction in fine needle aspirates from non-Hodgkin's lymphoma using anti-proliferating cell nuclear antigen - Correlation with the Kiel classification

OBJECTIVE: This study was undertaken to assess whether fine needle aspirates from non-Hodgkins lymphoma (NHL) could be used for growth fraction analysis with proliferating cell nuclear antigen (PCNA) staining and if there was a relationship between the growth fraction and cytomorphologic classification according to the Kiel classification.STUDY DESIGN: the study group consisted of 40 patients with NHL diagnosed by fine needle aspiration (FNA) cytology. The cytologic classification of the lymphomas was made by two cytopathologists on May-Grunwald-Giemsa-stained slides using the Kiel classification. There were 27 cases of low and 13 of high grade lymphoma. The estimation of the growth fraction was made by PCNA immunoreactivity. The PCNA index was quantitated in smears by counting an average of 1,000 cells, and the count teas correlated with the cytomorphologic classification.RESULTS: There was It strong correlation between the PCNA index and lymphoma grading. High grade lymphomas exhibited a mean PCNA positivity of 74.0%, which was significantly higher (P <.001) than that of low grade lymphomas (17.6%).CONCLUSION: Our study showed that PCNA evalua tion is suitable for smears obtained by FNA on NHL, correlates with increasing grades of lymphoma according to the Kiel classification and may offer a method of monitoring treatment of lymphoma.

A REAPPRAISAL OF GENERIC AND SUBGENERIC CLASSIFICATION IN THE BATRACHOSPERMACEAE (RHODOPHYTA)

The generic and subgeneric classification of the family Batrachospermaceae (Rhodophyta) has long been recognized as ambiguous and often inconsistent. One of the prime features used to delineate sections of Batrachospermum, trichogyne shape, is variable even within given species. However, characters associated with the carposporophyte and the carpogonial branch, as well as carpogonial symmetry, are practical and consistent taxonomic criteria. These features have been used to redefine sectional delineation in Batrachospermum. Based on phylogenetic reasoning and practicality, it is proposed that the three genera Nothocladus, Sirodotia and Tuomeya be reduced to sectional level within Batrachospermum. The genus Batrachospermum would thus become the sole member of the Batrachospermaceae and would include two subgenera, Batrachospermum and Acarposporophytum, the former with nine clearly defined sections (Aristata, Batrachospermum, Contorta, Hybrida, Nothocladus (Skuja) stat. nov., Sirodotia (Kylin) stat. nov., Tuomeya (Harvey) stat. nov., Turficola and Viridia). As a result, the following nomenclatural changes are proposed: Batrachospermum lindaueri (Skuja) comb. nov., B. nodosum (Skuja) comb. nov., B. delicatulum (Skuja) comb. nov., B. fennicum (Skuja) comb. nov., B. suecicum (Kylin) comb. nov. and B. americanum (Kutzing) comb. nov.

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.