No abstract

Corrigendum to "Functional and immunohistochemical characterization of CCEae3a, a carboxylesterase associated with temephos resistance in the major arbovirus vectors Aedes aegypti and Ae. albopictus" [Insect Biochem. Mol. Biol. 74 (July 2016) 61e67]

No abstract

Fine-needle aspiration (FNA) cytology is an important diagnostic tool in patients with thyroid lesions. Several systems have been proposed for the cyropathologic diagnosis of the thyroid nodules. However cases with indeterminate cytological findings still remain a matter of debate. In this review we analyze all literature regarding Thyroid Cytopathology Reporting systems trying to identify the most suitable methodology to use in clinical practice for the preoperative diagnosis of thyroid nodules. A review of the English literature was conducted, and data were analyzed and summarized and integrated from the authors' perspective. The main purpose of thyroid FNA is to identify patients with higher risk for malignancy, and to prevent unnecessary surgeries for benign conditions. The Bethesda System for Reporting Thyroid Cytopathology is the most widely used system for the diagnosis of thyroid FNA specimens. This system also contains guidelines for the diagnosis and treatment of indeterminate or suspicious for malignancy cases. In conclusion, patients who require repeated FNAs for indeterminate diagnoses will be resolved by repeat FNA in a percentage of 72%-80%.

Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management.