University of Ioannina

About: University of Ioannina is a education organization based out in Ioannina, Greece. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7654 authors who have published 20594 publications receiving 671560 citations. The organization is also known as: Panepistimio Ioanninon.

Melanin absorption spectroscopy: new method for noninvasive skin investigation and melanoma detection.

Abstract: We present a new method for studying melanin in vivo based on diffuse reflectance spectroscopy of human skin. We find that the optical absorption spectrum of in vivo melanin exhibits an exponential dependence on wavelength, consistent with, but with a higher decay slope than, in vitro results. We offer theoretical justification for this exponential dependence on the basis of a recently proposed model for the structure of eumelanin protomolecules. Moreover, we report on a new method for analysis of diffuse reflectance spectra, which identifies intrinsic differences in absorption spectra between malignant melanoma and dysplastic nevi in vivo. These preliminary results are confirmed both by analysis of our own clinical data as well as by analysis of data from three independent, previously published studies. In particular, we find evidence that the histologic transition from dysplastic nevi to melanoma in situ and then to malignant melanoma is reflected in the melanin absorption spectra. Our results are very promising for the development of techniques for the noninvasive detection of melanoma and, more generally, for the study and characterization of pigmented skin lesions. It is also a promising approach for a better understanding of the biological role, structure, and function of melanin.

Carcinoma of unknown primary (CUP)

Abstract: Carcinoma of unknown primary (CUP) is one of the 10 most frequent cancers worldwide. It constitutes 3-5% of all human malignancies. Patients with CUP present with metastases without an established primary site. CUP manifests as an heterogeneous group of mainly epithelial cancers recognised by distinct clinicopathological entities. The diagnostic work-up includes extensive histopathology investigations and modern imaging technology. Nevertheless, the primary tumour remains undetected most of the time. Molecular diagnosis with DNA microarrays demonstrates high sensitivity, but its prognostic contribution is still uncertain. Certain clinicopathological CUP entities are considered as favourable sub-sets responding to systemic platinum-based chemotherapy or managed by locoregional treatment. These sub-sets are: the poorly differentiated carcinomas involving the mediastinal-retroperitoneal nodes, peritoneal papillary serous adenocarcinomatosis in females, poorly differentiated neuroendocrine carcinomas, isolated axillary node adenocarcinomas in females or cervical nodal involvement by a squamous cell carcinoma. Patients who belong to the non-favourable sub-sets have a worse prognosis.

Attachment of Magnetic Nanoparticles on Carbon Nanotubes and Their Soluble Derivatives

Abstract: The magnetic modification of carbon nanotubes is described here. Pyrene can be noncovalently attached on the carbon nanotube surface. A carboxylic derivative of pyrene is used as interlinker for the binding of capped magnetic nanoparticles on the carbon nanotubes. The increased organophilic character of the capped nanoparticles induces high solubility in organic media for the modified carbon nanotubes.

A spatially constrained mixture model for image segmentation

Abstract: Gaussian mixture models (GMMs) constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation, where spatially constrained mixture models have been trained using the expectation-maximization (EM) framework. In this letter, we elaborate on this method and propose a new methodology for the M-step of the EM algorithm that is based on a novel constrained optimization formulation. Numerical experiments using simulated images illustrate the superior performance of our method in terms of the attained maximum value of the objective function and segmentation accuracy compared to previous implementations of this approach.