Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

The value of metabolic imaging to predict tumour response after chemoradiation in locally advanced rectal cancer

Background: We aim to investigate the possibility of using 18F-positron emission tomography/computer tomography (PET-CT) to predict the histopathologic response in locally advanced rectal cancer (LARC) treated with preoperative chemoradiation (CRT). Methods: The study included 50 patients with LARC treated with preoperative CRT. All patients were evaluated by PET-CT before and after CRT, and results were compared to histopathologic response quantified by tumour regression grade (patients with TRG 1-2 being defined as responders and patients with grade 3-5 as non-responders). Furthermore, the predictive value of metabolic imaging for pathologic complete response (ypCR) was investigated. Results: Responders and non-responders showed statistically significant differences according to Mandard's criteria for maximum standardized uptake value (SUVmax) before and after CRT with a specificity of 76,6% and a positive predictive value of 66,7%. Furthermore, SUVmax values after CRT were able to differentiate patients with ypCR with a sensitivity of 63% and a specificity of 74,4% (positive predictive value 41,2% and negative predictive value 87,9%); This rather low sensitivity and specificity determined that PET-CT was only able to distinguish 7 cases of ypCR from a total of 11 patients. Conclusions: We conclude that 18-F PET-CT performed five to seven weeks after the end of CRT can visualise functional tumour response in LARC. In contrast, metabolic imaging with 18-F PET-CT is not able to predict patients with ypCR accurately

Scimitar Syndrome in an Asymptomatic Adult: Fortuitous Diagnosis by Imaging Technique

Congenital cardiopathies in adults are a rare clinical entity in the cardiology consultations. Advances in imaging techniques allow the fortuitous diagnosis of mild forms of these congenital abnormalities. We describe a case of an asymptomatic 41-year-old man, with a medical history of recurrent pneumonia during childhood and an established diagnosis of scimitar syndrome by computed tomography.

Immune cell impact of three differently coated lipid nanocapsules: pluronic, chitosan and polyethylene glycol.

Lipid nanocapsules (NCs) represent promising tools in clinical practice for diagnosis and therapy applications. However, the NC appropriate functionalization is essential to guarantee high biocompatibility and molecule loading ability. In any medical application, the immune system-impact of differently functionalized NCs still remains to be fully understood. A comprehensive study on the action exerted on human peripheral blood mononuclear cells (PBMCs) and major immune subpopulations by three different NC coatings: pluronic, chitosan and polyethylene glycol-polylactic acid (PEG) is reported. After a deep particle characterization, the uptake was assessed by flow-cytometry and confocal microscopy, focusing then on apoptosis, necrosis and proliferation impact in T cells and monocytes. Cell functionality by cell diameter variations, different activation marker analysis and cytokine assays were performed. We demonstrated that the NCs impact on the immune cell response is strongly correlated to their coating. Pluronic-NCs were able to induce immunomodulation of innate immunity inducing monocyte activations. Immunomodulation was observed in monocytes and T lymphocytes treated with Chitosan-NCs. Conversely, PEG-NCs were completely inert. These findings are of particular value towards a pre-selection of specific NC coatings depending on biomedical purposes for pre-clinical investigations; i.e. the immune-specific action of particular NC coating can be excellent for immunotherapy applications.