Neuroendocrine differentiation in basal cell carcinoma.

Basal cell carcinoma (BCC) is the prototypical basaloid tumor of the skin. It may show various patterns simulating other cutaneous tumors due to its pleomorphism. It may have an unusal pattern of differentiation such as squamous, sebaceous, apocrine, eccrine, pilar, and endocrine differentiation. In order to establish the relative frequency of neuroendocrine differentiation in BCC, we performed a retrospective study of 33 consecutive BCCs using conventional immunohistochemistry with two neuroendocrine antibodies: Chromogranine A and synaptophysine. The age of the patients ranged from 17-83 years with mean of 65 years. The male to female ratio was 16:17. In immunohistochimestry, Chromogranine A was seen in 72.2% (24/33) while Synaptophysine was positive in 9.09% (3/33). Their expression was cytoplasmic and membranous and was seen in the periphery of these tumors in the overlying cells. Positive staining of chromogranine A was high (75-100% of tumors cells) in 9%, intermediate (25-75% of tumors cells) in 33% of cases and relatively low (<25%) in 30.3% of cases.

Detection of atypical attentional behaviors in young subjects.

BACKGROUND: Vigilance ability refers to the accuracy and speed with which a person performs a cognitive-motor task, either voluntarily (endogenous mode) or following a warning stimulus (exogenous mode). In the context of a force production task, our study focuses on the impact of the states of vigilance by proposing an original approach that allows distinguishing between good (inlier) and poor (outlier) participants. We assume that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. Our objective is particularly challenging in the context of a limited dataset with a high level of noise. NEW METHOD: Our original methodological approach consists of coupling the RANSAC (RANdom SAmple Consensus) algorithm with a statistical machine learning algorithm to handle noise. COMPARISON WITH EXISTING METHODS: Our clustering approach, based on the coupling of RANSAC methodology with ensemble classifiers, overcomes the limitations of conventional supervised algorithms that are either not robust to outliers (such as K-Nearest Neighbors) and/or not adapted to few-shot learning (such as Support Vector Machines and Artificial Neural Networks). RESULTS: The clustering results were validated in terms of reaction time distributions and force error distributions with respect to participant groups. We show that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. CONCLUSION: Our study has allowed us to detect atypical attentional patterns and succeeds in separating the inliers from the outliers.

[Inflammatory myofibroblastic tumor of the larynx: a case report]. / Tumeur myofibroblastique inflammatoire du larynx: a propos d'un cas.

BACKGROUND: Inflammatory myofibroblatic tumor is a recently caracterized solid mesenchymal tumor. They are frequently localised in the lung. However, the head and neck forms are uncommon. AIM: To report a new case of inflammatory myofibroblastic tumor of the larynx. CASE REPORT: This 47-year old man presented with dysphonia without dyspnea. Laryngoscopy showed a tumor of anterior vocal cord. CT scan of larynx confirmed diagnosis. The patient had a cordectomy. Histology concluded to the diagnosis of inflammatory myofibroblastic tumor. No relapses have been noted after 4 months. CONCLUSION: Only a few cases of laryngeal inflammatory myofibroblastic tumor have been reported in the literature. Clinical presentation depends of tumor location. Radiological images are not specific and diagnosis is based on histology. The expected course is essentially marked by the risk of local recurrences. Surgical excision seems to be the treatment of choice.

Optical flow computation and visualization in spherical context. Application on 3D+t bio-cellular sequences.

The main focus of this paper is the optical flow computation for 2D movements of objects embedded on 3D surfaces. For spheric-shaped supports, object motion has only two degrees of liberty, thus the 3D optical flow constraint is not relevant. Constancy assumption is formulated using a suitable parametrization of the 3D surface, leading to a 2D equation. Input temporal sequence is also transformed according to the 3D surface parametrization. We build a complete 2D model, taking into account the underlying spherical surface. It has the merit to estimate at a lower cost velocity field in the temporal input sequence. In order to analyze motion computation results, we design an adapted visualization tool, instead of carrying out an inverse transformation for the velocity field. Adapted to the selected parametrization, it displays rapidly moving objects and velocity field and improves the understanding of the displayed information. We display optical flow computation results for 3D+t cell wall simulation sequences.