Pheochromocytoma – clinical manifestations, diagnosis and current perioperative management

Pheochromocytoma is a neuroendocrine tumor characterized by the excessive production of catecholamines (epinephrine, norepinephrine, and dopamine). The diagnosis is suspected due to hypertensive paroxysms, associated with vegetative phenomena, due to the catecholaminergic hypersecretion. Diagnosis involves biochemical tests that reveal elevated levels of catecholamine metabolites (metanephrine and normetanephrine). Functional imaging, such as 123I-metaiodobenzylguanidine scintigraphy (123I-MIBG), has increased specificity in identifying the catecholamine-producing tumor and its metastases. The gold-standard treatment for patients with pheochromocytoma is represented by the surgical removal of the tumor. Before surgical resection, it is important to optimize blood pressure and intravascular volume in order to avoid negative hemodynamic events

Pheochromocytoma – clinical manifestations, diagnosis and current perioperative management

Pheochromocytoma is a neuroendocrine tumor characterized by the excessive production of catecholamines (epinephrine, norepinephrine, and dopamine). The diagnosis is suspected due to hypertensive paroxysms, associated with vegetative phenomena, due to the catecholaminergic hypersecretion. Diagnosis involves biochemical tests that reveal elevated levels of catecholamine metabolites (metanephrine and normetanephrine). Functional imaging, such as 123I-metaiodobenzylguanidine scintigraphy (123I-MIBG), has increased specificity in identifying the catecholamine-producing tumor and its metastases. The gold-standard treatment for patients with pheochromocytoma is represented by the surgical removal of the tumor. Before surgical resection, it is important to optimize blood pressure and intravascular volume in order to avoid negative hemodynamic events

A rare case of vulvar squamous cell carcinoma; case presentation

Objectives. Vulvar cancer is a rare gynecological malignancy, with an incidence of 1.5 per 100 000 women/year. The most common vulvar cancer is developed in squamous cells, the most encountered type of skin cells. Case report. We report a case of a 72-year-old female admitted in the Department of Plastic Surgery of Emergency Clinical Hospital “Prof. Dr. Agrippa Ionescu” with a 5/4.2 cm painful ulcerated tumoral mass located in the vulvar area. The lesion slowly increased in size over the past 12 months. The tumour was surgically removed with oncological safety margins and sent for histopathological evaluation. The histopathological examination revealed an ulcerated squamous carcinoma with lymphovascular and perineural invasion, but with negative margins. Postoperative results were favorable, and no local or general complications were observed. Conclusion. We highlight this case due to its unusual presentation in the clitoral area. Moreover, considering the potential for recurrence we point out the importance of the radical vulvectomy with regional lymphadenectomy and histopathological examination, in order to put a precise diagnosis and ensure the best possible treatment for the patient

A collaborative requirement mining framework.

Il est communément admis que 70 % des coûts du cycle de vie d’un produit sont engagés dès la phase de spécification. Or, aujourd’hui, dans chacune des relations contrac-tuelles client-fournisseur, le fournisseur doit faire face à un amas d’exigences à partir duquel il est difficile de prendre des décisions stratégiques avisées. Pour aider les sous-traitants, nous proposons une méthode outillée de synthèse des exigences, laquelle est supportée par un environnement numérique basé sur les sciences des données. Des modèles de classification extraient les exigences des documents. Les exigences sont ensuite analysées au moyen des techniques de traitement du langage naturel afin d’identifier les défauts de qualité qui mettent en péril le reste du cycle de vie. Pour faciliter leur exploitation, les exigences, dépourvues de leurs principaux défauts, sont non seulement classifiées automatiquement au sein de catégories métiers grâce aux techniques d’apprentissage machine, mais aussi segmentées en communautés au moyen des récentes avancées en théorie des graphes. Chacune des communautés d’exigences est caractérisée par un ensemble configurable de critères d’aide à la décision, dont l’estimation collaborative est assurée par des experts représentant les diverses fonctions de l’entreprise. Enfin, une synthèse graphique des estimations est restituée au décideur via un tableau de bord de résumés statistiques descriptifs facilitant la prise de décisions informées. La validation théorique et empirique de notre proposition corrobore l’hypothèse que les sciences des données est un moyen de synthétiser plusieurs centaines ou milliers d’exigences.It is broadly accepted that 70 % of the total life cycle cost is committed during the specification phase. However, nowadays, we observe a staggering increase in the number of requirements. We consider the tremendous volume of requirements as big data with which sub-contractors struggle to make strategic decisions early on. Thus, we propose to methodologically integrate data science techniques into a collaborative requirement mining framework, which enables decision-makers to gain insight and discover opportunities in a massive set of requirements. Initially, classification models extract requirements from prescriptive documents. Requirements are subsequently analysed with natural language processing techniques so as to identify quality defects. After having removed the quality defects, the analyst can navigate through clusters of requirements that ease the exploration of big data. Each cluster gathers the requirements that belong to a functional area (mechanics, electronics, IT, etc.). Each domain expert can therefore easily filter out the requirements subset that is relevant for him. A complementary approach consists in detecting communities of requirements by analysing the topology of a graph. Each community owns a customisable set of decision-making criteria which are estimated by all functional areas. A dashboard of statistical visuals distils the estimation results from which a decision maker can make informed decisions. We conclude that the theoretical and empirical validation of our proposition corroborates the assumption that data science is an effective way to gain insight from hundreds or thousands of requirements