Search

This study assesses the expression of all TNF-related apoptosis-inducing ligand (TRAIL) receptors in pancreatic ductal adenocarcinoma (PDAC) tumor tissue. We aimed to include TRAIL receptor expression as an inclusion parameter in a future clinical study using a TRAIL-based therapy approach for PDAC patients. Considering the emerging influence of PDAC desmoplastic stroma on the efficacy of anti-PDAC therapies, this analysis was extended to tumor stromal cells. Additionally, we performed PDAC stroma characterization. Our retrospective cohort study (N=50) included patients with histologically confirmed PDAC who underwent surgery. The expression of TRAIL receptors (DR4, DR5, DcR1, DcR2, and OPG) in tumor and stromal cells was evaluated by immunohistochemistry (IHC). The amount of tumor stroma was assessed by anti-vimentin IHC and Mallory’s trichrome staining. The prognostic impact was determined by the univariate Cox proportional hazards regression model. An extensive expression of functional receptors DR4 and DR5 and a variable expression of decoy receptors were detected in PDAC tumor and stromal cells. Functional receptors were detected also in metastatic tumor and stromal cells. A poor prognosis was associated with low or absent expression of decoy receptors in tumor cells of primary PDAC. After assessing that almost 80% of tumor mass was composed of stroma, we correlated a cellular-dense stroma in primary PDAC with reduced relapse-free survival. We demonstrated that TRAIL functional receptors are widely expressed in PDAC, representing a promising target for TRAIL-based therapies. Further, we demonstrated that a low expression of DcR1 and the absence of OPG in tumor cells, as well as a cellular-dense tumor stroma, could negatively impact the prognosis of PDAC patients.

International audience; One of the main tasks of Text Mining is organising a large number of unlabelled documents into a smaller set of meaningful and coherent clusters, similar with respect to their content. Clustering algorithms are usually carried on documents × terms matrices, algebraically representing each document as a vector. Nevertheless, a collection of documents can also be encoded differently, e.g. by considering a documents × documents representation. This peculiar data structure can be seen as an adjacency matrix and graphically displayed as a graph. In the framework of Network Analysis, community detection is performed on such graphs to find groups of nodes sharing common characteristics, and play similar roles. This paper aims at evaluating the use of different data structures and different grouping criteria, showing the effectiveness of the different alternatives in a text categorisation strategy. We performed a comparative study involving both classical text clustering approaches and community detection approaches, testing and discussing their performances.

Document Clustering is the peculiar application of cluster analysis methods on huge documentary databases. Document Clustering aims at organizing a large quantity of unlabelled documents into a smaller number of meaningful and coherent clusters, similar in content. One of the main unsolved problems in clustering literature is the lack of a reliable methodology to evaluate results, although a wide variety of validation measures has been proposed. If those measures are often unsatisfactory when dealing with numerical databases, they definitely underperform in Document Clustering. This paper proposes a new validation measure. After introducing the most common approaches to Document Clustering, our attention is focused on Spherical K-means, do to its strict connection with the Vector Space Model, typical of Information Retrieval. Since Spherical K-means adopts a cosine-based similarity measure, we propose a validation measure based on the same criterion. The new measure effectiveness is shown in the frame of a comparative study, by involving 13 different corpora (usually used in literature for comparing different proposals) and 15 validation measures.

Several evidences support the hypothesis that glutamatergic dysfunction may be implicated in the pathogenesis of schizophrenia and in the last few years great interest has been focused on the role of the N-methyl-D-aspartate receptor (NMDAR). Glutamate is the main excitatory neurotransmitter in human CNS and it plays a prominent role in synaptic plasticity, learning, and memory and other cognitive functions. Increasing interest in memantine add-on therapy in schizophrenic patients with negative and cognitive symptoms may suggest that memantine could be a new promising treatment in schizophrenia. The aim of this update was to evaluate clinical data about the memantine effectiveness in schizophrenic patients. Our systematic review of the literature highlights that memantine therapy in schizophrenic patients seems to improve mainly negative symptoms while positive symptoms and cognitive symptoms did not improve significantly.

Classificare i documenti appartenenti ad un determinato corpus, sulla base del loro contenuto, è uno dei compiti più importanti, se non il più importante, del Text Mining e dell'Information Retrieval. Quest'obiettivo può essere raggiunto facendo riferimento a tecniche di Classificazione automatica. Tali tecniche sono comunemente connotate come parte del più ampio contenitore dei metodi di tipo esplorativo, in quanto finalizzati a identificare strutture nei dati non note. La validazione dei risultati viene, quindi, a volte, percepita come un'aggiunta onerosa a quello che viene considerato un passo iniziale di un processo di conoscenza. Questa percezione è estremamente pericolosa, perché potrebbe indirizzare gli approfondimenti successivi in maniera non corretta. Inoltre, i metodi di Classificazione automatica individuano in ogni caso una partizione dei dati, anche quando i dati stessi non presentano alcun raggruppamento naturale. La motivazione del presente lavoro è, quindi, data dalla consapevolezza dell'importanza di disporre di strumenti di valutazione efficaci, così da fornire al ricercatore risultati caratterizzati da un certo grado di affidabilità. Questa tesi pone, quindi, in rassegna la vastissima letteratura relativa agli strumenti di validazione, ponendo in particolare l'attenzione sulle misure interne per la validazione, poiché rappresentano degli strumenti in grado di fornire un'informazione sintetica della qualità della soluzione ottenuta, valutandola in termini di coesione e separazione dei gruppi ottenuti. Così come la varietà dei metodi di Clustering discende principalmente dalla loro applicabilità in molti ambiti diversi, così diverse misure interne di validazione sono state prodotte in diversi contesti. Questa circostanza rende necessario per i ricercatori avere a disposizione delle linee guida per districarsi al meglio nelle scelte di uno strumento piuttosto che un altro che possa risultare più idoneo nei diversi contesti. In questo lavoro si propone un confronto dei punti di forza e dei punti di debolezza di numerosi indici, tra i più utilizzati e più recenti, valutandone la performance su un gran numero di configurazioni. Ne deriva la consapevolezza che la proposta di un nuovo indice sarebbe una operazione di poca efficacia. Si preferisce piuttosto perseguire l'obiettivo di individuare una strategia integrata che metta in relazione i tre elementi dai quali non si può prescindere per eseguire una Cluster Analysis: il tipo di dati, gli algoritmi utilizzati e gli indici di validazione. Questi elementi condizionano in maniera determinante vuoi la qualità dei risultati dell'analisi, vuoi la valutazione che se ne può ottenere.