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Among patients with non-small-cell lung cancer (NSCLC), those with pathological stage I have the best expectation of survival; however, survival is reduced to less than 50% in the long term. At present, it is unclear when patients can be reasonably defined as cured, and if they experience a higher incidence of malignant/nonmalignant diseases and a lower expectation of survival than the general population. A total of 134 stage I NSCLC patients, who had undergone resection at the Thoracic Surgery Unit of the General Hospital of Verona (north-eastern Italy) from October 1987 to December 1993, were still disease-free at 5 years. These subjects were further followed up, and morbidity and mortality rates were compared with those recorded in the general population of the same geographical area. The standardised incidence ratios (SIRs) for all malignancies and for lung cancer were higher than expected (2.39, 95% CI=1.6-3.5, P0.001; 10.1, 95% CI=6.2-15.6, P0.0001, respectively). The standardised mortality ratio (SMR) was also significantly increased (1.73, 95% CI=1.1-2.6, P=0.013). The excess mortality could be entirely explained by an increase in mortality from lung cancer (5.7, 95% CI=2.8-10.1, P0.0001). This study shows that patients, resected for pathological stage I NSCLC and tumour-free after 5 years, have a higher incidence of new lung cancer compared with the general population, which in turn determines an excess in all-cause mortality in the following years.

We present a novel probabilistic framework that jointly models individuals and groups for tracking. Managing groups is challenging, primarily because of their nonlinear dynamics and complex layout which lead to repeated splitting and merging events. The proposed approach assumes a tight relation of mutual support between the modeling of individuals and groups, promoting the idea that groups are better modeled if individuals are considered and vice versa. This concept is translated in a mathematical model using a decentralized particle filtering framework which deals with a joint individual-group state space. The model factorizes the joint space into two dependent subspaces, where individuals and groups share the knowledge of the joint individual-group distribution. The assignment of people to the different groups (and thus group initialization, split and merge) is implemented by two alternative strategies: using classifiers trained beforehand on statistics of group configurations, and through online learning of a Dirichlet process mixture model, assuming that no training data is available before tracking. These strategies lead to two different methods that can be used on top of any person detector (simulated using the ground truth in our experiments). We provide convincing results on two recent challenging tracking benchmarks.

We present a novel approach to computing Hamming distance and its kernelisation within Topological Quantum Computation. This approach is based on an encoding of two binary strings into a topological Hilbert space, whose inner product yields a natural Hamming distance kernel on the two strings. Kernelisation forges a link with the field of Machine Learning, particularly in relation to binary classifiers such as the Support Vector Machine (SVM). This makes our approach of potential interest to the quantum machine learning community.

BACKGROUND: In 1994 a change in drug reimbursement status was implemented in Italy according to cost-effectiveness criteria. The aim of this study was to examine the impact of these changes on the use of antipsychotic (AP) drugs. METHODS: Data concerning actual quantities of antipsychotic agents dispensed in Italy from 1995 to June 2003 were obtained from the Italian Ministry of Health. For each antipsychotic agent, the number of defined daily doses (DDDs) per 1,000 inhabitants per day was calculated, as well as the annual expenditure in Euros. RESULTS: From 1995 to June 2003 prescriptions for first-generation antipsychotic agents (FGAs) progressively decreased from 2.54 to 2.0 DDD/1,000/day; in contrast, prescriptions for second-generation antipsychotic agents (SGAs) progressively rose up to 1.75 DDD/1,000/day in 2003. Overall, from 1995 to 2003 antipsychotic prescriptions rose from 2.54 to 3.75 DDD/1,000/day. In 2003 the antipsychotic drug most frequently used was haloperidol, followed by olanzapine and risperidone. In 2003 the use of SGAs accounted for nearly 50% of overall DDD/1,000/day of AP agents. The cost of these new drugs, however, accounted for more than 80% of the total AP expenditure. CONCLUSIONS: In Italy, the progressive increase in the utilisation of SGAs has been accompanied by a moderate decrease in the utilisation of phenothiazines and by an almost constant use of butyrophenones. The policy of reimbursing the use of SGAs only in subjects who could not tolerate FGAs eventually failed to significantly affect the pattern of antipsychotic consumption and expenditure; moreover, when this policy was eliminated at the beginning of 2001, the pattern of consumption and expenditure did not change.

Autophagy is involved in different degenerative diseases and it may control epigenetic modifications, metabolic processes, stem cells differentiation as well as apoptosis. Autophagy plays a key role in maintaining the homeostasis of cartilage, the tissue produced by chondrocytes; its impairment has been associated to cartilage dysfunctions such as osteoarthritis (OA). Due to their location in a reduced oxygen context, both differentiating and mature chondrocytes are at risk of premature apoptosis, which can be prevented by autophagy. AutophagomiRNAs, which regulate the autophagic process, have been found differentially expressed in OA. AutophagomiRNAs, as well as other regulatory molecules, may also be useful as therapeutic targets. In this review, we describe and discuss the role of autophagy in OA, focusing mainly on the control of autophagomiRNAs in OA pathogenesis and their potential therapeutic applications.