Arterial and interstitial remodelling processes in non-specific interstitial pneumonia: systemic sclerosis versus idiopathic

Aims: To compare septal and vascular matrix remodelling, vascular occlusion, Pulmonary function tests and survival between two groups: one with idiopathic non-specific interstitial pneumonia (NSIP) and one with NSIP associated with systemic sclerosis (SSc). Methods and results: Pulmonary biopsy specimens were examined from 40 patients, 22 with NSIP and 18 with NSIP associated with SSc. The content of septal collagen and elastic fibres, as well as the elastic fibres in the vascular interstitium, were higher in the SSc group (P = 0.01, P = 0.001 and P < 0.0001, respectively). Among pulmonary function tests. the diffusing capacity for carbon monoxide/alveolar volume was affected to a greater extent in the SSc group (59%) of the predicted value in SSc and 97% in the idiopathic group). There were no differences in collagen content of the vascular interstitium, arterial occlusion, or survival between the two groups. Conclusions: Although the fibrotic process is more intense in the SSc group. it, does not affect the prognosis of these patients. Because the elastotic process is higher in the SSc group, this might suggest that autoimmune inflammatory mechanisms affecting the elastic fibre system play a greater role in the pathogenesis and pulmonary remodelling process of SSc NSIP than in idiopathic NSIP.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.