BackgroundNearest neighbor (NN) imputation algorithms are efficient methods to fill in missing data where each missing value on some records is replaced by a value obtained from related cases in the whole set of records. Besides the capability to substitute the missing data with plausible values that are as close as possible to the true value, imputation algorithms should preserve the original data structure and avoid to distort the distribution of the imputed variable. Despite the efficiency of NN algorithms little is known about the effect of these methods on data structure.MethodsSimulation on synthetic datasets with different patterns and degrees of missingness were conducted to evaluate the performance of NN with one single neighbor (1NN) and with k neighbors without (kNN) or with weighting (wkNN) in the context of different learning frameworks: plain set, reduced set after ReliefF filtering, bagging, random choice of attributes, bagging combined with random choice of attributes (Random-Forest-like method).ResultsWhatever the framework, kNN usually outperformed 1NN in terms of precision of imputation and reduced errors in inferential statistics, 1NN was however the only method capable of preserving the data structure and data were distorted even when small values of k neighbors were considered; distortion was more severe for resampling schemas.ConclusionsThe use of three neighbors in conjunction with ReliefF seems to provide the best trade-off between imputation error and preservation of the data structure. The very same conclusions can be drawn when imputation experiments were conducted on the single proton emission computed tomography (SPECTF) heart dataset after introduction of missing data completely at random.

Laser scanning confocal microscopy can provide an in vivo, noninvasive, high-resolution overview of the ocular surface morpho-functional unit. This confocal integrated approach may be useful in both research and clinical settings.

Objective. To assess fetal and maternal outcomes in women with systemic sclerosis (SSc). Methods. Prospectively collected data on

Digital ulcers (DUs) are a rather frequent and invalidating complication in systemic sclerosis (SSc), often showing a very slow or null tendency to heal, in spite of the commonly used systemic and local therapeutic procedures. Recently, stem cell therapy has emerged as a new approach to accelerate wound healing. In the present study, we have tentatively treated long-lasting and poorly responsive to traditional therapy SSc-related DUs by implantation of autologous adipose tissue-derived cell (ATDC) fractions. Fifteen patients with SSc having a long-lasting DU in only one fingertip who were unresponsive to intensive systemic and local treatment were enrolled in the study. The grafting procedure consisted of the injection, at the basis of the corresponding finger, of 0.5-1 ml of autologous ATDC fractions, separated by centrifugation of adipose tissue collected through liposuction from subcutaneous abdominal fat. Time to heal after the procedure was the primary end point of the study, while reduction of pain intensity and of analgesic consumption represented a secondary end point. Furthermore, the posttherapy variation of the number of capillaries, observed in the nailfold video capillaroscopy (NVC) exam and of the resistivity in the digit arteries, measured by high-resolution echocolor-Doppler, were also taken into account. A rather fast healing of the DUs was reached in all of the enrolled patients (mean time to healing 4.23 weeks; range 2-7 weeks). A significant reduction of pain intensity was observed after a few weeks (p < 0.001), while the number of capillaries was significantly increased at 3- and 6-month NVC assessment (p < 0.0001 in both cases). Finally, a significant after-treatment reduction of digit artery resistivity was also recorded (p < 0.0001). Even with the limitations related to the small number of patients included and to the open-label design of the study, the observed strongly favorable outcome suggests that local grafting with ATDCs could represent a promising option for the treatment of SSc-related DUs unresponsive to more consolidated therapies.

Intestinal microbiota has been associated with systemic autoimmune diseases, yet the functional consequences of these associations are elusive. We characterized the fecal microbiota (16S rRNA gene amplification and sequencing) and the plasma metabolome (high-performance liquid chromatography coupled to mass spectrometry) in 59 patients with systemic sclerosis (SSc) and 28 healthy controls (HCs). Microbial and metabolic data were cross-correlated to find meaningful associations after extensive data mining analysis and internal validation. Our data show that a reduced model of nine bacteria is capable of differentiating HCs from SSc patients. SSc gut microbiota is characterized by a reduction in protective butyrate-producing bacteria and by an increase in proinflammatory noxious genera, especially Desulfovibrio. From the metabolic point of view, a multivariate model with 17 metabolite intermediates well distinguished cases from controls. The most interesting peaks we found were identified as glycerophospholipid metabolites and benzene derivatives. The microbial and metabolic data showed significant interactions between Desulfovibrio and alpha-N-phenylacetyl-l-glutamine and 2,4-dinitrobenzenesulfonic acid. Our data suggest that in SSc, intestinal microbiota is characterized by proinflammatory alterations subtly entwined with the metabolic state. Desulfovibrio is a relevant actor in gut dysbiosis that may promote intestinal damage and influence amino acid metabolism.