Hospital admissions for vitamin D related conditions and subsequent immune-mediated disease: record-linkage studies

PMCID: PMC3729414The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7015/11/171. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Cervicite da clamydia trachomatis in gravidanza e possibilit\ue0 di contagio fetale

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Valutazione microbiologica e citologica vaginale ed endocervicale in donne portatrici di IUD

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Treatment of deep cartilage defects of the ankle with matrix-induced autologous chondrocyte implantation (MACI)

Matrix-induced Autologous Chondrocyte Implantation (MACI) was used for the treatment of six deep chondral defects of the ankle. In five cases the site of the lesion was the talar dome, while the sixth patient presented a kissing lesion. The mean size of the lesion was 3.4 cm2. Follow up averaged 33.8 months. AOFAS score was used for clinical-functional evaluation. The evolution of the implant was studied by MRI at 6, 12 and 24 months. With the exception of the patient with the kissing lesion, an improvement in the clinical-functional status and the presence of hyaline-like cartilage signal was observed in the treated ankles. All the patients underwent a second arthroscopic look at the time of malleolar screw removal: only in the ankle with the kissing lesion there was no evidence of repairing tissue. In our experience, MACI appears a reliable method for the treatment of chondral defects of the ankle. \ua9 2004 European Foot and Ankle Society. Published by Elsevier Ltd. All rights reserved

Ultrasound-guided alcohol injection for Morton's neuroma

Background: Ultrasonography-guided alcohol injection (USGAI) of Morton's neuroma represents an alternative to operative treatment. Nonetheless, the literature only reports few small studies evaluating the effectiveness of the treatment. The aim of the present retrospective study was to assess the effectiveness of USGAI to treat Morton's neuroma in 508 patients at 2 medical centers. Methods: Between January 2001 and January 2012, 508 patients with 540 Morton's neuroma had USGAI for Morton's neuroma. Only second and third web-space neuromas were included in this study. Results: A mean number of 3.0 (range, 1 to 4) injections were performed for each neuroma. Mean local inflammatory reaction was 0.7 (range, 0 to 2). There were no other local or systemic complications. The overall mean pre-USGAI VAS score was 8.7 (range, 6 to 10), while the post-USGAI VAS score at 1 year was 3.6 (range, 0 to 9). The delta VAS between the pre- and post-USGAI was statistically significant (P < .0001). At 1-year follow-up 74.5% of patients were satisfied with the procedure. Conclusion: USGAI produced encouraging results in over 500 patients affected by Morton's neuroma. The procedure proved to be safe in all patients.Background: Ultrasonography-guided alcohol injection (USGAI) of Morton's neuroma represents an alternative to operative treatment. Nonetheless, the literature only reports few small studies evaluating the effectiveness of the treatment. The aim of the present retrospective study was to assess the effectiveness of USGAI to treat Morton's neuroma in 508 patients at 2 medical centers.Methods: Between January 2001 and January 2012, 508 patients with 540 Morton's neuroma had USGAI for Morton's neuroma. Only second and third web-space neuromas were included in this study.Results: A mean number of 3.0 (range, 1 to 4) injections were performed for each neuroma. Mean local inflammatory reaction was 0.7 (range, 0 to 2). There were no other local or systemic complications. The overall mean pre-USGAI VAS score was 8.7 (range, 6 to 10), while the post-USGAI VAS score at 1 year was 3.6 (range, 0 to 9). The delta VAS between the pre- and post-USGAI was statistically significant (P < .0001). At 1-year follow-up 74.5% of patients were satisfied with the procedure.Conclusion: USGAI produced encouraging results in over 500 patients affected by Morton's neuroma. The procedure proved to be safe in all patients

Metastasi polmonari, linfonodali ed endobronchiali da epatocarcinoma: approccio multimodale ad un caso complesso

Il polmone \ue8 la localizzazione pi\uf9 frequente di metastasi da epatocarcinoma (HCC). Le metastasi endobronchiali sono tuttavia molto rare, in letteratura sono infatti descritti solo 8 casi. Lo scopo di questo lavoro \ue8 quello di presentare un caso singolare di metastasi endobronchiali da epatocarcinoma in paziente sottoposta a trapianto di fegato

A semi-supervised learning approach for pan-cancer somatic genomic variant classification

Cancer arises from the accumulation of particular somatic genomic variants known as drivers. New sequencing technologies allow the identification of hundreds of variants in a tumor sample. These variations should be classified as driver or passenger (i.e. benign), but functional studies could be time and cost demanding. Therefore, in the bioinformatics field, machine learning methods are widely applied to distinguish drivers from passengers. Recent projects, such as the AACR GENIE, provide an unprecedented amount of cancer data that could be exploited for the training process of machine learning algorithms. However, the majority of these variants are not yet classified. The development and application of approaches able to assimilate unlabeled data are needed in order to fully benefit from the available omics-resources. We collected and annotated a dataset of known 976 driver and over 84,000 passengers from different databases and we investigated whether unclassified variants from GENIE could be employed in the classification process. We characterized each variant by 94 features from multiple omics resources. We therefore trained different autoencoder architectures with more than 80000 GENIE variants. Autoencoder is a type of neural network able to learn a new features representation of the input data in an unsupervised manner. The trained autoencoders are then used to obtain new representations of the labeled dataset, with a reduced number of meta-features with the aim to reduce redundancy and extract the relevant information. The new representations are in turn exploited to train and test different machine learning techniques, such as Random Forest, Support Vector Machine, Ridge Logistic Regression, One Class SVM. Final results, however, does not show a significant increase in classification ability when meta-features are used