Objective• To study the influence of adjuvant androgen suppression and bisphosphonates on incident vertebral and non-spinal fracture rates and bone mineral density (BMD) in men with locally advanced prostate cancer. Patients and Methods• Between 2003 and 2007, 1071 men with locally advanced prostate cancer were randomly allocated, using a 2 × 2 trial design, to 6 months i.m. leuprorelin (androgen suppression [AS]) before radiotherapy alone ± 12 months additional leuprorelin ± 18 months zoledronic acid (ZdA), commencing at randomization. • The main endpoint was incident thoraco-lumbar vertebral fractures, which were assessed radiographically at randomization and at 3 years, then reassessed by centralized review.• Subsidiary endpoints included incident non-spinal fractures, which were documented throughout follow-up, and BMD, which was measured in 222 subjects at baseline, 2 years and 4 years. Results• Incident vertebral fractures at 3 years were observed in 132 subjects. Their occurrence was not increased by 18 months' AS, nor reduced by ZdA.• Incident non-spinal fractures occurred in 72 subjects and were significantly related to AS duration but not to ZdA.• Osteopenia and osteoporosis prevalence rates at baseline were 23.4 and 1.4%, respectively, at the hip. • Treatment for 6 and 18 months with AS caused significant reductions in hip BMD at 2 and 4 years (P < 0.01) and ZdA prevented these losses at both time points. Conclusion• In an AS-naïve population, 18 months of ZdA treatment prevented the sustained BMD losses caused by 18 months of AS treatment; however, the study power was insufficient to show that AS duration or ZdA influenced vertebral fracture rates.

BackgroundThe prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.Methods and FindingsThe microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.ConclusionsThe CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

BackgroundMulti-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset.FindingsIn this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our analysis is comprised of an iterative computation of these methods and statistical measures performed on a set of over 2000 samples. The refined labels assigned using this iterative approach revealed to be more consistent and in better agreement with clinicopathological markers and patients’ overall survival than those originally provided by the PAM50 method.ConclusionsThe assignment of intrinsic subtypes has a significant impact in translational research for both understanding and managing breast cancer. The refined labelling, therefore, provides more accurate and reliable information by improving the source of fundamental science prior to clinical applications in medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0078-9) contains supplementary material, which is available to authorized users.

Twitter has been one of the main sources of information and discussion during the COVID-19 pandemics. This paper characterizes a set of more than 56 million tweets written in Portuguese and collected over a period of 70 days. Our analysis includes the volume of messages, text of tweets, location of tweets, the main elements of tweets (e.g. hashtags and URLs) and the user profiles, including gender. The analyses showed the most discussed topics in the period were quarantine, hydroxychloroquine, agglomeration and social distance, and that the discussions were centered in political issues (e.g., most common hashtags include “fechadocombolsonaro" and “forabolsonaro").