Impact of androgen suppression and zoledronic acid on bone mineral density and fractures in the Trans-Tasman Radiation Oncology Group (TROG) 03.04 Randomised Androgen Deprivation and Radiotherapy (RADAR) randomized controlled trial for locally advanced prostate cancer.

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.

Political polarization on Twitter during the COVID-19 pandemic: a case study in Brazil.

The debate over the COVID-19 pandemic is constantly trending at online conversations since its beginning in 2019. The discussions in many social media platforms is related not only to health aspects of the disease, but also public policies and non-pharmacological measures to mitigate the spreading of the virus and propose alternative treatments. Divergent opinions regarding these measures are leading to heated discussions and polarization. Particularly in highly politically polarized countries, users tend to be divided in those in-favor or against government policies. In this work we present a computational method to analyze Twitter data and: (i) identify users with a high probability of being bots using only COVID-19 related messages; (ii) quantify the political polarization of the Brazilian general public in the context of the COVID-19 pandemic; (iii) analyze how bots tweet and affect political polarization. We collected over 100 million tweets from 26 April 2020 to 3 January 2021, and observed in general a highly polarized population (with polarization index varying from 0.57 to 0.86), which focuses on very different topics of discussions over the most polarized weeks-but all related to government and health-related events.

Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.

BACKGROUND: Multi-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. FINDINGS: In 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. CONCLUSIONS: The 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.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.

BACKGROUND: The 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 FINDINGS: The 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. CONCLUSIONS: The 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.

Identification of genome-wide SNP-SNP and SNP-clinical Boolean interactions in age-related macular degeneration.

We propose here a methodology to uncover modularities in the network of SNP-SNP interactions most associated with disease. We start by computing all possible Boolean binary SNP interactions across the whole genome. By constructing a weighted graph of the most relevant interactions and via a combinatorial optimization approach, we find the most highly interconnected SNPs. We show that the method can be easily extended to find SNP/environment interactions. Using a modestly sized GWAS dataset of age-related macular degeneration (AMD), we identify a group of only 19 SNPs, which include those in previously reported regions associated to AMD. We also uncover a larger set of loci pointing to a matrix of key processes and functions that are affected. The proposed integrative methodology extends and overlaps traditional statistical analysis in a natural way. Combinatorial optimization techniques allow us to find the kernel of the most central interactions, complementing current methods of GWAS analysis and also enhancing the search for gene-environment interaction.

An information theoretic clustering approach for unveiling authorship affinities in Shakespearean era plays and poems.

In this paper we analyse the word frequency profiles of a set of works from the Shakespearean era to uncover patterns of relationship between them, highlighting the connections within authorial canons. We used a text corpus comprising 256 plays and poems from the 16th and 17th centuries, with 17 works of uncertain authorship. Our clustering approach is based on the Jensen-Shannon divergence and a graph partitioning algorithm, and our results show that authors' characteristic styles are very powerful factors in explaining the variation of word use, frequently transcending cross-cutting factors like the differences between tragedy and comedy, early and late works, and plays and poems. Our method also provides an empirical guide to the authorship of plays and poems where this is unknown or disputed.