Reverting estrogen-receptor-negative phenotype in HER-2-overexpressing advanced breast cancer patients exposed to trastuzumab plus chemotherapy

INTRODUCTION: The amounts of estrogen receptor (ER) and progesterone receptor (PgR) in a primary tumor are predictive of the response to endocrine therapies of breast cancer. Several patients with ER-positive primary tumors relapse after adjuvant endocrine therapy with no ER expression in the recurrent tissue; much fewer with a recurrent disease after an ER-negative primary tumor may become endocrine responsive. These sequences of events indicate that a phenotype based on ER expression may not be a permanent feature of breast cancer. METHODS: Ten patients with advanced breast cancer whose tumors overexpressed HER-2, but not ER or PgR, were treated with weekly trastuzumab at standard doses with or without chemotherapy. RESULTS: Three out of 10 patients showed overexpression of ERs first appearing after 9, 12 and 37 weeks, respectively, from the initiation of trastuzumab. Two of these patients were subsequently treated with endocrine therapy alone: one of them received letrozole for 3 years without evidence of progression. CONCLUSION: Therapeutic targets enabling the appearance of an endocrine responsive disease may increase treatment options for patients with breast cancer. Furthermore, these clinical data suggest that an ER-negative phenotype is a multi-step process with a reversible repression modality, and that some ER-negative tumors may either revert to an ER-positive phenotype, allowing an endocrine treatment to be effective

Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples

Mobile element insertions (MEIs) are a known cause of genetic disease but have been underexplored due to technical limitations of genetic testing methods. Various bioinformatic tools have been developed to identify MEIs in Next Generation Sequencing data. However, most tools have been developed specifically for genome sequencing (GS) data rather than exome sequencing (ES) data, which remains more widely used for routine diagnostic testing. In this study, we benchmarked six MEI detection tools (ERVcaller, MELT, Mobster, SCRAMble, TEMP2 and xTea) on ES data and on GS data from publicly available genomic samples (HG002, NA12878). For all the tools we evaluated sensitivity and precision of different filtering strategies. Results show that there were substantial differences in tool performance between ES and GS data. MELT performed best with ES data and its combination with SCRAMble increased substantially the detection rate of MEIs. By applying both tools to 10,890 ES samples from Solve-RD and 52,624 samples from Radboudumc we were able to diagnose 10 patients who had remained undiagnosed by conventional ES analysis until now. Our study shows that MELT and SCRAMble can be used reliably to identify clinically relevant MEIs in ES data. This may lead to an additional diagnosis for 1 in 3000 to 4000 patients in routine clinical ES

Transferring research from a university to the United Kingdom National Health Service : The implications for impact

This is an Open Access article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.The aim of this article is to inform readers of the author's reflections on the experience of transferring universitybased research into the commercial sector, and of the processes and strategies employed when preparing for impact in so doing. Concepts for the transfer are illustrated by the author's reflection on aspects that arose during the birthing and subsequent start-up of a university spin-off, Pathways2Wellbeing, a form of reflection-on-action. This is the vehicle for the adaption required to transfer research into the delivery of a specialised clinic in the United Kingdom National Health Service for people with medically unexplained, persistent, bodily symptoms such as fibromyalgia, chronic fatigue and chronic pain. It is hoped that the article will provide readers with an insight into how knowledge transfer can take place through engagement with stakeholders to create an exchange of knowledges to result in impact on health service policy for service users, despite the challenges, and the enablers that facilitated this process. The reflections on the process of knowledge transfer and the implications for impact are underpinned by relevant theory.Peer reviewedFinal Published versio

Suppression of charged particle production at large transverse momentum in central Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}} = 2.76 TeV

Inclusive transverse momentum spectra of primary charged particles in Pb-Pb collisions at $\sqrt{s_{_{\rm NN}}}$ = 2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0-5% and 70-80% of the hadronic Pb-Pb cross section. The measured charged particle spectra in $|\eta|<0.8$ and $0.3 < p_T < 20$ GeV/$c$ are compared to the expectation in pp collisions at the same $\sqrt{s_{\rm NN}}$, scaled by the number of underlying nucleon-nucleon collisions. The comparison is expressed in terms of the nuclear modification factor $R_{\rm AA}$. The result indicates only weak medium effects ($R_{\rm AA} \approx$ 0.7) in peripheral collisions. In central collisions, $R_{\rm AA}$ reaches a minimum of about 0.14 at $p_{\rm T}=6$-7GeV/$c$ and increases significantly at larger $p_{\rm T}$. The measured suppression of high-$p_{\rm T}$ particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb-Pb collisions at the LHC.Comment: 15 pages, 5 captioned figures, 3 tables, authors from page 10, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/98

Two-pion Bose-Einstein correlations in central Pb-Pb collisions at sNN\sqrt{s_{\rm NN}} = 2.76 TeV

The first measurement of two-pion Bose-Einstein correlations in central Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV at the Large Hadron Collider is presented. We observe a growing trend with energy now not only for the longitudinal and the outward but also for the sideward pion source radius. The pion homogeneity volume and the decoupling time are significantly larger than those measured at RHIC.Comment: 17 pages, 5 captioned figures, 1 table, authors from page 12, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/388

Deep learning in classifying depth of anesthesia (DoA)

© Springer Nature Switzerland AG 2019. This present study is what we think is one of the first studies to apply Deep Learning to learn depth of anesthesia (DoA) levels based solely on the raw EEG signal from a single channel (electrode) originated from many subjects under full anesthesia. The application of Deep Neural Networks to detect levels of Anesthesia from Electroencephalogram (EEG) is relatively new field and has not been addressed extensively in current researches as done with other fields. The peculiarities of the study emerges from not using any type of pre-processing at all which is usually done to the EEG signal in order to filter it or have it in better shape, but rather accept the signal in its raw nature. This could make the study a peculiar, especially with using new development tool that seldom has been used in deep learning which is the DeepLEarning4J (DL4J), the java programming environment platform made easy and tailored for deep neural network learning purposes. Results up to 97% in detecting two levels of Anesthesia have been reported successfully