Search

Aims: In order to understand how sex differences impact the generalizability of randomized clinical trials (RCTs) in patients with heart failure (HF) and reduced ejection fraction (HFrEF), we sought to compare clinical characteristics and clinical outcomes between RCTs and HF observational registries stratified by sex. Methods and results: Data from two HF registries and five HFrEF RCTs were used to create three subpopulations: one RCT population (n = 16 917; 21.7% females), registry patients eligible for RCT inclusion (n = 26 104; 31.8% females), and registry patients ineligible for RCT inclusion (n = 20 810; 30.2% females). Clinical endpoints included all-cause mortality, cardiovascular mortality, and first HF hospitalization at 1 year. Males and females were equally eligible for trial enrolment (56.9% of females and 55.1% of males in the registries). One-year mortality rates were 5.6%, 14.0%, and 28.6% for females and 6.9%, 10.7%, and 24.6% for males in the RCT, RCT-eligible, and RCT-ineligible groups, respectively. After adjusting for 11 HF prognostic variables, RCT females showed higher survival compared to RCT-eligible females (standardized mortality ratio [SMR] 0.72; 95% confidence interval [CI] 0.62–0.83), while RCT males showed higher adjusted mortality rates compared to RCT-eligible males (SMR 1.16; 95% CI 1.09–1.24). Similar results were also found for cardiovascular mortality (SMR 0.89; 95% CI 0.76–1.03 for females, SMR 1.43; 95% CI 1.33–1.53 for males). Conclusion: Generalizability of HFrEF RCTs differed substantially between the sexes, with females having lower trial participation and female trial participants having lower mortality rates compared to similar females in the registries, while males had higher than expected cardiovascular mortality rates in RCTs compared to similar males in registries.

PDF file 45K, Association of high Ki67 with basal-like status

PDF file 26K, Association of node status with RFS and OS, stratified by menopausal status

PDF file 62K, MCL1 overexpression enhances docetaxel resistance in non-amplified TNBC cell lines

PDF file 77K, Association of gene alterations with gene expression

XLSX file 706K, This file contains 8 supplementary data tables which include patient demographics, genes and alterations assessed by NGS, Nanostring gene expression data, and bioinformatics analyses

PDF 118K, TSC1 truncation in the RD of a TNBC after NAC

PDF file 1474K, MEK activity is required for MYC-induced soft agar colony formation

PDF file 64K, Legends for all supplementary data figures (1-7)

Background Heart failure (HF) trials have stringent inclusion and exclusion criteria, but limited data exist regarding generalizability of trials. We compared patient characteristics and outcomes between patients with HF and reduced ejection fraction (HFrEF) in trials and observational registries. Methods and Results Individual patient data for 16 922 patients from five randomized clinical trials and 46 914 patients from two HF registries were included. The registry patients were categorized into trial-eligible and non-eligible groups using the most commonly used inclusion and exclusion criteria. A total of 26 104 (56%) registry patients fulfilled the eligibility criteria. Unadjusted all-cause mortality rates at 1 year were lowest in the trial population (7%), followed by trial-eligible patients (12%) and trial-non-eligible registry patients (26%). After adjustment for age and sex, all-cause mortality rates were similar between trial participants and trial-eligible registry patients [standardized mortality ratio (SMR) 0.97; 95% confidence interval (CI) 0.92–1.03] but cardiovascular mortality was higher in trial participants (SMR 1.19; 1.12–1.27). After full case-mix adjustment, the SMR for cardiovascular mortality remained higher in the trials at 1.28 (1.20–1.37) compared to RCT-eligible registry patients. Conclusion In contemporary HF registries, over half of HFrEF patients would have been eligible for trial enrolment. Crude clinical event rates were lower in the trials, but, after adjustment for case-mix, trial participants had similar rates of survival as registries. Despite this, they had about 30% higher cardiovascular mortality rates. Age and sex were the main drivers of differences in clinical outcomes between HF trials and observational HF registries.

ObjectivesWe investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.DesignInterim analysis of a prospective cohort study.Setting, participants and interventionsParticipants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.ResultsA total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.ConclusionWearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial.Trial registration numberISRCTN51255782; Pre-results.

Background: We investigated machine learning based identification of the pre-symptomatic coronavirus disease 2019 (COVID-19) and detection of infection-related changes in physiology using a wearable device (the Ava bracelet). Methods: Participants from an ongoing cohort study (GAPP) of the general population in Liechtenstein were included in the current sub-study (COVI-GAPP). Nightly they wore the fertility bracelet that measured every ten seconds skin temperature, heart rate, respiratory rate, skin perfusion, and heart rate variability. Participants reported daily symptoms in a complementary app. Laboratory reverse transcription polymerase chain reaction (RT-PCR) and/or COVID-19 serology samples were collected from all participants. Long short-term memory (LSTM) based recurrent neural networks (RNN) were chosen for the binary classification of an individual as healthy or infected on a given day in a derivation and validation procedure. Findings: A total of 1ž5 million hours of physiological data were recorded from 1163 participants (mean age 44 +/- 5ž5 years). COVID-19 was confirmed in 127 participants. Of these, 66 (52%) had worn their device from baseline to symptom onset and were included in the analysis and RNN. Multi-level modelling revealed significantly different values in pre- versus post-symptomatic respiratory rate, temperature, heart rate, heart rate variability ratio, and skin perfusion. The developed RNN algorithm had a recall of 0ž73 in the training set and 0ž68 in the testing set (overall recall of 0ž71) when detecting COVID-19 up to two days prior to symptom onset. Interpretation: Our proposed RNN algorithm identified 71% of COVID-19 positive participants two days prior to symptom onset. Wearable sensor technology can therefore enable COVID-19’ detection during the pre-symptomatic period. Funding: IMI grant agreement number 101005177, the Princely House of the Principality of Liechtenstein, the government of the Principality of Liechtenstein, and the Hanela Foundation in Switzerland. Declaration of Interest: Lorenz Risch, and Martin Risch are key shareholders of the Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. The other authors have no financial or personal conflicts of interest to declare. Ethical Approval: The local ethics committee approved the study protocol, and written informed consent was obtained from each participant (BASEC 2020-00786).

Despite the availability of a number of different classes of therapeutic agents with proven efficacy in heart failure, the clinical course of heart failure patients is characterized by a reduction in life expectancy, a progressive decline in health-related quality of life and functional status, as well as a high risk of hospitalization. New approaches are needed to address the unmet medical needs of this patient population. The European Medicines Agency (EMA) is undertaking a revision of its Guideline on Clinical Investigation of Medicinal Products for the Treatment of Chronic Heart Failure. The draft version of the Guideline was released for public consultation in January 2016. The Cardiovascular Round Table of the European Society of Cardiology (ESC), in partnership with the Heart Failure Association of the ESC, convened a dedicated two-day workshop to discuss three main topic areas of major interest in the field and addressed in this draft EMA guideline: (i) assessment of efficacy (i.e. endpoint selection and statistical analysis); (ii) clinical trial design (i.e. issues pertaining to patient population, optimal medical therapy, run-in period); and (iii) research approaches for testing novel therapeutic principles (i.e. cell therapy). This paper summarizes the key outputs from the workshop, reviews areas of expert consensus, and identifies gaps that require further research or discussion. Collaboration between regulators, industry, clinical trialists, cardiologists, health technology assessment bodies, payers, and patient organizations is critical to address the ongoing challenge of heart failure and to ensure the development and market access of new therapeutics in a scientifically robust, practical and safe way.

Context.—Expression profiling by microarrays and real-time polymerase chain reaction–based assays is a powerful tool for classification and prognostication of disease; however, it remains a research tool, largely reliant on frozen tissue. Limiting the utility of expression profiling is the isolation of quality nucleic acids from formalin-fixed, paraffin-embedded tissue. The collection, handling, and processing of tissue directly impacts the biomolecules that can be recovered from it. High-quality nucleic acids can be obtained from formalin-fixed, paraffin-embedded tissue, but greater attention to all steps in the process of tissue handling and preparation is required.Objective.—To summarize the current state-of-the-art of preanalytic factors in tissue handling and processing as they impact the quality of RNA obtainable from formalin-fixed, paraffin-embedded tissue. The goals are to provide recommendations that will improve RNA quality for expression profiling from formalin-fixed, paraffin-embedded tissue and highlight areas for additional research. Tissue is an analyte and it must be handled in a standardized fashion to provide consistent results.Data Sources.—The literature was reviewed. Consultation with industry and academic leaders in the use of RNA for expression profiling was obtained to identify areas for additional research.Conclusions.—Development of RNA-based assays from formalin-fixed, paraffin-embedded tissue is feasible. Greater attention to tissue handing and processing is essential to improve the quality of biospecimens for the development of robust RNA-based assays. Standardization of procedures and vigorous testing of alternative protocols are required to ensure that these assays function as designed.

A workshop entitled “Metrology and Standards Needs for Gene Expression Technologies: Universal RNA Standards” was held in March 2003 to define the requirements for standardizing RNA-based molecular assays, specifically microarray and quantitative reverse-transcriptase-PCR technologies. NIST sponsored the workshop, and participants represented government, industry, academia, and clinic. Workshop participants concluded that as a first step, two RNA reference materials could be defined that would help in standardization of gene-expression technologies: an Assay Process Reference Material, and a Universal Array Hybridization Reference Material. The specific characteristics of these two standardized materials were broadly outlined. The Assay Process Material was proposed to be a pool of 96 expressed human sequences of defined composition, cloned in a defined vector and pooled in prescribed ways. The Universal Array Hybridization Material was defined as a pool of 12 “alien” synthetic sequences not expressed in any known genome to be used to control for variability in array hybridization methods. Work is underway at NIST and among members of the gene expression array community to further define these materials and make them available.

Pharmacogenetics and pharmacogenomics deal with the genetic basis underlying variable drug response in individual patients. The traditional pharmacogenetic approach relies on studying sequence variations in candidate genes suspected of affecting drug response. On the other hand, pharmacogenomic studies encompass the sum of all genes, i.e., the genome. Numerous genes may play a role in drug response and toxicity, introducing a daunting level of complexity into the search for candidate genes. The high speed and specificity associated with newly emerging genomic technologies enable the search for relevant genes and their variants to include the entire genome. These new technologies have essentially spawned a new discipline, termed pharmacogenomics, which seeks to identify the variant genes affecting the response to drugs in individual patients. Moreover, pharmacogenomic analysis can identify disease susceptibility genes representing potential new drug targets. All of this will lead to novel approaches in drug discovery, an individualized application of drug therapy, and new insights into disease prevention. Current concepts in drug therapy often attempt treatment of large patient populations as groups, irrespective of the potential for individual, genetically-based differences in drug response. In contrast, pharmacogenomics may help focus effective therapy on smaller patient subpopulations which although demonstrating the same disease phenotype are characterized by distinct genetic profiles. Whether and to what extent this individual, genetics-based approach to medicine results in improved, economically feasible therapy remain to be seen. To exploit these opportunities in genetic medicine, novel technologies will be needed, legal and ethical questions must be clarified, health care professionals must be educated, and the public must be informed about the implications of genetic testing in drug therapy and disease management.

Biopharmaceutical R&D organizations characterize drug candidate target effects and modes of action and create molecular models of target diseases. These data-intensive activities are informed by vast data resources including publicly available data, internally generated data and partnered private data collections. However, rapid evolution in computing, data management tools, analytical and visualization methods, the complexity of data types and the data volumes that must be accommodated present significant technical and logistic hurdles to overcome. It is particularly difficult for a geographically dispersed R&D organization to make data resources easily available to scientists for search, visualization and exploration. Nevertheless, this is required for R&D scientists to gain insight into disease and drug mechanisms and to capture the knowledge needed to sustain the scientific enterprise. Standardized commercial solutions to R&D data challenges are unattractive since they require significant resource investment in platform configuration, user-training and system maintenance. This strategy necessarily creates delay in adopting newly emerging technologies and provides incentive not to adopt alternatives due to investment in existing systems. In contrast, our solution to R&D data demands was to build a cloud-deployed data platform using state of the art tools developed and maintained by the open source software community at the Apache Software Foundation. Partnering with academic data scientists, we selected the best available tools to fit our specific needs. We integrated them into a platform accessible to our federated R&D scientific community while allowing the system to be freely modified and updated on demand to meet evolving user requirements. Priorities for our data platform are to ingest, secure and index R&D source data of all types, make these indexed data assets available to computational scientists for analysis and provide faceted search capability based on a comprehensive metadata model. Three products: LabKey server, Apache OODT and ISATools have all been combined into a scientific data management system to provide a unified data resource enhanced by a search platform powered by Apache Solr. The platform supports both internally generated data and data imported from public, contracted or partnered sources. All data are available for interactive exploration by our R&D community, accessed via integrated search, analysis and visualization tools. Deployment of this system to our R&D organization has been met with enthusiastic adoption. Feedback for improvement or requests for system enhancements and additional capabilities are rapidly addressed in this open source environment, leading to further adoption among the R&D scientists and providing the basis for accessible, stable institutional knowledge collections. Citation Format: Lauren Intagliata, Selina Chu, Garth McGrath, Giuseppe Totaro, Daniel Civello, Nipurn Doshi, Shivika Thapar, Michael Livstone, Chris Mattmann, Paul Ramirez, Maureen Cronin. A cloud-enabled open source data management platform supporting a federated research and development organization. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5282.

Biomedical information is available to research and development scientists as unstructured text in the form of scientific manuscripts and reports published in the literature and elsewhere. Scientists focused on specific research programs are burdened with surveying vast numbers of publications and reports to acquire information relevant to their efforts. Employing technology as a research aid provides a mechanism to cope with information overload that characterizes the R&D environment. Text mining can extract knowledge from large corpora of biomedical text and make it available to support scientific research and knowledge collections [1, 2] and intelligent PDF reader tools able to search content and find related articles [3] are available; however, such reader tools are typically desktop applications limited to specific platforms and data sources so they cannot easily support broad based integrated scientific search needs for a dispersed R&D organization with a wide variety of content needs. Our team has developed a web-browser based document reader with a built-in exploration tool and automatic concept extraction from biomedical text content. This provides R&D scientists with a simple tool to aid finding, reading, and exploring documents relevant to focused research objectives. The tool, Shangri-Docs, combines a document reader with automatic concept extraction and highlighting of relevant terms based on carefully selected ontologies combined with our custom corporate enterprise taxonomy. Shangri-Docs provides the ability to evaluate a wide variety of document formats (e.g. PDF, Word, PPT, text, etc.) and exploits the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and privately cataloged databases simultaneously. Shangri-Docs incorporates Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific pathology, disease, drug, and biological terms mentioned in the text. cTAKES was originally designed specifically to extract information from clinical medical records. We have extended cTAKES automatic knowledge extraction process to include the R&D biomedical research domain by improving the ontology guided information extraction process. Shangri-Docs could be adapted to other science fields and further customized across our R&D scientific community via our open source, cloud-based, data management system. [1] Funk, et.al., BMC Bioinformatics 2014, 15:59 doi:10.1186/1471-2105-15-59 [2] Kang et al., BMC Bioinformatics 2014, 15:64 doi:10.1186/1471-2105-15-64 [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org Citation Format: Chris Mattmann, Lauren Intagliata, Selina Chu, Garth McGrath, Giuseppe Totaro, Daniel Civello, David Ballard, Jeffrey Long, Nipurn Doshi, Shivika Thapar, Michael Livstone, Paul Ramirez, Maureen Cronin. Shangri-Docs: a browser based tool for document exploration and automatic knowledge extraction from unstructured biomedical text. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5283.

Pharmaceutical R&D translational programs rely on analyzing DNA and RNA from patient specimens. High quality histopathology assessment is not possible from fresh and frozen tissues which are also difficult to store long term for follow up studies. FFPE preserves tissue architecture and allows easy archival storage and extracting genomic information from FFPE clinical trial samples is now commonly done, opening a variety of research possibilities. NGS is becoming the preferred approach to FFPE molecular profiling as it generates richer, more complete data than previously possible with microarray technologies. Our translational research objective was to quantify RNA isoforms for a number of genes requiring that we control for NGS coverage and analysis pipeline biases using embedded controls and comparing multiple calling algorithms. To optimize RNAseq library construction performance using FFPE samples we compared five commercially available rRNA depletion/mRNA selection methods for reducing rRNA abundance: Ovation® (Nugen), Ribo-Zero® (Epicentre), Ribo-Zero GOLD® (Epicentre), RiboGone® (Clonetech) and TruSeq® RNA Access (Illumina). We also evaluated DV200 as a performance metric for predicting NGS library quality for FFPE samples using TruSeq® (Illumina) and SMARTer® (Clonetech) library preparation methods. Finally, we compared RecoverAll® (Life Technologies) and Allprep® (Qiagen) RNA extraction method effects on NGS library quality. By performing the experimental comparisons using two NGS service providers, we were able to assess the impact of laboratory performance on NGS RNAseq results. Ultimately, we were able to define an NGS workflow based on sample QC assessment at multiple points in the process resulting in a highly reliable RNAseq workflow for clinical NGS specimens. Among the rRNA depletion methods, RiboGone and RNA Access outperformed alternative options. RiboGone virtually eliminated rRNA contamination from FFPE specimens with high reproducibility. RNA Access both eliminated rRNA and provided a high fraction of coding RNA sequences relative to the background of ncRNA allowing experimental designs with lower total read numbers. Depending on experimental requirements, either method is reliable; however, the two methods cannot be used interchangeably without computational normalization. The two library preparation and RNA extraction methods performed similarly in this experiment. DV200 was found to be an good metric for evaluating the quality of FFPE RNA and predicting success in generating RNAseq data. Our experience across NGS vendors showed that to get optimal results, vendor performance must be managed by using layers of controls with agreed on laboratory performance metrics and explicitly setting NGS experimental parameters in advance of performing the work. Citation Format: Konstantinos Mavrommatis, Lauren Intagliata, Garth McGrath, Daniel Civello, Maureen Cronin. Establishing a robust NGS laboratory workflow and analysis pipeline for FFPE specimen RNAseq to support biopharmaceutical translational research. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3626.

OBJECTIVES:: To determine whether prior oral-contraceptive use has a negative effect on the ability of women to conceive in both the short-term and long-term. METHODS:: The European Active Surveillance Study on Oral Contraceptives (EURAS-OC) was a controlled prospective noninterventional cohort study of 59510 users of oral contraceptives containing drospirenone or other progestins in clinical practice in seven European countries. In a planned secondary analysis pregnancy outcomes were investigated in 2064 participants in EURAS-OC who stopped oral-contraceptive use after study entry because of planned pregnancy. The influence of age parity progestin type ethinylestradiol dose duration of oral-contraceptive use and smoking status on the rate of pregnancy was assessed. RESULTS:: Overall 21.1% (95% confidence interval [CI] 19.4-23.0%) of the past oral-contraceptive users were pregnant one cycle after oral-contraceptive cessation. This rate increased to 79.4% (95% CI 77.6-81.1%) at 1 year (13 cycles). Progestin type ethinylestradiol dose duration of oral-contraceptive use and parity had no major influence on the rate of pregnancy after oral-contraceptive cessation. Up to age 35 years age had only a minor influence on the rate of pregnancy. Rates of pregnancy were reduced in women older than 35 years and in current smokers. CONCLUSION:: Previous oral-contraceptive use does not negatively affect initial and 1-year rates of pregnancy after oral-contraceptive cessation. A comparison of these data with data external to this study indicates that the negative effect of aging on fecundity is not amplified by oral-contraceptive use. LEVEL OF EVIDENCE:: II.

Over the last decade microscale technologies for molecular analysis have become the springboard for a newera in biological investigation. In parallel with nucleic acid sequencing technology improvements that enabled completion of the human genome project years ahead of schedule [1, 2], methods were developed for high throughput analysis of genetic variation and gene expression. These new molecular analytical tools have stimulated a resurgence of non-hypothesis driven biological research and promise to play a key role in the emerging field of personalized medicine [3]. Knowledge gained from the most common type of genetic variation inDNA, single nucleotide polymorphisms (SNPs), has had an enormous impact on the identification of genes involved in disease and is beginning to be of value in tailoring therapeutic regimens for an individual’s genetic composition [4]. Application of technologies for gene expression analysis, the subject of this review, has lagged behind analysis of genetic variation, primarily due to the intrinsic complexity of gene expression measurement. However, the number of studies employing gene expression analysis has expanded in the last few years as the available analytical methods mature and become more reliable and affordable.

This open-label study investigated whether drospirenone, a novel progestin with antimineralocorticoid properties, increases the risk for hyperkalemia in subjects with renal impairment. Women with normal renal function and mild or moderate renal impairment were given drospirenone 3 mg/d for 14 days. Mean serum potassium concentrations did not significantly change during steady-state drospirenone treatment. No difference in drospirenone effects on serum potassium concentrations in subjects with renal insufficiency versus subjects with normal renal function was found. Steady-state pharmacokinetics was similar in subjects with normal renal function and mild renal impairment, whereas, due to results from 1 subject, drospirenone exposure was slightly higher in the moderate renal impairment group. Given the reported tolerability of drospirenone, this small increase in serum drospirenone is not expected to be clinically significant. In conclusion, this study demonstrated that drospirenone has no significant effect on serum potassium levels in patients with mild to moderate renal insufficiency.

1. Techniques for identifying genetic polymorphisms: an historical perspective Peter J Wedlund 2. PCR: methods and limitations William B. Mattes and mark S. Miller 3. DNA oligonucleotide microarrays: application to genetic polymorphism analysis Maureen T. Cronin 4. Bioinformatics - genetic sites on the world wide web Mark O. Lively 5. Cancer susceptibility genes Keith W. Crawford and Peter G. Shields 6. Steroid and nuclear receptor polymorphism variants in hormone resistance and hormone independence Torsten A. Hopp and Suzanne A.W. Fuqua 7. Variation in genes regulating the immune system and relationship to disease Ann B. Begovich and Jorge R. Oksenberg 8. Polymorphisms in heart disease R Mark Payne 9. Genetic polymorphisms in the mitochondrial genome Gerrard T. berry and Paolo Fortina 10. Genetic polymorphisms affecting nervous system function Susan M. Mockus and Kent E. Vrana B Mattes, Parmacia and Upjohn Company, USA, Mark Steven Miller, Bowman Gray School of Medicine, USA, Fred Farin, University of Washington, USA, Maureen Cronin, Affymetrix, USA, David L Eaton, University of Washington, USA, Peter G Shields, National Cancer Institute, USA, Suzanne Fuqua, University of Texas, USA, Paolo Fortina, The Children's Hospital of Philidelphia, USA, Gerald T Nepom,Virgin Mason Research Centre, USA, Susan M Mockus & Kent E Vrana, Bowman Gray School of Medicine, USA, Mark Payne, Bowman Gray School of Medicine, USA.

Dr Wildemeersch refers to an estimated incidence of uterine perforations at insertion of an LNG-IUS (Mirena Schering AG Germany) of at least 2.6/ 1000 insertions reported in a paper by van Houdenhooven et al. The validity of this reported rate is questionable; indeed the numerator was derived from a retrospective questionnaire survey of only hospital gynaecologists and the denominator was estimated based on commercially available sales figures for Mirena for the surveyed region which did not account for many important factors such as for example parallel imports. (excerpt)

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

The human carcinoma cell line A431 is unusual in that physiologic concentrations of epidermal growth factor (EGF) inhibit proliferation. In the presence of 5-10 nM EGF proliferation of A431 cells is abruptly and markedly decreased compared to the untreated control cultures, with little loss of cell viability over a 4-day period. This study was initiated to examine how EGF affects the progression of A431 cells through the cell cycle. Flow cytometric analysis of DNA in EGF-treated cells reveals a marked change in the cell cycle distribution. The percentage of cells in late S/G2 increases and early S phase is nearly depleted. Since addition of the mitotic inhibitor vinblastine causes accumulation of cells in mitosis and prevents reentry of cells into G1, it is possible to distinguish between slow progression through G1 and G2 and blocks in those phases. When control cells, not treated with EGF, are exposed to vinblastine, the cells accumulate mitotic figures, as expected, and show progression into S, thus diminishing the number of cells in G1. In contrast, no mitotic figures are found among the EGF-treated cells in the presence or absence of vinblastine, and progression from G1 into S is not observed, as the number of cells in G1 remains constant. These results suggest that there are two EGF-induced blocks in cell cycle transversal; one is in late S and/or G2, blocking entry into mitosis, and the other is in G1, blocking entry into S phase. After 24 hours of EGF treatment, DNA synthesis is reduced to less than 10% compared to untreated controls as measured by the incorporation of [3H]thymidine or BrdU. In contrast, protein synthesis is inhibited by about twofold. Although inhibition of protein synthesis is less extensive, it occurs 6 hours prior to an equivalent inhibition of DNA synthesis. The rapid decrease in protein synthesis may result in the subsequent cell cycle arrest which occurs several hours later.