dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion To our knowledge, this is the first study to evaluate the inter-reader agreement for CTC detection in both M1 and M0 breast cancer in a large international multicenter study comprising 15 centers from Europe and the United States. Our results show that the inter-reader agreement is high for CTC definition and for characterizing HER2 expression on CTCs. Lower inter-reader agreement for CTC definition was observed for CellSearch® images of samples from patients with M0 disease, with low CTC counts, samples from the IJB dataset, and samples drawn after the administration of systemic treatment. CellSearch® images of samples with low CTC counts were more frequently observed in the IJB compared to the other datasets and in M0 compared to M1 disease and this can partly explain the lower inter-reader agreement in these groups.
Such an independent image review is applied in the ongoing European Organization for Research and Treatment of Cancer (EORTC) Treat CTC trial (ClinicalTrials. gov Identifier: NCT01548677). This trial is testing the value of CTC detection to decide for secondary adjuvant treatment in BC. We propose that investigators conducting studies on CTCs in non-metastatic breast cancer follow these guidelines for CellSearch® image interpretation. The present study might also be a model for similar experiments regarding other image-based CTC technologies. This could.
Acknowledgements Michail Ignatiadis has received grants from  Breast Cancer Research Foundation (BCRF) ,  Fonds de la recherche scientifique FNRS, MEDIC foundation and Les Amis de l' Institut Bordet. No further funding was provided for this study. Author details 1  Department of Medical Oncology ,  Institut Jules Bordet ,  Université Libre de Bruxelles, Brussels, Belgium. 2 Breast Cancer Translational Research Laboratory ,  Institut Jules Bordet ,  Université Libre de Bruxelles, Brussels, Belgium. 3 Institute of Tumor Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 4 Department of Medical Oncology, Institut Curie & Université Paris Descartes, Paris, France. 5 Division of Cancer, Imperial College, Hammersmith Hospital, London, UK. 6 Department of Gynecology, University of Tubingen, Tubingen, Germany.
Ponceau-S coloration was used as an internal control. Digitized images of the immunoblots obtained using an LAS-3000 imaging system (Fuji Photo Film, Paris, France) were used for densitometric measurements with an image analyser (UnScan It software, Silk Scientific, Orem, UT, USA). All assays were performed in duplicate. In addition, human brain frontal cortex was obtained (control from La Salpetrière Hospital Brain Bank) and proteins were analysed with the same method. Brain and plasma DYRK1A quantification. Using yeast two-hybrid, we screened a highly complex random-primed human adult brain cDNA library to identify genes encoding DYRK1A-interacting proteins. The assay identified 79 DYRK1A interactants (data not shown). Of these, 54 were from the extracellular matrix, like clusterin, an abundant protein in plasma.
To determine whether interactions with extracellular matrix proteins and other plasma proteins signalled the likely presence of DYRK1A in plasma, we performed western blotting using human albumin-depleted plasmas. Long DYRK1A isoforms migrating at the same apparent molecular weight (80-85 kDa) as the isoforms from human and mouse frontal cortex were detected using a DYRK1A mouse monoclonal antibody (Figures  1a  and b ) and two different rabbit DYRK1A polyclonal antibodies (data not shown), all raised against the carboxy terminus of DYRK1A. To assess the relative amount of DYRK1A in plasma we used a slot blot method previously developed for the quantification of a large number of brain homogenates from transgenic mice. 11 Along with quantifying plasma DYRK1A in wild-type mice, we used two different models carrying altered gene copy numbers for Dyrk1a: a mBACtgDyrk1a model with three copies of the murine Dyrk1a gene and a Dyrk1a(+/ -) model with only one copy.
Clinical research Population. Fifty-one participants were recruited in the Memory and Alzheimer Institute of the Pitié-Salpêtrière Hospital, including 30 subjects from the Biomage study (ANR-07-LVIE-002-01). Twenty-six biologically confirmed AD patients (n = 11 with clinical dementia rating (CDR) = 0. 5; n = 15 with CDR ⩾ 1) were included using the following criteria: (1) predominant and progressive episodic memory impairment, characterized by low free recall not normalized with cueing, associated or not with other cognitive deficits;  15  and (2) biological evidence of AD from CSF AD biomarker profile and/or significant amyloid retention on positron emission tomography (PET) with  11  C-labelled Pittsburgh compound B [11C-PiB]. A 'CSF AD biomarker profile' was defined as score below 0. 8 for the ratio of Aβ42:tau, calculated with the formula Aβ42/[240+(1.
19 This protein has been detected previously in plasma (plasma protein database). Plasma samples from a subgroup of CTRL and AD patients were analysed with the same slot blot method. Relative DYRK1B expression was similar between groups (Figure  2b ). Further, in the subgroup of patients with early AD (CDR = 0. 5), the same decrease in DYRK1A expression was found (Figure  2c ). When we compared DYRK1A levels between AD patients and controls for whom PiB-PET imaging was available (n = 17 AD patients with positive amyloid imaging and n = 13 controls with negative amyloid imaging), we observed similar results (Figure  3a ). No difference was found in plasma DYRK1A levels in AD patients at an early stage (CDR = 0. 5) and AD patients at a dementia stage (CDR ⩾ 1). DYRK1A protein levels were not correlated with the Mini-Mental State Exam scores.
The number of days to obtain a positive culture was also recorded  [46]. Non-ATB individuals were also enrolled as controls: blood donors, healthy community adults (HCA), healthy family contacts (HFC), health care workers (HCW-laboratory staff and nurses), hospitalized non-ATB patients and cured TB patients. Because the different areas of recruitment are highly endemic for TB, all subjects (except the blood donors) were asked to give one to three sputum samples and were subjected to radiological examinations to rule out the suspicion of ATB. All enrolled healthy individuals were stratified by risk for TB exposure. Subjects at high TB risk were included only if they agreed to participate in a six month follow-up to exclude ATB occurrence. Each individual's data were then recorded at each site using a standardized questionnaire involving 3 files (clinical/radiological evaluation, clinical/radiological follow-up and laboratory analysis) for both ATB patients and non-ATB individuals.
Data collection After collection, the data were subsequently transferred to EPIINFO files by one of the authors (TS). Each file included the patient's characteristics, risk of TB, clinical symptoms, whether TST was performed or not, chest X-ray findings, the effect of a 10 day antibiotic trial, the final clinical diagnosis, CSTB, the final therapeutic intervention with the therapy initiation date and the anti-TB drugs prescribed. The last part of the file consisted of the treatment outcome obtained during the 6 to 9 month follow-up of each individual with ATB and the absence of clinical symptoms in the non-ATB control groups. Patients with both pulmonary and extrapulmonary localizations (infiltrate and pleural effusion, for instance) were classified as pulmonary TB. The specific biological file consisted of the microbiological and the immunological results: HIV status (HIV-infected, HIV-uninfected or HIV-unknown), and in a subgroup of HIV-infected patients, the number of CD4 and CD8 cell counts/mL; OD values obtained by the ELISA using the PGL-Tb1 antigen and fusion protein ESAT-6/CFP10.
Flow cytometry After Fcc receptor blockade with the 2. 4G2 mAb, cells were stained with biotinylated CD1d:a-GalCer tetramers labeled with allophycocyanin, as previously described  [50]. Additional stainings were performed with the following mAbs: anti-b TCR (H57-597), anti-CD4 (RM4-5), anti-CD8 (53. 6. 2). Cells were acquired with a FACSCalibur (Becton Dickinson, Mountain View, CA) and analyzed using CellQuest or Flowjo software. For cell-sorting, cells were passed through a FACSAria flow-cytometer (Becton Dickinson, Mountain View, CA) using FACS-diva software.
CNS viral infection is lethal in mice enriched in iNKT cells The clinical consequence of neurotropic viral infection was assessed in Va14 Tg mice, Ja18 -/-mice, and C57Bl/6 wild-type mice over a period of 45 days. As shown in Figure  3A  By day 35 post infection, TMEV was no longer detected by immunohistochemistry in Va14 Tg mice (0 out of 7 mice) or wildtype mice (0 out of 6 mice), only in H2-D b -/-+ H2-K b -/-that lack a CD8 T cell compartment could we detect presence of TMEV in the hippocampus and cortex (4 out of 8 mice, data not shown). The wild-type and Ja18 -/-mice had similar very low viral load in the CNS at 8 and 45 days post infection (data not shown). These observations indicated that the enrichment in iNKT cells permitted a persistent infection of TMEV, which was correlated with an increased mortality.
The enrichment in iNKT cells inhibits the anti-viral CD8 T cell response In C57Bl/6 mice TMEV infection is fully cleared by an antiviral CD8 T cell response that essentially targets the immunodominant, H2-D b restricted epitope VP2 122-130  [42]. Inhibition of this anti-viral CD8 T cell response in the Va14 Tg mice could contribute to the defect in TMEV elimination. To test this hypothesis we enumerated TMEV-specific CD8 T cells using an IFN-c ELIspot assay. Figure  5  reveals the frequency (A) and absolute number (B) of effector CD8 T cells that responded to VP2 122-130 in the CNS of TMEV infected mice. These data indicate that a sizeable anti-viral CD8 T cell response infiltrates the CNS of both C57Bl/6 wild-type mice and Va14 Tg mice. However, the accumulation of TMEV-specific CD8 T cells is delayed in the CNS of TMEV infected Va14 Tg mice (peak at day 10) as compared to infected wild-type controls (peak at day 8) (Fig  5A ).
Acknowledgments The authors thank the  NIH tetramer facility  for the CD1d:PBS-57 tetramer,  M. Kronenberg  ( La Jolla ,  USA ) for the CD1d:a-GalCer tetramer, and  M. Taniguchi  ( Yokohama, Japan ) for the Ja18 -/-mice. We are grateful to  T. Yamamura  ( Tokyo, Japan ) for the use of  OCH , and  F. Lemonnier  ( Paris, France ) for the MHC-I and b2-microglobulin deficient mice.
This work was supported by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , the  Centre National de la Recherche Scientifique (CNRS) , the  French MS society (ARSEP) , the  Midi-Pyrenees Region , and the  Agence National de la Recherche  ( ANR-06-MIME ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study design and population A total of 521,330 men and women, 25-to 70-years old, were recruited between 1 January 1992 and 31 December 2000 from 23 centres in 10 European countries: Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden and the United Kingdom. Approval for the EPIC study was obtained from the ethical review boards of the International Agency for Research on Cancer and from all local institutions where subjects had been recruited for the EPIC study [see Additional file 1: Table  S1 ]. Written informed consent was obtained from all participants before joining the EPIC study. Details of the recruitment and study design have been published elsewhere  [22]. We excluded participants with missing data on dietary factors (n = 6,193), waist circumference measurements (n = 109,302), smoking history  (11, 746) , physical activity (n = 69,393), underweight participants (BMI <18; n = 95,381) and participants with prevalent diabetes reported at study baseline (n = 13,049).
Case ascertainment Cancer cases were identified through population cancer registries in Denmark, Italy, the Netherlands, Spain, Sweden and the United Kingdom. In France, Germany and Greece, a combination of methods was used including health insurance records, cancer pathology registries and active follow-up of study participants and their next of kin. Follow-up began at the date of enrolment and ended at the date of diagnosis of cancer, death or last complete follow-up. The last update of endpoint information was done up to 31 September 2010. Cancer incidence data were coded according to the 10th revision of the International Statistical Classification of Diseases, Injuries and Causes of Death  [23]  and the second revision of the International Classification of Diseases for Oncology  [24].
Assessment of lifestyle factors At baseline, participants filled out extensive medical, dietary and lifestyle questionnaires, including questions on alcohol use, smoking status, physical activity, education and previous illnesses. Body weight, height and waist circumference were measured in all centres except for EPIC-Oxford (health-conscious population) and France where anthropometric measurements were self-reported  [22]. Usual food intakes were measured by using countryspecific validated dietary questionnaires, and individual nutrient intakes were derived from foods included in the dietary questionnaires through the standardised EPIC Nutrient Database  [25]. All dietary variables used in the present study were calibrated by using an additive calibration method as previously described  [26].
c P-value for the linear trend was calculated using the Wald test treating the index as a continuous variable. Note: P-interaction by sex: 0. 40, for colon cancer; 0. 008 for rectal cancer; 0. 03 for colorectal cancer. P for interaction is assessed using the likelihood ratio test by generating a cross-product term between HLI and sex in the multivariable model. CI, confidence interval; EPIC, European Prospective Investigation into Cancer and Health; HR, hazard ratio. more adequately compared to BMI  [18]. Indeed, in our data when only BMI was used to define healthy weight, the estimated HR of CRC was 0. 93 (95% CI: 0. 87 to 1. 00), whereas the respective risk estimate for using only waist circumference was lower: HR = 0. 82 (95% CI: 0. 78 to 0. 87). Taking the above into consideration, in our study we used both BMI and waist circumference to define healthy weight.
Measurement error in selfreported variables cannot be ruled out; however, such error would likely lead to a non-differential bias potentially leading to underestimating the true effects. We used a simplified diet quality index that may not sufficiently account for the complexity of diets. A large proportion of participants were excluded because of missing information on main exposure variables which may have potentially biased the risk estimates if the participants with missing data are not similar to those with complete data. In our data overall there have not been major differences between participants with and without missing data according to the main study characteristics and exposure variables; therefore, it is unlikely that using the complete data analysis approach would have influenced our findings.
Acknowledgements The authors thank  Ellen Kohlsdorf  ( German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany  ) for her work on data management 25  Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 26  Hellenic Health Foundation ,  Athens, Greece. 27Bureau of  Epidemiologic Research, Academy of Athens, Athens, Greece. 28Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece. 29Department of  Epidemiology, Harvard School of Public Health, Boston, MA, USA. 30Molecular and  Nutritional Epidemiology Unit, Cancer Research and Prevention Institute -ISPO, Florence, Italy. 31Epidemiology and  Prevention Unit ,  Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Following these campaigns and as part of the overall response to measles epidemics, punctual household-based measles coverage surveys were conducted  [5] [6] [7] [8] [9]. The main objective of these surveys was to estimate vaccination coverage through ORI. These surveys also aimed to estimate measles vaccine coverage through previous vaccination opportunities: in EPI, SIA, or outbreak response campaigns. Here, we present a secondary data analysis of these different surveys to describe measles vaccine coverage through different vaccination opportunities in different settings in sub-Saharan African countries. The aim is to provide additional information on whether these different vaccination opportunities reach children in need.
Methods We considered all household-based measles vaccination coverage surveys conducted by Epicentre after a nonselective mass vaccination campaign that evaluated different opportunities and for which the complete dataset was available. We defined a survey as a unique analysis of measles vaccination coverage. We considered a total of 24 surveys conducted between 2005 and 2011 in 23 sites in 6 countries  (Chad, Central African Republic (CAR) , Democratic Republic of Congo (DRC), Cameroon, Malawi and Burundi). Surveys were conducted in different locations within a country in Chad, DRC, Malawi and CAR. Two surveys were conducted at two different times in N'Djaména  (Chad). A total of 13 of the study sites were considered as urban or a mix of urban-rural (N'djamena city, Matadi city, Mbuji-Mayi city, Blantyre, Lilongwe, Mzimba and Balaka/Machinga districts, Likasi and Lubumbashi cities and Kabo city), the others were considered as rural.
Data collection Trained surveyors conducted face-to-face interviews with child's main caregiver. Standardized, pre-piloted questionnaires were used and interviews were conducted in local language(s). Information was collected on demographic characteristics (age at the time of the survey or date of birth, sex) and vaccination status for different vaccine opportunities (Additional file 1: Table  S1 ). When possible, information on vaccination history was verified with vaccination card. When card was not available, probing questions, such as place were the vaccination took place or injection site, were asked to minimize the risk of misclassification. Vaccination status was collected for different opportunities. ORI implemented by MoH with the support of MSF (MSF/MoH ORI) and EPI were considered in all study sites.
Data analysis Children were considered as vaccinated through a specific opportunity if they were eligible for vaccination (i. e. in the target age group) and vaccination was reported by card examination or oral history. Vaccine coverage and 95% confidence interval (95% CI) were estimated for each vaccine opportunity. EPI coverage was defined as the proportion of children vaccinated through the routine program among children aged 9 to 11 months (i. e. in the targeted window for this program) and 12 to 23 months (i. e. who should have recently received their MCV through EPI). SIA, ORI and other potential MoH reactive campaigns coverage were defined as the proportion of children vaccinated amongst the target age group at the time of the campaign. ORI coverage was calculated by age group. If the delay between the last day of the MoH/MSF campaign and the first day of the vaccine coverage survey was <31 days, age at the time of the survey was considered to be similar to age at the time of the MoH/MSF campaign.
An alternative or additional possibility would be a prospective trial of NIV to prevent acute respiratory failure in patients recovering from cardiac, abdominal or possibly thoracic surgery  [53, 54]. Given that these patients would be emerging from surgical anaesthesia, there might be a larger role for sedation and that would facilitate rapid recruitment. Competing interests DL reports honoraria from Orion Pharma for guest lectures at international symposia in anaesthesia and intensive care and editorship of intensetimes, an electronic journal supported by Orion Pharma. GC reports honoraria for lectures from Orion Pharma (Italy), Covidien and Sylcomed. The Catholic University of Rome has received an institutional research grant from Orion Pharma. JM reports honoraria from Orion Pharma as a guest lecturer at international symposia in intensive care.
Acknowledgements The authors thank  Alexander Scott  ( Leeds, UK ),  Dorte Bulow Keld (Aarhus, Denmark), Alberto Sandiumenge (Tarragona, Spain), Ilona Bubek (Budapest, Hungary) and Per-Olof Joachimson (Uppsala, Sweden)  for their contributions to the roundtable debate that provided the intellectual framework of this article. The roundtable was made possible through the financial support of  Orion Pharma , Espoo, Finland. Editorial preparation of this manuscript was assisted by Hughes associates, Oxford, UK, on behalf of  Orion Pharma.
supplemented with known synthetic RNA (two-color RNA spike-in kit, Agilent Technologies). cRNAs were subsequently purified using RNeasy Mini Spin columns (Qiagen) and the purified cRNAs were then run onto a Bioanalyzer 2100 using RNA 6000 Nano Chip (Agilent technologies). cRNA yields and specific activities were measured using a NanoDrop 2000 (Thermo Scientific). An equal amount of 800 ng of cyanine 3 and cyanine 5 labeled cRNA were hybridized for each sample. The hybridization and the washing steps were performed following the manufacturer's recommendations. The arrays were scanned using an Agilent G2505C scanner, and the scan protocol used a resolution of 5 mm and a 20 bit dynamic range. The resulting. tiff images were analyzed using the Feature Extraction software version 10. 7. 3. 1 (Agilent) using the GE2_107_Sep09 protocol.
For each channel, the median of the signal intensity was used without background subtraction. Data were analyzed to define genes that are differentially expressed between wt-infected and DF2-infected samples. Differentially expressed genes were identified with a False Discovery Rate (FDR) equal to 5%. Microarray data are available in the Gene Expression Omnibus (GEO) database (http://www. ncbi. nlm. nih. gov/geo/) under GEO accession number GSE56506.
Acknowledgments We thank  Jerome Lecardonnel  for technical assistance in microarrays hybridization. Peter Marszal ,  Greg Smith  and  Tyler Hill  are gratefully acknowledged for technical assistance in molecular biology experiments. We also thank all the  CFIA/NCFAD  staff for technical assistance in animal experiments, in particular  Kim Azaransky ,  Jaime Bernstein ,  Christine De Graff ,  Maggie Forbes ,  Michelle French ,  Cory Nakamura ,  Marlee Phair  and  Kevin Tierney.
This work was partially funded by the  Conseil regional d'I ˆle-de-France  through  DIM Malinf (''Domaine d'Interet Majeur Maladies Infectieuses, parasitaires et nosocomiales emergentes '', Grant#: dim100157) and by the  OECD Co-operative Research Programme  (Contract No. :  TAD/PROG 67049 ). OL was supported by '' DIM Malinf '' doctoral fellowships awarded by the '' Conseil regional d'I ˆle-de-France ''. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
After enrollment into the study, data were collected from the patient's medical records: age, gender, past medical history, reason for hospitalization, date of microbiological specimen collection, date of Ig determination, and outcome during hospitalization (fatal or not). In cases with Ig abnormalities, no specific examinations were scheduled, but an etiologic diagnosis after hospitalization was sought from the patient's medical records or after contact with the practitioner in charge of the patient in order to diagnose a primary or secondary humoral deficiency. 12-15.