dataesr/scientific-paragraphs-categorization huggingface dataset:
Radiologic Study Fifty CT scans of the face with intravenous contrast were studied. Only 39 CT scans had sufficient contrast enhancement for the external carotid artery branches to be visible. The sex ratio was 18 females (46%) / 21 males (54%). Mean age was 46. 9 years (range 19 to 88 years). The images were acquired between January 2012 and May 2015. Indications for CT scanning were facial cellulitis and pretreatment investigation of oral tumor spread. Three-dimensional reconstructions were obtained using Osirix software. After launching the software and selecting each patient from the database, the contrast-enhanced images were visualized and the facial artery was identified. Intravenous contrast enhancement was considered optimal when the facial artery was visible at the inferior border of the mandible bilaterally.
Bioinformatics analyses Data from the Ion Proton sequencer were processed with the Ion Torrent built-in pipeline (TorrentSuite V4) to generate BAM files, and variant calling was done with the built-in ITVC in the somatic mode and with a minimum allele frequency threshold of 4%. Variants were annotated with Annovar and filtered to eliminate known single nucleotide polymorphisms (SNPs) (variants present in the Exome Aggregation Consortium [ExAC] or 1000 Genomes [1000G] databases at a frequency >0. 001) and sequencing artefacts using the MutSpec Galaxy package developed in-house  [14]. Further manual checks of BAM files using IGV were done when appropriate. All non-synonymous mutations found in the targeted regions and present in both duplicates of tumor samples but not in any blood samples of the 126 cases were retained for analysis.
Exome data from the HiSeq 4000 sequencer were analyzed with a pipeline developed inhouse and based on standard tools for quality control and processing (FastQC 0. 11. 3, Adapter-Removal 2. 1. 7, BWA-MEM 0. 7. 15, Qualimap 2, GATK 3. 5, and Picard 1. 131). Somatic variant calling was done on tumor-blood sample pairs with Strelka  [15]  using the default parameters. Variant annotation and filtering was done as described above with MutSpec  [14] , and only somatic indels and single nucleotide variants (SNVs) in coding regions were retained and analyzed. Pathway analysis of mutated genes was done with ConsensusPathDB (r32) using the KEGG, Biocarta, Reactome, and WikiPathways databases and a minimum of 3 overlapping genes and q-value <0. 05 as settings  [16]. To define cancer genes, we used the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (v82)  [17] , and genes identified as drivers for BC in the IntOGen database (r2014.
Public data on somatic mutations in breast cancer Data from The Cancer Genome Atlas (TCGA) breast and METABRIC genomic studies  [19, 20]  and from the IARC TP53 Database  [21]  were used as comparison datasets. Gene-specific mutation files (AKT1, CDH1, ERBB2, NOTCH1, PIK3CA, PTEN, RB1, and TP53) and related clinical files for the TCGA and METABRIC studies were retrieved from cBioPortal  [22, 23]  in February 2017. MAF files from exome sequencing data of TCGA BC cases were retrieved on 26 March 2015 via a https protocol at https://tcga-data. nci. nih. gov/tcgafiles/ftp_auth/distro_ ftpusers/anonymous/tumor/. Gene-specific data from TCGA and METABRIC were combined, including only cases with documented age and ER, PR, and HER2 status, and stratified by age (younger than 45 or older than 55 years).
For TCGA exome data, only data with documented age and ER, PR, and HER2 status were selected, resulting in a dataset of 453 samples, including 96 preBC and 357 postBC. Version R18 of the somatic dataset of the IARC TP53 Database was used to select for mutations reported in primary BC in women age 45 or younger and in studies using Sanger sequencing. Finally, another independent dataset, named hereafter 560BC, was assembled from public data obtained from whole-genome sequencing of 560 BC cases  [6]. For this dataset, mutation data were retrieved from COSMIC and clinical data were retrieved from the original publication. Only cases with documented ER, PR, and HER2 status and diagnosed at 45 years or younger were included (N = 123).
Potentially deleterious somatic mutations (affecting splicing, indels, nonsense, stop-loss, and non-synonymous substitutions) in the 8-gene panel were found in 63. 5% (80/126) of samples. TP53 was the most frequently mutated gene (32. 5%), followed by PIK3CA (21. 4%) and AKT1 (9. 5%), whereas other genes were mutated in less than 5% of samples. This distribution differed from that observed in preBC from the TCGA/METABRIC datasets (Fig  1A ). Indeed, there were fewer cases with TP53 or PIK3CA mutations and more cases with AKT1 and RB1 mutations in PRECAMA versus TCGA/METABRIC cases (p-values at best = 0. 03). These differences may be explained in part by a different distribution of subtypes between PRECAMA and TCGA/METABRIC cases (p-value = 8. 2 × 10 -6 ), because a higher proportion of luminal A cases (known to carry frequent AKT1 and infrequent TP53 mutations) and a lower proportion of TN cases (known to carry frequent TP53 mutations) was observed in PRECAMA compared with TCGA/METAB-RIC (p-value = 0.
Mutation patterns and signatures in premenopausal BC To study the underlying mutational processes involved in the development of preBC tumors in the studied populations, we analyzed somatic mutation patterns in the TP53 gene and at the exome-wide level. Fig 3A shows the distribution of TP53 mutation types in PRECAMA tumors and in tumors from young women (< = 45 years) from other datasets. There was a higher proportion of G:C>T:A mutations in PRECAMA compared with the IARC TP53 Database (p-value = 0. 004) or TCGA/METABRIC (p-value = 0. 05) datasets. In fact, G:C>T:A was the most frequent type, followed by indels, in PRECAMA, whereas G:C>A:T at CpG was the most frequent type in the other datasets. The overall distribution of TP53 mutation types was not significantly associated with IHC subtypes in PRECAMA samples (p-value = 0.
06), although cases with indels were more frequently of the TN subtype (Fig  3B ). TP53 indels were truncating mutations (predicted to result in loss of p53 protein expression) in 6/10 cases, and 5/6 of these truncating mutations were indeed associated with null p53 IHC staining (see Table A in S1 File). Therefore, although the presence of frequent TP53 truncating mutations in the TN subtype was similar to previous reports  [31] , the high frequency of G:C>T:A mutations in PRECAMA was unexpected. To validate this result, we used another dataset from a whole-genome sequencing study  [6] , and PRECAMA samples did show a significantly higher frequency of G:C>T:A mutations (S2B Fig) (p-value = 0. 02). Mutational signatures at the exome-wide level were analyzed using a dataset including the 12 PRECAMA samples and 453 BC samples from TCGA (including both preBC and postBC cases; see Materials and Methods).
The IHC analyses showed a majority of ER-positive cases and a proportion of TN subtype similar to previous reports in Hispanic women  [38]. The overall prevalence of ER-negative tumors in PRECAMA was substantiated by sequencing results on the 8-gene panel analyzed here. Indeed, TP53 mutations, which are strongly associated with ER-negative status  [7, 39] , were found in 32. 5% of the cases, consistent with an overall 28% of ER-negative cases. Also, the frequency of AKT1 mutations, typical of ER-positive cases  [20, 40] , was higher in PRE-CAMA than in the comparative dataset of young women. Continued enrollment will enable us to determine more precise estimates of subtype distribution in PRECAMA and to explore potential differences in tumor subtype distributions between countries. Although the overall tumor characteristics were more similar to those described in postBC than in preBC from other series, IHC staining with Ki67 showed high levels of staining in these preBC samples, even in luminal A cases (72% positive cases), which is consistent with previous reports on preBC  [41, 42].
Liao et al. (2015)   [43]  recently compared the molecular features of preBC versus postBC from the TCGA and METABRIC datasets using multi-omic data integration. They reported no difference in gene expression between preBC and postBC in ER-negative cases but significant differences in ER-positive cases, with activation of integrin signaling and EGFR pathways and TGFβ as the top upstream regulator in preBC. It would therefore be important in future studies to assess whether activation of these pathways drives the level of proliferation reflected by high Ki67 positivity in ER-positive preBC, because they may be potential clinical targets. The characteristics of the mutations found by target sequencing of the 8-gene panel were similar to those observed in other series of BC, with classical hotpots found in AKT1 and PIK3CA, a majority of missense mutations found in TP53, a higher proportion of truncating TP53 mutations in TN cases compared with other subtypes, and an expected distribution of mutated genes within IHC subtypes.
However, an interesting difference in the distribution of TP53 single base substitutions was observed. The most frequent TP53 mutation type was G: C>T:A, which represented 27% of all TP53 mutations. This proportion of G:C>T:A mutations was 1. 5-3. 3 times those observed in the comparative datasets used here, matching figures reported in lung cancers linked to exposure to polycyclic aromatic hydrocarbons  [44, 45]. This pattern is therefore unexpected in BC. These G:C>T:A mutations do not exhibit a strand bias, do not cluster at any hotspot, and seemed similarly distributed within IHC subtypes or country of origin, although the numbers are still too low to enable any conclusion to be drawn. Because these results may suggest a specific, as-yet unknown, mutational process at the origin of TP53 mutations, it will be important to confirm them in the full PRECAMA study.
(XLSX) S1 Fig. Distribution of IHC subtypes in PRECAMA samples and in preBC extracted from METABRIC and TCGA studies. Comparison of the distribution of IHC subtypes observed in PRECAMA and in preBC from a dataset extracted from METABRIC and TCGA (see Materials and Methods). Luminal A: ER+/HER2-; luminal B: ER+/HER2+; HER2-enriched: ER-/HER2.
Acknowledgments The  PRECAMA team includes International Agency for Research on Cancer  (Coordinating Center) investigators and staff:  Carine Biessy ,  Magali Olivier ,  Sabina Rinaldi ,  Isabelle Romieu ; investigators and staff in Chile:  Eva Bustamante ,  Fancy Gaete ,  Maria Luisa Garmendia ,  Jose Soto ; investigators and staff in Colombia:  Alberto Angel ,  Carlos Andres Ossa ,  William H. Arias ,  Gabriel Bedoya ,  Mauricio Borrero ,  Alicia Cock-Rada ,  Israel Dı ´az-Yunez ,  Carolina Echeverri ,  Fernando Herazo ,  Angel Hernandez ,  Roberto Jaramillo ,  Edgar Navarro ,  Yorlany Rodas Cortes ,  Gloria I. Sanchez ; investigators and staff in Costa Rica: Bernal Cortes,  Paula Gonzalez ,  Diego Guillen ,  Viviana Lorı ´a ,  Rebecca Ocampo ,  Carolina Porras ,  Ana Cecilia Rodriguez ; investigators and staff in Mexico:  Gabriela Torres-Meija ,  Angelica Angeles-Llerenas ,  Jenny Tejeda ; and investigators and staff in Seattle:  Peggy Porter.
Background The number of individuals living with dementia is increasing, negatively affecting families, communities, and health-care systems around the world. A successful response to these challenges requires an accurate understanding of the dementia disease burden. We aimed to present the first detailed analysis of the global prevalence, mortality, and overall burden of dementia as captured by the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, and highlight the most important messages for clinicians and neurologists. Methods GBD 2016 obtained data on dementia from vital registration systems, published scientific literature and surveys, and data from health-service encounters on deaths, excess mortality, prevalence, and incidence from 195 countries and territories from 1990 to 2016, through systematic review and additional data-seeking efforts.
To correct for differences in cause of death coding across time and locations, we modelled mortality due to dementia using prevalence data and estimates of excess mortality derived from countries that were most likely to code deaths to dementia relative to prevalence. Data were analysed by standardised methods to estimate deaths, prevalence, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs; computed as the sum of YLLs and YLDs), and the fractions of these metrics that were attributable to four risk factors that met GBD criteria for assessment (high body-mass index [BMI], high fasting plasma glucose, smoking, and a diet high in sugarsweetened beverages).
Although other efforts to estimate dementia prevalence  1, 2, 12  have used meta-analytic strategies to synthesise the available data, these analyses did not attempt to reconcile and make the combined best use of different types of data on incidence, prevalence, excess mortality, and causes of death reported in literature sources, claims databases, and vital registration systems. Furthermore, GBD assesses the strength of causal evidence for risks and includes estimates of attributable disease burden from risk factors using a comparative risk assessment framework. Global data and cross-country comparisons might help further current understanding of complex and multifactorial diseases such as dementia. The capacity of GBD to examine patterns across countries creates a unique opportunity to identify populations with different trends that in turn could reveal clustered risk or environmental factors, providing empirical evidence about factors that affect neurodegenerative diseases.
Overview and data sources 4] [15] [16]  In this Article, we have highlighted the methods pertaining to processes specific to the estimation of Alzhemier's disease and other dementias (hereafter referred to as dementia). For the GBD analyses, the reference case definitions were either those from the Diagnostic and Statistical Manual of Mental Disorders (DSM; DSM-III, DSM-IV, or DSM-5), which are used in surveys and cohort studies, or those from the International Classification of Diseases (ICD; ICD-8, ICD-9, and ICD-10), which are used in vital registration and claims data sources. 17, 18 For GBD analyses of dementia, the relevant ICD-9 codes were 290, 291. 2, 291. 8, 294, and 331, and the relevant ICD-10 codes were F00, F01, F02, F03, G30, and G31. In GBD, for each disease a reference case definition was chosen that represents the most recent consensus or the most commonly used.
Research in context Evidence before this study Over the past decade, there have been substantial research efforts to describe the global epidemiology of Alzheimer's disease and other dementias. Other efforts to summarise the evidence, including the World Dementia Report, have compiled evidence through systematic reviews and meta-analyses, but they report only on dementia prevalence by world region. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study has published regular updates of estimates for dementia in 195 countries and territories and subnational locations, such as the provinces of China and states in countries such as Brazil, the USA, and Mexico. However, there has been no dedicated and detailed publication of GBD methods and estimates for dementia. GBD 2016 estimates for dementia incorporated 43 new data sources on the prevalence and incidence of dementia, updating a previous review that covered 1980 to 2015; new sources were identified through a systematic review of English-language articles published in PubMed from 23 Jan, 2015, to Oct 7, 2016,with the search terms "dementia"[Title/Abstract] AND ("prevalence" OR "incidence") [Title/Abstract].
See Online for appendix definition. Data based on alternative case definitions were adjusted if systematic bias was detected. We included 237 sources of data from the scientific literature, and 3 years of medical claims data from the USA. We identified 176 sources reporting on prevalence, covering 17 of 21 world regions, and 64 sources on incidence, covering ten of 21 world regions (appendix). No input data were available for Oceania, central Asia, eastern Europe, or southern sub-Saharan Africa. We could not adjust the data for different study designs or case definitions because of the extreme heterogeneity in case-ascertainment methods. We identified 230 different methods of screening and diagnosis in the 237 scientific literature sources. Data points that had an age range of greater than 20 years were split into new data points for the 5-year age groups within the age range, using the age pattern from the USA as the country for which we had the most detailed information.
Natural-history model We did not use the typical GBD cause of death ensemble modelling (CODEm) approach for dementia because we noted large inconsistencies between cause of death data and prevalence data over time and between countries. ] [21]  Additionally, the highest age-standardised death rates were more than 50 times higher than the lowest agestandardised death rates across different locations for 2016, possibly indicating that the practice of coding a death to dementia as an underlying cause of death has not been consistent over time or between countries. To adjust for this bias, we jointly modelled cause of death and non-fatal outcomes for dementia. We first ran an initial cause of death model using CODEm, and an initial non-fatal model using DisMod-MR 2. 1, the Bayesian meta-regression tool used in most non-fatal models for GBD.
22, 23 DisMod-MR 2. 1 enforces consistency between the different parameters, because incidence determines the inflow into the pool of prevalent cases and excess mortality determines outflow via death. The rates of inflow and outflow then determine the average duration of disease. Both DisMod MR 2. 1 and CODEm use covariates and borrow strength from locations in the same region that have data to make estimates for locations where there are no input data. The initial CODEm model included 16 226 site-years of data (ie, data for a unique combination of location and calendar year). The covariates used in the initial CODEm model included diabetes prevalence, mean cholesterol, and mean body-mass index (BMI; full list in the appendix). The initial DisMod-MR 2. 1 model included settings of no remission (ie, no cure), and no incidence before age 40 years.
24, 25 From the initial model results we identified the locations with highquality vital registration systems that most often coded to dementia as a cause of death per prevalent case in the most recent year of estimates. For GBD 2016, these locations were the USA, Finland, Sweden, and Puerto Rico. We then used the log-transformed ratio between cause-specific mortality and prevalence for these locations to run a fixed-effects linear regression with dummy variables on 5-year age bins and sex. Because the ratio of cause-specific mortality and prevalence is an estimate of excess mortality rate, we used the results of this regression to predict data inputs for every GBD location and all years in a second DisMod-MR 2. 1 model. As an exception, we retained the 2016 ratios of causespecific mortality and prevalence for the four locations used in the regression analysis but assumed the 2016 ratios applied to the whole 1990-2016 period.
YLDs and risk estimation To derive YLDs for dementia, we first divided dementia prevalence into three severity categories. We used data from a systematic review of studies reporting on the Clinical Dementia Rating Scale (CDR)  26  and pooled proportions of mild (CDR 1), moderate (CDR 2), and severe dementia (CDR 3) in random-effect meta-analyses. We did this separately for ages 40-69, 70-79, and 80 years or more. For GBD 2016, we included seven studies identified through systematic review published from 1980 to 2015, covering two world regions. To calculate YLDs, we multiplied the prevalence at each severity level by the corresponding disability weight  24  and further corrected for comorbidity using a simulation assigning all non-fatal outcomes to hypothetical individuals for each age group, sex, location, and year.
2, 3 However, the previous studies reported results only at the region level and did not use data across regions to generate estimates for all countries. The increase in the number of cases of dementia is of even more importance given that there is currently no effective disease-modifying cure or treatment for the disease. 30 Additionally, even when clinical trials are initiated, many more end in failure than success in bringing a new disease-modifying drug to market, with ratios of more than 100:1 compared with the 14•6:1 pharmaceutical industry average. 31, 32 Without potential treatments, increasing numbers of cases will pose undue burden on individuals who have dementia, their caregivers, and health-care systems more generally. In GBD 2016, only four risk factors were judged to have sufficient evidence for a causal link to Alzheimer's disease and other dementias: high BMI, high fasting plasma glucose, smoking, and high intake of sugar-sweetened beverages.
Of the 237 available data sources, 230 different diagnostic procedures were used. Although most of the data ultimately classified dementia cases using the DSM or ICD definitions, differences between different versions of the DSM criteria could have led to differences in prevalence estimates, as suggested in a meta-regression analysis of prevalence studies in China. 38 ven if the same screening test was used, different studies often used different cutoff scores. For example, although 42% of studies used the Mini-Mental State Examination in the screening phase, the absolute cutoff scores that were used ranged from 18 to 28, and other studies used different cutoff scores by educational attainment level. In this round of GBD we did not find a way to correct for such bias because of the extreme variability in methods, so it is likely that part of the observed variation in prevalence was due to measure ment bias rather than reflecting true geographical variation.
A potential next step in the GBD is to consider dividing dementia into subtypes, as these might have different epidemiological features and potentially different prevention and treatment strategies. A first subdivision could be Alzheimer's disease dementia, vascular dementia, and remaining types. The challenges of subdividing include sparse data and the complication of how to handle mixed types of dementia. 39 However, increased use of biomarkers in the classification of dementia and Alzheimer's disease might help facilitate subdivision. 40, 41 dditionally, the data on severity distribu tions over age rely on few data sources and can be strengthened. ] [44]  Monitoring trends in dementia is difficult because of the extreme variation in cause of death coding practices and the large heterogeneity in case-ascertainment methods.
Although previous guidelines have been developed to systematise the reporting of neurological disorders generally, because of the diagnostic challenges noted with dementia, disease-specific guidelines are warranted and resources should be directed towards creating and implementing more systematic methods. 45 he GBD study will continue to update its estimates for dementia annually, and estimates might become more robust if data collection methods improve. Additionally, as new data become available on risk factors for dementia that meet GBD criteria for causal links, they can be incorporated into future iterations of GBD.